diff --git a/.buildinfo b/.buildinfo
index 5ac3f2529..b784ecfea 100644
--- a/.buildinfo
+++ b/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 9d2adf3db76a7702f5ae402a59452f1e
+config: ce863e59d78f087067ce6e4e14228fd2
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/.doctrees/apidocs/circuit_cutting.doctree b/.doctrees/apidocs/circuit_cutting.doctree
index 21c015a59..9d911ba84 100644
Binary files a/.doctrees/apidocs/circuit_cutting.doctree and b/.doctrees/apidocs/circuit_cutting.doctree differ
diff --git a/.doctrees/circuit_cutting/cutqc/index.doctree b/.doctrees/circuit_cutting/cutqc/index.doctree
index a13fc2a77..7b33520ed 100644
Binary files a/.doctrees/circuit_cutting/cutqc/index.doctree and b/.doctrees/circuit_cutting/cutqc/index.doctree differ
diff --git a/.doctrees/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.doctree b/.doctrees/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.doctree
index ba112ba8d..4dca75c25 100644
Binary files a/.doctrees/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.doctree and b/.doctrees/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.doctree differ
diff --git a/.doctrees/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.doctree b/.doctrees/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.doctree
index 080a4e029..cd39427cd 100644
Binary files a/.doctrees/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.doctree and b/.doctrees/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.doctree differ
diff --git a/.doctrees/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.doctree b/.doctrees/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.doctree
index 8dd4a225c..be12427dd 100644
Binary files a/.doctrees/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.doctree and b/.doctrees/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.doctree differ
diff --git a/.doctrees/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.doctree b/.doctrees/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.doctree
index 121452b7b..88e93328a 100644
Binary files a/.doctrees/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.doctree and b/.doctrees/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.doctree differ
diff --git a/.doctrees/circuit_cutting/how-tos/how_to_specify_cut_wires.doctree b/.doctrees/circuit_cutting/how-tos/how_to_specify_cut_wires.doctree
index c410042f6..2bc18608b 100644
Binary files a/.doctrees/circuit_cutting/how-tos/how_to_specify_cut_wires.doctree and b/.doctrees/circuit_cutting/how-tos/how_to_specify_cut_wires.doctree differ
diff --git a/.doctrees/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.doctree b/.doctrees/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.doctree
index 6936c097a..88c20c986 100644
Binary files a/.doctrees/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.doctree and b/.doctrees/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.doctree differ
diff --git a/.doctrees/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.doctree b/.doctrees/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.doctree
index e83bd5896..8ec35b737 100644
Binary files a/.doctrees/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.doctree and b/.doctrees/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.doctree differ
diff --git a/.doctrees/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.doctree b/.doctrees/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.doctree
index 282dee5ed..782acac14 100644
Binary files a/.doctrees/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.doctree and b/.doctrees/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.doctree differ
diff --git a/.doctrees/circuit_cutting/tutorials/04_automatic_cut_finding.doctree b/.doctrees/circuit_cutting/tutorials/04_automatic_cut_finding.doctree
new file mode 100644
index 000000000..83b1679d9
Binary files /dev/null and b/.doctrees/circuit_cutting/tutorials/04_automatic_cut_finding.doctree differ
diff --git a/.doctrees/circuit_cutting/tutorials/index.doctree b/.doctrees/circuit_cutting/tutorials/index.doctree
index f4095f1bd..45fd37474 100644
Binary files a/.doctrees/circuit_cutting/tutorials/index.doctree and b/.doctrees/circuit_cutting/tutorials/index.doctree differ
diff --git a/.doctrees/environment.pickle b/.doctrees/environment.pickle
index ba87bf2ff..5ca7aad7b 100644
Binary files a/.doctrees/environment.pickle and b/.doctrees/environment.pickle differ
diff --git a/.doctrees/install.doctree b/.doctrees/install.doctree
index fed359a44..90d123566 100644
Binary files a/.doctrees/install.doctree and b/.doctrees/install.doctree differ
diff --git a/.doctrees/nbsphinx/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb b/.doctrees/nbsphinx/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb
index 3a05d1bf2..19915683c 100644
--- a/.doctrees/nbsphinx/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb
+++ b/.doctrees/nbsphinx/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb
@@ -7,6 +7,8 @@
    "source": [
     "# CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding\n",
     "\n",
+    "**NOTE: CutQC is deprecated and will be removed no sooner than Circuit Knitting Toolbox v0.8.0.  The circuit cutting workflow in `circuit_knitting.cutting` now implements similar and improved functionalities, which will be maintained going forward.**\n",
+    "\n",
     "Circuit cutting is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output. \n",
     "\n",
     "The circuit knitting toolbox implements a wire cutting method presented in [CutQC](https://doi.org/10.1145/3445814.3446758) (Tang et al.). This method allows a circuit wire to be cut such that the generated subcircuits are amended by measurements in the Pauli bases and by state preparation of four Pauli eigenstates (see Fig. 4 of [CutQC](https://doi.org/10.1145/3445814.3446758)).\n",
@@ -30,18 +32,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "eb859bde",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:06.357187Z",
+     "iopub.status.busy": "2024-04-19T17:42:06.356901Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.721097Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.720238Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 1020.64x491.633 with 1 Axes>"
+       "<Figure size 786.525x491.633 with 1 Axes>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -82,9 +91,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "8c11457a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.724636Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.723928Z",
+     "iopub.status.idle": "2024-04-19T17:42:08.624148Z",
+     "shell.execute_reply": "2024-04-19T17:42:08.623218Z"
+    }
+   },
    "outputs": [],
    "source": [
     "%%capture\n",
@@ -115,9 +131,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "id": "465733e2",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:08.628048Z",
+     "iopub.status.busy": "2024-04-19T17:42:08.627669Z",
+     "iopub.status.idle": "2024-04-19T17:42:08.632223Z",
+     "shell.execute_reply": "2024-04-19T17:42:08.631531Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -141,18 +164,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "id": "938f7733",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:08.634712Z",
+     "iopub.status.busy": "2024-04-19T17:42:08.634506Z",
+     "iopub.status.idle": "2024-04-19T17:42:08.914796Z",
+     "shell.execute_reply": "2024-04-19T17:42:08.914094Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 523.664x270.9 with 1 Axes>"
+       "<Figure size 473.498x270.9 with 1 Axes>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -164,18 +194,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "id": "1c3a5712",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:08.918049Z",
+     "iopub.status.busy": "2024-04-19T17:42:08.917818Z",
+     "iopub.status.idle": "2024-04-19T17:42:09.173131Z",
+     "shell.execute_reply": "2024-04-19T17:42:09.172454Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 623.998x270.9 with 1 Axes>"
+       "<Figure size 473.498x270.9 with 1 Axes>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -206,9 +243,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "id": "5d1fb2ca",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:09.177033Z",
+     "iopub.status.busy": "2024-04-19T17:42:09.176313Z",
+     "iopub.status.idle": "2024-04-19T17:42:09.180279Z",
+     "shell.execute_reply": "2024-04-19T17:42:09.179623Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from qiskit_ibm_runtime import QiskitRuntimeService  # noqa: F401\n",
@@ -234,9 +278,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "id": "3cc622d9",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:09.183537Z",
+     "iopub.status.busy": "2024-04-19T17:42:09.182884Z",
+     "iopub.status.idle": "2024-04-19T17:42:09.188106Z",
+     "shell.execute_reply": "2024-04-19T17:42:09.187164Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from qiskit_ibm_runtime import Options\n",
@@ -258,10 +309,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "id": "2ae5160c",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:09.191528Z",
+     "iopub.status.busy": "2024-04-19T17:42:09.191111Z",
+     "iopub.status.idle": "2024-04-19T17:42:09.420212Z",
+     "shell.execute_reply": "2024-04-19T17:42:09.419296Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2967/4196057829.py:3: DeprecationWarning: The function ``circuit_knitting.cutting.cutqc.wire_cutting.evaluate_subcircuits()`` is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. \n",
+      "  subcircuit_instance_probabilities = evaluate_subcircuits(cuts)\n"
+     ]
+    }
+   ],
    "source": [
     "from circuit_knitting.cutting.cutqc import evaluate_subcircuits\n",
     "\n",
@@ -269,7 +336,7 @@
     "\n",
     "# Uncomment the following lines to instead use Qiskit Runtime Service as configured above.\n",
     "# subcircuit_instance_probabilities = evaluate_subcircuits(cuts,\n",
-    "#                                                          service_args=service.active_account(),\n",
+    "#                                                          service=service,\n",
     "#                                                          backend_names=backend_names,\n",
     "#                                                          options=options,\n",
     "#                                                         )"
@@ -287,9 +354,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "id": "fa22661e",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:09.424642Z",
+     "iopub.status.busy": "2024-04-19T17:42:09.423925Z",
+     "iopub.status.idle": "2024-04-19T17:42:09.428782Z",
+     "shell.execute_reply": "2024-04-19T17:42:09.427942Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -316,9 +390,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "id": "7e57f303",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:09.432758Z",
+     "iopub.status.busy": "2024-04-19T17:42:09.432111Z",
+     "iopub.status.idle": "2024-04-19T17:42:09.436940Z",
+     "shell.execute_reply": "2024-04-19T17:42:09.436250Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -348,9 +429,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "id": "3ec4d42c",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:09.440852Z",
+     "iopub.status.busy": "2024-04-19T17:42:09.440554Z",
+     "iopub.status.idle": "2024-04-19T17:42:09.445443Z",
+     "shell.execute_reply": "2024-04-19T17:42:09.444807Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -384,9 +472,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "id": "5aceecc0",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:09.449575Z",
+     "iopub.status.busy": "2024-04-19T17:42:09.449007Z",
+     "iopub.status.idle": "2024-04-19T17:42:10.686860Z",
+     "shell.execute_reply": "2024-04-19T17:42:10.685710Z"
+    }
+   },
    "outputs": [],
    "source": [
     "%%capture\n",
@@ -410,9 +505,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "id": "919958cb",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:10.691821Z",
+     "iopub.status.busy": "2024-04-19T17:42:10.691074Z",
+     "iopub.status.idle": "2024-04-19T17:42:10.697156Z",
+     "shell.execute_reply": "2024-04-19T17:42:10.696348Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -440,10 +542,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "id": "5353b0c8",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:10.700976Z",
+     "iopub.status.busy": "2024-04-19T17:42:10.700196Z",
+     "iopub.status.idle": "2024-04-19T17:42:10.712174Z",
+     "shell.execute_reply": "2024-04-19T17:42:10.711251Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2967/3419935587.py:3: DeprecationWarning: The function ``circuit_knitting.cutting.cutqc.wire_cutting_verification.verify()`` is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. \n",
+      "  metrics, exact_probabilities = verify(circuit, reconstructed_probabilities)\n"
+     ]
+    }
+   ],
    "source": [
     "from circuit_knitting.cutting.cutqc import verify\n",
     "\n",
@@ -462,26 +580,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "id": "673d3cb3",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:10.716298Z",
+     "iopub.status.busy": "2024-04-19T17:42:10.715820Z",
+     "iopub.status.idle": "2024-04-19T17:42:10.721924Z",
+     "shell.execute_reply": "2024-04-19T17:42:10.721135Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'nearest': {'chi2': 0,\n",
-       "  'Mean Squared Error': 8.13178352181795e-35,\n",
-       "  'Mean Absolute Percentage Error': 4.4880309854901524e-10,\n",
-       "  'Cross Entropy': 3.564551116068219,\n",
-       "  'HOP': 0.9945381353717198},\n",
+       "  'Mean Squared Error': 1.8155290178685046e-34,\n",
+       "  'Mean Absolute Percentage Error': 7.550336449277775e-10,\n",
+       "  'Cross Entropy': 3.5645511160682197,\n",
+       "  'HOP': 0.9945381353717202},\n",
        " 'naive': {'chi2': 0,\n",
-       "  'Mean Squared Error': 3.7794080473092745e-35,\n",
-       "  'Mean Absolute Percentage Error': 4.4880563544629694e-10,\n",
-       "  'Cross Entropy': 3.564551116068219,\n",
-       "  'HOP': 0.99453813537172}}"
+       "  'Mean Squared Error': 2.9965472816547853e-34,\n",
+       "  'Mean Absolute Percentage Error': 7.550351082551326e-10,\n",
+       "  'Cross Entropy': 3.5645511160682206,\n",
+       "  'HOP': 0.9945381353717199}}"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -502,18 +627,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "id": "c8cc97e9",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:10.725981Z",
+     "iopub.status.busy": "2024-04-19T17:42:10.725441Z",
+     "iopub.status.idle": "2024-04-19T17:42:11.074622Z",
+     "shell.execute_reply": "2024-04-19T17:42:11.073833Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -585,7 +717,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/.doctrees/nbsphinx/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb b/.doctrees/nbsphinx/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb
index 7518e53ff..c2cfd384f 100644
--- a/.doctrees/nbsphinx/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb
+++ b/.doctrees/nbsphinx/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb
@@ -7,6 +7,8 @@
    "source": [
     "# CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting\n",
     "\n",
+    "**NOTE: CutQC is deprecated and will be removed no sooner than Circuit Knitting Toolbox v0.8.0.  The circuit cutting workflow in `circuit_knitting.cutting` now implements similar and improved functionalities, which will be maintained going forward.**\n",
+    "\n",
     "Circuit cutting is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output. \n",
     "\n",
     "The circuit knitting toolbox implements a wire cutting method presented in [CutQC](https://doi.org/10.1145/3445814.3446758) (Tang et al.). This method allows a circuit wire to be cut such that the generated subcircuits are amended by measurements in the Pauli bases and by state preparation of four Pauli eigenstates (see Fig. 4 of [CutQC](https://doi.org/10.1145/3445814.3446758)).\n",
@@ -30,18 +32,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "eb859bde",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:17.192774Z",
+     "iopub.status.busy": "2024-04-19T17:42:17.192467Z",
+     "iopub.status.idle": "2024-04-19T17:42:18.587956Z",
+     "shell.execute_reply": "2024-04-19T17:42:18.587209Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 466.456x338.625 with 1 Axes>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -86,9 +95,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "5d1fb2ca",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:18.591613Z",
+     "iopub.status.busy": "2024-04-19T17:42:18.590975Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.254006Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.253128Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from qiskit_ibm_runtime import (\n",
@@ -115,9 +131,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "d409553d",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.259579Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.258036Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.264089Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.263354Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Set the Sampler and runtime options\n",
@@ -148,9 +171,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "8c11457a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.267512Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.266907Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.297080Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.296320Z"
+    }
+   },
    "outputs": [],
    "source": [
     "%%capture\n",
@@ -172,18 +202,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "5816c27f",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.300182Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.299635Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.506657Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.505891Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVkAAACXCAYAAAC/ZyirAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAXpklEQVR4nO3de1xUdd4H8M+BYRgGkJupkKI+5IpSKAZ4e57UQrqsl7VNpbYH2zJRCMv1eZnt1uZq67OlpUta0pr7ROmmaa6XqJDKXM1Cs2nlouSIpIIaxX0GYWbO88cISoLOCL85Z+Dz/kfmnDPn93WY32d+/M5lJFmWZRARkRAeShdARNSVMWSJiARiyBIRCcSQJSISiCFLRCQQQ5aISCCGLBGRQAxZIiKBGLJERAIxZImIBGLIEhEJxJAlIhKIIUtEJBBDlohIIIYsEZFADFkiIoEYskREAjFkiYgEYsgSEQnEkCUiEoghS0QkEEOWiEgghiwRkUAMWSIigRiyREQCMWSJiATSKF2AWsi1dYDJJLYRvR6Sv5/YNkj1LlbWorHOLLQNrZ8PvIP8nXpOdSNQbxFU0CW+GiBA6/zz3Ll/MmRh/wVaZqcB5gaxDfnooFm/lkHbjV2srMV78fNgqRP7XtP46TA973WHg7a6EZicC5gEh6xeA+xKcC5o3b1/croAsH9Civ4FAvY2RH8ak6o11pmFBywAWOoanBot11vEByxgb8Pp0bKb90+GLBGRQAxZIiKBOCdLpCIP5L0GS0MjbI0WeGg1KFi3C99t+kTpsqgDGLJEKvPZoy+h+kQZAgf3w+Scl3DmkyMwn69Uuiy6QZwuIFKpquOn0VhdD9/QEPQZHYW4JbMAACHDIjDqL48rXB05SpUhe+TIEcTHx0On0yEuLg5ZWVnQ6/Ww2WxKl0bkMr3iI3Gxqg4/FZ7CuYMF8O/fB/qwEAxb8AC+XbVV6fLIQaqbLjAYDBg3bhyWLl2KzZs3Izs7G2lpaYiKioKHh/KfCYNyd2PXyDsQ6d/jmstc7WKjFZuyjdiwvRg/VDZg+OBgpD8UhbExvRWryR3sOXgWa98txLGSaoT10mPOrwdjeuJAeHoq916bsGERJEmC/8A+2DdvNWyN9nOeDK9swZ0bFqF8378VnT6wmmpx/Pf/BQCwmevQVFkO77BBAICgMdMROuMPitSl1r6pupBNT0/H3LlzsWDBAgBAWloaMjIyEB0drXBl6mUyWzBxzofIK/gBFosMADh5pgZbckqwcmE8fpd8m8IVqtOiV/Kw8q2j8PCQYLXKKC6txr6vz2HjB0ZsX50AjUaZoG2ek+0/aTTGvjIP5w4WoqGiGlXHT8P35p4o/TBPkbqaeer9MXS1AQBQe3QvzmYtRuSKLxWtSc2UHxpewWg0Yv/+/UhPT2+1XKvVtoTsli1bMHr0aIwaNQqbNm1SokzVWb7egMMFFS0BCwBNFhmyDCxcmYejxT8pWJ06ffpVGVa+dRSyDFit9tet+eePvziL1zYXKVwhULr7IMr2fovo+dMAAJGz7sbxrBxEzZmkcGXkDFWNZA0GAwIDAxEeHt6yzGw24+TJk4iOjkZ1dTWef/55HDp0CJIkIT4+Hvfccw+Cg4NdWuf0wwfg7eHZ8riswQVXo7TDZpPx2uYiNFranq/WenngjW3H8eozo11cmbqtfbcQnh4SLFb5qnVNFhte/Uch5v8mSoHKWvt6+UZM/vhF5K/bhb4TY5EzcynGrEhB8G0D8dPREqXLUx019c1mqgpZSZJgtVphs9la5l8zMzNhMpkQHR2Nr776CmPGjIGfn/3a4jvuuAMHDhzA5MmTr7nfKVOmwGg0trs+TOOF7P6DHa7zvdixV837OCoxMRFlliaHt78eG7xQ6Zne7vrGJhv+vulDfPrO7E5rsysweiTDIvVsf31pJaKiOj9ke1i9kIJb2l2/NT611ePaU+ewafAsDF84A0VvZgOyjG9XbcXIFx7Dp7998ZptJU5MRI2nY+81KTAMuqf3OLRtRyVOnAi5qszh7Z3pnx3pm8D1+2dERAR27tzp1D5VFbKxsbEwm81YtmwZkpOTkZubi+XLlyM0NBQhISH48ccfERQU1LJ9cHAwKioqFKxYeRIskOQmyJJX2xvIVnjJNa4tyg1oUIuLcjAgtT1j5gl13WPC8PKWlp/rz1ZcN2BJPVQVsuHh4VizZg2WLFmCVatWYebMmUhKSkJxcTEAe6hWVl4+qlpZWYmePdsfjTS73iePfP4CLI+3PxrsTDk5OZB69+rUfT7x5y/wxrbjaGprykDyxAdvP4Mxw1d3apvubvsnpzB94aew2q6eLtB6eeC5lHF4ds5Tnd5u7ekL2Paz0aooOXty4N/PsfdamQmYkiu4oEty9uxBmN7x7d29f6rqwBcApKSkoLy8HFVVVcjMzERJSUnLQa9Ro0bhyy+/hMlkgtlsxr59+zBmzBiFK1bektQR6NfHF1qvy79OSQI8PSSkzRyC0cM6903TFUyd0B/T7uoPjUZqtdxL44GhEYF46uFbFaqMuhpVjWTbkp+fj5kzZwIAAgIC8Nxzz+HOO++EJElYvHgxQkJCXFrPdwlXH9lta5kr9QzS4dA/pmL1O/n43/XfwmqTMWJICBb896146L4ISJJ0/Z10Mx4eEt59aQLe3F6Mv24sQNHJKmg8JfwpdQTmPzQUvvp2pl+oFf/bxqvm9C019k1A5SFbX1+P0tLSVufIJiUlISkpScGq1Ck4wBtL027HttxTAIDD7/5K0XrcgaenB+Y8EIk5D0Qiato2AMAzs4cpXFVrUSmTYf6xGn1GDYW2hy8MKzejqviM0mWRE1Qdsr6+vryUlrqV8HvjEbMoCbWnzkMfFoxzBwpQkLkLJ7fuQ9DQ/gibMJwh62ZUHbJE3U392Qoc+/tHqCkph89NgegxMNS+QpIQOetuGF55T9kCyWkMWSIVCYoMR2VRKW4a8QvYLFac+6IAABC3ZBaKN+byloduSHVnFxB1Z4GR4ag8dhraQF+E3xuP84eOYcjs+9ArdjB+8ZsEhN8Tp3SJ5CSOZIlU5PDSLADANy++i9g/JkO2WFG0PhtF67MVroxuFEeyRCrVHLjk3hiyREQCMWQBQK8HfHTi2/HR2duibkvr5wONn/j3msZPB62fj8Pb+2oAvQsmD/Uae1vOPcm9+yfnZAFI/n7QrF8LmATfFESvh+TvJ7YNUjXvIH9Mz3sdjXVmoe1o/XzgHeTv8PYBWmBXAlBvEVgU7AEboHXuOe7ePxmyl0j+fgADkFzAO8jfqQB0lQCt8wHoKu7cPzldQEQkEEOWiEgghiwRkUAMWSIigRiyREQCMWSJiARiyBIRCcSQJSISiCFLRCQQQ5aISCBeVnuJXFvnttdGK+ViZa3wa/AB56/DV7vqRnXeI4B9QAyGLOxvLsvsNMDcILYhHx0069d2iTfZxcpavBc/D5Y6wa8Z7HeUmp73epcI2upGYHIuYBIcsnqN/YYvjgYt+4A4nC4A7J/eot9cgL0N0SMFF2msM7skYAHAUtfgkhGzK9RbxAcsYG/DqdEy+4AwDNkuorHJio/2n0FVzUX8VH0RGz84gW+KKpQui6jb43SBm/u+vA5vbD2GN7Yexw+Vl0ciDz/zOQAg/taeSJ05FDPuHggfHX/dRK7GXuemZFnGixv+jT+8ehg2GyBJbW93qKACjzy3D0+vzsOuVxMRd+tNLqvxgbzXYGlohK3RAg+tBgXrduG7TZ+4rH0iNWDIuiFZlrFw5VdY9XYBpJZl7W1r//fCTw0Y99sP8MHaREyID3NJnQDw2aMvofpEGQIH98PknJdw5pMjMJ+vdFn7RErjnKwbWvV2Pla9XQAA+Hm27shIwI6MhKueI8tAQ6MVv3oyF4VG14dc1fHTaKyuh29oCPqMjkLcklkAgJBhERj1l8ddXg+Rq6gyZI8cOYL4+HjodDrExcUhKysLer0eNptN6dIUV1lzEX949et210f07YGIvj3aXCfLQE19E55bc0RUee3qFR+Ji1V1+KnwFM4dLIB//z7Qh4Vg2IIH8O2qrS6vh8hVVBeyBoMB48aNw4MPPoiioiI88sgjSEtLQ1RUFDw8lC93UO5uHKutue4yUd7a8R0aLlo7tI8dn5XizLn6Tqro2iZsWIRp//or7nn/TzCs2Axbo/28IsMrW3DnhkWoLj7D6YM2WE21KHxqOAqfGo78lFvwzQzflsflW/6saG1K9wF3o7o52fT0dMydOxcLFiwAAKSlpSEjIwPR0dEKV6Y8WZbx2uYiSFL7c7COsNpk/G3bcfwpbUTnFdeO5jnZ/pNGY+wr83DuYCEaKqpRdfw0fG/uidIP84TX4I489f4YutoAAKg9uhdnsxYjcsWXitZEN0b5oeEVjEYj9u/fj/T09FbLtVptS8hOnToVoaGhSEpKUqJERRWcqMR339d0KGABQAKwLbekU2pyVOnugyjb+y2i508DAETOuhvHs3IQNWeSS+sgcjVVjWQNBgMCAwMRHh7essxsNuPkyZMtIbtmzRoYjUasW7fO4f1OmTIFRqOx3fVhGi9k9x/s8P6mHz4Abw/PlsdlDY5fKZOYmIgyS5PD21+pHn0Bzxktj3dkJFw1/xrRz/44//37Wy03nqnB1Pm5AOwHy46dKENUVNQN1QEAPaxeSMEtTj3n6+UbMfnjF5G/bhf6ToxFzsylGLMiBcG3DcRPR68d+okTE1HjeWOvmyOMHskA0KHXxBFSYBh0T+8R2kazxIkTIVeVObStu/QBpUVERGDnzp1OPUdVIStJEqxWK2w2W8v8a2ZmJkwmU0vI9uvX75qB6QrvxY5FpP/lcBuUu9sl7crwvP5GDu9L/B8xW+NTWz2uPXUOmwbPwvCFM1D0ZjYgy/h21VaMfOExfPrbF4XXQ51HqT7gjlQVsrGxsTCbzVi2bBmSk5ORm5uL5cuXIzQ0FCEhITe83+t98sjnL8DyePo1t+ksOTk5kHr3uqHn5h39ASN/c/n/0jwyvVLzCPbW+9+/5r7C+/ZEwYcFN1QHANSevoBtPwtRRxle3tLyc/3ZCocCNmdPDvz73djr5oioadsAAAXbb/w1cUSZCZhy9a9NiJw9exCmd2xbd+kD7khVc7Lh4eFYs2YN1q1bh5iYGBw+fBhJSUk86HVJ5MAA6Dvp0ti4KNdd+UXUnakqZAEgJSUF5eXlqKqqQmZmJkpKShiyl/Tw0+LhSRGdsq/UmUM6ZT9EdG2qC9mfy8/PbxWy8+fPx8KFC7F3714kJCS4fH72u4RJreai2lsmyrwZHQtHSbKPiMfF9umkikg0/9vGq+r0LaX7gLtR1Zzsz9XX16O0tLRVyGZkZChYkfKGR4YgcfTNyDl4ts31xjPXPiFcloGnH42G1N4dZQSKSpkM84/V6DNqKLQ9fGFYuRlVxWdcXgeRK6k6ZH19fXkpbRv+8dIEjH54J4pLrw7Utg6GXSn9oaGYNWWQqNJaCb83HjGLklB76jz0YcE4d6AABZm7cHLrPgQN7Y+wCcMZstTlqX66gK4WHOCNz968D8MGBwNo/zaHzZpX/y75VqxeNMplo9j6sxU49vePULQhG4WZu2Gpv3QupSQhctbdKPnnAZfUQaQkhqybCuvli3/93y+xNG0EQntePk9HkuyhemWO/ueI3tj68p14+X9GwsPDddMEQZHhqCwqRfDQAfAO8se5L+ynR8UtmYXijbm8ZwF1C6qeLqBr8/fV4rmUGDzz2DDs+vx7bMo24lyFGeaLVgT4eWHIfwQi5YFI3PaLYEXqC4wMR+mHebj5zhj0iovEsbc+xpDZ96FX7GBodFqcDfsG3390SJHaiFyFIdsFaDQemHbXAEy7a4DSpbRyeGkWAOCbF99F7B+TIVusKFqfjaL12QpXRuQ6nC4gl2gOXKLuhiFLRCQQQxYA9HrARye+HR+dva0uQOvnA42fC14zABo/HbR+Pi5pSzRfDaB3wSSdXmNvy/EnsA+IIslyR+9O2jXItXWAySS2Eb0ekr+f2DZc6GJlLRrrzMLb0fr5wDvIX2gbl28Q82uh7QBAdSNQbxHbhq8GCNA69xz2ATF44OsSyd8P6Ga//I7yDvIXHn5dUYDW+QB0BfYBMThdQEQkEEOWiEgghiwRkUAMWSIigRiyREQCMWSJiARiyBIRCcSQJSISiCFLRCQQr/i6RM2XFKr1Mkwiuj6GLOwBa5mdBpgbxDbko4Nm/Vqngra6EZicC5gEh6xeA+xKYNASdTZOFwD2EazogAXsbTg5Wq63iA9YwN6G6NEyUXfEkCUiEojTBdRtHSmswN+2HceJ0zU4eaYWHpKE5N9/juQpt+CukWEu+1Zf6toYstStyLKMTdlGvLqpEF8d/QGA/Zt9m++q/PbuE3h79wkMCu+B1JlDkJo0BFovTwUrJnfH6QLqNpqabJi9ZD8efuZz5OX/0LK8rdvWnzhdgwUrvsK98z5GVc1FF1ZJXQ1DlroFWZbx6PP7sGF78aXH19ve/u+neeW4N/VjmBt4VJBuDEOWuoWX38rHO7uN7a7fkZGAHRkJba778t8/YN4LX4gqjbo4VYbskSNHEB8fD51Oh7i4OGRlZUGv18NmsyldGgbl7sax2prrLnM1q6kWhU8NR+FTw5Gfcgu+meHb8rh8y58VrU1pjU1WrHzrKK51HCuibw9E9O3R7vp3dp/A2fP1Aqqjrk51B74MBgPGjRuHpUuXYvPmzcjOzkZaWhqioqLg4aHKzwRV8NT7Y+hqAwCg9uhenM1ajMgVXypak1r889NSnP+xY1/4aLXJ+Nu241iSOqKTqqLuQnWplZ6ejrlz52LBggUYOHAg0tLSEBYWhujoaKVLIzf12uYidPRkLEkCMrceQ1OT8n9NkXtR1UjWaDRi//792LhxY6vlWq0W0dHRKC4uxuzZsyHLMpqamvDss89i0qRJLq9z+uED8Pa4fFpPWYMLrhajG3YovwId/d57WQbOVZhRXmFCeCi/0ZUcp6qQNRgMCAwMRHh4eMsys9mMkydPIjo6GsHBwdi+fTtCQkJw4cIF3H777Q6F7JQpU2A0tn/QI0zjhez+gx2u873YsYj0vzx/Nyh3t8PPTUxMRJmlyeHtpcAw6J7e4/D2HZE4cSLkqjKXtOUqMjxg8nyq1bIdGQlXzb9G9LM/zn///lbLjWdqMHV+bsvjCQm/hA4VYool1YuIiMDOnTudeo6qQlaSJFitVthstpb518zMTJhMJkRHRyMkJKRlWx8fH16RQw6w2YehnfRekcDpAnKOqkI2NjYWZrMZy5YtQ3JyMnJzc7F8+XKEhoa2ClhZljFv3jwsXrzYof1e75NHPn8BlsfTO1S7o3JyciD17uXw9mUmYEru9bfrDDl79iBM75q2XKnnHe/gx6rLFxRcOTJt1jyCvfX+96+5ry/25aBXiE/nFkhdmqoOfIWHh2PNmjVYt24dYmJicPjwYSQlJV110OuJJ57AgAEDkJqaqlCl5E7uv2tAh/chARg7vBcDlpymqpAFgJSUFJSXl6OqqgqZmZkoKSlpFbJPPvkkdDodXnjhBUXq+y5hUqv52PaWKcn/tvE8fesKqTOHdHgfMoDUmUM7Xgx1O6oL2Z/Lz89vCdnc3FysXbsWX3/9NcaPH4/x48ejrq5O4QpJ7YZHhmD0sF43fBqXJAE9A73x64kDOrMs6iZUNSf7c/X19SgtLW0J2YSEBFgsvIacnLfid/GY8Fg2LFZbm/ctMJ5p/4o9WQZWLhwJby3vxkXOU3XI+vr6quJSWnJ/Y2N6Y+NfxuPBRZ/BBvmqoG3rYFjzLRCXPTECs6YOclGl1NWofrqAqLNMTxyID9Ymwk/vBaD9s7qal3t6SHj92TF4dk6MiyqkrkjVI1miznb32L44nZOEt3efwNp3C3GspPqqbUJ76jF3RiRm3z8YoTd1wXPayKUYstTtBPhr8cSDQ5GWNARfGC7gxPc1qK5rhJ/eCzf30uOukWHQaPhHHnUOhix1W5IkYWxMb4yN6a10KdSF8eOaiEgghiwA6PWAj058Oz46e1tO8NUAehf8vaHX2Nsios4lyfL1vu2oe5Br6wCTSWwjej0kf+dvk1fdCNQLPj3YVwMEaMW2QdQdMWSJiATidAERkUAMWSIigRiyREQCMWSJiARiyBIRCcSQJSISiCFLRCQQQ5aISCCGLBGRQAxZIiKBGLJERAIxZImIBGLIEhEJxJAlIhKIIUtEJBBDlohIIIYsEZFADFkiIoH+H7uOvmcy/PxSAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 423.331x170.567 with 1 Axes>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -195,18 +232,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "5f605d57",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.511147Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.510046Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.665574Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.664903Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 373.164x170.567 with 1 Axes>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -237,9 +281,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "7fd84c6e",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.671114Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.669747Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.684118Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.683353Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Use local versions of the primitives by default.\n",
@@ -263,9 +314,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "id": "aafc01ef",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.689377Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.688046Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.693864Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.693141Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Set the Sampler and runtime options\n",
@@ -285,10 +343,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "id": "2ae5160c",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.698916Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.697617Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.743023Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.742257Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3259/4196057829.py:3: DeprecationWarning: The function ``circuit_knitting.cutting.cutqc.wire_cutting.evaluate_subcircuits()`` is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. \n",
+      "  subcircuit_instance_probabilities = evaluate_subcircuits(cuts)\n"
+     ]
+    }
+   ],
    "source": [
     "from circuit_knitting.cutting.cutqc import evaluate_subcircuits\n",
     "\n",
@@ -296,7 +370,7 @@
     "\n",
     "# Uncomment the following lines to instead use Qiskit Runtime Service as configured above.\n",
     "# subcircuit_instance_probabilities = evaluate_subcircuits(cuts,\n",
-    "#                                                          service_args=service.active_account(),\n",
+    "#                                                          service=service,\n",
     "#                                                          backend_names=backend_names,\n",
     "#                                                          options=options,\n",
     "#                                                         )"
@@ -322,9 +396,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "id": "5aceecc0",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.746149Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.745620Z",
+     "iopub.status.idle": "2024-04-19T17:42:20.976210Z",
+     "shell.execute_reply": "2024-04-19T17:42:20.975201Z"
+    }
+   },
    "outputs": [],
    "source": [
     "%%capture\n",
@@ -350,10 +431,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "id": "5353b0c8",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:20.980252Z",
+     "iopub.status.busy": "2024-04-19T17:42:20.979628Z",
+     "iopub.status.idle": "2024-04-19T17:42:20.987941Z",
+     "shell.execute_reply": "2024-04-19T17:42:20.987149Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3259/3419935587.py:3: DeprecationWarning: The function ``circuit_knitting.cutting.cutqc.wire_cutting_verification.verify()`` is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. \n",
+      "  metrics, exact_probabilities = verify(circuit, reconstructed_probabilities)\n"
+     ]
+    }
+   ],
    "source": [
     "from circuit_knitting.cutting.cutqc import verify\n",
     "\n",
@@ -372,26 +469,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "id": "8d54b767",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:20.991193Z",
+     "iopub.status.busy": "2024-04-19T17:42:20.990628Z",
+     "iopub.status.idle": "2024-04-19T17:42:20.995648Z",
+     "shell.execute_reply": "2024-04-19T17:42:20.995000Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'nearest': {'chi2': 0,\n",
-       "  'Mean Squared Error': 1.554441697306333e-32,\n",
-       "  'Mean Absolute Percentage Error': 4.748059826691265e-14,\n",
+       "  'Mean Squared Error': 2.2429332936235563e-32,\n",
+       "  'Mean Absolute Percentage Error': 1.1722866042107947e-13,\n",
        "  'Cross Entropy': 2.599681088367844,\n",
        "  'HOP': 0.9004283905932716},\n",
        " 'naive': {'chi2': 0,\n",
-       "  'Mean Squared Error': 6.5760386594463524e-34,\n",
-       "  'Mean Absolute Percentage Error': 5.260720927719812e-14,\n",
+       "  'Mean Squared Error': 2.033924079765218e-33,\n",
+       "  'Mean Absolute Percentage Error': 1.212151701573308e-13,\n",
        "  'Cross Entropy': 2.5996810883678423,\n",
-       "  'HOP': 0.9004283905932736}}"
+       "  'HOP': 0.9004283905932735}}"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -412,18 +516,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "id": "c8cc97e9",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:20.998203Z",
+     "iopub.status.busy": "2024-04-19T17:42:20.997817Z",
+     "iopub.status.idle": "2024-04-19T17:42:21.418473Z",
+     "shell.execute_reply": "2024-04-19T17:42:21.417908Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -495,7 +606,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/.doctrees/nbsphinx/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb b/.doctrees/nbsphinx/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb
index 425b8b458..e807caf00 100644
--- a/.doctrees/nbsphinx/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb
+++ b/.doctrees/nbsphinx/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb
@@ -14,11 +14,18 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "072055cb",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:26.666751Z",
+     "iopub.status.busy": "2024-04-19T17:42:26.666047Z",
+     "iopub.status.idle": "2024-04-19T17:42:27.037654Z",
+     "shell.execute_reply": "2024-04-19T17:42:27.037075Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from qiskit import QuantumCircuit\n",
-    "from qiskit.quantum_info import PauliList\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
     "from circuit_knitting.cutting import (\n",
     "    partition_problem,\n",
@@ -38,15 +45,22 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "dc4af922",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:27.040845Z",
+     "iopub.status.busy": "2024-04-19T17:42:27.040384Z",
+     "iopub.status.idle": "2024-04-19T17:42:27.049464Z",
+     "shell.execute_reply": "2024-04-19T17:42:27.048947Z"
+    }
+   },
    "outputs": [],
    "source": [
     "circuit = QuantumCircuit(2)\n",
     "circuit.h(0)\n",
     "circuit.cx(0, 1)\n",
-    "observables = PauliList([\"ZZ\"])\n",
+    "observable = SparsePauliOp([\"ZZ\"])\n",
     "partitioned_problem = partition_problem(\n",
-    "    circuit=circuit, partition_labels=\"AB\", observables=observables\n",
+    "    circuit=circuit, partition_labels=\"AB\", observables=observable.paulis\n",
     ")\n",
     "subcircuits = partitioned_problem.subcircuits\n",
     "subobservables = partitioned_problem.subobservables"
@@ -64,7 +78,14 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "d095701f",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:27.051994Z",
+     "iopub.status.busy": "2024-04-19T17:42:27.051624Z",
+     "iopub.status.idle": "2024-04-19T17:42:27.130549Z",
+     "shell.execute_reply": "2024-04-19T17:42:27.129845Z"
+    }
+   },
    "outputs": [],
    "source": [
     "subexperiments, coefficients = generate_cutting_experiments(\n",
@@ -86,7 +107,14 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "7a74f709",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:27.133708Z",
+     "iopub.status.busy": "2024-04-19T17:42:27.133304Z",
+     "iopub.status.idle": "2024-04-19T17:42:27.137290Z",
+     "shell.execute_reply": "2024-04-19T17:42:27.136791Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.utils.simulation import ExactSampler\n",
@@ -106,7 +134,14 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "7019d781",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:27.139638Z",
+     "iopub.status.busy": "2024-04-19T17:42:27.139267Z",
+     "iopub.status.idle": "2024-04-19T17:42:27.151495Z",
+     "shell.execute_reply": "2024-04-19T17:42:27.150902Z"
+    }
+   },
    "outputs": [],
    "source": [
     "results = {\n",
@@ -132,7 +167,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/.doctrees/nbsphinx/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb b/.doctrees/nbsphinx/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb
index 72d59cd6a..7e4a31d1e 100644
--- a/.doctrees/nbsphinx/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb
+++ b/.doctrees/nbsphinx/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb
@@ -16,12 +16,19 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "dc54656b",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:28.788507Z",
+     "iopub.status.busy": "2024-04-19T17:42:28.787885Z",
+     "iopub.status.idle": "2024-04-19T17:42:29.225050Z",
+     "shell.execute_reply": "2024-04-19T17:42:29.224317Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "from qiskit.circuit.library import EfficientSU2\n",
-    "from qiskit.quantum_info import PauliList\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
     "from circuit_knitting.cutting import (\n",
     "    partition_problem,\n",
@@ -33,11 +40,18 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "dd147239",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:29.228363Z",
+     "iopub.status.busy": "2024-04-19T17:42:29.228093Z",
+     "iopub.status.idle": "2024-04-19T17:42:29.982364Z",
+     "shell.execute_reply": "2024-04-19T17:42:29.981635Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 831.997x294.311 with 1 Axes>"
       ]
@@ -50,7 +64,7 @@
    "source": [
     "circuit = EfficientSU2(4, entanglement=\"linear\", reps=2).decompose()\n",
     "circuit.assign_parameters([0.8] * len(circuit.parameters), inplace=True)\n",
-    "observables = PauliList([\"ZZZZ\"])\n",
+    "observable = SparsePauliOp([\"ZZZZ\"])\n",
     "circuit.draw(\"mpl\", scale=0.8)"
    ]
   },
@@ -66,11 +80,18 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "d4ccf5b8",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:29.986565Z",
+     "iopub.status.busy": "2024-04-19T17:42:29.985900Z",
+     "iopub.status.idle": "2024-04-19T17:42:30.190473Z",
+     "shell.execute_reply": "2024-04-19T17:42:30.189786Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 684.861x120.4 with 1 Axes>"
       ]
@@ -82,7 +103,7 @@
    ],
    "source": [
     "partitioned_problem = partition_problem(\n",
-    "    circuit=circuit, partition_labels=\"AABB\", observables=observables\n",
+    "    circuit=circuit, partition_labels=\"AABB\", observables=observable.paulis\n",
     ")\n",
     "subcircuits = partitioned_problem.subcircuits\n",
     "bases = partitioned_problem.bases\n",
@@ -94,11 +115,18 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "44956cbb",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:30.193586Z",
+     "iopub.status.busy": "2024-04-19T17:42:30.193094Z",
+     "iopub.status.idle": "2024-04-19T17:42:30.438300Z",
+     "shell.execute_reply": "2024-04-19T17:42:30.437592Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 684.982x120.4 with 1 Axes>"
       ]
@@ -126,7 +154,14 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "8c56282f",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:30.441244Z",
+     "iopub.status.busy": "2024-04-19T17:42:30.441021Z",
+     "iopub.status.idle": "2024-04-19T17:42:30.714499Z",
+     "shell.execute_reply": "2024-04-19T17:42:30.713655Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -199,7 +234,14 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "78539fcc",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:30.717844Z",
+     "iopub.status.busy": "2024-04-19T17:42:30.717338Z",
+     "iopub.status.idle": "2024-04-19T17:42:30.722870Z",
+     "shell.execute_reply": "2024-04-19T17:42:30.722190Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -225,7 +267,14 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "f07a6cc3",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:30.725747Z",
+     "iopub.status.busy": "2024-04-19T17:42:30.725290Z",
+     "iopub.status.idle": "2024-04-19T17:42:30.731306Z",
+     "shell.execute_reply": "2024-04-19T17:42:30.730545Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -268,7 +317,14 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "43d32869",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:30.734094Z",
+     "iopub.status.busy": "2024-04-19T17:42:30.733692Z",
+     "iopub.status.idle": "2024-04-19T17:42:30.983163Z",
+     "shell.execute_reply": "2024-04-19T17:42:30.982415Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -342,7 +398,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/.doctrees/nbsphinx/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb b/.doctrees/nbsphinx/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb
index f91e143ab..44a0bb67d 100644
--- a/.doctrees/nbsphinx/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb
+++ b/.doctrees/nbsphinx/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb
@@ -14,13 +14,20 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "1aa871cb",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:32.984407Z",
+     "iopub.status.busy": "2024-04-19T17:42:32.983921Z",
+     "iopub.status.idle": "2024-04-19T17:42:33.359403Z",
+     "shell.execute_reply": "2024-04-19T17:42:33.358807Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "from qiskit import QuantumCircuit\n",
-    "from qiskit.quantum_info import PauliList\n",
-    "from qiskit_aer.primitives import Estimator, Sampler\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "from qiskit_aer.primitives import EstimatorV2, SamplerV2\n",
     "\n",
     "from circuit_knitting.cutting import (\n",
     "    partition_problem,\n",
@@ -46,11 +53,18 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "0ae22516",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:33.362341Z",
+     "iopub.status.busy": "2024-04-19T17:42:33.361898Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.014510Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.013813Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1290.83x618.722 with 1 Axes>"
       ]
@@ -92,11 +106,18 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "631286a6",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.017159Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.016897Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.260375Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.259691Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAHwCAYAAADJpfudAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABk5ElEQVR4nO3deXhU5f3//+dMFhKSEJagARLWJAKBJAIiBBWDsRURBCtuSKtflIogtCLpYq3667cqSBVxhdrW+v18immlWpAiqIBiBAyyR/YQJMugIWELAbLM748jkUACmWFmzpzJ63FduULOnOU9Q3LmNfe5z33bnE6nExERERERsSS72QWIiIiIiIj7FOhFRERERCxMgV5ERERExMIU6EVERERELEyBXkRERETEwhToRUREREQsTIFeRERERMTCFOhFRERERCxMgV5ERERExMIU6EVERERELEyBXkRERETEwhToRUREREQsTIFeRERERMTCFOhFRERERCxMgV5ERERExMIU6EVERERELEyBXkRERETEwhToRUREREQsTIFeRERERMTCFOhFRERERCxMgV5ERERExMIU6EVERERELEyBXkRERETEwhToRUREREQsTIFeRERERMTCFOhFRERERCxMgV5ERERExMIU6EVERERELEyBXkRERETEwhToRUREREQsTIFeRERERMTCFOhFRERERCxMgV5ERERExMIU6EVERERELEyBXkRERETEwhToRUREREQsTIFeRERERMTCgs0uQBrmdEJtldlVNJ09BGw2s6sQERERaX4U6P1UbRWsnGt2FU2XMRWCQs2uQkRERKT5UZcbERERERELU6AXEREREbEwBXoREREREQtToBcRERERsTAFehERERERC1OgFxERERGxMAV6EREREREL0zj0AWTz3lU89kZGvWVhoRHEtU8is994Rg95hKAg/ZeLiIiIBBKluwCUkXY3A3vejBMn5cccfPTV27yx+FG++XY7v7x9vtnliYiIiIgHKdAHoMRO/cjsf2/dzyPTH2bCrJ4s/fJN7r/pj7SObG9idSIiIiLiSepD3wyEh0bQs8sgnE4nxYf2ml2OiIiIiHiQAn0zUfJ9kG/Vsq3JlYiIiIiIJ6nLTQA6WXWCIxWlOJ1GH/rFa95gT9FGesYPJK59ktnliYiIiIgHNYsW+tLSUrKyskhISCAsLIz4+HimTZtGRUUFEyZMwGaz8corr5hdpse8vfxJbn+qPWOfvoyJL6SweM1rXNPnNp6+7z9ml2aq8grYXwoHyuD4SbOrERERX6s4BYVlxnvBoeNmVyPiOQHfQr9p0yaGDx+Ow+EgIiKC3r17U1xczNy5c9m7dy9lZWUApKWlmVuoB424eiLXpYyluraKfSVbyV41k9IjhYSGhNWtc7r6FA/P6UfGlfcw7obH65bPeuc+Dh8/yDMPLDWjdI+rroHN30DObsj/7ofldhukxMOQJEi4DGw282oUERHvyv/WeB/Y9A3U1P6wvEs7433gyi4QEmRefSKXKqADfWlpKSNHjsThcDB9+nSefPJJoqKiAJg1axa/+tWvCA4OxmazkZKSYnK1ntMpJpF+SZkADOw5nD7druGXr13DSwsf4vF73wEgNLgFWXe9zfTXrmNQr1vo0TGVnG3vs3b7YuY/utXM8j2m4hT85dP6Qf6MWqdxYt/0DVyTCLcNAHuzuF4lItJ81Drhg42wYnvDj+8/BPvXwOe74MHrISqs4fVE/F1AR5ipU6dSWFjIlClTmD17dl2YB8jKyiI1NZXq6mq6du1Kq1atTKzUu5K7ppPZbzyrNmeTV/BF3fKkuP7cPvQxZr3zU747XMicdyfyyJhXiYnuaGK1nnG6GuavbDjMn+vz3fDeV+B0er8uERHxnSWbGg/zZ/vmELyxAk5Web0kEa8I2EC/fft2srOziYmJ4dlnn21wnf79+wOQmppab/m+ffsYNWoUUVFRtGnThp/+9KccOnTI6zV707jMJ7Dbg/j7st+fs/x3BNmDmTTnSlITMshIu8ukCj3r0x1Gy0tTrd4F+5oQ/kVExBoKy+CTr5u+flE5rHBhfRF/ErCBfsGCBdTW1jJu3DgiIyMbXCc8PByoH+iPHTtGRkYGhYWFLFiwgPnz57N69WpuueUWamtrG9yPFXSKSSAj9S427vmErfmr65YHB4XQu2s6RypK+fGA+02s0HNqa+GLPa5vl7Pb87WIiIg53Dmnr9lj3HslYjUBG+hXrFgBQEZGRqPrFBYWAvUD/fz58ykqKuL999/nlltuYezYsfzjH/9g7dq1LFq0yLtFe9ndNzyO3Wbn78t/aKXfmr+a5evf4tYhU3ht0TROVVWaWKFn7D5ojGjjqk3fQOVpz9cjIiK+dboavtrn+nbHTsLXxZ6vR8TbbE5nYPYcjo+Pp7CwkI0bNzY4gk11dTUdOnSgtLSUvXv30r17d+CHDwArV66st36PHj24/vrr+ctf/uJyLQMGDMDhcLi0TWhwOPOneLfJuPLUcX7+Qio/ue5RRg6exPQ3hpIUN4BJo150eV8TX0nkdLV/fBjodvU4+t82061tl/3peo5960bzvoiI+I2WrTtx82/WubXt5g+eZvfqP3u4IpGLi42NZf369W5tG7Cj3FRUGE20lZUNh8zs7GxKS0uJioqiW7dudcu//vprxo4de976ycnJfP21e53rHA4HRUVFLm0TFtLSrWO5Yt7i6cS27cao9Iex2WzMuOMtHpqTxpA+Y0jpfp1L+yopLuZk1QkvVeqadkfdH1z4u+8OccjF/ysREfEvrU6Hur3t0WPHXX7PFjFbwAb62NhYysvL2bBhA4MHD673WElJCTNmzAAgJSUF21mDkJeXl9O6devz9te2bVt27tzpdi2uCg0Od+tYTfXljqWs2pzN/Ee31D3/jjE9mDD8OWZn38+86VsID41o8v46dOzoNy30Leyn3N62VUsbYZ06ebAaERHxteDQEGprqrEHuR5zWthO0UnvA2ICd/LiGQEb6DMzM9m+fTszZ87kxhtvJCkpCYDc3FzGjx9PaWkp4JsJpdy5fFJzGlbO9UIx3xvYczjv/+HwectvHTKZW4dMdnl/u3ftJsj9BhGPOl0NT70HJ1zsD9+zA8zZucU7RYmIiE/99TPYcsC1bVoEw4p/zyUsxItvwCJeELA3xWZlZdGuXTsOHDhAcnIyffv2JTExkYEDB9K9e3eGDRsGnD9kZZs2bTh8+PB5+ysrK6Nt27a+KF0uUWgwXN3D9e2uSfJ8LSIiYg53zukDukFYiOdrEfG2gA30cXFxrF69mhEjRhAWFkZBQQFt27Zl3rx5LFmyhF27dgHnB/pevXo12Ff+66+/plevXj6pXS7dsF7Qpuk9hujZAXpbfz4tERH5XuLlkBLf9PWjw+HGPt6rR8SbAjbQgxHOP/jgA44dO8axY8dYt24dEydOpKKigoKCAux2O3361P/rveWWW/j888/rhrQEWLduHXv37mXkyJG+fgripqhwmDQM2jYh1CfFwn3Xgj2g/xpERJoXmw3uTYfkJnSHjw6Hh4ZBa++PRyHiFQE7bOWFrFu3jkGDBnHFFVewY8eOeo8dPXqUvn37EhMTw9NPP83JkyfJysqiffv2rFmzBruPUp+3+9B7WsZU/KYP/dmOnTRmjV27B46fc6/s5a1gSBKkJ0BwkDn1iYiId9XUGu8Bn++GksP1H2sZCoN6wNCeEK0wLxYWsDfFXsjWrVuB87vbALRq1YoVK1Ywbdo07rrrLoKDg7nlllt48cUXfRbmxXOiwuCWNLipL+x0wP/kQGUVRITCr28xWnBERCRwBdm/b7xJhP2HYN4K432gZSg8Nca470rE6prlr/GFAj0Yk0h98MEHvixJvCw4yLjsGhpsnMiDgxTmRUSaE5sNusb88D4QEqQwL4GjWTY5XyzQB7LPtrzLSwsn1Vv2Ye7fuHGGjZxt75tTlIiIiIi4rVl+Nl2xYoXZJZgmZ9t7ZPb/ad3PjrIClq77M706DzKxKhERERFxV7MM9IHseOVhHvxTH05VVdI+Op6qmlM4DuVzQ//xTLvtdfIKcphx51sA1NbW8sK/HmDy6JeZt3i6uYWLiIiIiFsU6ANMZHhrhqXdQ3iLKO698Qlydy5jwYpnmD72TdbvXE7vLukEBxmzZiz87AWSuw4hKa6/yVWLiIiIiLuaZR/6QLeneBMJna4EYHfhVyR0NP79Rd77DOkzBoB9jm2s3rqQcZm/M61OEREREbl0aqEPQPnnBPrByaNwOp2s37mMB0fMAmBb/moOlhdw38xEAMqOOZjz7kTKjpYwMn1So/sWEREREf+iQB9gSo8Ugc1GTLQxNV6+Ywv33PA4Ow58SefLexHeIhKAkemT6gX36a9fz23X/oIhfUabUbaIiIiIuEmBPsDsKdpY18UGIDKsNYvWvEZ0RAzpyaPNK0xEREREvEKBPsAM6n0Lg3rfUvfzq9NyAXhgdjLPP7Sy0e3+NGmVt0sTERERES9QoG8m3nwsz+wSRERERMQLNMqNiIiIiIiFKdCLiIiIiFiYAr2IiIiIiIWpD72fsodAxlSzq2g6e4jZFYiIiIg0Twr0fspmg6BQs6sQEREREX+nLjciIiIiIhamQC8iIiIiYmEK9CIiIiIiFqZALyIiIiJiYQr0IiIiIiIWpkAvIiIiImJhCvQiIiIiIhamQC8iIiIiYmEK9CIiIiIiFqZALyIiIiJiYQr0IiIiIiIWpkAvIiIiImJhCvQiIiIiIhamQC8iIiIiYmEK9CIiIiIiFqZALyIiIiJiYQr0IiIiIiIWpkAvIiIiImJhwWYXIA1zOqG2yuwqms4eAjab2VWIiIiIND8K9H6qtgpWzjW7iqbLmApBoWZXISIiItL8qMuNiIiIiIiFKdCLiIiIiFiYAr2IiIiIiIUp0IuIiIiIWJgCvYiIiIiIhSnQi4iIiIhYmAK9iIiIiIiFaRz6ALJ57yoeeyOj3rKw0Aji2ieR2W88o4c8QlCQ/stFREREAonSXQDKSLubgT1vxomT8mMOPvrqbd5Y/CjffLudX94+3+zyRERERMSDFOgDUGKnfmT2v7fu55HpDzNhVk+Wfvkm99/0R1pHtjexOhERERHxJPWhbwbCQyPo2WUQTqeT4kN7zS5HRERERDxIgb6ZKPk+yLdq2dbkSkRERETEk5pFoC8tLSUrK4uEhATCwsKIj49n2rRpVFRUMGHCBGw2G6+88orZZXrMyaoTHKko5fDx79hXspW5/57MnqKN9IwfSFz7JLPLExEREREPCvg+9Js2bWL48OE4HA4iIiLo3bs3xcXFzJ07l71791JWVgZAWlqauYV60NvLn+Tt5U/WW3ZNn9t4ZMyrJlUkZnI6oaAUcnaD4zBU1UBEC0iJh4HdoWULsysUERFvKzsOa/bATgecqoLQYOh+GQxJhMtamV2dXKqADvSlpaWMHDkSh8PB9OnTefLJJ4mKigJg1qxZ/OpXvyI4OBibzUZKSorJ1XrOiKsncl3KWKprq9hXspXsVTMpPVJIaEhY3Tqnq0/x8Jx+ZFx5D+NueLxu+ax37uPw8YM888BSM0oXD/v2KLydA4Vl5z+W/x0s2QzDesOP+4Ld5vv6RETEu05Vwz/XwYb9RgPP2Q6Uwac7ILkTjBusBh4rC+guN1OnTqWwsJApU6Ywe/bsujAPkJWVRWpqKtXV1XTt2pVWrQLn42mnmET6JWUysOdw7szI4g/3L2ZnYS4vLXyobp3Q4BZk3fU273zyDHuLNwOQs+191m5fzKNj/2JW6eJBjiMwZ1nDYf6MqhpYttU42Z97ohcREWs7XQ1vfAJfFVz4HJ9XBHM/ghOnfFaaeFjABvrt27eTnZ1NTEwMzz77bIPr9O/fH4DU1NS6ZWc+AAwcOJAWLVpgs1m/2TK5azqZ/cazanM2eQVf1C1PiuvP7UMfY9Y7P+W7w4XMeXcij4x5lZjojiZWK55QXQPzV8KJ001bf+1eo0uOiIgEjn+vh32lTVvXcQT+d4136xHvCdhAv2DBAmpraxk3bhyRkZENrhMeHg7UD/R79uxh4cKFxMbGctVVV/mkVl8Yl/kEdnsQf1/2+3OW/44gezCT5lxJakIGGWl3mVSheNKWA1BW4do2q7ZDrVrpRUQCwrFKyN3n2jZ5RXDwiHfqEe8K2EC/YsUKADIyMhpdp7CwEKgf6K+77jpKSkpYtGgRmZmZ3i3ShzrFJJCRehcb93zC1vzVdcuDg0Lo3TWdIxWl/HjA/SZWKJ70+S7Xtyk9DjtLPF+LiIj43tq9UFPr+na6WmtNAXtT7P79+wHo0qVLg49XV1eTk5MD1A/0drvnP+MMGDAAh8Ph0jahweHMn+LZv6q7b3iclZsW8Pflv2f2QysB2Jq/muXr3+LWIVN4bdE03uixiRYh4S7vOzEpkdPVlR6t1xtu/m0uLaM7UOIoIS4ucK7A1GOz8ZNn9mNz43f5F0/MJW/ZLC8UJSLiH5rF+wBwzf/5f8Re0XijZmP+88kWpo6+2QsVycXExsayfv16t7YN2EBfUWH0N6isbDhkZmdnU1paSlRUFN26dfNqLQ6Hg6KiIpe2CQtp6fJxUntcz0fPN95nosvlvVg2q6bu58pTx3k++z4mDH+OkYMnMf2Nofx16W+ZNOpFl49dUlzMyaoTLm/nazU1NXXfXf0/sYqQFhFuhXmAk1XOgH1dRESgebwPANS4G/GCwgL6dQlUARvoY2NjKS8vZ8OGDQwePLjeYyUlJcyYMQOAlJQUr9/4Ghsb6/I2ocGut5K7at7i6cS27cao9Iex2WzMuOMtHpqTxpA+Y0jpfp1L++rQsaMlWuiDgoLqvnfq1MnkarzEZsNZW4PNHuTypmHBBO7rIiJCM3kfAOy1TRwV4RzOmsqAfl38mTt58YyADfSZmZls376dmTNncuONN5KUZMyQmpuby/jx4yktNW779sWEUu5cPqk5DSvneqGY7325YymrNmcz/9EtdR9oOsb0YMLw55idfT/zpm8hPDSiyfvbvWs3QaHeqtZznvw3HKmEDrEd6u6hCESvfQK7XOvlBcArz0wj8a/TPF+QiIifaC7vAyu3w382uL7d7T9K5e1fBe7rEqgC9qbYrKws2rVrx4EDB0hOTqZv374kJiYycOBAunfvzrBhw4D6/eebk4E9h/P+Hw5zWZvO9ZbfOmQyb/9mr0thXvzPkETXt7msFSRc7vlaRETE9wZ2h2AXL9TagPQEr5QjXhawgT4uLo7Vq1czYsQIwsLCKCgooG3btsybN48lS5awa5cxDEhzDfQS2PrEuT6V9w29IQCmXRARESCiBQx2MZyndoaYqIuvJ/4nYLvcAPTq1YsPPvjgvOXHjx+noKAAu91Onz59TKhMxLuC7DDxenj5I+PS8sVk9IKre3i9LBER8aFbr4RvjzZtSOLO7eDuQd6vSbwjoAN9Y/Ly8nA6nSQlJdGy5fmjybz77rsAfP311/V+7tq1KwMGDPBdoSKXICYKfvFjWLC28f70LUPhR31gaE/f1iYiIt4XHAQPDjX60q/ZA9UNjEtvt0H/rnD7QGjRLFNhYGiW/3Vbt24FGu9uM3bs2AZ//tnPfsZbb73l1dpEPKlNBDx8gzGl9xe7jQlDamoh2A5jB8KVXSC0WZ4FRESah+Ag+MlVcFMKrNtrNPDsPmi8F7QIht+OhGjXR8oWP9Ms38ovFuidzsbHchexothouG0AbP7G6IIT0UJdbEREmpOIFjCst/F1ZqSfsBCF+UARsDfFXsjFAn0g+2zLu7y0cFK9ZR/m/o0bZ9jI2fa+OUWJiIiIiNuaZQv9ihUrzC7BNDnb3iOz/0/rfnaUFbB03Z/p1Vl3woiIiIhYUbMM9IHseOVhHvxTH05VVdI+Op6qmlM4DuVzQ//xTLvtdfIKcphx51sA1NbW8sK/HmDy6JeZt3i6uYWLiIiIiFsU6ANMZHhrhqXdQ3iLKO698Qlydy5jwYpnmD72TdbvXE7vLukEB4UAsPCzF0juOoSkuP4mVy0iIiIi7mqWfegD3Z7iTSR0uhKA3YVfkdDR+PcXee8zpM8YAPY5trF660LGZf7OtDpFRERE5NKphT4A5Z8T6Acnj8LpdLJ+5zIeHDELgG35qzlYXsB9MxMBKDvmYM67Eyk7WsLI9EmN7ltERERE/IsCfYApPVIENhsx0Z0AyHds4Z4bHmfHgS/pfHkvwltEAjAyfVK94D799eu57dpfMKTPaDPKFhERERE3KdAHmD1FG+u62ABEhrVm0ZrXiI6IIT15tHmFiYiIiIhXKNAHmEG9b2FQ71vqfn51Wi4AD8xO5vmHVja63Z8mrfJ2aSIiIiLiBQr0zcSbj+WZXYKIiIiIeIFGuRERERERsTAFehERERERC1OgFxERERGxMPWh91P2EMiYanYVTWcPMbsCERERkeZJgd5P2WwQFGp2FSIiIiLi79TlRkRERETEwhToRUREREQsTIFeRERERMTCFOhFRERERCxMgV5ERERExMIU6EVERERELEyBXkRERETEwhToRUREREQsTIFeRERERMTCFOhFRERERCxMgV5ERERExMIU6EVERERELEyBXkRERETEwhToRUREREQsTIFeRERERMTCFOhFRERERCxMgV5ERERExMIU6EVERERELCzY7AKkYU4n1FaZXUXT2UPAZjO7ChEREZHmR4HeT9VWwcq5ZlfRdBlTISjU7CpEREREmh91uRERERERsTAFehERERERC1OgFxERERGxMAV6ERERERELU6AXEREREbEwBXoREREREQtToBcRERERsTCNQx9ANu9dxWNvZNRbFhYaQVz7JDL7jWf0kEcICtJ/uYiIiEggUboLQBlpdzOw5804cVJ+zMFHX73NG4sf5Ztvt/PL2+ebXZ6IiIiIeJACfQBK7NSPzP731v08Mv1hJszqydIv3+T+m/5I68j2JlYnIiIiIp6kPvTNQHhoBD27DMLpdFJ8aK/Z5YiIiIiIBynQNxMl3wf5Vi3bmlyJiIiIiHiSutwEoJNVJzhSUYrTafShX7zmDfYUbaRn/EDi2ieZXZ6IiIiIeFCzaKEvLS0lKyuLhIQEwsLCiI+PZ9q0aVRUVDBhwgRsNhuvvPKK2WV6zNvLn+T2p9oz9unLmPhCCovXvMY1fW7j6fv+Y3ZpIqYpr4A1e2DF17B6JxSUgtNpdlUiIuIrp6phQwGs3A6rdsC2QqipNbsqzwj4FvpNmzYxfPhwHA4HERER9O7dm+LiYubOncvevXspKysDIC0tzdxCPWjE1RO5LmUs1bVV7CvZSvaqmZQeKSQ0JKxundPVp3h4Tj8yrryHcTc8Xrd81jv3cfj4QZ55YKkZpYt4XEEpfJwHeUXnB/i4NnBdT7iqG9hs5tQnIiLedeQEfPw15ObDyar6j7UKg0EJMKw3hIWYU58nBHQLfWlpKSNHjsThcDB9+nRKSkrYsGEDDoeDmTNnsmTJEnJzc7HZbKSkpJhdrsd0ikmkX1ImA3sO586MLP5w/2J2Fuby0sKH6tYJDW5B1l1v884nz7C3eDMAOdveZ+32xTw69i9mlS7iUV/tg7nLjVaYhlrjC8vhH2sgex3UqrVeRCTgOI7Ai8uMK7PnhnmAoydh+TZ4+SM4dtL39XlKQAf6qVOnUlhYyJQpU5g9ezZRUVF1j2VlZZGamkp1dTVdu3alVatWJlbqXcld08nsN55Vm7PJK/iibnlSXH9uH/oYs975Kd8dLmTOuxN5ZMyrxER3NLFaEc/YWQL/u6ZpQX3tXvhgk9dLEhERHzpWCfNWwOETF1+3qBz+vAqqarxellcEbKDfvn072dnZxMTE8Oyzzza4Tv/+/QFITU2tW/buu+/yk5/8hC5dutCyZUt69uzJ448/zvHjx31St7eMy3wCuz2Ivy/7/TnLf0eQPZhJc64kNSGDjLS7TKpQxHOcTli00bVW91Xbm3bSFxERa/h0J5S7cF7/5hBs3O+9erwpYAP9ggULqK2tZdy4cURGRja4Tnh4OFA/0M+ePZugoCCeeeYZli5dyqRJk3j99de56aabqK217p0TnWISyEi9i417PmFr/uq65cFBIfTums6RilJ+POB+EysU8ZyCUqO1xRW1TuOmWRERsb7qGljrxjn9812er8UXAjbQr1ixAoCMjIxG1yksLATqB/rFixfzz3/+k3HjxjF06FCmTZvGK6+8Qk5ODp9//rl3i/ayu294HLvNzt+X/9BKvzV/NcvXv8WtQ6bw2qJpnKqqNLFCEc/Y4GYLy4YCj5YhIiIm2eWA46dc3+6bQ1B6zPP1eFvAjnKzf7/xjt6lS5cGH6+uriYnJweoH+jbt29/3roDBgwAoKioyK1aBgwYgMPhcGmb0OBw5k/Z7dI2qT2u56PnG+9j0OXyXiyb9UPnsMpTx3k++z4mDH+OkYMnMf2Nofx16W+ZNOpFl44LkJiUyOlq//8wcPNvc2kZ3YESRwlxcVeZXY7PNZfnf/U9rxKfeqvL2xV/d5S4uN5eqEhE/EVzOQ9eSHN4Dbr0u52r7pzj1rbDfjyKsm82eLagJoiNjWX9+vVubRuwgb6iogKAysqGQ2Z2djalpaVERUXRrVu3C+5r5cqVAPTq1cutWhwOh8sfBsJCWrp1LFfMWzyd2LbdGJX+MDabjRl3vMVDc9IY0mcMKd2vc2lfJcXFnKzy/w7INTU1dd/d/YBmZc3l+Vccd695per0qYB+XUSk+ZwHL6Q5vAaRXb5ze9uDjmK+tdjrErCBPjY2lvLycjZs2MDgwYPrPVZSUsKMGTMASElJwXaBAaiLiop44oknuOmmm9weqz42NtblbUKDw906VlN9uWMpqzZnM//RLXXPv2NMDyYMf47Z2fczb/oWwkMjmry/Dh07WqKFPigoqO57p06dTK7G95rL8685cdCt7SrL9wf06yIizec8eCHN4TUIqTkCgNPpvGDOO1dtTRURwadMeV3cyYtn2JzOwJwrcerUqbz88svEx8fz8ccfk5SUBEBubi7jx48nPz+fqqoqJk+e3OgsscePH+f666/H4XCQm5tLhw4dfFZ/zWlYOddnh7tkGVMhKNTsKi7uyX/DkUqIDoenbzO7Gt9rLs+/vAL+v/+4PhPs3YPg6h7eqUlE/ENzOQ9eSHN4DZxOeH4pFLs4QEJaZ7jvWu/U5E0Be1NsVlYW7dq148CBAyQnJ9O3b18SExMZOHAg3bt3Z9iwYUD9/vNnq6ysZOTIkezbt4/ly5f7NMyLyKVpEwF9XGxcaRkKVzZ8y42IiFiMzQbXJLq+3TVJnq/FFwI20MfFxbF69WpGjBhBWFgYBQUFtG3blnnz5rFkyRJ27TLGJWoo0FdVVXH77bezfv16li5dSu/euklOxGp+cpXR+tQUNmDcYAgN2E6IIiLNz9U9oLcLc2VedwUkXO69erwpoN++evXqxQcffHDe8uPHj1NQUIDdbqdPnz71Hjszdv0nn3zCf//7XwYOHOirckXEg1q3hCk3wryVFx6CLDgIfjoEkuN8V5uIiHhfkN3oPvM/X8CWAxde9/qeMKqfb+ryhoAO9I3Jy8vD6XSSlJREy5b1R5OZPHky//rXv/j1r39Ny5YtWbt2bd1jPXr0aHBYSxHxT+2jIOtmY1z6z3dBYdkPj9mAH/WFwQlG+BcRkcATGmyE+t0OyNkNWwvr3181sLvRzaZzO/Nq9IRmGei3bt0KNNzdZunSpQA899xzPPfcc/Ue+9vf/sZ9993n9fpExHNCg2FQD7i6Oxw/CTOXGJONRIXB8BSzqxMREW+z2+CKDsZX5Wn44yLjfaBVGNwz+OLbW0HA9qG/kAsF+oKCApxOZ4NfgRDmP9vyLi8tnFRv2Ye5f+PGGTZytr1vTlEiPmCzQVS4cQn2zM8iItK8hIcG5vuAAn0zk7PtPdL7jK772VFWwNJ1f6ZX50HmFSUiIiIibmuWXW5WrFhhdglec7zyMA/+qQ+nqippHx1PVc0pHIfyuaH/eKbd9jp5BTnMuPMtwLgB+IV/PcDk0S8zb/F0cwsXEREREbc0y0AfyCLDWzMs7R7CW0Rx741PkLtzGQtWPMP0sW+yfudyendJJzgoBICFn71ActchJMX1N7lqEREREXFXs+xyE+j2FG8iodOVAOwu/IqEjsa/v8h7nyF9xgCwz7GN1VsXMi7zd6bVKSIiIiKXTi30ASj/nEA/OHkUTqeT9TuX8eCIWQBsy1/NwfIC7ptpTKNWdszBnHcnUna0hJHpkxrdt4iIiIj4FwX6AFN6pAhsNmKijXnv8x1buOeGx9lx4Es6X96L8BaRAIxMn1QvuE9//Xpuu/YXDDnrhlkRERER8X8K9AFmT9HGui42AJFhrVm05jWiI2JITx5tXmEiIiIi4hUK9AFmUO9bGNT7lrqfX52WC8ADs5N5/qGVjW73p0mrvF2aiIiIiHiBAn0z8eZjeWaXICIiIiJeoFFuREREREQsTIFeRERERMTCFOhFRERERCxMgV5ERERExMJ0U6yfsodAxlSzq2g6e4jZFYiIiIg0Twr0fspmg6BQs6sQEREREX+nLjciIiIiIhamQC8iIiIiYmEK9CIiIiIiFqZALyIiIiJiYQr0IiIiIiIWpkAvIiIiImJhCvQiIiIiIhamQC8iIiIiYmEK9CIiIiIiFqZALyIiIiJiYQr0IiIiIiIWpkAvIiIiImJhCvQiIiIiIhamQC8iIiIiYmEK9CIiIiIiFqZALyIiIiJiYQr0IiIiIiIWFmx2AdIwpxNqq8yuounsIWCzmV2FiIiISPOjQO+naqtg5Vyzq2i6jKkQFGp2FSIiIiLNj7rciIiIiIhYmAK9iIiIiIiFKdCLiIiIiFiYAr2IiIiIiIUp0IuIiIiIWJhGuZGA5XRCYRl8U2Z8P3gEjp80Hjt+Ct77CuLbQtcYiIkyt1ZvOXIC9pXCgUNQVF7/+f/PF8bzj28LXWIgKAA/3lfVQEHp978Hh+BwBRz7/jWoOAX/3Ww8/27tITLM3FpFxDsOHf/hPHDueXDBWuMc0LkdxLUFewAOv3yqynj+B8qM94IjlfXPg8u2fv9e2B5aBuBodU6n8f9+5vkfPFr/d+Df63/IAu1bmVvrpbA5nU6n2UXI+WpOa9hKd504Dbn58Pku+O5Y07bpcRlckwQp8dYPtrVO2FkCObshr8g4mV1MdDgMToTBCca/re7QceP5r9trvGFdTJDd+L+/NskI95pTQcTaamqN89/nu2CXo2nbxERCeiJc3QMiWni3Pl9wHIGcXfBlPpyqvvj6IUHQvysMSTICrtWdrDKyQM5u47Voim7t4ZpESO0MwUHerc/TFOj9lDuBfvPeVTz2Rka9ZWGhEcS1TyKz33hGD3mEoCDvXJTxh0DvdMIXe2DRhqadvBrSLhLuHgQJl3u2Nl8pLjdanA6Uubd9kB1uTIYb+1jzg83paliyGT7bAe6e2BIvh7sGGb8LImI9+74zzoPfHnVv+9AgGJEG115hzRb7E6fgvQ1GmHVXSjzcfhW0smADj9MJufuMq/CVp93bR5uWcOcg6NnBs7V5kwK9n7qUQJ+RdjcDe96MEyflxxx89NXbFDi2cfPVD/LL2+d7pV6zA315hXECb2pLzMVcmwQjr4RQi3RKq3XCJ3nw4VajZepSdWoD96ZDh9aXvi9f2fcd/O8aKG3iVZkLCQ2GUVfCkES11otYRXWN8YF+1Y6mXZm8mO7tYVy6tT7cf10E2euMbjWXqmWoEer7db30ffnK0Up4Zy18XeyZ/Q1OgNH9oEWIZ/bnTRZsg5OLSezUj8z+93Jj//Hccf0M5j6ylvbRcSz98k0OH//O7PI87uAReGm558I8wOpdMG+lccnO39XUwv9+YbyReSLMg9HfcM4y2HvQM/vzti0H4JWPPRPmwWjpfzcX3t/gmWAgIt51qhr+/Cms3O65v9n874zzYFG5Z/bnbWv2wJ9XeSbMg9F99e0c+DjPM/vzttJjRhbwVJgH4zV97ZOmdd00mwJ9MxAeGkHPLoNwOp0UH9prdjkeVXoMXv0EDp/w/L73fgvzVxrhzl/Vfh/mvyrw/L5PVcO8VUbLtz/LK4K3Vnvuw8zZPt2hUC/i76pr4K+fGvcOedqxk0aga2ofbLOs22u0zHvjVPXBJljxtRd27EHlFfDqx8b9U562/5A1GvgU6JuJku+DfKuWAXCny/eqa+AvnxqX2Lwl/ztYuN57+79Un3wNG/Z7b/+nq43X+MyIAP6m9Bj8fbXR5chbPt1h9McUEf/0n42w04NXaM9VcQre/NR/G3e+OWSEeW9atBG2e7Dl25NqauGvn0G5Fxr2zvDFa3ypLNJDWFxxsuoERypKcTqNPvSL17zBnqKN9IwfSFz7JLPL85hlW6HExVaTR28ybvI5WgkvfNi0bdbthbTO0Kuj6zV6U8lho8+8K9x5/sdPGR9qfnaNyyV6Va3TuG/idI1r27nzGvx7PSTFQuuWrtcpIt6z5yCs3unaNu6cA0qPGd0ax/R3vUZvqq6Bf6xxvVHDndcgex38agSE+8mIdmes+Nr1gSDcef4b9xtZILWz6zX6QrNooS8tLSUrK4uEhATCwsKIj49n2rRpVFRUMGHCBGw2G6+88orZZXrM28uf5Pan2jP26cuY+EIKi9e8xjV9buPp+/5jdmkeU1hmtE67qlW4EcpcvXM/e50xlq+/cH4fZl3tZuLu89+43+in7k++2G10i3KVO6/BySr415euH8vf1DqNUR9OnPbuVQ1/drraaHH1RhctK6itNZ7/qSrrdyWrrjHOg65y9zz42Q7/64K4fJt73YHceQ0OnzBa6v3JwaOuN2yB+78D//rSGEXIHwV8C/2mTZsYPnw4DoeDiIgIevfuTXFxMXPnzmXv3r2UlRkf69LS0swt1INGXD2R61LGUl1bxb6SrWSvmknpkUJCQ36YOed09SkentOPjCvvYdwNj9ctn/XOfRw+fpBnHlhqRulNtnK7bwPJ4RNGP/X0RN8d80J2HzQuAfrSJ18bQ5n5g9pa3/fpzCsy3jhjo317XE84ePSH8ajP9ANtEQxXdTPGnLbSaEbuOFUF6/fB57uNK1tnJF5uzD/RJ86aw7Q2ldNpfPj9fJfxwfzMubN1S+OcNrgHRFlweMJN33inz3RjnBjvPd3a++6YF3KqGj5z8erEpfoyH25O8Z/fl0+3+/bD+fFTxmtwfS/fHbOpAvgUZrTMjxw5EofDwfTp0ykpKWHDhg04HA5mzpzJkiVLyM3NxWazkZKSYna5HtMpJpF+SZkM7DmcOzOy+MP9i9lZmMtLCx+qWyc0uAVZd73NO588w97izQDkbHuftdsX8+jYv5hVepMcP2mcyH3t893+06KVs8v3x9xf6v749p62vQTKKnx/XDNe90vhdMKSTfDsYuON/+ybuk5VG7/TM5cY4zUHaov9vu/g//sP/Cu3fpgH44Px31bD8/81bqoLRCerYP4qYxSoTd/U/38+fMKYLfmp9+ErC94nYsbf49ZC7wzC4I4NBb6/UbOmFtb6ydgaladhfYHvj5uz2z/PlwEd6KdOnUphYSFTpkxh9uzZREVF1T2WlZVFamoq1dXVdO3alVatLDzf70Ukd00ns994Vm3OJq/gi7rlSXH9uX3oY8x656d8d7iQOe9O5JExrxIT7Wedxc+Ru8+cy+XF5f4RaI+dNN5UzLBmjznHPZdZdeTuMy7zW8WijfBRE4ac+3SHMUynv3xg9ZT9pU0bcs5xBOZ+5Lnh/vxFVY0xUtfFbmasqYX/94VxFcMqHEdgX6nvj+t0+k+gXWvSedBf3gc27DfnRuXvjvnnkM4BG+i3b99OdnY2MTExPPvssw2u07+/cXdLampq3bLVq1eTmZlJhw4daNGiBXFxcdx5551s377dJ3V7y7jMJ7Dbg/j7st+fs/x3BNmDmTTnSlITMshIu8ukCpvOzD6M+X7Qf3J/qXmtA/7Qf9TpNK+Ok1Xnt/L6q50lRveApvpiN2wz6YOiN9TWwlufG6G2KcorAuM+ibN9nOfaOWvBWut8qMl34/4ZT/GH8+DpavMamMoq4IgfXKXY18x/B84VsIF+wYIF1NbWMm7cOCIjG57mLTzc6AR2dqAvLy+nb9++zJ07l+XLlzNz5kzy8vIYPHgwhYXWfbfrFJNARupdbNzzCVvzV9ctDw4KoXfXdI5UlPLjAfebWGHTHfBx3/GzFZp47DPMvEpw8Ij5Q7eVV5g7yYc/XKVpis/d6I7gzjb+6uti17vR5BVBmQ/7ZHtTdQ2s2e3aNjW15rX6usrMv8PCMvOvZhUfNrfbhz+cB82swR+e/7kCNtCvWLECgIyMjEbXORPQzw70o0aN4sUXX2Ts2LEMHTqUcePG8e9//5sjR46wcOFC7xbtZXff8Dh2m52/L/+hlX5r/mqWr3+LW4dM4bVF0zhV5d/NMxWnvDvW7MUU+sGMgWbOWljrNL+F2uxZG/3hd+BijpyAbUWub7fT4bnZds32hYthFvyrO8WlyiuCo27MH/GFn/YPPpeZ54GKU+b3oy8yOVCafR48XQ3fHjXv+Ga/DzXE5nSa/TnTO+Lj4yksLGTjxo0NjmBTXV1Nhw4dKC0tZe/evXTv3r3RfR06dIiYmBheeeUVJk+e7HItAwYMwOFwbdaL0OBw5k9x4x3JBZWnjvPzF1L5yXWPMnLwJKa/MZSkuAFMGvWiy/ua+Eoip6u9/2Egom1nhv/qi0YfPzO2bGNahYHdblyOv9CbXWNj01YecbDkmQEuVOx5Q3/+Lu27D2rwMU89f2j8NVj95j0c3P2ZCxV7Vpd+t3PVnXMafOxizx8u/XfgwOb/sO4frp8HfKl993SG/vyfbm2b87efUbLjEw9X5HvDf7WGiLauD8tUuPW/rP2fiV6oyLd63fALkn/0mFvbvv9kL6pP+vcnux8/9ilR7Xs0+JgvzoPLX8zkqGOHCxV71hXXT6bv8N80+JgvzoO7V/+ZzR887ULFntUiMoaRT2xq9HFvZ4HTlUdY9FRy0wtuotjYWNavd282y4AdtrKiwrjWWlnZcMjMzs6mtLSUqKgounXrdt7jNTU11NbWsn//fn7zm98QGxvLHXfc4VYtDoeDoiLXmsvCQrw/g828xdOJbduNUekPY7PZmHHHWzw0J40hfcaQ0v06l/ZVUlzMySrvN1m0rrnw63JmbNmLsdvdmyTIic3l/0tPq6pqvM+Lt58/QFn5YVNfg9YJjQeNpj5/cP81OHmqyvTfgYsJbud+09Xho8f9/vk1id29t7eq6tqAeP6dK93vl/btd4eoPOqHnYTPcqGBEXxxHiwtPcR3Jv6edDzeeH8yX5wHKypPmvp3EtH6wiNjeP13wBbkd+eJgA30sbGxlJeXs2HDBgYPHlzvsZKSEmbMmAFASkoKNpvtvO2HDh1KTk4OAAkJCaxYsYL27d0bfDY2NtblbUKDvTvI65c7lrJqczbzH91S9/w7xvRgwvDnmJ19P/OmbyE8NKLJ++vQsaNPWujDoy88CPjRi5TgyqfyhjhrTtOpU6eLVOldQfbGL6p56vlfaF+tW0Wa+hpERTb+t3Gx5w+X/jsQGmwz/XfgYqLC3O9NGdHC/59fU1RXHoHWHVzezlZzIiCefwu7e+MZOmtraNc6AmeUn00Heg6bs/Hn54vzYLs2rQitNu/3JOIC07X64jwYHhps6t9JSHjrCz7u9SxQfcorz9+dvHhGwHa5mTp1Ki+//DLx8fF8/PHHJCUlAZCbm8v48ePJz8+nqqqKyZMnNzhL7M6dOzl8+DD79u3j+eef59tvvyUnJ4fOnX0z52/NaVg51yeH8oiMqRDkg/N/TS38+p9NH7niXE+NMT6NHz4BT73n+vZJsfDwDe4d21P+uQ6+cPPGtUt9/gBPjoY2Tf+s53EFpTBnmfvbX+prcFMK3NTX/eP7Qm0t/HGx65PutAqDJ8cExiRLH25xbwbJB4dCcpzn6/G1Q8fh//7HmAzJFSnx8H9cu0Brijc/dX9Upks9B9ht8NwdEGpik+jWA/CXS+j5eKmvwZ1Xw+AE949/qZxOePxdY9Zrd1zq8+8SA7/8sXvH9pYAOG03LCsri3bt2nHgwAGSk5Pp27cviYmJDBw4kO7duzNs2DCg/g2xZ7viiiu4+uqrueuuu/jkk084duwYs2bN8uVTkAYE2aFTG/OOH9/WvGPX1dDOvGNHtnD/ErWndGxtvKGaxR9+By7Gbod0N95sBycGRpgHI2y4+nvSJgJ6+fc0HE3WLhJ6u9GAOMRPZsO+mDgT/w5jo80N82Du+wCY+/oD2Gzm1uCP7wMBcuo+X1xcHKtXr2bEiBGEhYVRUFBA27ZtmTdvHkuWLGHXLmN8tsYC/dlat25NQkICe/ZYZDyvAGfmicwf/ojNrCGurXEiNVNoMMS2Nu/4/vA70BSDE4xQ11StW8I1Sd6rx9eiW8LQnq5tMyLV+DAUKH7cF4KDmr5+UqzxZQWdTT4Pmi06HKLCzDl2kB06XLj3q0+YeS72x/eBADp1na9Xr1588MEHHDt2jGPHjrFu3TomTpxIRUUFBQUF2O12+vTpc9H9fPvtt+zcuZMePRq+o158K831gSs8okUw9PSD1ruObSDGhaDmSWldzDnuucz6Heje/uKjR/iLli3g5xlNu6LSKsxY16yA4C0j02BA16atO+pKGHD++AiW1rkd3HdN00J9lxi4/1rzP7A3VcLl0NKkbv5pvul5e0E2G6SaVEffONc+KHqLWf8PwXZI9sPbbAL2ptgLycvLw+l0kpSURMuW9d/t7r33XhISEkhLS6N169bs3r2bF198keDgYH75y1+aVLGcrftlxiVPxxHfHndANwgL8e0xG2K3wZAk+M8G3x43PBT6+UmgH5Rg9I/29XjZQyzWgn1ZK6Of59It8FXB+feeBNuhX1cYnmLufRHeYrfDPelGWP10B5Q2cE9Bl3aQmQx9TfqQ6G194mDqjcY9BduLz+9TH9nCuJpzYx/zu5G4IjQYru7h2mzIntAu0j8adsDoHmXGZHD+ch6Mb2d8aP3GxxM+pnWBSD9s/LDQn6/nbN1q3CnVUHebQYMG8fbbb/PSSy9x8uRJ4uPjycjI4Le//S1duvhJmmnmbDaja8C7ub49rj/1LR3YHf672f2bg91xdXf/ecNvFW60Tm3c77tjRoVBqgVDX3RLuGsQjOoHm/YbHwRPVRsfTp+4FSJamF2hd9ltcO0VRgjZWQJvrTaef4tgmJJpfl9kX+jcDiZmGDfKbjlghPtT1RAeYtwc6A+tre5IT4RVO3w7a+uQRHPv4Tlbh9bGlYo9B313zNhoSLjMd8e7mGuS4B9rfH9MfxTQXW4ac6FAP2XKFL788kvKy8uprKxk165dzJs3T2HezwxOgDgf3hx7TaLR1cVfRLQw+vv6Sqtw+NHFe6f51KgrjVDmK2P6Wzf4gNE9IT3xh6tMLYIDP8yfzW4zbng98/zDQppHmD9bu0jI6PXDaxAabO3f6fZRkOHifRKX4vJWxodDfzKmv28/YPzkKv/qljWgK3SL8d3xruoOXX14PFco0Dczn215l5cWTqq37MPcv3HjDBs52943pyg3BNnhnsG+GZGjbQSMvNL7x3HVdVdAN/emRnDZnQONPtn+pE0E3NrPN8dKiYcr9ZlexO/clGJ0LfM2m814zwnxsw9AndrAj3w0jO41SZB4uW+O1VR2O9zto/+X6HAY46P3HHc0y0C/YsUKnE4nI0aMMLsUn8vZ9h7pfUbX/ewoK2Dpuj/Tq/Mg84pyU8c2rgfto5XGuLNNmXgDjD7G96ZDCz/oO38u+/cfalxpZXX1+YNxdcJfx+UenGCEbVe4+hq0iYCxA/2rVUpEDKHBxjk61IVA5855cHhf414Mf3RjsnHDvitcfQ06tDZuMvdHl7UyrlS4wtXnb//+A52/NWydzU96xIqnHK88zIN/6sOpqkraR8dTVXMKx6F8bug/nmm3vU5eQQ4z7nwLgNraWl741wNMHv0y8xZPN7dwN13fE06cguXbmrb+Cx82fd9BdrjvWuMmXH/VPsoYneT1FVDZhAk2XHn+AP27wm0D3CrNJ2w2GD/EmGRmZ0nTtnHlNYgONyYSC7TRX0QCSed2MGGocR5oyn1Frp4Hh/Y0bhr2V0F2ePB6eO0TOFDWtG1ceQ0uawWThvlnw9YZ6YlQcQqWbG7a+q48f/v37zNXuD7xtE81yxb6QBYZ3pphafdw2zW/YN6jm5g0ag49uwxi+tg32bRnJb27pBMcZPxVLvzsBZK7DiEpzsWPtn7m5lSjP7UnW1DDQ40TZB8/bZk+W+d28Eim5yd8uiYJxg32/3G5Q4KM2T093SXmslYw9UfGhyYR8W9XdICHMjx7X4gNY1bo0f38/wpdeChMzoQrPDyPQOd28MiN1hiu98Y+8JMBnr2noEWwMXOyFbpc+vlbtbhjT/EmEjoZfVF2F35FQkfj31/kvc+QPmMA2OfYxuqtCxmX+TvT6vSkYb2N4fliPTDZRXIn+PUI6Onnn8bP1rEN/GoEDPLAVAnR4TDxerj9Kv8P82cEB8FPh8D49Esfm9pmg2G94LHhrk3MJCLm6nE5/PoWz4xG1T7KCLI3pfh/mD8jLAR+PswItZc6IlmQ3Rh4YdqPrHWF8tor4NGbPDOIxRWxxu+TFRr2QF1uAlL+OYF+cPIonE4n63cu48ERswDYlr+ag+UF3DfTGIux7JiDOe9OpOxoCSPTJzW6b3/WuR1MH26MN/35LqN/nCvi2xojQFzZxTon8LOFhxrDE17ZBT75GnY5XNs+ooUxrnNmsnkTtlwKmw36d4PEWKMLVm6+MTRfk7cHencyWnn8dRQDEbmwqDC4/zpjeM4VX0NBqWvbR4cb3TcyevnPML2uODNMa6+Oxnlw437Xhje2fz9h1Y/6GP3mrSiuLTz6Y1i9y8gChxqYf+JCOrYxRk8a0M1aWcCCv65yIaVHisBmIybamMYs37GFe254nB0HvqTz5b0Ib2E0OY5Mn1QvuE9//Xpuu/YXDDnrhlkrCgkyAmlGL/i6CDbshwOHGp5Qxm6D2NbQtZ0xUVHnABnC7ooOxtfBI7B2L+R/C0XlUF17/rqtWxonv9R4Y7IMfxvBwR2two2rC7ekwfp9xu/BgTI4dvL8dUODjVEiEi4zfgfUIi8SGFLija/CMuM8WFAKJYehpoHzYLtI4zzYr4vRGuuL0dO8LSbKuInz1n7wZT7sKDbOgycauNcqLMR4/kmxxlVeK3SvuZjgICMHDL0CdpQYE+t9cwi+O3b+unYbXB5tTDJ3dQ+jQcdKQf4MBfoAs6doY10XG4DIsNYsWvMa0RExpCePNq8wHwuyGzM/npn98cRp+PYoVFUbf7wtQow/4EAIsI25PPqHYR1rao3nf+K08e+QIOOEb6VLqa4KCzHuA7gmyZh45kgllFcYrVXBduOKRPso63QrEhHXxbWF29sa/66ugYNH4eRpY5bpkGDjHBDI8zFEtDCCbUYv4zxYVgFHThgNPMF2iAo3PtD4y2RZnma3G1deexttnFR+nwVO1xhXZVuEGPMLWPFqzLkC4CnI2Qb1voVBvW+p+/nVacZ0qg/MTub5h1Y2ut2fJq3ydmmmahnavLtRBNmte/nUE2w242qEp28cFhHrCA4yrsg1VzabEd6b85XI8FD/HX70UinQNxNvPpZndgkiIiIi4gW62CwiIiIiYmEK9CIiIiIiFqZALyIiIiJiYepD76fsIZAx1ewqms7ux1NCi4iIiAQyBXo/ZbNBkAUn9xERERER31KXGxERERERC1OgFxERERGxMAV6ERERERELU6AXEREREbEwBXoREREREQtToBcRERERsTAFehERERERC1OgFxERERGxMAV6ERERERELU6AXEREREbEwBXoREREREQtToBcRERERsTAFehERERERC1OgFxERERGxMAV6ERERERELU6AXEREREbEwBXoREREREQtToBcRERERsbBgswuQhjmdUFtldhVNZw8Bm83sKkRERESaHwV6P1VbBSvnml1F02VMhaBQs6sQERERaX7U5UZERERExMIU6EVERERELEyBXkRERETEwhToRUREREQsTDfFiogEuOoaKDkCjsNwqtpYdroavjkEHVtDcJCZ1YmIyKVSoBcRCUAnq2D9PsjNh8JyqKmt/3hlFbzwIQTZjVA/oBtc1R1aarQqERHLUaAXEQkgJ6tg6RZYu+eH1vgLqamFA2XG15JNMLAH3JyqYC8iYiUK9AFk895VPPZGRr1lYaERxLVPIrPfeEYPeYSgIP2XiwSqnSXwzlooP+He9qdr4PNdsPUA3Hk19O7k2fpERMQ7lO4CUEba3QzseTNOnJQfc/DRV2/zxuJH+ebb7fzy9vlmlyciHuZ0wrKt8OFWz+zvSCXMXwXDesPINM0CLSLi7xToA1Bip35k9r+37ueR6Q8zYVZPln75Jvff9EdaR7Y3sToR8bQPNsEnX3t+vyu+hqpquG2AQr2IiD/TsJXNQHhoBD27DMLpdFJ8aK/Z5YiIB3220zth/ozVu+DjPO/tX0RELp1a6JuJku+DfKuWbU2uREQ85eARWLTBtW0evQlahcPRSmOUm6ZYugV6dYQ4nT5ERPySWugD0MmqExypKOXw8e/YV7KVuf+ezJ6ijfSMH0hc+ySzyxMRD6ithX+sherai697tlbh0Lql8b3Jx3J+f6wa144lIiK+EfCBvrS0lKysLBISEggLCyM+Pp5p06ZRUVHBhAkTsNlsvPLKK2aX6VFvL3+S259qz9inL2PiCyksXvMa1/S5jafv+4/ZpYmIh2z6BvaX+u54xeWwvsB3xxMRkaYL6C43mzZtYvjw4TgcDiIiIujduzfFxcXMnTuXvXv3UlZWBkBaWpq5hXrYiKsncl3KWKprq9hXspXsVTMpPVJIaEhY3Tqnq0/x8Jx+ZFx5D+NueLxu+ax37uPw8YM888BSM0oXkSbK2e37Y36+C67urhtkRUT8TcC20JeWljJy5EgcDgfTp0+npKSEDRs24HA4mDlzJkuWLCE3NxebzUZKSorZ5XpUp5hE+iVlMrDncO7MyOIP9y9mZ2EuLy18qG6d0OAWZN31Nu988gx7izcDkLPtfdZuX8yjY/9iVuki0gQlh2Hvt74/bmEZ7D/k++OKiMiFBWygnzp1KoWFhUyZMoXZs2cTFRVV91hWVhapqalUV1fTtWtXWrVqZWKl3pfcNZ3MfuNZtTmbvIIv6pYnxfXn9qGPMeudn/Ld4ULmvDuRR8a8Skx0RxOrFZGLySsy79hfm3hsERFpWEAG+u3bt5OdnU1MTAzPPvtsg+v0798fgNTU1Eb3M3z4cGw2G0899ZQ3yvSpcZlPYLcH8fdlvz9n+e8Isgczac6VpCZkkJF2l0kVikhTFZY1z2OLiEjDAjLQL1iwgNraWsaNG0dkZGSD64SHG0M8NBbo//nPf7Jp0yZvlehznWISyEi9i417PmFr/uq65cFBIfTums6RilJ+POB+EysUkaY6YGKoPlBmzEwrIiL+IyAD/YoVKwDIyMhodJ3CwkKg4UB/9OhRfvGLXzB79mzvFGiSu294HLvNzt+X/9BKvzV/NcvXv8WtQ6bw2qJpnKqqNLFCEWmK8grzjn3spDGMpYiI+I+AHOVm//79AHTp0qXBx6urq8nJyQEaDvSPP/44SUlJjBs3jnvvvfeS6xkwYAAOh8OlbUKDw5k/xbVhLFJ7XM9Hzzf+Ttvl8l4sm/XDQNKVp47zfPZ9TBj+HCMHT2L6G0P569LfMmnUiy4dFyAxKZHT1fowIOJ1Nhu3P3eg0YfPTBzVmFZhP3x/akzj611o4qkeCVdQfdrETxWX4Obf5tIyugMljhLi4q4yuxxT6DUQ8U+xsbGsX7/erW0DMtBXVBhvNJWVDQfM7OxsSktLiYqKolu3bvUeW79+PX/+85/56quvPFaPw+GgqMi1O8nCQlp67PiNmbd4OrFtuzEq/WFsNhsz7niLh+akMaTPGFK6X+fSvkqKizlZdcJLlYrI2WprqrEHNXz6PjNx1MXY7U1bryGFBwqoqT7t3sYmq6mpqfvu6nk5UOg1EAk8ARnoY2NjKS8vZ8OGDQwePLjeYyUlJcyYMQOAlJQUbGcNqFxTU8PPf/5zpkyZQnJyskfrcVVosAvTOLrhyx1LWbU5m/mPbql7DTrG9GDC8OeYnX0/86ZvITw0osn769Cxo1roRXzk9IkywqIua/Cxoxf5M2wVZoT52lo4erLx9RrbT9XJY8Re3r6JlfqfoKCguu+dOnUyuRpz6DUQ8U/u5MUzAjLQZ2Zmsn37dmbOnMmNN95IUlISALm5uYwfP57SUmN6xXMnlHrllVc4ePCgx0e1cefySc1pWDnXo2XUM7DncN7/w+Hzlt86ZDK3Dpns8v5279pNUKgHChORi5q/Er4ubvixxrrJnPHUGKNl/uhJeOo914/ds3MUr35/D5IVPflvOFIJHWI71N1L1dzoNRAJPAF5U2xWVhbt2rXjwIEDJCcn07dvXxITExk4cCDdu3dn2LBhQP3+86WlpTzxxBP8/ve/p7q6msOHD3P48GEATp48yeHDh6mtrTXj6YiI1BPX1rxjx5t4bBERaVhABvq4uDhWr17NiBEjCAsLo6CggLZt2zJv3jyWLFnCrl27gPqBvrCwkGPHjvHzn/+cNm3a1H0BzJw5kzZt2vDNN9+Y8nxERM6WeHnzPLaIiDQsILvcAPTq1YsPPvjgvOXHjx+noKAAu91Onz596pYnJCSwcuXK89bPyMjgZz/7Gffdd98l9W0SEfGUhMvhslbw7VHfHrdNS+iliaRFRPxOwAb6xuTl5eF0OklKSqJlyx+GeIiMjOT6669vcJuuXbs2+piIiK/ZbDAkEd7z3GBcTZKeaNxQKyIi/qXZnZq3bt0KND5DrIiIFVzdw/1hJ90RFWYEehER8T8K9BfhdDo9PuqNmT7b8i4vLZxUb9mHuX/jxhk2cra9b05RIuKysBC4a5Dvjjd2IES08N3xRESk6RTom5mcbe+R3md03c+OsgKWrvszvTr7MBmIiEf07ADpCa5tc7QSDp+4+Hj1Z+vfFVLiXTuOiIj4TrPrQ79ixQqzS/Cq45WHefBPfThVVUn76Hiqak7hOJTPDf3HM+2218kryGHGnW8BUFtbywv/eoDJo19m3uLp5hYuIm65bQCUVcCOkqatf7Fx6s/V4zK482rX6xIREd9pdoE+0EWGt2ZY2j2Et4ji3hufIHfnMhaseIbpY99k/c7l9O6STnBQCAALP3uB5K5DSIrrb3LVIuKu4CD4P9fB3z+HvCLP7vuKWGPfoXqnEBHxa82uy01zsKd4EwmdrgRgd+FXJHQ0/v1F3vsM6TMGgH2ObazeupBxmb8zrU4R8YzQYCN435wKQR44q9tt8KM+8OD10CLk0vcnIiLepXaXAJR/TqAfnDwKp9PJ+p3LeHDELAC25a/mYHkB9800hq0oO+ZgzrsTKTtawsj0SY3uW0T8U5DdCOF9OkH2l7C/1L39xLU1uthoRlgREetQoA8wpUeKwGYjJroTAPmOLdxzw+PsOPAlnS/vRXiLSABGpk+qF9ynv349t137C4acdcOsiFhPxzbwix/B/kOQsws27ofq2gtvE2SHtM7G2Pbd2hvj3IuIiHUo0AeYPUUb67rYAESGtWbRmteIjoghPXm0eYWJiM/YbNA1xvi642ooLocDZeA4DKeqwYnRTadDNMS3g46t1U9eRMTKdAoPMIN638Kg3rfU/fzqtFwAHpidzPMPrWx0uz9NWuXt0kTEBCFB0CXG+BIRkcCkQN9MvPlYntkliIiIiIgXaJQbERERERELU6AXEREREbEwBXoREREREQtToBcRERERsTDdFOun7CGQMdXsKprOrtkkRUREREyhQO+nbDYICjW7ChERERHxd+pyIyIiIiJiYQr0IiIiIiIWpkAvIiIiImJhCvQiIiIiIhamQC8iIiIiYmEK9CIiIiIiFqZALyIiIiJiYQr0IiIiIiIWpkAvIiIiImJhCvQiIiIiIhamQC8iIiIiYmEK9CIiIiIiFqZALyIiIiJiYQr0IiIiIiIWpkAvIiIiImJhCvQiIiIiIhamQC8iIiIiYmHBZhcgDXM6obbK7Cqazh4CNpvZVYiIiIg0Pwr0fqq2ClbONbuKpsuYCkGhZlchIiIi0vyoy42IiIiIiIUp0IuIiIiIWJgCvYiIiIiIhSnQi4iIiIhYmAK9iIg0G05n/e8iIoFAo9yIiEhAqnXCzhLYfRAOHILCcqg8bTx29CT8/t8Q39b46t0JOrczt14REXcp0IuISEA5cRrW7oGc3XDoeOPrHa2EvCLj68OtRrC/Jgn6d4XgIJ+VKyJyyRToA8jmvat47I2MesvCQiOIa59EZr/xjB7yCEFB+i8XkcCVVwT/XAdHKl3f9kAZLFgLn+6EcYOhUxvP1yci4g1KdwEoI+1uBva8GSdOyo85+Oirt3lj8aN88+12fnn7fLPLExHxuKoa+NeX8GX+pe+ruBz+tBRuToUbemsWbBHxfwr0ASixUz8y+99b9/PI9IeZMKsnS798k/tv+iOtI9ubWJ2IiGedroY3P4VdDs/ts9YJH2wyWvpv669QLyL+TaPcNAPhoRH07DIIp9NJ8aG9ZpcjIuIxNbXw1888G+bPtnonLN7knX2LiHiKAn0zUfJ9kG/Vsq3JlYiIeM7HebCjxLvHWPG10TdfRMRfqctNADpZdYIjFaU4nUYf+sVr3mBP0UZ6xg8krn2S2eWJiHhEcTks3+baNo/eBK3CjRFuXviw6dtlr4Nfj4CWLVw7noiILzSLFvrS0lKysrJISEggLCyM+Ph4pk2bRkVFBRMmTMBms/HKK6+YXabHvL38SW5/qj1jn76MiS+ksHjNa1zT5zaevu8/ZpcmIuIRTqcRsmtqXduuVTi0bml8d8XRSliy2bVtRER8JeBb6Ddt2sTw4cNxOBxERETQu3dviouLmTt3Lnv37qWsrAyAtLQ0cwv1oBFXT+S6lLFU11axr2Qr2atmUnqkkNCQsLp1Tlef4uE5/ci48h7G3fB43fJZ79zH4eMHeeaBpWaULiLSJPsPGV++9GU+jEiDlqG+Pa6IyMUEdAt9aWkpI0eOxOFwMH36dEpKStiwYQMOh4OZM2eyZMkScnNzsdlspKSkmF2ux3SKSaRfUiYDew7nzows/nD/YnYW5vLSwofq1gkNbkHWXW/zzifPsLfYaHbK2fY+a7cv5tGxfzGrdBGRJvl8l++PWVUDuR4YFlNExNMCOtBPnTqVwsJCpkyZwuzZs4mKiqp7LCsri9TUVKqrq+natSutWrUysVLvSu6aTma/8azanE1ewRd1y5Pi+nP70MeY9c5P+e5wIXPencgjY14lJrqjidWKiFxYVQ1s2m/OsXP3mXNcEZELCdhAv337drKzs4mJieHZZ59tcJ3+/fsDkJqaWrds1apV2Gy2876s3iVnXOYT2O1B/H3Z789Z/juC7MFMmnMlqQkZZKTdZVKFIiJNU3IYql3sO+8pxeXGBwoREX8SsH3oFyxYQG1tLePGjSMyMrLBdcLDjbuizg70Z7z66qv069ev7ueIiAjvFOojnWISyEi9i082/i9b81fTt/u1AAQHhdC7azq7czbw4wH3m1yliMjFHfBx3/mz1TqNUN8lxrwaRETOFbAt9CtWrAAgIyOj0XUKCwuBhgN97969GTRoUN1X3759vVOoD919w+PYbXb+vvyHVvqt+atZvv4tbh0yhdcWTeNUVaWJFYqIXJzjSPM+vojIuQK2hX7/fqODZZcuXRp8vLq6mpycHKDhQO9JAwYMwOFwbRrD0OBw5k/Z7dI2qT2u56PnnY0+3uXyXiyb9cO14spTx3k++z4mDH+OkYMnMf2Nofx16W+ZNOpFl44LkJiUyOlqfRgQEe8bMPZPdB1wZ4OPnRlnvjGtwn74/tSYCx+nsbHqs37zBHu/+FsTq/U/N/82l5bRHShxlBAXd5XZ5YjI92JjY1m/fr1b2wZsoK+oqACgsrLhkJmdnU1paSlRUVF069btvMfvvPNOSktLadeuHaNGjeK5554jJsa9a6wOh4OiItemGQwLaenWsVwxb/F0Ytt2Y1T6w9hsNmbc8RYPzUljSJ8xpHS/zqV9lRQXc7LqhJcqFRH5Qa/vz+8NOTPO/MXY7U1bryGHD5e7fE73JzU1NXXfrfw8ROQHARvoY2NjKS8vZ8OGDQwePLjeYyUlJcyYMQOAlJQUbDZb3WPR0dHMmDGD6667jsjISNasWcOzzz7L2rVrWb9+PWFhYbgqNjbW5W1Cg12c9cRFX+5YyqrN2cx/dEvd8+8Y04MJw59jdvb9zJu+hfDQpt830KFjR7XQi4hPhF7gnevoRU5DrcKMMF9bC0dPXnjdxvYV2TKUTp06XXhjPxYUFFT33crPQyTQuJMXz7A5nc7G+2hY2NSpU3n55ZeJj4/n448/JikpCYDc3FzGjx9Pfn4+VVVVTJ48+aKzxC5evJhRo0bx17/+lfvv982NozWnYeVcnxzKIzKmQpAmWxERH8jZDf/60r1tnxpjtMwfPgFPvefePh69CTq3c29bf/Dkv+FIJUSHw9O3mV2NiHhCwN4Um5WVRbt27Thw4ADJycn07duXxMREBg4cSPfu3Rk2bBjQtP7zt9xyCxEREW73axIREc+Jb2vese026NDavOOLiDQkYAN9XFwcq1evZsSIEYSFhVFQUEDbtm2ZN28eS5YsYdcuY5pBV26IPbtrjoiImKNjawgJMufYndqYd2wRkcYEbB96gF69evHBBx+ct/z48eMUFBRgt9vp06fPRfezaNEiKioqGDhwoDfKFBERFwQHQVoXyM33/bGv6u77Y4qIXExAB/rG5OXl4XQ6SUpKomXL+sMc3HvvvXTv3p1+/frV3RQ7a9Ys0tLSuOsuzaIqIuIPrkn0faAPDYKrzh8UTUTEdM0y0G/duhVouLtNcnIy//jHP5gzZw6VlZXExcXx4IMP8uSTTxIaqrs+RUT8QZcY6BYD+0p9d8yre0C43gZExA8FbB/6C7lQoP/Nb37D1q1bOXr0KFVVVezbt48XXniB6OhoX5fpFZ9teZeXFk6qt+zD3L9x4wwbOdveN6coERE33HE1BPnoXax1S7jZu3MQioi4TYG+mcnZ9h7pfUbX/ewoK2Dpuj/Tq/Mg84oSEXFDh9ZwU1/XtjlaaQxZebHx6s9159VqnRcR/9Usu9ysWLHC7BK85njlYR78Ux9OVVXSPjqeqppTOA7lc0P/8Uy77XXyCnKYcedbANTW1vLCvx5g8uiXmbd4urmFi4i4YVhvKCiFvCZOePrCh64f48Zk6NXR9e1ERHylWQb6QBYZ3pphafcQ3iKKe298gtydy1iw4hmmj32T9TuX07tLOsFBIQAs/OwFkrsOISmuv8lVi4i4J8gOP7sG/voZ7Cjx/P6H9lRXGxHxf82yy02g21O8iYROVwKwu/ArEjoa//4i732G9BkDwD7HNlZvXci4zN+ZVqeIiCeEBsMDQ2Fwguf2GWSHUVfC6H6gKUhExN+phT4A5Z8T6Acnj8LpdLJ+5zIeHDELgG35qzlYXsB9MxMBKDvmYM67Eyk7WsLI9EmN7ltExB8FBxn93FPiIXud0U/eXfFt4Z7BmhFWRKxDgT7AlB4pApuNmOhOAOQ7tnDPDY+z48CXdL68F+EtIgEYmT6pXnCf/vr13HbtLxhy1g2zIiJW06sj/PoWWLsXcnbBd8eavm23GBiSBFd28d3oOSIinqBAH2D2FG2s62IDEBnWmkVrXiM6Iob05NHmFSYi4iNhIXB9T7juCthzEHY74EAZFJbD8ZPGOjagdYTRGh/f1vggENfW1LJFRNxmczqdTrOLkPPVnIaVcz23vwdmJ/P8QytpE3mZ53Z6loypEKQh3UTEzzmdUOsEu6359o1/8t9wpBKiw+Hp28yuRkQ8QS30zcSbj+WZXYKIiOlsNghqpkFeRAKXegmKiIiIiFiYAr2IiIiIiIUp0IuIiIiIWJgCvYiIiIiIhemmWD9lDzFGjrEKe4jZFYiIiIg0Twr0fspm0zCQIiIiInJx6nIjIiIiImJhCvQiIiIiIhamQC8iIiIiYmEK9CIiIiIiFqZALyIiIiJiYQr0IiIiIiIWpkAvIiIiImJhCvQiIiIiIhamQC8iIiIiYmEK9CIiIiIiFqZALyIiIiJiYQr0IiIiIiIWpkAvIiIiImJhCvQiIiIiIhamQC8iIiIiYmEK9CIiIiIiFqZALyIiIiJiYcFmFyANczqhtsrsKprOHgI2m9lViIiIiDQ/CvR+qrYKVs41u4qmy5gKQaFmVyEiIiLS/KjLjYiIiIiIhSnQi4iIiIhYmAK9iIiIiIiFKdCLiIiIiFiYAr2IiIiIiIVplBsREZEAdvwkHCgzvkqPwYnTxvLK07BmD8S1hQ7REBxkbp0i4j4FehERkQBTXQNbC+HzXbD324bXOV0D2euMf4eFwFXdYUgixEb7rk4R8QwFehERkQCy+Rv493o4Utn0bU5WweqdxlefOBh7FUS39F6NIuJZCvQBZPPeVTz2Rka9ZWGhEcS1TyKz33hGD3mEoCD9l4uIBKLjJ+HdXNj0zaXtZ1uh0ao/pj9c1U2zgItYgdJdAMpIu5uBPW/GiZPyYw4++upt3lj8KN98u51f3j7f7PJERMTDDh2H1z4xvntC5Wn4xxooKofR/RTqRfydAn0ASuzUj8z+99b9PDL9YSbM6snSL9/k/pv+SOvI9iZWJyIinlReAS9/BIdPeH7fn+4Ap9NorVeoF/FfGrayGQgPjaBnl0E4nU6KD+01uxwREfGQ6hqYv8o7Yf6Mz3ZCzm7v7V9ELp1a6JuJku+DfKuWbU2uREREPGXZVig57No2j94ErcLhaCW88GHTtlm0AXp2gJgol0sUER8I+Bb60tJSsrKySEhIICwsjPj4eKZNm0ZFRQUTJkzAZrPxyiuvmF2mR52sOsGRilIOH/+OfSVbmfvvyewp2kjP+IHEtU8yuzwREfGAA4fgk69d365VOLRuaXxvqtM18M46o/uNiPifgG6h37RpE8OHD8fhcBAREUHv3r0pLi5m7ty57N27l7KyMgDS0tLMLdTD3l7+JG8vf7Lesmv63MYjY141qSIREfG0j/Kg1ocBe89ByP8Welzuu2OKSNMEbAt9aWkpI0eOxOFwMH36dEpKStiwYQMOh4OZM2eyZMkScnNzsdlspKSkmF2uR424eiIzH/yIP074Lw/cPJOolm0pPVJIaEhY3Tqnq0/xwOxk/veTP9bbdtY79/HbN4f7umQREXHB4RPGxFG+9rn60ov4pYAN9FOnTqWwsJApU6Ywe/ZsoqJ+6PiXlZVFamoq1dXVdO3alVatWplYqed1ikmkX1ImA3sO586MLP5w/2J2Fuby0sKH6tYJDW5B1l1v884nz7C3eDMAOdveZ+32xTw69i9mlS4iIk2wdo853V82fwPHTvr+uCJyYQEZ6Ldv3052djYxMTE8++yzDa7Tv39/AFJTU8977L333iM9PZ2IiAiio6MZMmQIeXl5Xq3Zm5K7ppPZbzyrNmeTV/BF3fKkuP7cPvQxZr3zU747XMicdyfyyJhXiYnuaGK1IiJyMbsPmnPcWifs+86cY4tI4wIy0C9YsIDa2lrGjRtHZGRkg+uEhxt3A50b6OfOncsdd9zBNddcw6JFi1iwYAGZmZlUVrowh7YfGpf5BHZ7EH9f9vtzlv+OIHswk+ZcSWpCBhlpd5lUoYiINEWtEwrLzDv+gUPmHVtEGhaQN8WuWLECgIyMjEbXKSw0Oh+eHej37t3LjBkzePHFF5kyZUrd8ptvvtlLlfpOp5gEMlLv4pON/8vW/NX07X4tAMFBIfTums7unA38eMD9JlcpIiIXc+gYnKo27/iF5eYdW0QaFpCBfv/+/QB06dKlwcerq6vJyckB6gf6v/71r4SEhPDggw96tJ4BAwbgcDhc2iY0OJz5Uzx799HdNzzOyk0L+Pvy3zP7oZUAbM1fzfL1b3HrkCm8tmgab/TYRIsQF8Yy+15iUiKnq619FUNExAradelPxsP/afCxM2PMX0irsB++PzWm8fUaG6c+Z90m/jD+liZWKyJNFRsby/r1693aNiADfUVFBUCj3WSys7MpLS0lKiqKbt261S3/4osvuOKKK/if//kf/u///b8cOHCAxMREfv/733P33Xe7XY/D4aCoqMilbcJCWrp8nNQe1/PR843fJdXl8l4sm1VT93PlqeM8n30fE4Y/x8jBk5j+xlD+uvS3TBr1osvHLiku5mSVF6cqFBERQ1SPRh86M8Z8U9jtTV/3bDW1uPyeJiLeFZCBPjY2lvLycjZs2MDgwYPrPVZSUsKMGTMASElJwWaz1XusqKiI3/zmN8ycOZP4+Hj+8pe/cM8999C+fXsyMzPdrsdVocGut5K7at7i6cS27cao9Iex2WzMuOMtHpqTxpA+Y0jpfp1L++rQsaNa6EVEfKBtm+hGHzvahNNwqzAjzNfWwtELjFjT2L7stlo6dep08QOJiEvcyYtn2JzOwJv3berUqbz88svEx8fz8ccfk5RkzI6am5vL+PHjyc/Pp6qqismTJ9ebJTYpKYndu3fz3nvvMXr0aACcTidpaWm0bt2aTz/91GfPoeY0rJzrvf1/uWMpz/zv3cx/dAuXtelct/w/Oa+y8LMXmDd9C+GhEU3eX8ZUCAr1RqUiInK2Iyfgyffc3/6pMUbL/OET8JQb+0nrDPdd6/7xRcTzAnKUm6ysLNq1a8eBAwdITk6mb9++JCYmMnDgQLp3786wYcOA80e4adu2LUC9lnibzUZmZibbtm3z3RPwgYE9h/P+Hw7XC/MAtw6ZzNu/2etSmBcREd+JbnnxfvLeFNfWvGOLSMMCMtDHxcWxevVqRowYQVhYGAUFBbRt25Z58+axZMkSdu3aBZwf6JOTkxvd58mTmklDRET8Q7yJodrMY4tIwwIy0AP06tWLDz74gGPHjnHs2DHWrVvHxIkTqaiooKCgALvdTp8+feptc+uttwKwfPnyumW1tbV89NFHXHXVVT6tX0REpDHJJnVhbxkK3dqbc2wRaVxA3hR7IXl5eTidTpKSkmjZsv7t/SNHjuTaa69l4sSJHDp0iM6dO/Pmm2+Sl5fHRx99ZFLFIiIi9fXvCv/Z4Pvx6Ad2h9BmlxxE/F/AttA3ZuvWrcD53W3A6C+/aNEifvKTn/Db3/6WUaNGsX//fv773//W9bsXERExW4sQI1z72pBE3x9TRC6u2X3OvlCgB2jdujXz5s1j3rx5vixLRETEJTf2ga8K4MRp3xzvmkRo38o3xxIR16iFvpn5bMu7vLRwUr1lH+b+jRtn2MjZ9r45RYmIiMtahcNPBvjmWG0jYOSVvjmWiLiu2bXQr1ixwuwSTJWz7T0y+/+07mdHWQFL1/2ZXp0HmViViIi4o19X2FYEG/c3fZszE0Y1ZRIqgCA73DPY6OYjIv6p2QX6QHe88jAP/qkPp6oqaR8dT1XNKRyH8rmh/3im3fY6eQU5zLjzLcAYweeFfz3A5NEvM2/xdHMLFxERl9lsMG6w0e1mZ0nTtnnhw6bv326D8emQcLl79YmIbyjQB5jI8NYMS7uH8BZR3HvjE+TuXMaCFc8wfeybrN+5nN5d0gkOMppZFn72Asldh5AU19/kqkVExF3BQfDAUPh/ObDlgOf2GxIEPx0CfeM9t08R8Y5m14e+OdhTvImETkZnx92FX5HQ0fj3F3nvM6TPGAD2ObaxeutCxmX+zrQ6RUTEM0KC4P5r4Y6B0MIDTXXd2kPWzQrzIlahFvoAlH9OoB+cPAqn08n6nct4cMQsALblr+ZgeQH3zTTGICs75mDOuxMpO1rCyPRJje5bRET8k80G6YnQswMs3gSbv4Fap2v7aN0ShvU2RrSxq8lPxDIU6ANM6ZEisNmIiTamEcx3bOGeGx5nx4Ev6Xx5L8JbRAIwMn1SveA+/fXrue3aXzCkz2gzyhYREQ9pGwk/uwaOVMLaPbDpGzh4pPFwHx4K3WJgcAL07mTcBCsi1qJAH2D2FG2s62IDEBnWmkVrXiM6Iob05NHmFSYiIj4VHQ4/7mt8na6GonIoPQZVNUbre3gIdGoD7SKN1n0RsS6b0+l08YKc+ELNaVg513P7e2B2Ms8/tJI2kZd5bqdnyZgKQaFe2bWIiIiIXIBa6JuJNx/LM7sEEREREfEC9ZQTEREREbEwBXoREREREQtToBcRERERsTDdFOunnE6orTK7iqazh2iUBBEREREzKNCLiIiIiFiYutyIiIiIiFiYAr2IiIiIiIUp0IuIiIiIWJgCvYiIiIiIhSnQi4iIiIhYmAK9iIiIiIiFKdCLiIiIiFiYAr2IiIiIiIUp0IuIiIiIWJgCvYiIiIiIhSnQi4iIiIhYmAK9iIiIiIiFKdCLiIiIiFiYAr2IiIiIiIUp0IuIiIiIWJgCvYiIiIiIhSnQi4iIiIhYmAK9iIiIiIiFKdCLiIiIiFiYAr2IiIiIiIUp0IuIiIiIWJgCvYiIiIiIhSnQi4iIiIhYmAK9iIiIiIiFKdCLiIiIiFjY/w+ccESucliUCAAAAABJRU5ErkJggg==",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 956.385x618.722 with 1 Axes>"
       ]
@@ -115,19 +136,26 @@
    "id": "dcd8dea0",
    "metadata": {},
    "source": [
-    "### Specify some observables\n",
+    "### Specify an observable\n",
     "\n",
-    "These observables have 7 qubits, just like the original circuit."
+    "This observable has 7 qubits, just like the original circuit."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "id": "847a3205",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.262953Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.262735Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.266201Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.265626Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "observables_0 = PauliList([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])"
+    "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])"
    ]
   },
   {
@@ -146,11 +174,18 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "e4ee1559",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.268783Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.268324Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.594163Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.593481Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1290.83x785.944 with 1 Axes>"
       ]
@@ -170,9 +205,9 @@
    "id": "c22b9572",
    "metadata": {},
    "source": [
-    "### Update the observables\n",
+    "### Update the observable terms to account for the extra qubit\n",
     "\n",
-    "The transformed circuit contains additional qubits (one for each `CutWire` instruction), so the observables must be updated for the new circuit.  This can be done using the `expand_observables` function.\n",
+    "The transformed circuit contains additional qubits (one for each `CutWire` instruction), so the observables must be updated for the new circuit.  This can be done using the `expand_observables` function. Since the observable coefficients are ignored until reconstruction of the final expectation value, the input and output types of the observables for ``expand_observables`` are ``PauliList``s.\n",
     "\n",
     "The resulting observables have 9 qubits, just like the transformed circuit."
    ]
@@ -181,7 +216,14 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "95fbeda0",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.596688Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.596481Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.601335Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.600793Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -195,8 +237,8 @@
     }
    ],
    "source": [
-    "observables_1 = expand_observables(observables_0, qc_0, qc_1)\n",
-    "observables_1"
+    "observable_expanded_paulis = expand_observables(observable.paulis, qc_0, qc_1)\n",
+    "observable_expanded_paulis"
    ]
   },
   {
@@ -213,10 +255,19 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "99bef123",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.603720Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.603374Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.609287Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.608662Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "partitioned_problem = partition_problem(circuit=qc_1, observables=observables_1)\n",
+    "partitioned_problem = partition_problem(\n",
+    "    circuit=qc_1, observables=observable_expanded_paulis\n",
+    ")\n",
     "subcircuits = partitioned_problem.subcircuits\n",
     "subobservables = partitioned_problem.subobservables"
    ]
@@ -243,7 +294,14 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "abeee650",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.612002Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.611552Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.615983Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.615292Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -265,13 +323,20 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "aaef5b3d",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.618472Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.618111Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.917676Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.916967Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 974.414x451.5 with 1 Axes>"
+       "<Figure size 974.616x451.5 with 1 Axes>"
       ]
      },
      "execution_count": 9,
@@ -287,13 +352,20 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "975a3ca9",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.920262Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.919872Z",
+     "iopub.status.idle": "2024-04-19T17:42:35.089754Z",
+     "shell.execute_reply": "2024-04-19T17:42:35.089072Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 723.783x367.889 with 1 Axes>"
+       "<Figure size 723.984x367.889 with 1 Axes>"
       ]
      },
      "execution_count": 10,
@@ -317,7 +389,14 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "459dcee8",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:35.092542Z",
+     "iopub.status.busy": "2024-04-19T17:42:35.092086Z",
+     "iopub.status.idle": "2024-04-19T17:42:35.426066Z",
+     "shell.execute_reply": "2024-04-19T17:42:35.425092Z"
+    }
+   },
    "outputs": [],
    "source": [
     "subexperiments, coefficients = generate_cutting_experiments(\n",
@@ -331,12 +410,19 @@
    "cell_type": "code",
    "execution_count": 12,
    "id": "dc9fe287",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:35.429165Z",
+     "iopub.status.busy": "2024-04-19T17:42:35.428747Z",
+     "iopub.status.idle": "2024-04-19T17:42:36.349289Z",
+     "shell.execute_reply": "2024-04-19T17:42:36.348638Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "sampler = Sampler(run_options={\"shots\": 2**12})\n",
+    "sampler = SamplerV2()\n",
     "results = {\n",
-    "    label: sampler.run(subexperiment).result()\n",
+    "    label: sampler.run(subexperiment, shots=2**12).result()\n",
     "    for label, subexperiment in subexperiments.items()\n",
     "}"
    ]
@@ -345,49 +431,58 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "e317a998",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:36.352558Z",
+     "iopub.status.busy": "2024-04-19T17:42:36.352328Z",
+     "iopub.status.idle": "2024-04-19T17:42:38.830508Z",
+     "shell.execute_reply": "2024-04-19T17:42:38.829874Z"
+    }
+   },
    "outputs": [],
    "source": [
     "reconstructed_expvals = reconstruct_expectation_values(\n",
     "    results,\n",
     "    coefficients,\n",
     "    subobservables,\n",
-    ")"
+    ")\n",
+    "final_expval = np.dot(reconstructed_expvals, observable.coeffs)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "id": "5ae568ca",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:38.833471Z",
+     "iopub.status.busy": "2024-04-19T17:42:38.833025Z",
+     "iopub.status.idle": "2024-04-19T17:42:38.844321Z",
+     "shell.execute_reply": "2024-04-19T17:42:38.843788Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reconstructed expectation values: [0.17901284, 0.70971423, 0.68885177]\n",
-      "Exact expectation values: [0.1767767, 0.70710678, 0.70710678]\n",
-      "Errors in estimation: [0.00223614, 0.00260745, -0.01825501]\n",
-      "Relative errors in estimation: [0.01264952, 0.00368749, -0.02581648]\n"
+      "Reconstructed expectation value: 1.6174469\n",
+      "Exact expectation value: 1.59099026\n",
+      "Error in estimation: 0.02645664\n",
+      "Relative error in estimation: 0.01662904\n"
      ]
     }
    ],
    "source": [
-    "estimator = Estimator(run_options={\"shots\": None}, approximation=True)\n",
-    "exact_expvals = (\n",
-    "    estimator.run([qc_0] * len(observables_0), list(observables_0)).result().values\n",
-    ")\n",
-    "print(\n",
-    "    f\"Reconstructed expectation values: {[np.round(reconstructed_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Exact expectation values: {[np.round(exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Errors in estimation: {[np.round(reconstructed_expvals[i]-exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
+    "estimator = EstimatorV2()\n",
+    "exact_expval = (\n",
+    "    estimator.run([(qc_0.decompose(\"cut_wire\"), observable)]).result()[0].data.evs\n",
     ")\n",
+    "print(f\"Reconstructed expectation value: {np.real(np.round(final_expval, 8))}\")\n",
+    "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n",
+    "print(f\"Error in estimation: {np.real(np.round(final_expval-exact_expval, 8))}\")\n",
     "print(\n",
-    "    f\"Relative errors in estimation: {[np.round((reconstructed_expvals[i]-exact_expvals[i]) / exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
+    "    f\"Relative error in estimation: {np.real(np.round((final_expval-exact_expval) / exact_expval, 8))}\"\n",
     ")"
    ]
   }
@@ -408,7 +503,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/.doctrees/nbsphinx/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb b/.doctrees/nbsphinx/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb
index 7b0d38c4f..129cd3812 100644
--- a/.doctrees/nbsphinx/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb
+++ b/.doctrees/nbsphinx/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "## Gate Cutting to Reduce Circuit Width\n",
     "\n",
-    "In this tutorial we will simulate expectation values of a four-qubit circuit using only two-qubit experiments by cutting gates in the circuit.\n",
+    "In this tutorial we will simulate the expectation value of a four-qubit circuit using only two-qubit experiments by cutting gates in the circuit.\n",
     "\n",
     "Like any circuit knitting technique, gate cutting can be described as three consecutive steps:\n",
     "\n",
@@ -28,11 +28,18 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "96f5b72a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:40.888918Z",
+     "iopub.status.busy": "2024-04-19T17:42:40.888715Z",
+     "iopub.status.idle": "2024-04-19T17:42:41.860986Z",
+     "shell.execute_reply": "2024-04-19T17:42:41.860261Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApQAAAD2CAYAAABobBdEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABDdElEQVR4nO3deXhM9/4H8PfMZEKSSYQgE0kIItYoUT9CY6e2oguqLdrb3VJbtXRRLdXqguqiiobSWtp7LXVRRGm1pFSoWBK7JDKRXfZl5vz+yJWIbDM5c+acGe/X83gec+bMdz7hvL/5zJxNJQiCACIiIiKiWlLLXQARERER2Tc2lEREREQkChtKIiIiIhKFDSURERERicKGkoiIiIhEYUNJRERERKKwoSQiIiIiUdhQEhEREZEobCiJiIiISBQ2lEREREQkChtKIiIiIhKFDSURERERicKGkoiIiIhEYUNJRERERKKwoSQiIiIiUdhQEhEREZEobCiJiIiISBQ2lEREREQkChtKIiIiIhKFDSURERERicKGkoiIiIhEYUNJRERERKKwoSQiIiIiUdhQEhEREZEobCiJiIiISBQ2lEREREQkChtKIiIiIhKFDSURERERicKGkoiIiIhEYUNJRERERKKwoSQiIiIiUdhQEhEREZEoTnIX4GgiJn6IrKsGucuAe4Ae/dfNETXGjEggIddKBYnk6wos7SZ3FcqhlO0McKxtjduZY2NupMHcEMCG0uqyrhqQERsvdxlWkZALXM6SuwqqjCNtZwC3NbIN5oZIOtzlTURERESisKEkIiIiIlHYUBIRERGRKGwoiYiIiEgUnpQjkweWTUbg2L4AAJPRiLykDCT+EY0Ti75HriFN5uosd/Wzp5F6YF3JA7Ua2vo+cA/uB98JH8DZy1fe4u5xjrStcTsjW2FuiCzDbyhlZDh6Fps7Poef7n8Zv01eBq8OAejzzSy5y6o1XbswdFybiODV19F81g/IvRKFy4tHy10WwbG2NW5nZCvMDZH52FDKyFRYjLzkDOQa0pB09BxiNuxH466todW5yF1araicnKGtr4ezly/c2/dCo0EvICfmCIy5t+Qu7Z7nSNsatzOyFeaGyHxsKBXCxbs+AoZ3h6nYCMFokrsc0QpTbyD9z58AtabkDymGI21r3M7IVpgbourxGEoZ6Xu0x5MX10OlVsPJpQ4AIHrFDhTnFQAA+qyahRuHTiF2w34AQIMOzdHrq2n4eeBsGAuKZKu7KlnRBxE1VgfBZIJQmAcA8B41C5q6bgCA9CNbkbj53XKvyY87C//nPkOjIS/bvN57iSNta9zOyFaYG+aGzGfXDeWpU6cwb948HDx4EIIgoF+/flixYgWCgoIwbNgwbNq0Se4Sq5V84gIOT/sCmjpaBIzogSZhHRG1eGPp83+9HY4h2xfg2q5IFKRnI/TD5xH5xhrFTVS3uQV1Q8D0dRAK85F+eAtundqPJk8uLH2+fujDqB/6cOnjjKPbkLD+DXj1myhHuRYRBAFnLqbjZlo+dK5ahLT1gpOT/XzB70jbmiNvZ45GEAScvZSBpNQ86Fy16NzGC1otcyMH5oakZrcNZUREBIYPH45mzZrhrbfegouLC9auXYshQ4YgOzsbnTp1krvEGhnzC0vvK3vy481wD9Cj2/vP4s9XvwYA5BrScGblTtz/9nikRF1E5uVEJB4+LWfJ1VI7u6CuTyAAwKVZBxQYLiHum6loNmVVhXULU+JxfeVkBL6zG+o6rrYu1WyCIOC7HRexZH00/oktO7OzSSNXvDy2DWY/3RF1nJW/y8iRtjVH3M4cjSAI+P6/l/DputM4GVOWG5+GLnhpTFu89kww6tZR/q8f5oa5IfPZz0fFOyQnJ2Ps2LEICQlBVFQUZs+ejSlTpiAiIgLXr18HALtoKO928pPNCBzbF173tSxddj58Dzxb+yN4yigce3edjNVZzmfcfKREhCPnwvFyywWTCVeWPgX9o3PgGtBRpupqJggCZn4ciaff/g2nL5S/TEhiSi7e/uIEBr+0B3n5xTJVWHuOtK3Z+3bmaARBwGtLjmH8G4dwKrZ8bgypeXjnqxMY9OIe5OYxN3Jibsja7LKhXLx4MdLT0xEeHg4Xl7Kz7erVq4eQkBAA9tlQZl0xIG7fcYTMGVe2UBAQ890+xEecQEGqfZ2NV7dJK3h2fQg3NrxZbnniloXQuHig8fCpMlVmnh92XcKyDWcAAIJQ/rnbjw8eN+C1pcdsXJl4jrSt2ft25mi2/HIFn6wr+Zauqtz8fiIJMz+JtHFl4jE3RFWzy4Zy06ZNCAsLQ1BQUKXPe3t7Q6/XAwCKi4sxbdo0NGjQAJ6ennj22WeRn59vy3ItEv3VDvj26QR9aPuyhSYTBJNQ9YsUzPvh2bh1ci+yTh8EAGSf+wOp+9cg4JVwWeuqiSAIWPJdNFSqmtdd858YZGYVSl+UlTnStmav25kjWrrevNys3R6LtMwC6QuyMuaGqHLKP4jlLgaDAQkJCRg7dmyF50wmE06fPo3OnTuXLlu0aBF+/fVXnD59Gs7OzhgxYgRee+01LF++3Kz3Ky4uhsFgMLu+oiLzduMcnv5lpcuTj8dgrc9jZr9fdXXEx8eLHMMbgNasdQOmra10ua5tD3TZXjLRFmdn4MrS8Qh4ZS2cPLwsrKUI8fFJFr1GjMvxOThxLtWsdfMKjAj/TxQe62+7O06Yu50BjrWtOdp25miuJuYi8nSyWesWFJrw7U9RePxBP4mrKsPclMfcUGX0ej2cnCxvD+2uoczJyQEAqCr5CLx9+3bcvHmz3O7u1atX46OPPoKvb8kv+/nz52P06NFYunQpNJqaT6YwGAzw9/c3u76FXgPhq/Uwe32pxMbGYowFdVem3efRcGnavuYVzZS8ZwWK0hMR9+2Mcsu9+k6E98gZVbyqRGxsLPwf7GC1WmrkGgi0nGP26jNenYcZKXslLKg8pWxngPK2NbvazhyNSwsg8A2zV5899z3Mfm63hAWVx9xUjbmh2+Li4uDnZ/kHPbtrKP39/aHRaHDo0KFyy69du4apU0uO+bjdUGZkZCAuLq5cgxkSEoKsrCxcvXoVLVu2hD24uOUgLm45KHcZovk8Nhc+j82VuwzzmCw8LMKo3MMoLOEI25pdbWeOxpRn2frMjWIwNySW3TWUzs7OmDBhAsLDwzFy5EgMGzYMcXFxWLVqFby9vZGQkFDaQGZlZQEAPD09S19/+++3n6uJXq9HXFyc2fUdGfMhcq6Yv4tcKkFBQYjb8q2oMaae9UacQub7oKAg/GLB/4NYJpOAB579HfE38yqcWHA3tRqI/HUN9F51bVMclLOdAY61rdl6O3M0JpOAPi8cxtXE3JpzowL+3LcSvo1tdxtD5kYazI1juX0OiqXsrqEEgOXLl0Or1WL79u04cOAAQkNDsXXrVrz33nu4ePFi6ck67u7uAIDMzMzSf6CMjIxyz9XEycnJoq9+tVpl/JNqtZbVXekYFwAoYLICAK1WK/rnsdT08cGY9clfNa736IDmuP++QBtUVEYp2xngWNuaHNuZo5k+viOmLT5a43oj+jZDt5BWNqioDHMjDeaGADs9y1un02HlypUwGAzIysrC3r17ERoaiujoaAQHB0OtLvmxPD094e/vj5MnT5a+NioqCu7u7ggICJCneLIbU8e1x9Cw6ifJln7u+GJuqI0qIlK+SWPbYkSfptWu09xXhxVv9bBRRURkC3bZUFYmIyMD8fHxFa4/+dxzz+GDDz7AjRs3kJycjPnz5+Ppp58264QcurdptWpsXTYArz0TDA+38mdSajQqPD64Bf5c/xAae9lulx2R0jk5qfHTp/0x59mOFXOjVmHMg81xZMMI6BvyDixEjsRhGsrTp0supHt3Q/nGG2+gV69eaN++PQIDA9G2bVssXrxYhgrJHjlrNVg84/9wI2Icvnqz7BuVv74fgY0f9WUzSVQJrVaND6Z1rZCbo9+PwOaP+8GbuSFyOA7fUDo5OWH58uVIT09HZmYm1qxZU+7uOnJr9UR/DN3xPoZsXwDPNpXvJhr873cRuvgFG1dWOyl7V+P8az1wfs4DyLta+T1tY97sg2tfvWTjysRxc9Xiod5l/z+NGyhnGzKXI21rjrqdOZq7c6O3w0aSuSEyj8M0lJMmTYIgCOjevbvcpZjN2VOH1hMHYfcj8/DHzBXotuCZCuv4DeiComwLL8Uhk+KsNCTvWYHWiw4hYMoaxK2eVmGdjGM7oXEx74Qosh5H2ta4nZGtMDdE5nOYhtIeNeocCMOfZyAUG3Hr0g3UaeCBcvcsU6nQ5pnBOL92j3xFWiDnwl/QdegDlZMWdf1ao/hWCgSTqfR5wWRC8q4v0WjoZBmrvDc50rbG7YxshbkhMh8bShk5e+pQmJlT+rgoOw/OHmUHqgeO6YNruyJhzC+SozyLGbPS4KSrX/pY7eIOY25m6ePUA+vgGfoI1FrbXa+RSjjStsbtjGyFuSEyHxtKGRVm5sDZw630sVbngsJbuQAATR0tWjwShoubDshVnsU0uvow5mSUPjblZUHjWq/k74X5SDv0PRr2r7jLiKTnSNsatzOyFeaGyHzKucrrPSj5xAV0enUMVBo1dP6NUZB2C7dvL6Fr2hjO9dwwYP1cOHvq4NLYEy1H98alHw/VMKp83IK64cbGdyAYi1Fw8yqcPBpC9b9rghYkXYExJwMXFwxHcXYaitINSD3wHbz6TZC56nuDI21r3M7IVpgb5obMx4ZSRoUZ2bjwQwSGbF0AQTDh6NzV8O3bCc6eOlzZehg7B78OANCHtkfzUT0VO1Hd5uTeAA0HPoeYub0AtRpNX/wSmSf2wJiVhga9n0DbJccBAFmnDyLt902crGzIkbY1bmdkK8wNkflUglDTHVfJEtt6T0dGbLzcZcAzyA+jDi0TNcaYX4HL5t3yXHIt3IEtfeWtId6QA/9BmwAAcXsfh5/erYZXSEcp2xngWNuaErYzR8PcVI65IUfDYyiJiIiISBQ2lEREREQkCo+htDL3AL3cJQCwTh2+CrrVrpJqUQKlbGeAY21rSqmDpMHcSEMpdZC8eAwlkZmUdCwYkb1gbojuDdzlTURERESisKEkIiIiIlHYUBIRERGRKGwoiYiIiEgUNpREREREJAobSiIiIiIShQ0lEREREYnChpKIiIiIRGFDSURERESisKEkIiIiIlHYUBIRERGRKGwoiYiIiEgUNpREREREJAobSiIiIiIShQ0lEREREYniJHcBjiZi4ofIumqQuwy4B+jRf90cUWPMiAQScq1UkEi+rsDSbnJXQVJhbqTB3Dg25kYazE3tsKG0sqyrBmTExstdhlUk5AKXs+Sugu4FzA2R5ZgbUhLu8iYiIiIiUdhQEhEREZEobCiJiIiISBQeQ0lUjeS0PPx6LBHHz6Qg6lxq6fI3Pz+OPl190KuLHi39PWSskEh5UtLz/5ebZJw4W5abN5aX5SawKXND5EjYUBJV4u+zKVjyXTR+3HsFRcWmCs9/9/NFfPfzRQDAgO5NMHVcOzzUpylUKpWtSyVSjBNnU7B0QzS2/HIFhUUVc7N+50Ws31mSm37/54OpT7TDyL7NmBsiB8CGUiYPLJuMwLF9AQAmoxF5SRlI/CMaJxZ9j1xDmszVWe7qZ08j9cC6kgdqNbT1feAe3A++Ez6As5evvMVZIC+/GG9/8TeWrI+GIJj3mv1Hb2D/0RsY0acpvn67J3wauUpb5D2MuVGm/IJivPPVCXyyLhomk3nBOfBXIg78lYhhvfzxzbyeaNLYTeIq713MDdkCj6GUkeHoWWzu+Bx+uv9l/DZ5Gbw6BKDPN7PkLqvWdO3C0HFtIoJXX0fzWT8g90oULi8eLXdZZjOk5KL7Uz/j0+/MbybvtOPgdQQ/+h8ci062fnFUirlRlqTUPPQYvxMfhZ82u5m8039/i0Pwo1tx9NRNCaqj25gbkhobShmZCouRl5yBXEMako6eQ8yG/WjctTW0Ohe5S6sVlZMztPX1cPbyhXv7Xmg06AXkxByBMfeW3KXVKCU9H32f3YV/Yqv+tK7RqODr7Qpfb1doNJXvokvNKMCAF3bjxNkUqUq95zE3ypGWWYD+z+1C1PnUKtcxJzdpmQUY+OJufhiTEHNDUmNDqRAu3vURMLw7TMVGCMaKxx7Zm8LUG0j/8ydArSn5o2CCIGDiW4dw/kpmtevpG7ogft84xO8bB33DqifhW9lFeGRmBLJyCq1dKt2FuZGPIAj417zfcOZSRrXrmZub7NxiPDozAplZzI3UmBuSAo+hlJG+R3s8eXE9VGo1nFzqAACiV+xAcV4BAKDPqlm4cegUYjfsBwA06NAcvb6ahp8HzoaxoEi2uquSFX0QUWN1EEwmCIV5AADvUbOgqVtybFT6ka1I3Pxuudfkx52F/3OfodGQl21e723rdlzArt+te7eJazey8dqSY1jxdk+rjkvMDaCM3Pyw6xK2/3rdqmPGGXLw6qeRWDU/zKrjEnMDKCM3jsyuG8pTp05h3rx5OHjwIARBQL9+/bBixQoEBQVh2LBh2LRpk9wlViv5xAUcnvYFNHW0CBjRA03COiJq8cbS5/96OxxDti/AtV2RKEjPRuiHzyPyjTWKDDcAuAV1Q8D0dRAK85F+eAtundqPJk8uLH2+fujDqB/6cOnjjKPbkLD+DXj1myhHuQCAwiIj5iw7LsnYX/94HjPGd0BQQD1Jxq+t64nZ+Oan8zj6TzKMJgGtA+rhhUdbI6RdQ7lLMwtzI39uiotNeH3pMUnGXv2fWMwY3wHtWtaXZPzaijfk4Jt/n8eRUzdRbBTQqqkHXnisNe5v30ju0szC3MifG0dnt7u8IyIi0L17d8TExOCtt97CokWLEB8fjyFDhiA7OxudOnWSu8QaGfMLS+7FGhOHkx9vRlbcTXR7/9nS53MNaTizcifuf3s8Wo8fiMzLiUg8fFrGiqundnZBXZ9AuDTrgCZPvoc63s0R983UStctTInH9ZWT0Xz2JqjryHdW9NaIa0hKzZNs/K9/PCfZ2JYSBAFvfX4czQdvxvurTuHAXzdw6HgiVv54Hl0e345R0/YhO1eZvzzuxNzIn5sdB68j4WauZON//eN5yca2lCAImP/VCQQM3owFK08i4mhJblb9OwZdx+3AQ1P22sXhLcyN/LlxdHbZUCYnJ2Ps2LEICQlBVFQUZs+ejSlTpiAiIgLXr5fsgrGHhvJuJz/ZjMCxfeF1X8vSZefD98CztT+Cp4zCsXfXyVid5XzGzUdKRDhyLpT/BlAwmXBl6VPQPzoHrgEdZaquxHc/X5B4/IsQanPKuATe+eoE3l91CrdPxBUElDubffuv1/HozAgY7eyYKubG9qTOzfqfL9bqjHEpLFh5Eu9+HQXj/+oRUD43O3+Lw6hp+1FUyXU3lYy5IWuzy4Zy8eLFSE9PR3h4OFxcyg7yrlevHkJCQgDYZ0OZdcWAuH3HETJnXNlCQUDMd/sQH3ECBan2dfZa3Sat4Nn1IdzY8Ga55YlbFkLj4oHGwyv/NGkrgiAg8rS0Z5WmZhTgcnyWpO9hjoSkHCxadarG9fb+mYCdv8XZoCLrYW5sT+rcZGQV4sK16k+SswVDSi4WfBNV43oH/krE9l+v2aAi62FuyNrs8hjKTZs2ISwsDEFBQZU+7+3tDb1eDwDYsmULli9fjpMnT6Jhw4a4evWqRe9VXFwMg8Fg9vpFRcUWjX+36K92YNjP70Mf2h6GI2dKFppMECz8tF5UVIz4eHEnmhQVeQPQihrD++HZiJnTE1mnD8I9uA+yz/2B1P1r0HbJCQtrKUJ8fJKoWu6WcDMPqRkF5ZZpNKoqz0T1uWO5TxXrGFLyYDSW/7/adzgWdcL0IqsVZ8mGi6XfsNS47roodGll2zMlmZvylJybpLQCGFLKHyYiRW72Ho6Fm7aJyGrFWb7pEoqLzduGln53Et3bift/txRzU56Sc2NP9Ho9nJwsbw9VglL2x5nJYDDAx8cHM2fOxKefflruOZPJBB8fH3Tu3Bl79uwBAOzbtw+pqalISkrC0qVLLW4o4+Pj4e/vb/b6C70Gwldr3XvUBo7pA6/7WiLyzTVmvyah6BbeSt0n6n3bfR4Nl6btRY1xp+LsDJybGYKAKWvg3rGvRa/Nu34GZ6d2sFotAACXZkDg2+UW+Xq7In7fuCpeUDO/gRuRkHTXsWUJ3wNpv9Z6TKto9grgHgyYc4s7Yy5w9hXpa7oDc1M1xeWmrj/Q6p1yiyTJzY1NQOr+Wo9pFU0nAx6dzMxNPnB2iuQl3Ym5qZricmNH4uLi4OfnZ/Hr7O4bypycHACo9N6v27dvx82bN8vt7h44cCAAYNu2bbYoj6qRvGcFitITEfftjHLLvfpOhPfIGVW8Sko2un+wEu5TrLLk6BYF1EullJcbG7G33FiUMZLaPZsbGdndN5SFhYVwdXVF586dcexY2WUrrl27hp49eyIhIQEbN27E448/Xu5127Ztw/Tp0yXf5X1kzIfIuWL++lJxa65H6JY5osaYetYbcfm23YVTFf+6Rfi8nXV3QVxJyEGv5w+XW1bTrrtjG0cBALqO24bElIpnh1e26+6zV4PxSD95d93N/+Y81myr+RgvFYCOQR7YuSxU+qLuwNxIQ4rcXDfkoue/fi+3TIrcfDK9A8YOkve+zAvXxGDlv6/WuJ5KBbRr7o49X/SQvqg7MDfSkCI39qS2u7zt7htKZ2dnTJgwAeHh4Rg5ciSGDRuGuLg4rFq1Ct7e3khISLDqCTlOTk4WffWr1Srjn1SrtazuSse4ACDfOvWIpdVqRf88d2vSRIDONbLcpXKMRqHirrdKJKbkmbUeAPQLDYSfX4Na12kNs57WmdVQCgCmPXWf1f+ta8LcSEOK3Pj6CqjnHlnujjZS5KZ/j0D4+XnVuk5rmDnR3ayGUhCAV57qyNyIGcPBc3MvsMvv6JcvX44XXngBkZGRmDVrFiIjI7F161Y0adIErq6uVZ6sQ3QntVqFkLbS/sJyreuENs09JX0Pc7Rt4Yknh7U0a72xD7awQUVkr1QqFe6X+CL4deto0F4BFzYPCqiHiSNa1bxeMw88ObTmfBE5MrtsKHU6HVauXAmDwYCsrCzs3bsXoaGhiI6ORnBwMNRqu/yxSAaPD5a2eRo9qDmcnJSxPa6e/wBG9WtWYfntI9XatfDE3q8Hw9VFGd96kHJJnZtHBwRAq1VGblbO64lHBwRUWH47N60D6mHvysFwc1XG7loiuSgjsVaQkZGB+Pj4Cru7jUYj8vPzUVRUBEEQkJ+fj4KCgsoHoXvOU8NbQifhL4JJY9tKNral6tZxwr+X9McvXz+IgaFlx6aFtPPCuoW9cHzTSPjp3WSskOzFuCEtUM/dWbLxlZSbOs4abPmkH/Z9MxgP9ijLTee2XghfEIaoLaPQrIm7jBUSKYPDNJSnT5fcIuruhnL9+vVwcXHBmDFjcP36dbi4uKB169YyVFi5Vk/0x9Ad72PI9gXwbNO00nUG//tdhC5+wcaV1U7K3tU4/1oPnJ/zAPKuVn7brpg3++DaVy/ZuLLKubs5Y/bTwZKMPTTMD107KOv+2Gq1CoN6+OHbd8NKl21bNhATRrSCS137+WaSuZGXm6sWrz8jzV1HBvXwReh9jSUZu7bUahUGdPfF6vlludn+2UA8PTKIuZGRveXG0Tl8Q/n0009DEIRyfyw901sqzp46tJ44CLsfmYc/Zq5AtwXPVFjHb0AXFGVLd69payrOSkPynhVovegQAqasQdzqaRXWyTi2ExoXZX2an/vsfejUxronzdRzd8Y38x6o9PJWJA5zowyznw7G/e2t+4HJ3U2LVe8wN1JgbkhqDtNQTpo0CYIgoHv37nKXYrZGnQNh+PMMhGIjbl26gToNPMpfe02lQptnBuP82j3yFWmBnAt/QdehD1ROWtT1a43iWykQTGX3txVMJiTv+hKNhk6WscqKtFo1Ni7uCy/POtWuZ0jJg9/AjfAbuLHCnULupNGosG5hL/h6c/exFJgbZXByUuOHD/ugUf261a5nbm7UahXC3wtDUx+dtUslMDckPYdpKO2Rs6cOhZk5pY+LsvPg7OFa+jhwTB9c2xUJY35RZS9XHGNWGpx0ZWdmql3cYcwtux9v6oF18Ax9BGpt9b+A5NCmuSf2fzOk2l+Oty+NkpCUW+Gaebc5aVT44cM+GNm34skvZB3MjXK0alYP+1cNgbdX5degBMzLjUajwnfv98KjA5tLVeo9j7khqbGhlFFhZg6cPcq+xdLqXFB4q+QabZo6WrR4JAwXNx2QqzyLaXT1YczJKH1sysuCxrVeyd8L85F26Hs07F9xN4tSdGrjhb83jyx34L0l2rbwxOF1wzGGl92RFHOjLB2DGuD4xpEY8kDtrtsX1KwefgsfhieHBVq5MroTc0NSY0Mpo+QTF+DdvS1UGjXcA/QoSLtVcoVcALqmjeFczw0D1s9Fl7efgm//zmg5urfMFVfPLagbss78BsFYjPzEi3DyaAjV/y7hVJB0BcacDFxcMBzx615D5t+7kHrgO5krrshfr8PuFQ/iu/d7IbiVedfBa9LYFQundMGJzSPRraOyTiZwRMyN8nLjp3fDf78chA0f9MZ9rc07HtmnkSvemxyCkz+OQo9O3hJXSMyN8nLjaOzn9DQHVJiRjQs/RGDI1gUQBBOOzl0N376d4Oypw5Wth7Fz8OsAAH1oezQf1ROXfjwkc8XVc3JvgIYDn0PM3F6AWo2mL36JzBN7YMxKQ4PeT6DtkuMAgKzTB5H2+yZ49Zsgb8FVUKlUGP9QKzw1PBB/nryJPX/E4++zKTh3OQO5+cVw1mrQws8dXdp5oVcXPYaFNVXMNfPuBcyNcnPz5LBAPDG0JY7+cxO7D5fk5uylktxondT/y01D9Oqix/BezI0tMTfKzI0jsbt7eSvdtt7TkREbL3cZ8Azyw6hDy0SNMeZX4HKWdeoRq4U7sKWv3FU4jnhDDvwHbQIAxO19XPbrTzI30mBurIu5qRxzQwB3eRMRERGRSGwoiYiIiEgUNpREREREJApPyrEy9wC93CUAsE4dvq41r2MrSqqFrI+5kYaSaiHrY26koaRa7AlPyiG6Bynt5AIie8DcEFWNu7yJiIiISBQ2lEREREQkChtKIiIiIhKFDSURERERicKGkoiIiIhEYUNJRERERKKwoSQiIiIiUdhQEhEREZEobCiJiIiISBQ2lEREREQkChtKIiIiIhKFDSURERERicKGkoiIiIhEYUNJRERERKKwoSQiIiIiUZzkLsDRREz8EFlXDXKXAfcAPfqvmyNqjBmRQEKulQoSydcVWNpN7iqIaqaUOQBwrHmAc4BjY26kYcvcsKG0sqyrBmTExstdhlUk5AKXs+Sugsi+ONIcAHAeINtgbuwfd3kTERERkShsKImIiIhIFDaURPcYQRAQZ8gufXzheiaKi00yVkSkfHfnJvYac0N0Jx5DSXQPKCwy4j/7r2LdjouIPH0T6bcKS5/r99xuuNTVoFNrL4wZ1BwTR7ZCfY86MlZLpAyFRUZsO3ANa7dfQOTpZKRlFpQ+1//53ahbR4NOrRtg9KDmeHpkEBrUY27o3sWGUiYPLJuMwLF9AQAmoxF5SRlI/CMaJxZ9j1xDmszVWe7qZ08j9cC6kgdqNbT1feAe3A++Ez6As5evvMXdwwRBwLodFzBn2XEkpeZVuV5evhFHTt3EkVM38cbnxzH9yQ545+XOqOOssWG19x5HmgccaQ4QBAEbdl7Ea0uPwZBSdW7yC4w4+k8yjv6TjDc//xuvPNEO704KQd06/NUqJeZGmbjLW0aGo2exueNz+On+l/Hb5GXw6hCAPt/MkrusWtO1C0PHtYkIXn0dzWf9gNwrUbi8eLTcZd2zUtLzMXzKXjzz9u/VNpN3y8s34oM1pxAydhtOx9rX5GyPHGkecIQ5IC2zAKOm7ceEN3+rtpm8W36BER+Fn0bnMdtw8nyqhBUSwNwoERtKGZkKi5GXnIFcQxqSjp5DzIb9aNy1NbQ6F7lLqxWVkzO09fVw9vKFe/teaDToBeTEHIEx95bcpd1zklLz0OuZ/2LX77W/DMfZSxkIe+a/iPznphUro7s50jxg73NAcloeej/zX+w4eL3WY5y/kolez/wXf55MsmJldDfmRnnYUCqEi3d9BAzvDlOxEYLR/g/0Lky9gfQ/fwLUmpI/ZDP5BcUY/PIenLucUeU6Go0Kvt6u8PV2hUajqnK9zKxCDJn0Cy7F2dfEZq8caR6wtzmgoNCIoZP3IvpiepXrmJubrJwiDJ28F7FXM6Uole7C3CgDD/SQkb5Hezx5cT1UajWcXEoO5o5esQPFeSUHfvdZNQs3Dp1C7Ib9AIAGHZqj11fT8PPA2TAWFMlWd1Wyog8iaqwOgskEobBkV5H3qFnQ1HUDAKQf2YrEze+We01+3Fn4P/cZGg152eb1Oqr5K6Jw8nz1u6r1DV0Qv28cAMBv4EYkJFV9S4f0W4X417zf8euaoVCrq/4lSrXjSPOAPc8BC1ZG4fiZlGrXsSQ3mVmF+Nc7v+PQt0Oh0fC7G2tjbpSRmzvZdUN56tQpzJs3DwcPHoQgCOjXrx9WrFiBoKAgDBs2DJs2bZK7xGoln7iAw9O+gKaOFgEjeqBJWEdELd5Y+vxfb4djyPYFuLYrEgXp2Qj98HlEvrFGcWG4zS2oGwKmr4NQmI/0w1tw69R+NHlyYenz9UMfRv3Qh0sfZxzdhoT1b8Cr30Q5ynVIp2JS8fHa01Yf97e/DVj9nxi88Fgbq48tliAIOH8lE0mpeXB30+K+oAZwcrKfX+CONA/Y6xwQfSENH377j9XH/SMqCSt/PI9Jj7ez+tjWEHMlA4kpedC5OqFTay/mRib2mpu72W1DGRERgeHDh6NZs2Z466234OLigrVr12LIkCHIzs5Gp06d5C6xRsb8wtJ7l578eDPcA/To9v6z+PPVrwEAuYY0nFm5E/e/PR4pUReReTkRiYet3yxYi9rZBXV9AgEALs06oMBwCXHfTEWzKasqrFuYEo/rKycj8J3dUNdxtXWpDmvZhjMwmQRJxv50XTSef7Q1VCplfEspCAJ+2HUJS76LxolzZSdB+DZ2xctj2uLVp4Pt4ix1R5oH7HUO+Oz7MzAapcnNkvXReGlMW0V9u7/xf7k5frbsG9kmjVzx0pg2mP10sF2cpc7cyJ+bu9nPx5E7JCcnY+zYsQgJCUFUVBRmz56NKVOmICIiAtevlxxMbQ8N5d1OfrIZgWP7wuu+lqXLzofvgWdrfwRPGYVj766TsTrL+Yybj5SIcORcOF5uuWAy4crSp6B/dA5cAzrKVJ3jScsswKY9lyUbP/ZaJg5EJko2viUEQcDrS4/hqbmHEHXXGbU3knPx1hd/Y8jLvyAvv1imCmvPkeYBe5gDMm4V4PtdlyQb/1JcFvb+mSDZ+JZ647PjeGLOQfx9rvzu/cSUXMz78gQefOkX5OYxN3Kyh9xUxi4bysWLFyM9PR3h4eFwcSk7o6tevXoICQkBYJ8NZdYVA+L2HUfInHFlCwUBMd/tQ3zECRSk2teJEXWbtIJn14dwY8Ob5ZYnblkIjYsHGg+fKlNljunQ8UTkFxglfY9f/qz9WePWtHnP5dJd+8JdXyzdfvzrsUS8+ulfNq5MPEeaB+xhDjgclYS8/HsjN//edwUfrDkFoOrc/Pa3ATM+PmrjysRjbuRnlw3lpk2bEBYWhqCgoEqf9/b2hl6vR0FBAZ5//nm0aNEC7u7uCAoKwueff27jai0T/dUO+PbpBH1o+7KFJhMEiXZjSs374dm4dXIvsk4fBABkn/sDqfvXIOCVcFnrckR/n63+hAJ7eQ9zLF0fDXP2vH+7LRYZtwpqXlFhHGkeUPocwNxUtG7HBaRm5EtfkJUxN/JS/oESdzEYDEhISMDYsWMrPGcymXD69Gl07twZAFBcXAy9Xo+9e/eiRYsW+Oeff/Dggw/C29sbY8aMMev9iouLYTAYzK6vqMi8XQWHp39Z6fLk4zFY6/OY2e9XXR3x8eI+FRcVeQPQmrVuwLS1lS7Xte2BLttLwlycnYErS8cj4JW1cPLwsrCWIsTH87pu1Yk6W3471WhU0Des/JpsPncs96liHQAwpOSVO7Ys+mKa6O1KrCs3cvBXtHm/oPMLjAj/z0mMHmC7O06YOwcAjjUP2Osc8PeZ8odxSJGbMwrITXxSHv44ad41ZQsKTfj2p5MYN9hP4qrKMDflyZkbvV4PJyfL20O7ayhzcnIAoNITA7Zv346bN2+W7u52c3PDggULSp/v1KkTRowYgcOHD5vdUBoMBvj7+5td30KvgfDVepi9vlRiY2MxxoK6K9Pu82i4NG1f84pmSt6zAkXpiYj7dka55V59J8J75IwqXlUiNjYW/g92sFotDqnZK4BH2XE1d17ipDrHNo6q8rm7L41yMznNojxIwrUF0PINs1efOXs+ZqbskbCg8pQyBwDKmwcUOQc0mwx4dC59KEVu0tKz5M+NSwAQ+JbZq7/2xgK89vwu6eq5C3NTNVvnJi4uDn5+ln+YsLuG0t/fHxqNBocOHSq3/Nq1a5g6teS4gqqOnywqKsLvv/+OV199VeoyreriloO4uOWg3GWI5vPYXPg8NlfuMhyXIO1xYAAAkw3eoyZGC3fFmcy/fZ6SOcI8oMg5wBa5scV71MRoYQ5M9rfLuzLMje2oBOHuQ3OV71//+hfCw8MxYsQIDBs2DHFxcVi1ahW8vb3xzz//4Ny5c2jTpuL18l588UWcOHECf/zxB5ydnc16L0t3eR8Z8yFyrpi/vlTcmusRumWOqDGmnvVGXL55u7yl5l+3CJ+34y7v6ry36jxWbb1W+rimXXe3v2HpOm4bEqu4Z/Hdu+66tPXEtk+7Wa/oWjCZBIQ99zvikvIqnFhwN7UaOLq2N3wa1rVNcVDOHAA41jwg1Ryw6NsYrPjpauljKXLTsZUH/vtZqNVqrg1BENDnhcO4ciO3xtyoVMAf3/aCv7ftbmPI3EijNrm5Z3Z5A8Dy5cuh1Wqxfft2HDhwAKGhodi6dSvee+89XLx4sdKTdWbOnIkjR47gwIEDZjeTAODk5GTRV79arTL+SbVay+qudIwLABTyIVWr1Yr+eRxdn/8rKNdQGo1CtXfyuC0xJc+s9QAgtFMTRfw/TB/fETM/jqxxvZF9m6Frp0AbVFRGKXMA4FjzgFRzQJ9uheUaSkfOzYwJ92HqB0dqXG94r6YI7dLKBhWVYW6kYcvfnXZ5lrdOp8PKlSthMBiQlZWFvXv3IjQ0FNHR0QgODoZaXf7Hmj59Ovbt24eIiAg0bNhQpqqJpBXWxdusMzjF6N1FL+0bmGnK4+0wNKz6SbK5rw5fvdnDRhWRvXqgs7fkFx1XSm5eGt0GI/o0rXadpno3fP02c0OWs8uGsjIZGRmIj4+vcPzkK6+8gv379+PAgQNo1KiRPMUR2YC/XodhYdId+O/t5YIRfav/ZWQrWq0aW5cNwOyng+HuVn63kkatwuhBzfHn+oegb6isO0mQ8jRp7FZjkyVGw/p18ciAAMnGt4STkxo/fdofr/+rIzzuyo1arcKjAwJw9PsRaNLYTaYKyZ45TEN5+nTJRY7vbCivXbuGzz//HBcvXkTz5s2h0+mg0+kwZMgQmaokktYrT1rvrPy7vTS6DZy1yrmVobNWg49m/h9uRIzDF3PLjk87+v0IbPmkH5tJMtsrT0h3r+0XH2utqFuAarVqfDi9K25EjMOXb5R9E3l0w0P4aUl/+DRibqh2HLqhbNasGQRBQH5+PrKzs0v/7N69W6YqK2r1RH8M3fE+hmxfAM82lX9KHvzvdxG6+AUbV1Y7KXtX4/xrPXB+zgPIu1r5fVNj3uyDa1+9ZOPK7g0DQ30xdnBzq48b1KweXv+X8m71BQA6Vy1G9m1W+ljvZbsTCazFkeYBe5wD+v5fEzw5rGXNK1qopb875j57n9XHtQY3V225b2Z97PADGHOjLA7TUE6aNAmCIKB79+5yl2I2Z08dWk8chN2PzMMfM1eg24JnKqzjN6ALirLt47InxVlpSN6zAq0XHULAlDWIWz2twjoZx3ZC4+IuQ3X3ji/m9qjyLNXbDCl58Bu4EX4DN8JQxZmqtzlpVAhfEAaXuso5aN6RONI8YM9zwPI5ofBtXH1TZUluNBoVvn03DG6u8p/p64iYG+VxmIbSHjXqHAjDn2cgFBtx69IN1GnggXJnVahUaPPMYJxfa7uLMouRc+Ev6Dr0gcpJi7p+rVF8KwWCyVT6vGAyIXnXl2g0dLKMVTq+hvXr4pevB6NBvTpVrnP7TNaEpNxylze5m1qtwvpFvdGjk7cUpRIcax6w5zmgQb062LPiQXh5is+NSgWEvxeGXvf7SFEqgblRIjaUMnL21KEwM6f0cVF2Hpw9yj4hB47pg2u7ImHML5KjPIsZs9LgpKtf+ljt4g5jbmbp49QD6+AZ+gjUWttdE/Be1TGoAQ59OxSBTWt/54l67s7495J+eHyI9XcFUhlHmgfsfQ7o0KoBfl87HEHN6tV6DA+dFls+7ofxD9n2sjv3GuZGedhQyqgwMwfOHmVn02l1Lii8VXJdM00dLVo8EoaLmw7IVZ7FNLr6MOZklD425WVB41oyMZsK85F26Hs07F9xtwRJo0OrBjj148OYMb69xZcTGtbLH2f+8whG9QuQpDYq40jzgCPMAW1beOLkj6Pw6sRgiy8nNLinH6L/8wgeG2T945ipPOZGeXhQlIyST1xAp1fHQKVRQ+ffGAVpt3D7Fga6po3hXM8NA9bPhbOnDi6NPdFydG9c+vFQDaPKxy2oG25sfAeCsRgFN6/CyaMhVP+7JmhB0hUYczJwccFwFGenoSjdgNQD38Gr3wSZq3Zsri5OWDK7O155oj1W/nQea7dfqPLYL52rFo8NDMCksW3RtQMvsWUrjjQPOMoc4FLXCR/P+j9MGdcW3/wUg/DtF5CYXPlFzN1cnPDogABMfrwdunZoCJXUF4MlAMyNEnPDhlJGhRnZuPBDBIZsXQBBMOHo3NXw7dsJzp46XNl6GDsHvw4A0Ie2R/NRPRUbhtuc3Bug4cDnEDO3F6BWo+mLXyLzxB4Ys9LQoPcTaLvkOAAg6/RBpP2+SZGBcFQBvu74YFpXLHrlfsQn5eDvsylISs2HySTA090Zndp4IaiZBzQa7rSwNUeaBxxtDmjWxB3vv3I/Fk7tgoSkXPx9LgVJqSW3VfR0d8Z9rRugdUA95kYGzI3y2OW9vJVsW+/pyIiNl7sMeAb5YdShZaLGGPMrcDnLOvWI1cId2NJX7ipIqeINOfAftAkAELf3cfjp5bsws1LmAMCx5gHOAdbH3FSOuakdfqwiIiIiIlHYUBIRERGRKDyG0srcA/RylwDAOnX4KujGCUqqhag6SpkDAMeaB5RSB0mDuZGGLevgMZREZPeUdCwYkb1gbsiauMubiIiIiERhQ0lEREREorChJCIiIiJR2FASERERkShsKImIiIhIFDaURERERCQKG0oiIiIiEoUNJRERERGJwoaSiIiIiERhQ0lEREREorChJCIiIiJR2FASERERkShsKImIiIhIFDaURERERCQKG0oiIiIiEsVJ7gIcTcTED5F11SB3GXAP0KP/ujmixpgRCSTkWqkgkXxdgaXd5K6CqGZKmQMAx5oHOAc4NuZGGrbMDRtKK8u6akBGbLzcZVhFQi5wOUvuKojsiyPNAQDnAbIN5sb+cZc3EREREYnChpKIiIiIRGFDSURERESi8BhKIrJLqRn5+PWvRPx9LgUnzqaWLn/7y7/R+349enXRo4Wfh4wVEilPWmYBfv3rBv4+m4q/z6aULn/ri5LchIXoEdiUuSHLsaEkIrsSdS4FS9efwZa9V1BQaKzw/NrtF7B2+wUAwMDQJpg6rj2G9/aHSqWydalEinEqJhVL15/Bpj2XK83Nuh0XsG5HSW76d2uCqU+0w4g+TZkbMhsbSpk8sGwyAsf2BQCYjEbkJWUg8Y9onFj0PXINaTJXZ7mrnz2N1APrSh6o1dDW94F7cD/4TvgAzl6+8hZHDiG/oBjvfHUCn6yLhskkmPWafUduYN+RGxjRpylWzusJfUNXiau0jCPNA5wDlKmg0Ij3vo7C4vB/YDSal5uIyBuIiLyB4b38sXJeTzRp7CZxlZZhbpSJx1DKyHD0LDZ3fA4/3f8yfpu8DF4dAtDnm1lyl1VrunZh6Lg2EcGrr6P5rB+QeyUKlxePlrsscgA3U/PQY/xOfBR+2uxm8k47Dl5H8KNbcSw6WYLqxHGkeYBzgLKkpOfjgYk7sWj1KbObyTvt/C0OwY9uxdFTNyWoThzmRnnYUMrIVFiMvOQM5BrSkHT0HGI27Efjrq2h1bnIXVqtqJycoa2vh7OXL9zb90KjQS8gJ+YIjLm35C6N7FhqRj76PbcLUedTq1xHo1HB19sVvt6u0Ggq30WXkp6PAS/sxok7jhtTAkeaBzgHKEf6rQL0f343jp+pens3JzdpmQUY+OJuxX0YY26Uhw2lQrh410fA8O4wFRshGE1ylyNaYeoNpP/5E6DWlPwhqgVBEPDM27/jzKWMatfTN3RB/L5xiN83DvqGVf9CuZVdhEdmRiArp9DKlVqHI80DnAPkIwgCnnvnMP6JrX73r7m5yc4txiMzIpCZxdxIzZ5zw2MoZaTv0R5PXlwPlVoNJ5c6AIDoFTtQnFcAAOizahZuHDqF2A37AQANOjRHr6+m4eeBs2EsKJKt7qpkRR9E1FgdBJMJQmEeAMB71Cxo6pYcf5N+ZCsSN79b7jX5cWfh/9xnaDTkZZvXS8r3/X8v4edD16065rUb2XhtyTGseLunVcetLUeaBzgHKMOWX67gPxFXrTpmfFIOZn0SidXvhll13NpibpSXG7tuKE+dOoV58+bh4MGDEAQB/fr1w4oVKxAUFIRhw4Zh06ZNcpdYreQTF3B42hfQ1NEiYEQPNAnriKjFG0uf/+vtcAzZvgDXdkWiID0boR8+j8g31iguDLe5BXVDwPR1EArzkX54C26d2o8mTy4sfb5+6MOoH/pw6eOMo9uQsP4NePWbKEe5pHBFRSa8vuyYJGN//eN5zBjfAUEB9SQZ3xKONA9wDpCf0WjC7CV/STL2mq2xmDmhA9q1rC/J+JZgbpSXG7vd5R0REYHu3bsjJiYGb731FhYtWoT4+HgMGTIE2dnZ6NSpk9wl1siYX1hy/9KYOJz8eDOy4m6i2/vPlj6fa0jDmZU7cf/b49F6/EBkXk5E4uHTMlZcPbWzC+r6BMKlWQc0efI91PFujrhvpla6bmFKPK6vnIzmszdBXUdZZ96SMuw4eA03buZKNv7XP56TbGxLONI8wDlAfv/9LQ5xhhzJxl+x5bxkY1uCuVFebuyyoUxOTsbYsWMREhKCqKgozJ49G1OmTEFERASuXy/ZPWYPDeXdTn6yGYFj+8Lrvpaly86H74Fna38ETxmFY++uk7E6y/mMm4+UiHDkXDhebrlgMuHK0qegf3QOXAM6ylQdKd26HRclH782Z4xLzZHmAc4Btid1btbvvAijAo9TZG7kZ5cN5eLFi5Geno7w8HC4uJQdSFyvXj2EhIQAsM+GMuuKAXH7jiNkzriyhYKAmO/2IT7iBApS7euMr7pNWsGz60O4seHNcssTtyyExsUDjYdX/gmMSBAERJ6W9lIlaZkFuByfJel71IYjzQOcA2xP6txkZhUi9prytkHmRn52eQzlpk2bEBYWhqCgoEqf9/b2hl6vBwBMmjQJP//8MzIzM+Hu7o7Ro0fjo48+grOzs1nvVVxcDIPBYHZtRUXFZq9bmeivdmDYz+9DH9oehiNnShaaTBAs/CalqKgY8fHxomopKvIGoBU1hvfDsxEzpyeyTh+Ee3AfZJ/7A6n716DtkhMW1lKE+PgkUbWQ/UhMycfNtPxyyzQaVZVnovrcsdyninUMKXkVrsX3y+8xqNvbR2S15YmdAwDHmgc4B9hOSkYBEu46TESK3Ow7HAt35yYiqy2PuSlPztzo9Xo4OVneHqoEQVDePp9qGAwG+Pj4YObMmfj000/LPWcymeDj44POnTtjz549AICzZ8+iWbNmcHNzQ0pKCkaPHo3evXtj/vz5Zr1ffHw8/P39za5voddA+Gqtex/UwDF94HVfS0S+ucbs1yQU3cJbqftEvW+7z6Ph0rS9qDHuVJydgXMzQxAwZQ3cO/a16LV518/g7NQOVquFFK5uU6DVvHKLfL1dEb9vXBUvqJnfwI1ISLrrmMwbPwCpB2o9ZmWkmAMAx5gHOAdIrI4vEFT+bGBJcpO4GUgRt13djbmpmq1zExcXBz8/P4teA9jhN5Q5OSUHG1d2f9Ht27fj5s2b5XZ3t2vXrvTvgiBArVbjwoULktdJFSXvWYGi9ETEfTuj3HKvvhPhPXJGFa+ie5Ot7h/M+xTbEucAidnsvtvMjS3ZS27s7hvKwsJCuLq6onPnzjh2rOySIteuXUPPnj2RkJCAjRs34vHHHy997sMPP8TChQuRk5MDLy8v7N69G127djXr/Szd5X1kzIfIuWL++lJxa65H6JY5osaYetYbcfnidnlbi3/dInzejru77hVXE3MR9uzv5ZbVtOvu2MZRAICu47YhMSWvwjqV7bpbMrMDRg+w7v1ylTIHAI41D3AOqFnCzTx0f/q3csukyM3H09rj8Qct/warOsyNNGqTm9ru8ra7byidnZ0xYcIEhIeHY+TIkRg2bBji4uKwatUqeHt7IyEhocIJOXPmzMGcOXNw7tw5fP/99/DxMf+YKScnJ4u++tVqlfFPqtVaVnelY1wAkF/jajah1WpF/zxkP5o0EeDudhRZOWXXjDMahYq73iqRmJJn1noA0C80EH5+XrWuszJKmQMAx5oHOAfUzNdXQH2Po0i/VXZHGyly0zc0EH5+DWtdZ2WYG2nYMjd2eZb38uXL8cILLyAyMhKzZs1CZGQktm7diiZNmsDV1bXKk3Xatm2L++67D+PHj7dxxURkCbVahS7trPsL625162jQroX8F2gmshaVSoX720ubG2etGh0CmRuqyC4bSp1Oh5UrV8JgMCArKwt79+5FaGgooqOjERwcDLW66h+rqKgIsbGxNqyWiGpj7IPNJR3/sYEB0GrtcgokqtLYB1tIOv7D/ZuhjrN93WOabMNhZtOMjAzEx8eX292dmZmJtWvXIiMjA4Ig4J9//sHChQvx4IMPylcoEZnlyWEt4e4m3TFIk8a2lWxsIrmMG9IS9dzNuyxebUwaw9xQ5RymoTx9uuSWSnc2lCqVChs2bECLFi3g7u6OUaNGYejQofj8889lqrKiVk/0x9Ad72PI9gXwbNO00nUG//tdhC5+wcaV1U7K3tU4/1oPnJ/zAPKuVn6bq5g3++DaVy/ZuDKyN+5uznjtmWBJxn6why+6d2wsydi14UjzAOcAebm6OGHOv6S5i0r/bk0Q1kUvydi1wdwoi0M3lB4eHti/fz/S0tKQnZ2Ny5cv45NPPoGbm5tMVZbn7KlD64mDsPuRefhj5gp0W/BMhXX8BnRBUXbFM++UqDgrDcl7VqD1okMImLIGcaunVVgn49hOaFzcZaiO7NHrz9yHzm2se9KMh06LVfMfqPTSY3JwpHmAc4AyvDoxGF07WPdYSp2rFquZG0k4Sm4cpqGcNGkSBEFA9+7d5S7FbI06B8Lw5xkIxUbcunQDdRp4lL+OmEqFNs8Mxvm1e+Qr0gI5F/6CrkMfqJy0qOvXGsW3UiCYyu75KphMSN71JRoNnSxjlWRPtFo1fljcB16edapdz5CSB7+BG+E3cCMMlVz65Da1WoXw93rBX6+zdqm15kjzAOcAZXByUuP7D/qgUf261a5nbm5UKmDNuw8gwFc5DQ1zozwO01DaI2dPHQozc0ofF2XnwdnDtfRx4Jg+uLYrEsb8osperjjGrDQ46crO/lO7uMOYm1n6OPXAOniGPgK1tvpJjuhObZp7Yv83Q6r95Xj70igJSbkVrpl3m0ajwvr3e+ORAQESVVo7jjQPcA5QjlbN6iFi1RB4e1V+DUrA/NysXdALYyQ+2cdSzI3ysKGUUWFmDpw9yna/a3UuKLxVch0wTR0tWjwShoubrHtbOClpdPVhzMkofWzKy4LGtV7J3wvzkXboezTsX3G3BFFNOrXxwvFNIzEwtHb3Dw5qVg+/hQ/DE8NaWrky8RxpHuAcoCzBQQ1wfONIDO5Zu+sQBjb1wK+rh2LCiFZWrkw85kZ52FDKKPnEBXh3bwuVRg33AD0K0m4B/7txka5pYzjXc8OA9XPR5e2n4Nu/M1qO7i1zxdVzC+qGrDO/QTAWIz/xIpw8GkL1v0s4FSRdgTEnAxcXDEf8uteQ+fcupB74TuaKyZ409dHhl68HY+2CXmZfB0/f0AXzX+6Mkz+OQo9O3hJXWDuONA9wDlAeP70bdn01COsX9UbHoAZmvcbbywXzXuyMUz8+rKiTcO7E3CiPci5Nfw8qzMjGhR8iMGTrAgiCCUfnroZv305w9tThytbD2Dn4dQCAPrQ9mo/qiUs/HpK54uo5uTdAw4HPIWZuL0CtRtMXv0TmiT0wZqWhQe8n0HbJcQBA1umDSPt9E7z6TZC3YLI7KpUKE0e2woQRgTh8Igl7/ojH32dTcO5KBnLzjXDWqtHc1x1d2nqh9/0+eKh3U8Vfa9KR5gHOAcqkUqnw1PBAPDmsJf48eRO7D8fh77OpOHs5Hbn5Rmid1Gjuq0OXdg3RK0SPEX2bwlmr7GtNMjfKY3f38la6bb2nIyM2Xu4y4Bnkh1GHlokaY8yvwOUs69QjVgt3YEtfuasgqplS5gDAseYBzgGOjbmRhi1zo+yP7kRERESkeGwoiYiIiEgUNpREREREJApPyrEy9wBlnBFnjTp8XWtex1aUVAtRdZQyBwCONQ8opQ6SBnMjDVvWwZNyiIiIiEgU7vImIiIiIlHYUBIRERGRKGwoiYiIiEgUNpREREREJAobSiIiIiIShQ0lEREREYnChpKIiIiIRGFDSURERESisKEkIiIiIlHYUBIRERGRKGwoiYiIiEgUNpREREREJAobSiIiIiIShQ0lEREREYnChpKIiIiIRGFDSURERESisKEkIiIiIlHYUBIRERGRKGwoiYiIiEgUNpREREREJAobSiIiIiIShQ0lEREREYny/+nhttSBnktAAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 831.997x294.311 with 1 Axes>"
       ]
@@ -56,21 +63,26 @@
    "id": "8638fdf1",
    "metadata": {},
    "source": [
-    "### Specify some observables\n",
-    "\n",
-    "Currently, only `Pauli` observables with phase equal to 1 are supported. Full support for `SparsePauliOp` is expected in CKT v0.7.0."
+    "### Specify an observable"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "id": "f75e8dd1",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:41.864097Z",
+     "iopub.status.busy": "2024-04-19T17:42:41.863622Z",
+     "iopub.status.idle": "2024-04-19T17:42:41.867665Z",
+     "shell.execute_reply": "2024-04-19T17:42:41.867037Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit.quantum_info import PauliList\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
-    "observables = PauliList([\"ZZII\", \"IZZI\", \"IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])"
+    "observable = SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])"
    ]
   },
   {
@@ -78,22 +90,31 @@
    "id": "162a5629",
    "metadata": {},
    "source": [
-    "### Separate the circuit and observables according to a specified qubit partitioning\n",
+    "### Separate the circuit and observable according to a specified qubit partitioning\n",
     "\n",
-    "Each label in `partition_labels` corresponds to the `circuit` qubit in the same index. Qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut."
+    "Each label in `partition_labels` corresponds to the `circuit` qubit in the same index. Qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut.\n",
+    "\n",
+    "**Note:** The ``observables`` kwarg to `partition_problem` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "id": "30326299",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:41.871005Z",
+     "iopub.status.busy": "2024-04-19T17:42:41.870615Z",
+     "iopub.status.idle": "2024-04-19T17:42:41.898352Z",
+     "shell.execute_reply": "2024-04-19T17:42:41.897640Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.cutting import partition_problem\n",
     "\n",
     "partitioned_problem = partition_problem(\n",
-    "    circuit=qc, partition_labels=\"AABB\", observables=observables\n",
+    "    circuit=qc, partition_labels=\"AABB\", observables=observable.paulis\n",
     ")\n",
     "subcircuits = partitioned_problem.subcircuits\n",
     "subobservables = partitioned_problem.subobservables\n",
@@ -112,7 +133,14 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "6b54be63",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:41.901553Z",
+     "iopub.status.busy": "2024-04-19T17:42:41.901119Z",
+     "iopub.status.idle": "2024-04-19T17:42:41.905691Z",
+     "shell.execute_reply": "2024-04-19T17:42:41.905183Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -134,11 +162,18 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "b7e06fac",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:41.908126Z",
+     "iopub.status.busy": "2024-04-19T17:42:41.907769Z",
+     "iopub.status.idle": "2024-04-19T17:42:42.076871Z",
+     "shell.execute_reply": "2024-04-19T17:42:42.076174Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 913.147x160.533 with 1 Axes>"
       ]
@@ -156,11 +191,18 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "11e45e83",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:42.079604Z",
+     "iopub.status.busy": "2024-04-19T17:42:42.079205Z",
+     "iopub.status.idle": "2024-04-19T17:42:42.296509Z",
+     "shell.execute_reply": "2024-04-19T17:42:42.295819Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 913.309x160.533 with 1 Axes>"
       ]
@@ -197,7 +239,14 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "3d606ef8",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:42.299255Z",
+     "iopub.status.busy": "2024-04-19T17:42:42.298869Z",
+     "iopub.status.idle": "2024-04-19T17:42:42.302431Z",
+     "shell.execute_reply": "2024-04-19T17:42:42.301774Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -229,7 +278,14 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "2029d18e-0e91-4160-b8c9-02cb9e1ba3cb",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:42.304898Z",
+     "iopub.status.busy": "2024-04-19T17:42:42.304537Z",
+     "iopub.status.idle": "2024-04-19T17:42:42.678677Z",
+     "shell.execute_reply": "2024-04-19T17:42:42.677821Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.cutting import generate_cutting_experiments\n",
@@ -241,68 +297,112 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d8870454-2173-4454-90b4-a034779510e0",
+   "id": "444b0038-b3d5-469c-85b2-4c1d9d8fce05",
    "metadata": {},
    "source": [
-    "### Run the subexperiments using the Qiskit Sampler primitive"
+    "### Choose a backend\n",
+    "\n",
+    "Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "7430eef1",
-   "metadata": {},
+   "id": "36c89aa0-70aa-4615-8198-77ec85e8aa93",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:42.682386Z",
+     "iopub.status.busy": "2024-04-19T17:42:42.681780Z",
+     "iopub.status.idle": "2024-04-19T17:42:43.193076Z",
+     "shell.execute_reply": "2024-04-19T17:42:43.192515Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit_aer.primitives import Sampler\n",
-    "\n",
-    "# Set up a Qiskit Aer Sampler primitive for each circuit partition\n",
-    "samplers = {\n",
-    "    label: Sampler(run_options={\"shots\": 2**12}) for label in subexperiments.keys()\n",
-    "}\n",
+    "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n",
     "\n",
-    "# Retrieve results from each partition's subexperiments\n",
-    "results = {\n",
-    "    label: sampler.run(subexperiments[label]).result()\n",
-    "    for label, sampler in samplers.items()\n",
-    "}"
+    "backend = FakeManilaV2()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "68eb0522-8c01-4f56-aacc-0bfc60159896",
+   "id": "85a1d76a-5c47-4210-a742-9b22f02f7200",
    "metadata": {},
    "source": [
-    "To use the Qiskit Runtime Sampler, replace the code above with this commented block."
+    "### Prepare the subexperiments for the backend\n",
+    "\n",
+    "We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "3c9a7e78-4bf9-4dbd-a8f9-3db185591d9c",
-   "metadata": {},
+   "id": "184e0f36-1279-48a3-aab7-b7603bb71f66",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:43.195989Z",
+     "iopub.status.busy": "2024-04-19T17:42:43.195647Z",
+     "iopub.status.idle": "2024-04-19T17:42:45.516007Z",
+     "shell.execute_reply": "2024-04-19T17:42:45.515210Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# from qiskit_ibm_runtime import Session, Options, Sampler, QiskitRuntimeService\n",
-    "# from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
-    "#\n",
-    "# service = QiskitRuntimeService()\n",
-    "# backend = service.least_busy(operational=True, simulator=False)\n",
-    "#\n",
-    "# # Prepare transpiler for target backend\n",
-    "# pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
-    "#\n",
-    "# with Session(backend=backend) as session:\n",
-    "#     # Set up Qiskit Runtime Sampler primitives.\n",
-    "#     samplers = {\n",
-    "#         label: Sampler(Options(execution={\"shots\": 2**12})) for label in subexperiments.keys()\n",
-    "#     }\n",
-    "#\n",
-    "#     # Retrieve results from each subexperiment\n",
-    "#     results = {\n",
-    "#         label: sampler.run(pass_manager.run(subexperiments[label])).result()\n",
-    "#         for label, sampler in samplers.items()\n",
-    "#     }"
+    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
+    "\n",
+    "# Transpile the subexperiments to ISA circuits\n",
+    "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
+    "isa_subexperiments = {\n",
+    "    label: pass_manager.run(partition_subexpts)\n",
+    "    for label, partition_subexpts in subexperiments.items()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8870454-2173-4454-90b4-a034779510e0",
+   "metadata": {},
+   "source": [
+    "### Run the subexperiments using the Qiskit Runtime Sampler primitive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3bc1c07b-b286-4a5f-8b0a-67efd4a6309e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:45.519227Z",
+     "iopub.status.busy": "2024-04-19T17:42:45.518797Z",
+     "iopub.status.idle": "2024-04-19T17:42:54.689525Z",
+     "shell.execute_reply": "2024-04-19T17:42:54.688868Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/runner/work/circuit-knitting-toolbox/circuit-knitting-toolbox/.tox/docs/lib/python3.9/site-packages/qiskit_ibm_runtime/session.py:157: UserWarning: Session is not supported in local testing mode or when using a simulator.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit_ibm_runtime import SamplerV2, Batch\n",
+    "\n",
+    "# Set up a Qiskit Runtime Sampler primitive for each circuit partition\n",
+    "samplers = {label: SamplerV2(backend=backend) for label in subexperiments.keys()}\n",
+    "\n",
+    "# Submit each partition's subexperiments as a single batch\n",
+    "with Batch(backend=backend):\n",
+    "    jobs = {\n",
+    "        label: sampler.run(isa_subexperiments[label], shots=2**12)\n",
+    "        for label, sampler in samplers.items()\n",
+    "    }\n",
+    "\n",
+    "# Retrive results\n",
+    "results = {label: job.result() for label, job in jobs.items()}"
    ]
   },
   {
@@ -312,23 +412,34 @@
    "source": [
     "### Reconstruct the expectation values\n",
     "\n",
-    "Use the subexperiment results, subobservables, and sampling coefficients to reconstruct the expectation value of the original circuit."
+    "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "7d57339c",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:54.692693Z",
+     "iopub.status.busy": "2024-04-19T17:42:54.692460Z",
+     "iopub.status.idle": "2024-04-19T17:42:57.601371Z",
+     "shell.execute_reply": "2024-04-19T17:42:57.600628Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.cutting import reconstruct_expectation_values\n",
     "\n",
+    "# Get expectation values for each observable term\n",
     "reconstructed_expvals = reconstruct_expectation_values(\n",
     "    results,\n",
     "    coefficients,\n",
     "    subobservables,\n",
-    ")"
+    ")\n",
+    "\n",
+    "# Reconstruct final expectation value\n",
+    "final_expval = np.dot(reconstructed_expvals, observable.coeffs)"
    ]
   },
   {
@@ -336,44 +447,43 @@
    "id": "53beaca3",
    "metadata": {},
    "source": [
-    "### Compare the reconstructed expectation values with the exact expectation values from the original circuit"
+    "### Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "e3385ba5",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:57.604408Z",
+     "iopub.status.busy": "2024-04-19T17:42:57.603973Z",
+     "iopub.status.idle": "2024-04-19T17:42:57.617317Z",
+     "shell.execute_reply": "2024-04-19T17:42:57.616698Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reconstructed expectation values: [0.37551016, 0.52859873, 0.58741736, 0.12230408, 0.29614246, -0.1243059]\n",
-      "Exact expectation values: [0.36916216, 0.52511814, 0.59161991, 0.10927476, 0.28001606, -0.12940509]\n",
-      "Errors in estimation: [0.00634799, 0.00348059, -0.00420255, 0.01302933, 0.01612639, 0.00509918]\n",
-      "Relative errors in estimation: [0.01719568, 0.0066282, -0.00710346, 0.11923456, 0.05759096, -0.0394048]\n"
+      "Reconstructed expectation value: 0.70903873\n",
+      "Exact expectation value: 0.56254612\n",
+      "Error in estimation: 0.14649261\n",
+      "Relative error in estimation: 0.26040996\n"
      ]
     }
    ],
    "source": [
-    "from qiskit_aer.primitives import Estimator\n",
+    "from qiskit_aer.primitives import EstimatorV2\n",
     "\n",
-    "estimator = Estimator(run_options={\"shots\": None}, approximation=True)\n",
-    "exact_expvals = (\n",
-    "    estimator.run([qc] * len(observables), list(observables)).result().values\n",
-    ")\n",
-    "print(\n",
-    "    f\"Reconstructed expectation values: {[np.round(reconstructed_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Exact expectation values: {[np.round(exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Errors in estimation: {[np.round(reconstructed_expvals[i]-exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
+    "estimator = EstimatorV2()\n",
+    "exact_expval = estimator.run([(qc, observable)]).result()[0].data.evs\n",
+    "print(f\"Reconstructed expectation value: {np.real(np.round(final_expval, 8))}\")\n",
+    "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n",
+    "print(f\"Error in estimation: {np.real(np.round(final_expval-exact_expval, 8))}\")\n",
     "print(\n",
-    "    f\"Relative errors in estimation: {[np.round((reconstructed_expvals[i]-exact_expvals[i]) / exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
+    "    f\"Relative error in estimation: {np.real(np.round((final_expval-exact_expval) / exact_expval, 8))}\"\n",
     ")"
    ]
   }
@@ -394,7 +504,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/.doctrees/nbsphinx/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb b/.doctrees/nbsphinx/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb
index 3f69d56ca..143a39dad 100644
--- a/.doctrees/nbsphinx/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb
+++ b/.doctrees/nbsphinx/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb
@@ -28,11 +28,18 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "54ed0f13",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:00.038466Z",
+     "iopub.status.busy": "2024-04-19T17:43:00.038247Z",
+     "iopub.status.idle": "2024-04-19T17:43:01.073530Z",
+     "shell.execute_reply": "2024-04-19T17:43:01.072796Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAABGQAAAD2CAYAAACQlVGeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABejElEQVR4nO3deXhMZ/8G8HuyyiokZCIJUcS+JGIJRVBFqaKW6oKitEUp1WqVKqp0r76ltqK01lbtitgV1doSWxKCJDJklz2znN8f+UlEtplkzjkzk/tzXb0uc+aZZ77e17kz+c5znqMQBEEAERERERERERFJxkruAoiIiIiIiIiIqho2ZIiIiIiIiIiIJMaGDBERERERERGRxNiQISIiIiIiIiKSGBsyREREREREREQSY0OGiIiIiIiIiEhibMgQEREREREREUmMDRkiIiIiIiIiIomxIUNEREREREREJDE2ZIiIiIiIiIiIJMaGDBERERERERGRxNiQISIiIiIiIiKSGBsyREREREREREQSY0OGiIiIiIiIiEhibMgQEREREREREUmMDRkiIiIiIiIiIomxIUNEREREREREJDE2ZIiIiIiIiIiIJMaGDBERERERERGRxNiQISIiIiIiIiKSGBsyREREREREREQSY0OGiIiIiIiIiEhibMgQEREREREREUmMDRkiIiIiIiIiIomxIUNEREREREREJDE2ZIiIiIiIiIiIJMaGDBERERERERGRxNiQISIiIiIiIiKSGBsyREREREREREQSY0OGiIiIiIiIiEhibMgQEREREREREUmMDRkiIiIiIiIiIonZyF2ApQkdtQjpt1VylwEXPyV6rptZqTnePQvEZRmpoErydgS+7SB3FSQWnjfi4HlDJA9mmjiYaUTyYKaJg5lGABsyRpd+W4XUiFi5yzCKuCzgVrrcVVBVwPOGiCwJM42ILAkzjUg8vGSJiIiIiIiIiEhibMgQEREREREREUmMlywR6SE9Mw83bqchO0cLWxsrPOXjgtruDnKXRURUIRlZalyPTi3ItPo+LvBkphGRmcrMUuP67TRkZqlha2sFvzou8KrlKHdZRETlYkOGqBQ3olPx09br2H8qFjdup0EQij7vq3RCtyAlJgxpgs4BnlAoFPIUSkSkh6i7D/HTlmvYezIW16NTi2Wad21HdG2rxIShTdC1rZKZRkQm7VbsQ/y05Tr2nojBteg06HRFQ61ObUd0CfTE+BeboHt7L2YaEZkkNmRk8vR3E9FweHcAgE6rRfb9VMSfCsf5hb8iS5Usc3WGu/39aCQdXpf/wMoKtjW84NKyB7xHfg47d295izNQjCoDkxaexs6jd8sZl4kNu29iw+6bCGjijp9md0b7lrUkqrJq4nlDZLh7DzIx6fPT2B56p8xxcQ+ysHHfLWzcdwstG9XAT7M7o1MbT4mqrJqYaUSGUyVm4Z1FZ7DtYHSxxvLj7j3Iwub90di8PxrNGrhh2axO6BrkJV2hVZQl5RozjaTAPWRkpDpzFZtbjcO2oLdwfOJ3cG/hh5AV0+Uuq8Kcm3VBq7XxaLnqLupP/w1Z0Rdwa/FQucsyyK97otBi8B/lNmOedOF6EoJf24VZS/6FVqsTqToCeN4QGWLLX7fQfNAf5TZjnhQWmYKnR+3G+9/8w0wTGTONSH9/HLqN5oP+wNYDZTdjnnT1ZipCxu7Fu1+cgVrNTBObJeUaM43ExoaMjHR5GmQnpCJLlYz7Z67hxoZDqN2uMWydzfM6foWNHWxrKGHn7g2X5l1R69nxyLxxGtqsh3KXppdv14fj1Q+P4WGGukKv1+kELFx1CSNnHecvMCLieUOkn6WbrmL4jCNITc+r0OsFAfhybRhGfHAUGg0zTSzMNCL9rNx2HUOmhyI5LbdCrxcE4LsNVzD0vVDkqbVGro4eZ0m5xkwjsbEhYyIcPGvAr39H6DRaCBbwy3xe0j2k/L0NsLLO/8/E/bonCtO+PFvmGGtrBbw9HeHt6Qhr69KvQ/5t781y5yLj4HlDVLJtB6IxceHpMsfom2lbD0Rj0udlz0XGwUwjKtnOI3cwYf6pMlfF6JtpO47cxYR5p0SokkpiSbnGTCMxcA8ZGSk7NccrUeuhsLKCjYM9ACB82U5osvM7/yErp+PesUuI2HAIAFCzRX10XToFu3rNgDa3Yqs4xJQefhQXhjtD0Okg5GUDADwHTod1NScAQMrp7Yjf/GmR1+TEXIXvuO9Rq+9bktf7SKwqE29/9ne545QeDog9OAIA4NNrI+LuZ5U6dslvV/F8SF0805HXlxobzxvTOG/IdKkSszBhfvm/bBiSacu3XseAkLp4rouv0eqkfMw0ZhqVLSE5G+Pmniz3EiVDMm3tjkgMCKmLQT39jFgpPWJJucZMI7GZdUPm0qVLmDNnDo4ePQpBENCjRw8sW7YM/v7+6NevHzZt2iR3iWVKOB+Jk1P+B2t7W/gN6IQ6XVrhwuKNBc//M3sN+u6Yjzt7zyI3JQPBi97A2Y9Wm1xQPeLk3wF+U9dByMtByskteHjpEOq8sqDg+RrBg1AjeFDB49QzfyJu/Udw7zFKjnILTF50usKXKZVl7CcnELFrKOztTK+DnvowF2GRKdDqBDT0dYWP0knukvTG88Y0zhsyXVO/OFPhJf1lGf/pSUTuHgqHaqb30eFhRh4uRyRDoxXQwNcFvkpnuUvSGzONmUZle+/rf5CQkmP0ed9a8Dd6BXvD2dHW6HNXVnpmHi7dSIZao8NTPi6oV8dF7pIMYkm5xkwjsZnepyo9hYaGon///qhXrx4+/vhjODg4YO3atejbty8yMjLQpk0buUsslzYnD+m3VQCAi19uhoufEh0+G4u/3/sJAJClSsaV5bsRNPs1JF6IQtqteMSfDJOz5DJZ2TmgmldDAIBDvRbIVd1EzIrJqDdpZbGxeYmxuLt8Ihp+sg9W9o5Sl1og6u5D/HnYsM0u9XU3PhN/HLqNEc81EGX+irgbn4H5yy9gw56byMnNv35aoQD6dfHFrDfaoGPr2jJXWD6eN/KfN2S67txLx9YDt0WZO+5BFrb8FY1RLzQSZf6KiFVlYsHKi1i/KwpZORoA+ZnWp7MPZr3RBp0DTP8uUcw0ZhqV7t6DTPy276Yoc99PysZve29i/JAmosxfEfEJWViw4iLW7YxEZram4HjvTt74aFxrs7lLlCXlGjONxGaWe8gkJCRg+PDhCAwMxIULFzBjxgxMmjQJoaGhuHs3/+445tCQedLFrzaj4fDucG9d+Av89TX74dbYFy0nDcS5T9fJWJ3hvEbMRWLoGmRG/lvkuKDTIfrbV6F8cSYc/VrJVF2+5Vuvizr/0s3XRJ3fEDeiU9F+xA6s+iOioBkD5G9yt/t4DLq+vgc7jojTnBITzxuiQit/vwGdzoBbjxjIlDLtZsxDdHhlJ5ZvvV7QjAHyM23fyViEjNmDbQeiZaywYphpRIVWb4+ARlM1Mu12XDo6vLITSzdfK9KMAYC//o5Dj3H78NsecZpTYrOkXGOmkbGZZUNm8eLFSElJwZo1a+DgULhbd/Xq1REYGAjAPBsy6dEqxBz8F4EzRxQeFATc+OUgYkPPIzfJvHbzrlanEdzaPY97G2YVOR6/ZQGsHVxRu/9kmSor9NffsaLOf+rifWRkyb/8UqvVYcA7B3E/ufQlvxqtDsNnHMbd+AwJK6s8njdEhf76O07U+f8JT0DKQ+NfDmUonU7AwCmHcC+h9D0itDoBL888ipsx5pUBzDSiQmJn2qUbybiflC3qe+hDEAQMfjcUMarMUsfoBAGjZh/D9ehU6QozEkvKNWYaGZtZNmQ2bdqELl26wN/fv8TnPT09oVQqAQAajQZTpkxBzZo14ebmhrFjxyInx/jXoRpL+NKd8A5pA2Vw88KDOh0EEb/xFJPnoBl4ePEA0sOOAgAyrp1C0qHV8Htnjax1AUBWtgZXb6WK+h6CAFy8niTqe+hj/6lYRNwp+4edIAC5eTqs2CbuqiEx8LwhAvLUWlyOSBb9fc5flT/TDv9zD+FRKWWOEQRArdHhpy3MNLkx06gitFodLlwTP2/+u5oo+nuU58R/Klwo5/OiIAAajWBSq3oMYUm5xkwjYzK7PWRUKhXi4uIwfPjwYs/pdDqEhYUhICCg4NjChQtx5MgRhIWFwc7ODgMGDMD777+PJUuW6PV+Go0GKpVK7/rUak35gwCcnPpjiccT/r2BtV5D9H6/suqIja3c6g+12hOAfhud+U1ZW+Jx56ad0HZHftBqMlIR/e1r8HtnLWxc3Q2sRY3Y2PsGvaY816LTodUW/SFgba2A0sOhxPFejx33KmWMKjG72Jx/n4+GX239/l2IZdW2cL3HrttxA28OUopYTXE8b4oy5fOGTNfN2EzkqYveUlSMTDt9IRqNfeS9denKrQZk2s4bmDK8jojVFMdMK4qZRhVxV5VV5HJEQKxMu4NW9Uu/TbYUVm67qvfYX3ZG4P1Xpb3jnb6ZBoiba8w0MmVKpRI2Noa3V8yuIZOZmb+UT6EoHpw7duzAgwcPilyutGrVKnzxxRfw9s6//fDcuXMxdOhQfPvtt7C2Lv/uNyqVCr6++ofeAvde8LZ11Xu8WCIiIjDMgLpL0uyHcDjUbV7+QD0l7F8GdUo8Yn5+t8hx9+6j4PnCu6W8Kl9ERAR8e7cwWi0AAIf6QMOiyw0fv2ViWc5tHFji8ZJus/jBzNn44I3QCpdpFH7TAOem+btdliNWlWbQv3lj4HlTOpM7b8h0VfMFGn1S5JAYmTZ7znzMfvuvCpdpFPUmAy6t9Mq0hORsZlolMNNINvZegP/8IofEyLQFCxZhwZQ9FS7TKOq+BVRvq9fQtAwNfH3rApBuZQkzrXTMNHokJiYGPj4+Br/O7Boyvr6+sLa2xrFjx4ocv3PnDiZPzr9m71FDJjU1FTExMUUaNIGBgUhPT8ft27fRoIHp3P2mLFFbjiJqy1G5y6g0ryEfwmvIh3KXUUiQaNWKVO9TFp2e10cLAqA13Uv6DMHzhqqcKpVpOXo1YyAI+WMtADONqhxBW/4Yc3qfsuj72UsQACEPUjZjxGQJucZMo8pSCIJgdmf0mDFjsGbNGgwYMAD9+vVDTEwMVq5cCU9PT1y+fBnXrl1DkyZNEBMTg7p16yI+Pr5gTxm1Wg07OztcuHBBr41/Db1k6fSwRciM1n+8WJzqKxG8ZWal5ph81RMxOfot6RObbzU1fmhm3CV9DzPVaD70cJFj5S2FffSNS7sRfyI+sXiTo6SlsL8uaIuugR7GKbqCfg+9h6lf63c7wdf6+WLhxGYiV1QUzxtxiHHekOnKztGiyZBD0D12NZEYmbbmkwA806G20equiF3HVXh70SW9xg5/1htfTZX2G0hmmjiYaVVLrlqHpi8egvqxuyyJkWnLP2qN556W9lLtJ/11+gHGzb+g19hB3b2wZIa0d/FhpomDmWZZqswlSwCwZMkS2NraYseOHTh8+DCCg4Oxfft2zJs3D1FRUQWb/bq4uAAA0tLSChoyqampRZ4rj42NjUFLj2xtTeN/Ultbw+oucY5IACbyxaKtrW2l/z4laVjXFVF3Cze71WqFYktZSxKfmK3XOADo3bUJ3N2qVbhGY5jwkhILVkcgKS0XpbVgFcj/vmXG60Hw8akpZXk8b0Qi1nlDpqtpfTdcuZla8FisTPOq5VjREo1i7FAvzF8VAVVSdqmZ9siMMUHw8ZG2Kc5MEwczrepp2agmzj+2sa8YmfZsl8bw8dbv9wKxjBpUB5+ujEDs/Uw9Mq0tfHw8pSns/zHTxMFMI8BM77Lk7OyM5cuXQ6VSIT09HQcOHEBwcDDCw8PRsmVLWFnl/7Xc3Nzg6+uLixcvFrz2woULcHFxgZ+fnzzFk0kJCRL3G5EWDWvI3owBgGr2NtjyVQ/Y2VqVuMpfochvxnz9Xnu09Je2GUNExhPSzkvU+f3rVZe9GQMAdrbW2PJVD9jbWaOkC5ce5dznU4LQtpm8KxSJqOLEzrR6dZxRr46zqO+hDxsbK2z9qgcc7G3KzLS5bwUguLW0zRgiEpdZNmRKkpqaitjY2GKXIY0bNw6ff/457t27h4SEBMydOxejR4/Wa0NfsnwThjYRdf43h4k7vyG6t6+DI6ufQ3Dr4pca1Pd2wfqF3TBtZEsZKiMiYxk/pOpk2tOBShxf0w9PBxb/5aSelzPWzO+CmWNby1AZERnL+Bcbizr/m0OblHijEDl0aFUbJ9f1Q7cSmlC+SiesmNMZn7wVKENlRCQmi2nIhIXl74/xZEPmo48+QteuXdG8eXM0bNgQTZs2xeLFi2WokExRUPNaJTYojKG6ix1e699QlLkrKri1J0798jz2L+tdcOy3RSGI3D0Ur5pYrURkuFb+NUX7RtnFyRajX2gkytwV1a5FLRxf2x8HlvcpOLZhYTdE7RmK0S/4y1gZERlD4/pu6N3JW5S5HavZYMxA08qJgKYeOLL6ORxa0bfg2C+fdcOtvcPwhsgNdyKSh8U3ZGxsbLBkyRKkpKQgLS0Nq1evhoNDyZuByaHRyz3x3M7P0HfHfLg1qVvimD6/f4rgxeMlrqxiEg+swvX3O+H6zKeRfbvkTWRvzArBnaVvSlxZ6X6a3Rk2Nsb/duT79zvC1dnO6PMaQ/MGNQr+3CVQCSsr0/h2SF88b4hKt3RWJ9jbGX8V6FfT26OGq73R5zWGpvXdCv7cLcgL1tbm9fGGmUZUuh8+DIZDNeNn2qKpQajtbjq/EzyusV/1gj93b8dMkxszjcRkXmd3Gd5++20IgoCOHTvKXYre7Nyc0XjUs9g3eA5OTVuGDvNfLzbG55m2UGfoectimWnSk5GwfxkaLzwGv0mrEbNqSrExqed2w9pB3o3TntTKvybmvln+ElBVYjZ8em2ET6+NUJWwc//jnu9WFyMHcMWJGHjeEJWt6VNuWDCpbbnjDMm03p288YbIlw5UVcw0orI1qlcdi6e2K3ecIZnWvZ0XJr4k7R0lqwpmGpFhLKYhY45qBTSE6u8rEDRaPLx5D/Y1XVFkx1WFAk1e74Pra/fLV6QBMiP/gXOLEChsbFHNpzE0DxMhPHb/VUGnQ8LeH1HruYkyVlmyj95ojfFDyv5l49HO/nH3s4rdMvFxwa1r47fFISZzTbKl4XlDVL7po1pg0oiyf9nQN9OCmntgy1c9mGkiYaYRlW/SiGaYNrLs29frm2ltmtTE79/2NLvVweaCmUZkGDZkZGTn5oy8tMyCx+qMbNi5Ft69ouGwENzZexbaHLUc5RlMm54MG+fCS2GsHFygzUoreJx0eB3cggfDylb+uw49SaFQYNnHnfHRuNYl3oVIXy90r4sDy/vA2dHWeMVRETxviMqnUCiwZGZHzH0roFK/dPTr6ovQlX1N9vJLS8BMIyqfQqHAV9Pb47PJbWFtXfFMe7aTN46ses5kL7+0BMw0IsOwISOjvLRM2Lk6FTy2dXZA3sMsAIC1vS2eGtwFUZsOy1Wewayda0CbmVrwWJedDmvH/GtgdXk5SD72Kzx6Fl+2aCqsrBT47J0gnFzXH/71qpf/gsfUcLXD+oXdsP27Z9iMERnPGyL9KBQKfPJWIP7+pT+aPuVm0Guru9jh53ldsOuHXmzGiIyZRqQfhUKBj95ogzMbnkeLhjXKf8FjXJxssXxOZ+xf1htubMaIiplGZBgbuQuoyhLOR6LNe8OgsLaCs29t5CY/BIT8JZbOdWvDrroTnln/IezcnOFQ2w0NhnbDza3HZK66dE7+HXBv4ycQtBrkPrgNG1cPKKzye36596OhzUxF1Pz+0GQkQ52iQtLhX+DeY6TMVRfXqY0nrmwfjD0nYrB08zUc/uceNJqSl74GNHHHm8Oa4OXnGrARIxGeN6Z53pDp6tCqNsJ+H4R9J2Px46ZrCD17D2qNrsSxrRvXxIQhTfBq/wZwcWIjRgrMNGYaGSaoeS1c3DoQf/0dh6Wbr+Hg6TjkqUvOtJaNamD8kCZ4rX9DVHdhpkmBmcZMI8OwISOjvNQMRP4Wir7b50MQdDjz4Sp4d28DOzdnRG8/id19PgAAKIObo/7AziYdVgBg41ITHr3G4caHXQErK9Sd8CPSzu+HNj0ZNbu9jKbf/AsASA87iuQTm0w6rGxsrPBC93p4oXs95ORqEBaZglMX7uPdL88CALZ82R3PdvLhD3cZ8Lwx3fOGTJe1tRX6d6uL/t3qIjdPi/CoFJz4T1WQaZu/6I5nO3nzm2MZMNOYaWQ4a2srPNfFF8918UWeWovwyBScOK/C1C/yM23j4hD07uzDS5NkwExjppFhFIIglL7rFRnsz25TkRoRK3cZcPP3wcBj31VqjmFHgFvpxqmnsp5yAbZ0l7eGWFUmfJ/dBACIOfASfJRO5bzCdJna34XnjThM4bwh02VqOVAZpvZ3YaaJg5lGZTG1HKgMU/u7MNPEwUwjgHvIEBERERERERFJjg0ZIiIiIiIiIiKJcQ8ZI3PxU8pdAgDj1OHtWP4YqZhSLWR8PG/EYUq1EFUlzDRxmFItRFUJM00cplQLyYcNGSPruW6m3CUYzbcd5K6AqgqeN0RkSZhpRGRJmGlE4uElS0REREREREREEmNDhoiIiIiIiIhIYmzIEBERERERERFJjA0ZIiIiIiIiIiKJsSFDRERERERERCQxNmSIiIiIiIiIiCTGhgwRERERERERkcTYkCEiIiIiIiIikhgbMkREREREREREEmNDhoiIiIiIiIhIYmzIEBERERERERFJjA0ZIiIiIiIiIiKJsSFDRERERERERCQxNmSIiIiIiIiIiCTGhgwRERERERERkcRs5C7A0oSOWoT02yq5y4CLnxI9182s1BzvngXisoxUUCV5OwLfdpC7CqLymUoGAJaVA8wAInkw08TBTCOSBzNNHMy0imNDxsjSb6uQGhErdxlGEZcF3EqXuwoi82JJGQAwB4iqOmYaEVkSZhqZGl6yREREREREREQkMTZkiIiIiIiIiIgkxoYMEREREREREZHEuIcMURUhCALOXk7AmcsPcPLC/YLjb84/heDWtRHU3AMh7bxgb2ctY5VERPoRBAHnwhNx5vIDnPivcIPGCfNOomPr2ghq5oHu7b1QzZ4fdYjI9AmCgP+uJuL0pQc4/limjZ93Eh1b5X9O697OCw7VmGlEloRnNJGFy87RYMW261i25Tpu3E4r9vyeEzHYcyIGAFCrRjWMHeyPKa80h9LDUepSiYjKlZunxcrfb2Dp5mu4diu12PN7T8Zi78n8DRvd3ewxZmB+pnl7OklcKRFR+fLUWqz+IwJLN19DeFRKsef3nYzFvv/PtBqudnh9oD+mvtocvkpnqUslIhGwISOTp7+biIbDuwMAdFotsu+nIv5UOM4v/BVZqmSZqzPc7e9HI+nwuvwHVlawreEFl5Y94D3yc9i5e8tbXBV2+tJ9vD77RImNmJIkpORg0erLWL71OpbMDMYr/RpAoVCIXGXVZUk5wAwgKZwLT8Do2cdx9WaqXuOTUnPx5dowrPj9Br6d0QGjX2jETBMRM43IMOevJmL07OMIiyzeiClJysM8fPNLOFb+fgNfT2+PcS82ZqaJiJlGUuAeMjJSnbmKza3GYVvQWzg+8Tu4t/BDyIrpcpdVYc7NuqDV2ni0XHUX9af/hqzoC7i1eKjcZVVZSzddxdOj9ujdjHlcysM8vPbRMYz95AQ0Gp0I1dEjlpQDzAAS08pt19Hx1V16N2Mel5aehzFzTmDkrGNQq5lpYmKmEeln3Y5IdHhlp97NmMelZ6oxft4pvPT+EeSptSJUR48w00hsbMjISJenQXZCKrJUybh/5hpubDiE2u0aw9bZQe7SKkRhYwfbGkrYuXvDpXlX1Hp2PDJvnIY266HcpVU5SzddxcSFp6HTCSU+b22tgLenI7w9HWFtXfo3K2v+jMSYOSdKnYcqz5JygBlAYln9xw2Mn3eq0pm2YfdNvDbrKLRaNmXEwkwjKt8vOyMxevZxaLSVy7Qtf0XjpfeP8MszETHTSGxsyJgIB88a8OvfETqNFoIFfFDMS7qHlL+3AVbW+f+RZM5ceoDJi86UOUbp4YDYgyMQe3AElB5l/0BZvzsKP266aswSqRSWlAPMADKW81cTMWH+qTLHGJJpm/dH49v1V4xZIpWCmUZUXFhEMsbNPVnmGEMybXvoHSz++bIxS6RSMNNIDNxDRkbKTs3xStR6KKysYONgDwAIX7YTmuxcAEDIyum4d+wSIjYcAgDUbFEfXZdOwa5eM6DNVctWd2nSw4/iwnBnCDodhLxsAIDnwOmwrpa/kWLK6e2I3/xpkdfkxFyF77jvUavvW5LXa4lycjV4fc5xo69omfndv3iuiy8a+LoadV6yrBxgBpCx5am1GD37OLSlfItcUR//7z/07+aLJvXdjDovMdOYaVQWtVqH0bOPQ23kFS2f/nQBA0LqoqV/TaPOS8w0Zpr4zLohc+nSJcyZMwdHjx6FIAjo0aMHli1bBn9/f/Tr1w+bNm2Su8QyJZyPxMkp/4O1vS38BnRCnS6tcGHxxoLn/5m9Bn13zMedvWeRm5KB4EVv4OxHq03u5H7Eyb8D/Kaug5CXg5STW/Dw0iHUeWVBwfM1ggehRvCggsepZ/5E3PqP4N5jlBzlWqTV2yNwPdrwPWPKk5Wjwez//YffFnc3+tyVodXqsOd4DDbsuYn7SdlwdrTBgJB6eKVfAzg72spdnl4sKQeYAWRs63ZEVmh/hfLk5mkxa8l/+P3bnkafuzK0Wh32nYzF+t1RUCVmw8nBBv27+uLV/g3h6mwnd3l6YaYx06h0G/fdxPlrSUafV63R4aMl/2LX/541+tyVodMJ+OtULH7ZFYV7CVlwrGaD57r4YOTzjVDdhZkmNWaaaTLbhkxoaCj69++PevXq4eOPP4aDgwPWrl2Lvn37IiMjA23atJG7xHJpc/KQflsFALj45Wa4+CnR4bOx+Pu9nwAAWapkXFm+G0GzX0PihSik3YpH/MkwOUsuk5WdA6p5NQQAONRrgVzVTcSsmIx6k1YWG5uXGIu7yyei4Sf7YGXP2ysbgyAIWLr5mmjzbzt4G9+9n43a7qZxzeyN6FQ8P/kgIu/mX/eqUACCAOw9EYsZ3/yD3xaFoH+3ujJXWT5LygFmABmTIAhYukW8TNtx9A7i7meazO2wb8Y8RP9JBwqa6goFACH/lrcffHcOv3zWDYN6+slaoz6Yacw0Kp2Yn9P2nIjB7bh0+Hm7iPYehrgdl47+kw7gyv9vxP7oc9r+U7GY+d2/WDO/C4b1fkreIvXATGOmic0s95BJSEjA8OHDERgYiAsXLmDGjBmYNGkSQkNDcffuXQAwi4bMky5+tRkNh3eHe+sGBceur9kPt8a+aDlpIM59uk7G6gznNWIuEkPXIDPy3yLHBZ0O0d++CuWLM+Ho10qm6izPf1cTK3T3EX2pNTps2n9LtPkNEavKRMiYvQXNGCD/h/wjGVlqDJp6CKFn7slQXeVYUg4wA6gywiNTcPG6eLcV1WoF/Lb3pmjzGyI+IQshY/YUWeEoCMCjWMvM1mDo9MPYfzJWngIrgZlGlC/idhrOhiWINr8gABv2RIk2vyESkrMRMmZvQTMGKPo5LTtXg5feP4KdR+5IX1wlMdPI2MyyIbN48WKkpKRgzZo1cHAo/La+evXqCAwMBGCeDZn0aBViDv6LwJkjCg8KAm78chCxoeeRm2ReO2BXq9MIbu2ex70Ns4ocj9+yANYOrqjdf7JMlVkmMX/IP/JPuPjvoY/PV1+CKim71OcFAdDqBLz75RkIgnndIcqScoAZQJVRlTLty7WXEXs/q9TnBQHQCQKmfsFMkxMzjSrjbNgD0d/jn7BE0d9DH9+sD8ed+IxSn38UY1O/OGN2d/JkppGxmeUlS5s2bUKXLl3g7+9f4vOenp5QKpUAgC1btmDJkiW4ePEiPDw8cPv2bYPeS6PRQKVS6T1erdYYNP+TwpfuRL9dn0EZ3Byq0/9/FwidDoKBYaVWaxAbW7lv0tRqTwCV24fDc9AM3JjZGelhR+HSMgQZ104h6dBqNP3mvIG1qBEbe79StVRWfGJO4Z9V8YCmmozVFHfi37tFHltbK0rdmd/rseNeZezer0rMLrKZ5j+XVZX+d1VZGVkarN0RUe44QQDCIlOw42A4gprVkKCyfJXNAMCycsCSMsDSmHqmHf+36DenomRa2H3ZMy07V4tVv98od5wgADdup2Hr3svo1NpdgsryMdOKYqaZLlPPtGPnxM+0c+HyZ1quWoflelxuKghAdFwGft15Ed2DaklQWT5mWlHMNONRKpWwsTG8vaIQzOyrFpVKBS8vL0ybNg1ff/11ked0Oh28vLwQEBCA/fv3AwAOHjyIpKQk3L9/H99++63BDZnY2Fj4+vrqPX6Bey942xr3TjQNh4XAvXUDnJ21Wu/XxKkf4uOkg5V632Y/hMOhbvNKzfE4TUYqrk0LhN+k1XBpZdjmsNl3r+Dq5BZGq6VCbGoATb/M//O1GYDG+BtNVkq9yYBr64KH3p6OiD04oowXlM+n10bEPf6trSYduPZupeasNId6QMPZ+o+/txFIChWvnieIkQGAZeSA2WeApTH1TKv7JlA9qOChKJmmzQGuTqrUnJVm7w34f1r+uEfitwGJ+8Wr5wnMtNIx00yMqWea7zjArWPBQ1EyTacBrrxZqTkrzc4TaPyZ/uPv/wk82C1aOU9ippWOmVY5MTEx8PHxMfh1ZrdCJjMzEwCgUCiKPbdjxw48ePCgyOVKvXr1AgD8+eefUpRHZUjYvwzqlHjE/Fz0F3r37qPg+YLMv+STnkzhKkcDa1BYi1MGGYwZQIYp/nPe+G8hwXuUW4OhmWYCNRMAZhoZiplWMlP4bEkAM00uZrdCJi8vD46OjggICMC5c+cKjt+5cwedO3dGXFwcNm7ciJdeeqnI6/78809MnTpV9EuWTg9bhMxo/ceLxam+EsFbZlZqjslXPRGTYxq3DvatpsYPzeS/ZKn9yGMAgH9+6QYvD9NaCvvOl5ex/Uh8wePylsKe2zgQANBuxJ+ITyx5P5Ynl8LWVTrg1M9djVd0BSSn5aHtq0eh0eoXXes+DUSPdtIthTWVDAAsKwdMIQMsjaln2nvfhWPzgbiCx2JkmtLdHufWhxit5opIy1Aj8OUjyNPol2krPm6Dvp08Ra6qEDNNHMw04zP1TPvox6tYvyem4LEYmVbT1RaXNvUwXtEVkJmtQZsRR5CTp9Nr/P8+aIUXunmJXFUhZpo4mGkVv2TJ7FbI2NnZYeTIkVizZg1eeOEF9OvXDzExMVi5ciU8PT0RFxdn1A19bWxsDFp6ZGtrGv+T2toaVneJc0QCyCl3mCRsbW0r/fepNJvMgj96Kb3gozSNW6U+0ikgpUhDRqsVii5jLUV8YrZe4wAgqLmn7P8/+PgAQ3rdKfeOTwoF4OPphFcGtIa1tXTfvphKBgCWlQMmkQGWxsQzrXNgWpGGjBiZ1rZ5bdn/XfkAGPFcDNbtjCxznEIBKN0dMHpQAGxtmWkVnoOZZrlMPNOebptRpCEjRqYFNqtlEv+uRg5ohBXbyt4bSwHA3c0e44YGwt5OutXMzDRxMNMqzizXiC1ZsgTjx4/H2bNnMX36dJw9exbbt29HnTp14OjoWOpmv0SWLKi5h0W8hz4+HNcaDvbWpa7MVSB/s7h5EwMlbcYQkfFIkmnNTCPT3n+9JZwcbMq82kAQgLlvB0rajCEi45Eib0zlc9p7o1rCxdG27EwDMOfNAEmbMUSmyCx/qjs7O2P58uVQqVRIT0/HgQMHEBwcjPDwcLRs2RJWVmb51yKqlKcDPFGntqOo7zGsd31R59dXK/+a2PlDLzg55H/L8eTPewHAV9PbY/QLbM4Smav2LWrBr46zqO8xvM9Tos6vr2YNamDPj8/CxTF/2XlJv8QsfCcI44c0kbgyIjKWNk3c4V+vuqjvMby3aWRao3rVsW9Zb1R3tgNQcqZ98mYAJo1oJnFlRKbHYjoXqampiI2NLXa5klarRU5ODtRqNQRBQE5ODnJzc+UpkkhENjZWmCDih/U+nX3QwNf4u9JX1DMdvRG1Zxg+m9wWT/m6FBwf/UIjhP8xGNNHtZSxOiKqLGtrK7w5TLxM697OC02fchNtfkN1C/JC1J6h+HxKEBr4FGbta/0b4vK2QfhwXOsyXk1Eps7KSoG3RMy0Tm1qo00Td9HmN1TnAE9E7h6KL95th0Z1CzPtleca4MKWgZj7dmCJN2khqmospiETFhYGAMUaMuvXr4eDgwOGDRuGu3fvwsHBAY0bN5ahwpI1erknntv5GfrumA+3JnVLHNPn908RvHi8xJVVTOKBVbj+fidcn/k0sm+HlTjmxqwQ3Fkq8y35LNTEl5qidk3jb2KnUABz3mxj9Hkry9PdAR+90QZHV/crODZ/Yls0b1hDxqoMZ0k5wAwgY5owpAm8aomz8u+TtwJEmbcyatV0wMyxrXFk9XMFxxa+E4SW/jVlrMpwzDSiko0d7A9fkfa2+fTtQFHmrQyPGtUw4/VWCF1ZmGmLprYzqcaRPphpJCaLb8iMHj0agiAU+c/QOy2Jxc7NGY1HPYt9g+fg1LRl6DD/9WJjfJ5pC3VGyTurmxpNejIS9i9D44XH4DdpNWJWTSk2JvXcblg7uJTwajIGd7dqWPZxZ6PPO+21FghuLd1dPaoSS8oBZgAZm5urPVbMMX6mTRrRDN2CpLurR1XCTCMqnYuTHVbNfdro844f0hjPdPQ2+rzETCPxWUxD5u2334YgCOjYsaPcpeitVkBDqP6+AkGjxcOb92Bf07XoRZYKBZq83gfX1+6Xr0gDZEb+A+cWIVDY2KKaT2NoHiZC0BXe8k7Q6ZCw90fUem6ijFVavsHP+JV7Ta4qMRs+vTbCp9dGqEq5leIjnQM8MX9SW2OWSI+xpBxgBpAY+neri+kjW5Q5xpBM69CyFhZNCTJmifQYZhpR2Z7t5IOPyrkE0ZBMC2zqjq+mtzdmifQYZhqJzWIaMubIzs0ZeWmFt+hTZ2TDzrVwaXbDYSG4s/cstDlqOcozmDY9GTbOhZeKWDm4QJuVVvA46fA6uAUPhpWt8S+poaK+/6Aj3hxa+nXKj261GHc/C1qtUOq4pwM8sed/z8KhmuncItDSWFIOMANILF9Ob493Xi690axvpnVoWQv7lvWG0/9vnkvGx0wjKt+CyW3xXhl73embaW2beeCvn/rAxclOjDIJzDQSHxsyMspLy4Sda+F1pLbODsh7mAUAsLa3xVODuyBq02G5yjOYtXMNaDNTCx7rstNh7Zi/m7wuLwfJx36FR8/iy/zI+KysFFj6cSesmvs0XJ0N/8XDykqB919viYMr+qC6C3/Ii8mScoAZQGJRKBT47oOOWLegK9wqkEkKBTBtZAscXvUcarjai1AhPcJMIyqfQqHAF9Pa4dfPQ1CzesUyafLLzXDs5+fgUYO/PIuJmUZiY0NGRgnnI+HZsSkU1lZw8VMiN/khIOR3wZ3r1oZddSc8s/5DtJ39Krx7BqDB0G4yV1w2J/8OSL9yHIJWg5z4KNi4ekDx/7cgz70fDW1mKqLm90fsuveR9t9eJB3+ReaKLZtCocDYwY0R/sdgjB3kD4dq1nq8Bujf1Ren1z+Pxe+2RzV7rowRmyXlADOAxKRQKDByQCNc2T4Y44c0hqMeK/cUCqDv0z44ua4/vn6vAxwdmGliY6YR6UehUODlfg1wZftgvDWsCZz1XLn3bCdvHPv5OSyZGczVfhJgppHY+MlERnmpGYj8LRR9t8+HIOhw5sNV8O7eBnZuzojefhK7+3wAAFAGN0f9gZ1xc+sxmSsum41LTXj0GocbH3YFrKxQd8KPSDu/H9r0ZNTs9jKafvMvACA97CiST2yCe4+R8hZcRfgqnbHq0y74cnp7bD0QjX/CEvDftUTcT8qBTifAzcUObZrURNumHhjSqz7q+3AjLylZUg4wA0gKdWo7Yfmcp/HFu/mZdjbsAf67moT7ydnQagVUdy7MtBd7+aGBr2v5k5LRMNOIDKP0cMTSjztj0dR22HbwNs5cfoD/riZClZSfaa7OtmjtXxNtm3lgcE8/+PtVl7vkKoWZRmJjQ0ZmERsOIWLDoYLHKVfvFBujOn0FqtNXpCyrwmr1Ho9avR+75Vv94puWubQMgUvLEOmKIgBADVd7jB/SBOOHlL63DMnDknKAGUBSqe5ih3EvNsa4FxvLXQo9gZlGZDhXZzuMGeSPMYP85S6FnsBMIzHxkiUiIiIiIiIiIomxIUNEREREREREJDE2ZIiIiIiIiIiIJMY9ZIzMxU8pdwkAjFOHt6MRCjESU6qFqCymkgGAZeWAqdRBVNUw08RhKnUQVTXMNHGYSh3miA0ZI+u5bqbcJRjNtx3kroDI/FhSBgDMAaKqjplGRJaEmUamhpcsERERERERERFJjA0ZIiIiIiIiIiKJsSFDRERERERERCQxNmSIiIiIiIiIiCTGhgwRERERERERkcTYkCEiIiIiIiIikhgbMkREREREREREEmNDhoiIiIiIiIhIYmzIEBERERERERFJjA0ZIiIiIiIiIiKJsSFDRERERERERCQxNmSIiIiIiIiIiCTGhgwRERERERERkcTYkCEiIiIiIiIikhgbMkREREREREREErORuwBLEzpqEdJvq+QuAy5+SvRcN7NSc7x7FojLMlJBleTtCHzbQe4qiKoeZpo4mGlE8mCmiYOZRiQPZpo4pMw0NmSMLP22CqkRsXKXYRRxWcCtdLmrICI5MdOIyJIw04jIkjDTzB8vWSIiIiIiIiIikhgbMkREREREREREEmNDhojMWmaWGuFRKQWPk9NyZayGiKhysrI1uHKzMNOSUnNkrIaIqHKyc4pmWmIKM43ocdxDhojMTtTdh/hpyzXsOxWL69Fp0OmEgudaD92Oul5O6NbWCxOGNkGnNrWhUChkrJaIqGy3Yh9i+dbr2HsiFldvpRbJtDbD/oSv0gld2yox/sXG6NJWyUwjIpN2Oy4dy7ddx57jMbh6KxVabWGmBQz/Ez6eTng6wBPjhzRGSDsvZhpVaWzIyOTp7yai4fDuAACdVovs+6mIPxWO8wt/RZYqWebqDHf7+9FIOrwu/4GVFWxreMGlZQ94j/wcdu7e8hZHFiPufiYmLzqN7aF3yhx3Nz4T63dHYf3uKAQ0ccfyOZ3RrkUtiaqsmphpRIZTJWbhnUVnsO1gNASh9HExqkz8uucmft1zE638a+Kn2Z0Q3NpTukKrIGYakeEeJGVjyuIz2PzXrTIzLfZ+Jjbtv4VN+2+heQM3LPu4M7q0VUpXaBVlSblmSZnGS5ZkpDpzFZtbjcO2oLdwfOJ3cG/hh5AV0+Uuq8Kcm3VBq7XxaLnqLupP/w1Z0Rdwa/FQucsiC7Fp3000H/xHuc2YJ124noSOr+7CrCX/FvnWmYyPmUakv98PRqPZwN+x9UDZzZgnXY5IxtOj9uCDb/+BVqsTr0BiphEZYOeRO2g++A9s2l92M+ZJV26motuYPXj3izNQq5lpYrOkXLOUTGNDRka6PA2yE1KRpUrG/TPXcGPDIdRu1xi2zg5yl1YhChs72NZQws7dGy7Nu6LWs+OReeM0tFkP5S6NzNwPv13BiA+OIi09r0Kv1+kELFx1Ca99dIy/wIiImUaknxXbrmPI9MNIeVjxTPtiTRhGfHAUGg0zTSzMNCL9rN0RgYFTD1V4fxhBAL7bcAVD3wtlU0ZklpRrlpJpbMiYCAfPGvDr3xE6jRaCBfzCmJd0Dyl/bwOsrPP/I6qgzftv4Z1FZ8ocY22tgLenI7w9HWFtXfp1yL/tvYlpX541dolUAmYaUcm2h97Gm/NPlTlG30zbeiAakz4/bewSqQTMNKKS7Tl+F2M/OVnmqhh9M23HkbsYP++kCFVSSSwp18w507iHjIyUnZrjlaj1UFhZwcbBHgAQvmwnNNn5d4kJWTkd945dQsSGQwCAmi3qo+vSKdjVawa0uWrZ6i5NevhRXBjuDEGng5CXDQDwHDgd1tWcAAApp7cjfvOnRV6TE3MVvuO+R62+b0leL5m++ISscn9xAQClhwNiD44AAPj02oi4+1mljl3y21U8H1IXz3Q0r+tLzQEzjZlGZXuQlI3x806Vu5zfkExbvvU6nu/mi35d6xqzVAIzDWCmUdmSUnMw9pOT5V4Sbkimrd0RiQEhdTGop58xS6X/Z0m5ZimZZtYNmUuXLmHOnDk4evQoBEFAjx49sGzZMvj7+6Nfv37YtGmT3CWWKeF8JE5O+R+s7W3hN6AT6nRphQuLNxY8/8/sNei7Yz7u7D2L3JQMBC96A2c/Wm1yJ8MjTv4d4Dd1HYS8HKSc3IKHlw6hzisLCp6vETwINYIHFTxOPfMn4tZ/BPceo+Qol8zAO4tOI7WClymVZewnJxCxayjs7Uyvg/4wIw/hUSnQaHRoWNcVdWo7yV2S3phpzDQq27tfnhXllq/j551C5K46cHQwvY916Zn5maZW6/CUjyt8lMw0uTDTyNhmfPMP7idlG33eN+efwjMd68DFyc7oc1dWZpYalyOToVbrUN/HBb5KZ7lLMogl5ZqlZJrp/eTWU2hoKPr374969erh448/hoODA9auXYu+ffsiIyMDbdq0kbvEcmlz8pB+WwUAuPjlZrj4KdHhs7H4+72fAABZqmRcWb4bQbNfQ+KFKKTdikf8yTA5Sy6TlZ0Dqnk1BAA41GuBXNVNxKyYjHqTVhYbm5cYi7vLJ6LhJ/tgZe8odalkBqJj0/H7oduizH03PhO/H7yNl/s1EGX+iohVZWLByotYvysKWTkaAICVAujXtS5mvdEaHVrVlrnC8jHTmGlUulhV/l1FxHDvQRY2/3ULrw/0F2X+iohPyMKCFRfxy65IZGTlZ5pCAfR92gcfjWuDzgGmf5coZhozjUqnSszCht03RZn7QXIOft1zE28OayrK/BVxPykbn628iLU7IpGeWdic6NPZBx+ObYWuQV4yVqc/S8o1S8k0s9xDJiEhAcOHD0dgYCAuXLiAGTNmYNKkSQgNDcXdu3cBwCwaMk+6+NVmNBzeHe6tC39JvL5mP9wa+6LlpIE49+k6GasznNeIuUgMXYPMyH+LHBd0OkR/+yqUL86Eo18rmaojU7d823WDduk31NIt18Sb3ECRd9LQ/uUdWL71ekEzBgB0ArDr2F10Gb0HO44YdncpU8BMIyq04vfrot7pbelm08m06Nh0tH95B5ZuvlbQjAHyN+7ceyIWIWP2YNuBaBkrrBhmGlGh1X9EQC3ipuKmlGkxqgx0fGUnfvjtapFmDADsPxWLHuP24bc94jSnxGZJuWaumWaWDZnFixcjJSUFa9asgYND4Y7Q1atXR2BgIADzbMikR6sQc/BfBM4cUXhQEHDjl4OIDT2P3CTz2jG6Wp1GcGv3PO5tmFXkePyWBbB2cEXt/pNlqozMwf5TsaLO//fF+0jPNP7lUIbS6QQMeOcg4hNLX/Kr0eowfMZh3LmXLmFllcdMIyr016k4Uef/90oiklKNfzmUoQRBwMCphxBbxh4RWp2Al2ceRdRd88oAZhpRob/+FvdzWlhkCuITSs8RqQiCgBenheL2vYxSx+gEAaNmH8O1W6nSFWYklpRr5pppZtmQ2bRpE7p06QJ//5KX5np6ekKpVCI3NxdvvPEGnnrqKbi4uMDf3x8//PCDxNUaJnzpTniHtIEyuHnhQZ0OgojfqonJc9AMPLx4AOlhRwEAGddOIenQavi9s0bWusi05eRqcOVmiqjvIQjAhWtJor6HPg78HYfr0WlljhEEIDdPh+Vbb0hUlfEw04gAtVqHSxHJor/PeRPItKPn4nG5nL+rIABqjQ7LTGilor6YaUT5XyZduC5+pv13NVH09yjP6UsPcC687DoEAdBoBJNa1WMIS8o1c8w0s9tDRqVSIS4uDsOHDy/2nE6nQ1hYGAICAgAAGo0GSqUSBw4cwFNPPYXLly+jd+/e8PT0xLBhw/R6P41GA5VKpXd9arWm/EEATk79scTjCf/ewFqvIXq/X1l1xMZWrnOtVnsCsNVrrN+UtSUed27aCW135J/MmoxURH/7GvzeWQsbV3cDa1EjNva+Qa8xtvjEwm8e41XxgKaajNVYtht3MqDRFP0hYG2tgNLDocTxXo8d9ypljCoxG1pt0TlP/XcbTym1lay2clZuvazXOAWAdTtu4O0XleIW9ARmWlHMNKqI6HuZyM0rmjViZNrp87fR1FfeD9ArtoTrPfaXnRF49yVp73jHTCuKmUYVcS8hGxlZRS/dESPTzly4gzYN5F0/sHzzVb3H/rIzAh+85itiNcXpm2mAuLnGTAOUSiVsbAxvr5hdQyYzMxMAoFAUv4f9jh078ODBg4LLlZycnDB//vyC59u0aYMBAwbg5MmTejdkVCoVfH31P7EWuPeCt62r3uPFEhERgWEG1F2SZj+Ew6Fu8/IH6ilh/zKoU+IR8/O7RY67dx8FzxfeLeVV+SIiIuDbu4XRaqkQmxpA0y8BAO3btQc04q7gqNIc6gENZxc59PgtE8tybuPAEo+XdJvFj2bNwUdvHqpwmUbhNxVwbp6/22UZBAD3HqQblEfGwEwrHTON9GbvDfgXvfWmGJn2ydwF+GTS/gqXaRT1JgIubcrNNABITMlhplUCM+0JzDTp2HkCjT8rckiMTPvs8y/w2bu7K1ymUfhOANza6TX0YaYGvr71AIi3t86TmGmlkzrTYmJi4OPjY3CdZteQ8fX1hbW1NY4dO1bk+J07dzB5cv51YaXtH6NWq3HixAm89957YpdpVFFbjiJqy1G5y6g0ryEfwmvIh3KXQeZAkGjVilTvUxadnns+CIL+Y00cM42qniqUadpcvZox+ZmWK349EmCmUZXDz2mljFVDymaMmCwh18wl0xSCIOZ9TMQxZswYrFmzBgMGDEC/fv0QExODlStXwtPTE5cvX8a1a9fQpEmTYq+bMGECzp8/j1OnTsHOTr/72ht6ydLpYYuQGa3/eLE41VcieMvMSs0x+aonYnL0WzYmNt9qavzQTP6lsO1H5jcC//mlG7w8uBRWLOlZGjQbElrkWHlLYR9949JuxJ8lbpBb0lLYX+YFontQLeMUXUF/HL6HKV/pdzvBl/v4YPE7xvvmQB/MNHEw06qW7Bwtmg4Jhfax6/HFyLTVcwLwbMfaxiu8AnafUOGtzy/pNXbIM3Xw7bSWIldUFDNNHMy0qiVPrUPTFw8hTyNupi37sDX6d5H2Uu0nHTjzAGPnXdBr7IBuSvz4QWuRKyqKmSaOimRalblkCQCWLFkCW1tb7NixA4cPH0ZwcDC2b9+OefPmISoqqsTNfqdNm4bTp0/j8OHDejdjAMDGxsagpUe2tqbxP6mtrWF1lzhHJAAT+ULe1ta20n+fSrPJLPijl9ILPkonGYuxfP71qiPiTuFmt1qtUGwpa0niE7P1GgcAfbo2Qa2aJX94kMr44UosWB2JxNScUm/zrUD+JUszxgTBx8ewa2Ari5kmDmZa1dOsgRvCIgsvoRAj03p3aQxvT3n/fxw7pA7mrYyAKim71Ex7ZMbrQfDxkbYpzkwTBzOt6mnV2B3/Xinc7FaMTHu2iz98fOS9HGfUoDqYtzISd1UZemRaW/j4SNtAYqaJQ8pMM8u7LDk7O2P58uVQqVRIT0/HgQMHEBwcjPDwcLRs2RJWVkX/WlOnTsXBgwcRGhoKDw8PmaomIkN0CxL3B1rTp9xkb8YAQDV7G2z+sjvsbK1KXOWvUOQ3YxZPbYc2TaRtxhCR8XQL8hJ1/ga+LqhT21HU99CHra0Vtn7dE/Z21ijpwqVHOTdvYiDat5R3hSIRVVy3tuJ+TvPxdEJ9bxdR30Mf1tZW2PJVDzjY25SZaR+Pb4OnA+VdzUPmySwbMiVJTU1FbGxssf1j3nnnHRw6dAiHDx9GrVr8wU9kLt4cWvyyQ3Oa3xDd29fBkdXPoWOr4pca1FU6Y838Lnh/TCsZKiMiYxn/YmNR558wpEmJNzyQQ+cATxxf0w+dAzyLPefj6YSVnzyN2RMCZKiMiIzlDbEzbWhjk8m09i1r4eS6fuhSQhOqTi1HLJ3VCfMmBspQGVkCi2nIhIXl78HweEPmzp07+OGHHxAVFYX69evD2dkZzs7O6Nu3r0xVEpG+Apt5lPhh3hhcnW0x8vmGosxdUcGtPfH3+uexb2nvgmPrF3bDzb1DMfqF4pdhEpF5aelfE93bibNKxsnBBq8PNK2caNeiFk6s64+/lhVm2i8LuiJ63zCME/kXOSISX+P6bujTWZxLOqrZW2PcYNPKiYCmHji2ph8OLu9TcGzdgq64vX843hre1GSaR2R+LLohU69ePQiCgJycHGRkZBT8t2/fPpmqLK7Ryz3x3M7P0HfHfLg1qVvimD6/f4rgxeMlrqxiEg+swvX3O+H6zKeRfbvkjUpvzArBnaVvSlwZmaOlszrB1sb4MfXNex3g5mpv9HmNoUXDGgV/DgnygrW1ecU0M42odD/O6gR7O2ujz/vFu+3gUcM0NzBt1qAw07q3r8NMkxkzjYzphw+D4VDN+Jm28J0gKD3kvwSzJE3quxX8uUf7OrAR4XOqmJhppse8/gWV4e2334YgCOjYsaPcpejNzs0ZjUc9i32D5+DUtGXoMP/1YmN8nmkLdUbxnchNkSY9GQn7l6HxwmPwm7QaMaumFBuTem43rB3kvx6UzEMr/5qY+1b5y9pVidnw6bURPr02QlXCzv2Pe66LD8YMMq1vki0FM42obE2fcsNnk9uWO86QTHumYx28OaypsUqkxzDTiMrWsK4rvni3fbnjDMm0rm2VeOflZsYqkR7DTDNNFtOQMUe1AhpC9fcVCBotHt68B/uariiyq6dCgSav98H1tfvlK9IAmZH/wLlFCBQ2tqjm0xiah4kQdLqC5wWdDgl7f0St5ybKWCWZmw/Htcb4IWUvW320s3/c/axit0x8XPsWtbBxcXcuKxUJM42ofNNGtsCkEWX/sqFvpgU2dcfWr3rAyoqZJgZmGlH5Jr7UFNNHtihzjL6Z1rJRDWz/7hmzW0lnLphppon/2mVk5+aMvLTCW/SpM7Jh51q4PK/hsBDc2XsW2hy1HOUZTJueDBvnwqXJVg4u0GYV3rY46fA6uAUPhpWtaS6rJtOkUCiw7OPO+HBs6xLvQqSvfl19cWhlH7g663/bezIMM42ofAqFAktmdsQnbwZUqpHSu5M3Qlf2NdnLLy0BM42ofAqFAl9Ob48Fk9rC2rrimdajvReO/twPNasz08TCTDNNbMjIKC8tE3auTgWPbZ0dkPcwCwBgbW+LpwZ3QdSmw3KVZzBr5xrQZqYWPNZlp8PasXr+n/NykHzsV3j0LL40jqg8VlYKLJwShBNr+8O/XnWDXlvdxQ4/z+uCXT/0gosTmzFiYqYR6UehUGDu24H4+5f+aPqUm0GvdXGyxYo5nbFvWW82Y0TGTCPSj0KhwKzxbXBmw/NF9sLTh7OjLZbO6oSDK/qyGSMyZpppspG7gKos4Xwk2rw3DAprKzj71kZu8kNAyF/G51y3NuyqO+GZ9R/Czs0ZDrXd0GBoN9zcekzmqkvn5N8B9zZ+AkGrQe6D27Bx9YDCKr/nl3s/GtrMVETN7w9NRjLUKSokHf4F7j1Gylw1mZPOAZ64sn0wdh27ix83XcPRf+NLXfrayr8mJgxpjFf7N+SqGIkw05hpZJgOrWoj7PdB2HM8Bj9uvobD/9yDRlNypjVv4IYJQ5tg5PONUN2FmSYFZhozjQwT1LwWLm4diP2nYvHjpms4dOYe1BpdiWObPuWG8S82xugXGrG5LBFmmmlmGhsyMspLzUDkb6Hou30+BEGHMx+ugnf3NrBzc0b09pPY3ecDAIAyuDnqD+xs0icEANi41IRHr3G48WFXwMoKdSf8iLTz+6FNT0bNbi+j6Tf/AgDSw44i+cQmkzwhyPTZ2FhhUE8/DOrph+wcDS5HJONadCqysjWws7VGfW8XBDZzRw3+cJccM42ZRoaztrbCgO71MKB7PeTkahAWmYIrN1OQla2BrY0V6vu4ILCpB785lgEzjZlGhrO2tkK/rnXRr2td5OZpERaZjCtRqcjMVsPWxgp+3i4IbOoOdzfTvozEEjHTTDPT2JCRWcSGQ4jYcKjgccrVO8XGqE5fger0FSnLqrBavcejVu/HbpNWv3WxMS4tQ+DSMkS6oshiOVSzQYdWtdGhVW25S6H/x0wjqrhq9jZo16IW2rWoJXcp9P+YaUQVZ29njaDmtRDUnJlmKphppod7yBARERERERERSYwNGSIiIiIiIiIiifGSJSNz8VPKXQIA49Th7Vj+GKmYUi1EVQkzTRymVAtRVcJME4cp1UJUlTDTxCFlLWzIGFnPdTPlLsFovu0gdwVEJDdmGhFZEmYaEVkSZpr54yVLREREREREREQSY0OGiIiIiIiIiEhibMgQEREREREREUmMDRkiIiIiIiIiIomxIUNEREREREREJDE2ZIiIiIiIiIiIJMaGDBERERERERGRxNiQISIiIiIiIiKSGBsyREREREREREQSY0OGiIiIiIiIiEhibMgQEREREREREUmMDRkiIiIiIiIiIomxIUNEREREREREJDE2ZIiIiIiIiIiIJMaGDBERERERERGRxGzkLsDShI5ahPTbKrnLgIufEj3XzazUHO+eBeKyjFRQJXk7At92kLsKoqqHmSYOZhqRPJhp4mCmEcmDmSYOKTONDRkjS7+tQmpErNxlGEVcFnArXe4qiEhOzDQisiTMNCKyJMw088dLloiIiIiIiIiIJMaGDBERERERERGRxNiQISIiIiIiIiKSGPeQISKSmSAIOBeeiLNhD3Di/P2C4299dgqdWnsiqLkHurZVwt7OWsYqiYj0IwgC/ruaiDOXE3DifOFmk299dgodW9ZGUHMPdAtSopo9P4YSkekTBAEXriXhzOUHOP7fY5m24BQ6tKqFtk09ENLOCw7VmGlkOP6rISKSSU6uBqv+iMDSzddw7VZqsed3H4vB7mMxAIBaNaph3ODGeOeVZlB6OEpcKRFR+XLztPh5e36mhUelFHv+8UzzqFENYwY2wpRXmqNObSepSyUiKpdarcOaHfmZdulGcrHndx+Pwe7j+ZlWs7o9xgz0x5RXmsNHyUwj/bEhI5Onv5uIhsO7AwB0Wi2y76ci/lQ4zi/8FVmq4ie8qbv9/WgkHV6X/8DKCrY1vODSsge8R34OO3dveYsjMkH/hCVg9OzjJTZiSpKQkoPPV1/CT1uvYcnMYLzSrwEUCoW4RRqAmUZUtf13NRGjPz5eYiOmJIkpOfhiTRiWb7uB797vgFEDGjHTRMRMIzLMpRtJGD37OC5e1+98T07LxVfrwrB823V8Pb09xr3Y2KQyDbCsXLOkTOMeMjJSnbmKza3GYVvQWzg+8Tu4t/BDyIrpcpdVYc7NuqDV2ni0XHUX9af/hqzoC7i1eKjcZRGZnBXbriP4tV16N2Mel/IwD699dAxjPzkBjUZn/OIqgZlGVDWt+TMCHV7ZqXcz5nFp6Xl4ffYJjJx1DGo1M01MzDQi/fy6JwpBI3bo3Yx5XHqmGuPnncJL7x9BnlorQnWVY0m5ZimZxoaMjHR5GmQnpCJLlYz7Z67hxoZDqN2uMWydHeQurUIUNnawraGEnbs3XJp3Ra1nxyPzxmlosx7KXRqRyVix7TomzDsFnU4o8XlrawW8PR3h7ekIa+vSv1lZ82ckxsw5Ueo8cmCmEVU9a3dEYMycE9BqK5dpG3bfxGuzjkKrNZ2mDDONqOr5bc9NvPbRMWg0lcu0LX9F46X3j5jcl2eWlGuWkmlsyJgIB88a8OvfETqNFoIJfRipqLyke0j5extgZZ3/HxHhXHgC3lrwd5ljlB4OiD04ArEHR0DpUfYPx/W7o/C/jVeNWaLRMNOILN+lG0l449OTZY4xJNM274/GN7+EG7NEo2GmEVm+K1EpeH3OcQhlfNdlSKZtD72DRT9fMnKVxmNJuWbOmcY9ZGSk7NQcr0Sth8LKCjYO9gCA8GU7ocnOBQCErJyOe8cuIWLDIQBAzRb10XXpFOzqNQPaXLVsdZcmPfwoLgx3hqDTQcjLBgB4DpwO62r5G1ulnN6O+M2fFnlNTsxV+I77HrX6viV5vURSys3T4vXZx42+omXm9+fwXBdfNKzratR5K4KZxkyjqkOt1mH07OOlfotcUbN/PI/nQ+qiSX03o85bEcw0ZhpVHRqNDq/POY48I186Oe+ni3ghpB5a+tc06rwVZUm5ZimZZtYNmUuXLmHOnDk4evQoBEFAjx49sGzZMvj7+6Nfv37YtGmT3CWWKeF8JE5O+R+s7W3hN6AT6nRphQuLNxY8/8/sNei7Yz7u7D2L3JQMBC96A2c/Wm1yJ8MjTv4d4Dd1HYS8HKSc3IKHlw6hzisLCp6vETwINYIHFTxOPfMn4tZ/BPceo+QoV29arQ57T8Ri5e83Co5t3HcTE19qBkcHsz6FSEJr/ozAlZupRp83O0eLOT/+h98Wdzf63IZipplPpv31dxyWb71ecOzXPVGYOKIZnB1tZayMzMmGPVEV2l+hPLl5Wsxa8h9+/7an0ec2FDPNPDJNpxNw8HQcftpSmGm/7IrE5JebwcXJTsbKyJxs+Ssa58ITjT6vWqPDh0v+xe7/PWv0uSvCknLNUjLNbC9ZCg0NRceOHXHjxg18/PHHWLhwIWJjY9G3b19kZGSgTZs2cpdYLm1OHtJvq5B6IwYXv9yM9JgH6PDZ2ILns1TJuLJ8N4Jmv4bGr/VC2q14xJ8Mk7HislnZOaCaV0M41GuBOq/Mg71nfcSsmFzi2LzEWNxdPhH1Z2yClb3p3sI34nYamg38HQPeOYhdx+4WHH//23PwfmYj9p+MlbE6MheCIGDp5muizb/t4G08SMoWbX59MdNMP9NuxjxEyxe3o9/EA9h5tDDTZn7/L7x7bsSux44RlUXMTNtx9A7i7meKNr++mGmmn2m349LRZuh29HnrL/x55E7B8Vk//Ic6PTfij0O35SuOzMrSLeJl2t4TMbgdly7a/IawpFyzlEwzy4ZMQkIChg8fjsDAQFy4cAEzZszApEmTEBoairt38z9MmkND5kkXv9qMhsO7w711g4Jj19fsh1tjX7ScNBDnPl0nY3WG8xoxF4mha5AZ+W+R44JOh+hvX4XyxZlw9GslU3Xli1VlotvrexBxp+SNodIy8vD8Owdw9Fy8xJWRubl4PQlhkYbffURfao0OG/fdFG3+imKmmZb4hCyEjNlT6t290rPUGPzuIRw8HSdtYWR2rt5Mwb9XjP9N8iNarYBf9zDTxGbumfYgKRshY/aW+vM1M1uDodNDsec4G81Utqi7D3Hqwn3R5heE/H3/TJEl5Zq5ZppZNmQWL16MlJQUrFmzBg4OhZspVa9eHYGBgQDMsyGTHq1CzMF/EThzROFBQcCNXw4iNvQ8cpPMa8foanUawa3d87i3YVaR4/FbFsDawRW1+5fcwTQVC1ddhKqMVQeCkP+hcdqXZyCUtfsXVXlnwxJEf49/RFhmW1nMNNPyxZrLiL2fVerzggBodQKmfsFMo7JJk2niv4ehmGmm5etfwnAnPqPU5wUBEABM/eKsSd2RkEzPP1JkmgTvURGWlGvmmmlmuQHGpk2b0KVLF/j7+5f4vKenJ5RKJQDg7bffxq5du5CWlgYXFxcMHToUX3zxBezs9LumVKPRQKVS6V2bWq3Re2xJwpfuRL9dn0EZ3Byq01fyD+p0EAz8QaJWaxAbW7nLadRqTwCV20/Ac9AM3JjZGelhR+HSMgQZ104h6dBqNP3mvIG1qBEbK17n+kkZWRqs2xlZ7jhBAC5cT8bu0CsIaOImfmFklk78W/TbOWtrRak783s9dtyrjN37VYnZRW4ze+ZSfKXP+ZIw04oy10zLztFi9R83yh0nCMDVm6n4fX8YOrY0jQ0IyfQcP3enyGMxMu2fsPvMtHLnqLqZlqvWYcW261Agv+lSGkHIX/2wafcldA30kKo8MjPHzt0u8liUTAt/YJKZBhgn15hpgFKphI2N4e0VhWBmX4OpVCp4eXlh2rRp+Prrr4s8p9Pp4OXlhYCAAOzfvx8AcPXqVdSrVw9OTk5ITEzE0KFD0a1bN8ydO1ev94uNjYWvr6/e9S1w7wVvW+Pe7aThsBC4t26As7NW6/2aOPVDfJx0sFLv2+yHcDjUbV6pOR6nyUjFtWmB8Ju0Gi6tDNuANPvuFVyd3MJotZTLoT7QcFb54x659xuQdFi8esi81ZsIuAYUPPT2dETswRFlvKB8Pr02Iu7x1Q7aTODqlErNWRJmWunMKtOq+QCN5uo/Pn4LkHhAtHLIzPlOANzaFTwUJdN0ucCViZWasyTMtNKZVabZewH+8/Ufr9oOJOwRrx4ybz5jgRrBBQ9FyTRBC4RPqNScJREj0wDDc42ZBsTExMDHx8eg1wBmuEImMzN/kzeFQlHsuR07duDBgwdFLldq1qxZwZ8FQYCVlRUiI8tf+UDGl7B/GdQp8Yj5+d0ix927j4LnC++W8iqZlPDvq2xmefUfScbQf0+m+h70OLPKNIMziplGZTD4Z2SF3kSC96DHmVemGfjvQ5J/s2S+mGmWyFwyzexWyOTl5cHR0REBAQE4d+5cwfE7d+6gc+fOiIuLw8aNG/HSSy8VPLdo0SIsWLAAmZmZcHd3x759+9CuXbuSpi/G0EuWTg9bhMxo/ceLxam+EsFbZlZqjslXPRGTYxq3QPWtpsYPzaRbCpuYmougV49Bq+dSvV/mBaJ7UC2RqyJzNfWrMPx++F7B4/KWwp7bOBAA0G7En4hPLHkfoyeXwvrUrobTa7sZr+j/x0wTh9SZlpquRuArR6DW6Jdpq2YHoHdwbZGrInP1/vfh2PhX4ebPYmRa7Zr2+G9DiNFqfoSZJg6pMy09S4OAl48gN0+n1/ilM1vj+a5Kkasic/Xx0qtYtzum4LEYmebmYouwzT2MV/T/Y6aJoyKZVtFLlsxuhYydnR1GjhyJNWvW4IUXXkC/fv0QExODlStXwtPTE3FxccU29J05cyZmzpyJa9eu4ddff4WXl5fe72djY2PQ0iNbW9P4n9TW1rC6S5wjEkCOceqpLFtb20r/fQzh4wMMfuYOth6ILnOcQgHUVTrj5edbw9qa3yhTyToFphRpyGi1QtFlrKWIT8zWaxwAtG1eW5RzhJkmDskzDcDwPnewYXfZd65RKACluwNGDWoDGxtmGpWsc9uHRRoyomRas1rMtPLmqMKZBgCv9GuIn7dHlDlGAcCjRjWMGRIAeztraQojs/N028wiDRkxMi2wqQczrbw5qmimmeWnrSVLlmD8+PE4e/Yspk+fjrNnz2L79u2oU6cOHB0dS93st2nTpmjdujVee+01iSsmc/Th2Fawt7MqdZWrAvmbxX36diCbMVSmoGbibyQY1JybFVLZPni9FRzsrctcuf8o09iMobJIkWltJXgPMm8zRreEs6NN2ZkGYM6bbMZQ2aT4DMVMo9KY5ScuZ2dnLF++HCqVCunp6Thw4ACCg4MRHh6Oli1bwsqq9L+WWq1GRETZ3XQiAAho6oEd3/eCg31+5/nJH/gCgK+mt8eoFxpJXxyZlU5tPOFd21HU9xj27FOizk/mr0Wjmtj9v2fh5FBypgHAwneC8MaQJhJXRuYmqLkH6nu7iPoew3vXF3V+Mn9N6rthz/+ehatT/iUOJWXaJ28GYOJLTSWujMxN68Y10divuqjv8VIffk6jkpllQ6YkqampiI2NLXK5UlpaGtauXYvU1FQIgoDLly9jwYIF6N27t3yFklnp3dkHUXuGYt7EQDT0dYWrky28azvi7eFNEfb7IEwf1VLuEskM2NhYYcJQ8X7J7RVcB/4if5Agy9CjQx1E7RmGzya3RaO6+ZlWp5Yj3hzaBJe2DcKH41rLXSKZAWtrK7wpYqZ1batEi0a87TqVr2uQFyJ2DcXnU4LQ2K86XJ1s4eXhgDdebIzzm1/A3LcDS7wRCNHjFAoF3homXqZ1aFkLgVwhQ6UwjYvOjCAsLAwAijRkFAoFNmzYgGnTpiEvLw+1a9fG4MGD8emnn8pUZXGNXu6JRi/1gCDocPqDlUi9frfYmD6/f4q0qDic/mCFDBUaJvHAKiQe+hmwskK9N5fBwa94w+LGrBBU826Cem//JEOFhvOq5YjZEwIwe0JA+YOJSvH28Kb438areJBs3ItjFQpgjgn922SmmT5Pdwd89EYbfPRGG7lLITM2fkhjfP/bFdx7oN/+CYb45E1mmlgsMdNquztg5tjWmDmWDWWquDGD/PHN+nDcjc80+txz3wo0+pwVxUwzPRazQqakhoyrqysOHTqE5ORkZGRk4NatW/jqq6/g5OQkU5VF2bk5o/GoZ7Fv8BycmrYMHea/XmyMzzNtoc4oefduU6NJT0bC/mVovPAY/CatRsyqKcXGpJ7bDWsHcZc5E5kid7dq+Gl2Z6PPO+WV5ng60DTuHMFMI6o63FztsWKO8TPtrWFN0KNDHaPPWxHMNKKqw8XJDqvmdjH6vGMG+aPP09JueF0aZpppspiGzNtvvw1BENCxY0e5S9FbrYCGUP19BYJGi4c378G+pmvRC2AVCjR5vQ+ur90vX5EGyIz8B84tQqCwsUU1n8bQPEyEoCu8HaGg0yFh74+o9dxEGaskks+gnn6Y/HKzMseoErPh02sjfHpthKqUWyk+0rFVLXw2OciYJVYKM42oaunXtS7eK+fSXUMyLai5B76Y1t6YJVYKM42oaukV7I1Zb5S90sqQTGvduCa+ea+DMUusFGaaabKYhow5snNzRl5a4bI4dUY27FwLN/5sOCwEd/aehTZHLUd5BtOmJ8PGuUbBYysHF2iz0goeJx1eB7fgwbCyrSZHeUQm4bv3O5a598KjWy3G3c+CViuUOi64dW3sXdobjg6mc+UpM42o6vliWju8U0ajWd9MC2rugf3LesPZ0VaMMiuEmUZU9cyf1BYzRpfeaNY309o0qYkDP/VBdRc7McqsEGaaaWJDRkZ5aZmwcy28fMrW2QF5D/Ovxba2t8VTg7sgatNhucozmLVzDWgzUwse67LTYe2Yv9GoLi8Hycd+hUfP4kvjiKoSKysFln7cCSs/eRouTob/4qFQANNGtkDoyr6o4WovQoUVx0wjqnoUCgW++6Aj1s7vWuFfPN55uRmOrn4O7m6m9aGZmUZU9SgUCix+tx1+/TwENVwrlmlvDWuC42v6oba7g5GrqxxmmmliQ0ZGCecj4dmxKRTWVnDxUyI3+SEg5HdanevWhl11Jzyz/kO0nf0qvHsGoMHQbjJXXDYn/w5Iv3IcglaDnPgo2Lh6QPH/tyDPvR8NbWYqoub3R+y695H2314kHf5F5oqJ5KFQKDDuxcYI/2MwXh/YCNXsrfV6XZ/OPji5rj++fq8DHKqZzsqYR5hpzDSqmhQKBUa90AhX/hiMcYP94ahnPvUKroPja/rh+5nBcDKhlTGPMNOYaVQ1KRQKvNyvAa7++SLeHNoETnquRu7R3guHV/XF0o87w8XJdFbGPMJMM81MUwiCUPpaKzLYn92mIjUiVu/x/q8+g4bDukMQdDjz4So4etaAnZszorefLBijDG6O+gM7G7TTtZu/DwYe+86Q0osZdgS4lW7YaxL+WoGk0LWAlRXqTvgR6pR4aNOTUbPbywVj0sOOIvnEJoN2un7KBdjS3bBaiMxFclouth6IxtmwB/jvahIeJGdDqxXg5mqHNo3d0baZO4b0qo8Gvq6S18ZMY6YRGSr1YX6mnQlLwH9XE3E/KT/TqrvYobV/TbRt5oEhvfzQqF51yWtjpjHTiAyVlp6HbQejceZy/ue0+MT8y5VcnW3R2j//c9rgZ/zQpL6b5LUx08w/09iQMTJDTwqxyHVSiIU/6InkwUwTBzONSB7MNHEw04jkwUwTh5SZxkuWiIiIiIiIiIgkxoYMEREREREREZHE2JAhIiIiIiIiIpKY6d2mw8y5+CnlLgGAcerwdix/jFRMqRaiqoSZJg5TqoWoKmGmicOUaiGqSphp4pCyFm7qS0REREREREQkMV6yREREREREREQkMTZkiIiIiIiIiIgkxoYMEREREREREZHE2JAhIiIiIiIiIpIYGzJERERERERERBJjQ4aIiIiIiIiISGJsyBARERERERERSYwNGSIiIiIiIiIiibEhQ0REREREREQkMTZkiIiIiIiIiIgkxoYMEREREREREZHE2JAhIiIiIiIiIpIYGzJERERERERERBJjQ4aIiIiIiIiISGJsyBARERERERERSYwNGSIiIiIiIiIiibEhQ0REREREREQkMTZkiIiIiIiIiIgkxoYMEREREREREZHE2JAhIiIiIiIiIpIYGzJERERERERERBJjQ4aIiIiIiIiISGL/B+xSd6bhojH9AAAAAElFTkSuQmCC",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1434x294.311 with 1 Axes>"
       ]
@@ -55,21 +62,26 @@
    "id": "452cd19e",
    "metadata": {},
    "source": [
-    "### Specify some observables\n",
-    "\n",
-    "Currently, only `Pauli` observables with phase equal to 1 are supported. Full support for `SparsePauliOp` is expected in CKT v0.7.0."
+    "### Specify an observable"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "id": "105e858d",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:01.076216Z",
+     "iopub.status.busy": "2024-04-19T17:43:01.075949Z",
+     "iopub.status.idle": "2024-04-19T17:43:01.079736Z",
+     "shell.execute_reply": "2024-04-19T17:43:01.079184Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit.quantum_info import PauliList\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
-    "observables = PauliList([\"ZZII\", \"IZZI\", \"IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])"
+    "observable = SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])"
    ]
   },
   {
@@ -79,41 +91,26 @@
    "source": [
     "### Specify a backend\n",
     "\n",
-    "You can provide either a fake backend from Qiskit or a hardware backend from Qiskit Runtime."
+    "You can provide either a fake backend or a hardware backend from Qiskit Runtime."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "id": "756f2130-6947-479a-9fe7-97443c87a904",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:01.082117Z",
+     "iopub.status.busy": "2024-04-19T17:43:01.081912Z",
+     "iopub.status.idle": "2024-04-19T17:43:01.558662Z",
+     "shell.execute_reply": "2024-04-19T17:43:01.558071Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit.providers.fake_provider import GenericBackendV2\n",
+    "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n",
     "\n",
-    "backend = GenericBackendV2(num_qubits=4, coupling_map=[[0, 1], [1, 2], [2, 3]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b5debc22-7e92-465e-b37d-05763946b6e6",
-   "metadata": {},
-   "source": [
-    "To use a hardware backend, replace the code above with the following commented block."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "322fb191-6b85-4887-9883-71f94bb0be87",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from qiskit_ibm_runtime import QiskitRuntimeService\n",
-    "#\n",
-    "# service = QiskitRuntimeService()\n",
-    "# # backend = service.backend(\"ibm_cairo\")\n",
-    "# backend = service.least_busy(operational=True, simulator=False)"
+    "backend = FakeManilaV2()"
    ]
   },
   {
@@ -128,15 +125,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "b394da7a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:01.561886Z",
+     "iopub.status.busy": "2024-04-19T17:43:01.561340Z",
+     "iopub.status.idle": "2024-04-19T17:43:01.596696Z",
+     "shell.execute_reply": "2024-04-19T17:43:01.596116Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Transpiled circuit depth: 101\n"
+      "Transpiled circuit depth: 30\n"
      ]
     }
    ],
@@ -148,23 +152,30 @@
     ")\n",
     "\n",
     "transpiled_qc = pass_manager.run(circuit)\n",
-    "print(f\"Transpiled circuit depth: {transpiled_qc.depth()}\")"
+    "print(f\"Transpiled circuit depth: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "4fe4af43",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:01.598991Z",
+     "iopub.status.busy": "2024-04-19T17:43:01.598785Z",
+     "iopub.status.idle": "2024-04-19T17:43:02.317434Z",
+     "shell.execute_reply": "2024-04-19T17:43:02.316773Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 3447.07x147.156 with 1 Axes>"
+       "<Figure size 2008.96x147.156 with 1 Axes>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -185,18 +196,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "12e73c69",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:02.320250Z",
+     "iopub.status.busy": "2024-04-19T17:43:02.319784Z",
+     "iopub.status.idle": "2024-04-19T17:43:02.631638Z",
+     "shell.execute_reply": "2024-04-19T17:43:02.630944Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1634.66x294.311 with 1 Axes>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -226,14 +244,23 @@
     "\n",
     "`generate_cutting_experiments` accepts a circuit containing `TwoQubitQPDGate` instances and observables as a `PauliList`.\n",
     "\n",
-    "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst)."
+    "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst).\n",
+    "\n",
+    "**Note:** The ``observables`` kwarg to `generate_cutting_experiments` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "83b1efed-bafa-48c4-bbf0-cf7eb9027ac5",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:02.634186Z",
+     "iopub.status.busy": "2024-04-19T17:43:02.633980Z",
+     "iopub.status.idle": "2024-04-19T17:43:04.963805Z",
+     "shell.execute_reply": "2024-04-19T17:43:04.962860Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -241,7 +268,7 @@
     "\n",
     "# Generate the subexperiments and sampling coefficients\n",
     "subexperiments, coefficients = generate_cutting_experiments(\n",
-    "    circuits=qpd_circuit, observables=observables, num_samples=np.inf\n",
+    "    circuits=qpd_circuit, observables=observable.paulis, num_samples=np.inf\n",
     ")"
    ]
   },
@@ -259,9 +286,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "c7b28e2c",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:04.967500Z",
+     "iopub.status.busy": "2024-04-19T17:43:04.966963Z",
+     "iopub.status.idle": "2024-04-19T17:43:04.970995Z",
+     "shell.execute_reply": "2024-04-19T17:43:04.970300Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -287,26 +321,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "70e2f1b6",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:04.973543Z",
+     "iopub.status.busy": "2024-04-19T17:43:04.973161Z",
+     "iopub.status.idle": "2024-04-19T17:43:05.463994Z",
+     "shell.execute_reply": "2024-04-19T17:43:05.463230Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Original circuit depth after transpile: 101\n",
-      "QPD subexperiment depth after transpile: 26\n"
+      "Original circuit depth after transpile: 30\n",
+      "QPD subexperiment depth after transpile: 7\n"
      ]
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 2320.28x428.089 with 1 Axes>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -315,8 +356,12 @@
     "# Transpile the decomposed circuit to the same layout\n",
     "transpiled_qpd_circuit = pass_manager.run(subexperiments[100])\n",
     "\n",
-    "print(f\"Original circuit depth after transpile: {transpiled_qc.depth()}\")\n",
-    "print(f\"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth()}\")\n",
+    "print(\n",
+    "    f\"Original circuit depth after transpile: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth(lambda x: len(x[1]) >= 2)}\"\n",
+    ")\n",
     "transpiled_qpd_circuit.draw(\"mpl\", scale=0.8, idle_wires=False, fold=-1)"
    ]
   },
@@ -325,46 +370,36 @@
    "id": "fd9a126c",
    "metadata": {},
    "source": [
-    "### Run the subexperiments using the Qiskit Sampler primitive"
+    "### Prepare subexperiments for the backend and run them using the Qiskit Runtime Sampler primitive"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "80dd2a66",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:05.466804Z",
+     "iopub.status.busy": "2024-04-19T17:43:05.466360Z",
+     "iopub.status.idle": "2024-04-19T17:43:37.025494Z",
+     "shell.execute_reply": "2024-04-19T17:43:37.024692Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit_aer.primitives import Sampler\n",
+    "from qiskit_ibm_runtime import SamplerV2\n",
     "\n",
-    "# Set up Qiskit Aer Sampler primitive.\n",
-    "sampler = Sampler(run_options={\"shots\": 2**12})\n",
+    "# Transpile the subeperiments to the backend's instruction set architecture (ISA)\n",
+    "isa_subexperiments = pass_manager.run(subexperiments)\n",
     "\n",
-    "# Retrieve results from each subexperiment\n",
-    "results = sampler.run(subexperiments).result()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "878c9d46-53b9-4ac5-8170-f4abd1072a1c",
-   "metadata": {},
-   "source": [
-    "To use the Qiskit Runtime Sampler, replace the code above with this commented block, and be sure to specify a hardware backend (above) when specifying a backend."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "a437de20-2042-4e62-87a7-804058cff5db",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from qiskit_ibm_runtime import Session, Options, Sampler\n",
-    "#\n",
-    "# with Session(backend=backend) as session:\n",
-    "#     sampler = Sampler(options=Options(execution={\"shots\": 2**12}))\n",
-    "#\n",
-    "#     results = sampler.run(pass_manager.run(subexperiments)).result()"
+    "# Set up the Qiskit Runtime Sampler primitive.  For a fake backend, this will use a local simulator.\n",
+    "sampler = SamplerV2(backend=backend)\n",
+    "\n",
+    "# Submit the subexperiments\n",
+    "job = sampler.run(isa_subexperiments)\n",
+    "\n",
+    "# Retrieve the results\n",
+    "results = job.result()"
    ]
   },
   {
@@ -374,14 +409,21 @@
    "source": [
     "### Reconstruct the expectation values\n",
     "\n",
-    "Use the subexperiment results, subobservables, and sampling coefficients to reconstruct the expectation value of the original circuit."
+    "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "ace12f7f",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:37.029345Z",
+     "iopub.status.busy": "2024-04-19T17:43:37.028881Z",
+     "iopub.status.idle": "2024-04-19T17:43:39.308072Z",
+     "shell.execute_reply": "2024-04-19T17:43:39.307244Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.cutting import reconstruct_expectation_values\n",
@@ -389,8 +431,10 @@
     "reconstructed_expvals = reconstruct_expectation_values(\n",
     "    results,\n",
     "    coefficients,\n",
-    "    observables,\n",
-    ")"
+    "    observable.paulis,\n",
+    ")\n",
+    "# Reconstruct final expectation value\n",
+    "final_expval = np.dot(reconstructed_expvals, observable.coeffs)"
    ]
   },
   {
@@ -398,44 +442,43 @@
    "id": "3fc2327f",
    "metadata": {},
    "source": [
-    "### Compare reconstructed expectation values to exact expectation values from the original circuit"
+    "### Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "id": "4928e703",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:39.311175Z",
+     "iopub.status.busy": "2024-04-19T17:43:39.310719Z",
+     "iopub.status.idle": "2024-04-19T17:43:39.324322Z",
+     "shell.execute_reply": "2024-04-19T17:43:39.323777Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reconstructed expectation values: [0.52972412, 0.5692749, 0.37542725, -0.20349121, 0.25061035, -0.24511719]\n",
-      "Exact expectation values: [0.50983039, 0.56127511, 0.36167086, -0.23006544, 0.23416169, -0.20855487]\n",
-      "Errors in estimation: [0.01989373, 0.00799979, 0.01375639, 0.02657423, 0.01644866, -0.03656231]\n",
-      "Relative errors in estimation: [0.03902028, 0.01425288, 0.03803565, -0.11550725, 0.07024487, 0.17531269]\n"
+      "Reconstructed expectation value: 0.66918945\n",
+      "Exact expectation value: 0.50497603\n",
+      "Error in estimation: 0.16421342\n",
+      "Relative error in estimation: 0.32519053\n"
      ]
     }
    ],
    "source": [
-    "from qiskit_aer.primitives import Estimator\n",
+    "from qiskit_aer.primitives import EstimatorV2\n",
     "\n",
-    "estimator = Estimator(run_options={\"shots\": None}, approximation=True)\n",
-    "exact_expvals = (\n",
-    "    estimator.run([circuit] * len(observables), list(observables)).result().values\n",
-    ")\n",
-    "print(\n",
-    "    f\"Reconstructed expectation values: {[np.round(reconstructed_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Exact expectation values: {[np.round(exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Errors in estimation: {[np.round(reconstructed_expvals[i]-exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
+    "estimator = EstimatorV2()\n",
+    "exact_expval = estimator.run([(circuit, observable)]).result()[0].data.evs\n",
+    "print(f\"Reconstructed expectation value: {np.real(np.round(final_expval, 8))}\")\n",
+    "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n",
+    "print(f\"Error in estimation: {np.real(np.round(final_expval-exact_expval, 8))}\")\n",
     "print(\n",
-    "    f\"Relative errors in estimation: {[np.round((reconstructed_expvals[i]-exact_expvals[i]) / exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
+    "    f\"Relative error in estimation: {np.real(np.round((final_expval-exact_expval) / exact_expval, 8))}\"\n",
     ")"
    ]
   }
@@ -456,7 +499,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/.doctrees/nbsphinx/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb b/.doctrees/nbsphinx/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb
index dc25adb29..ce326d5e9 100644
--- a/.doctrees/nbsphinx/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb
+++ b/.doctrees/nbsphinx/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb
@@ -30,12 +30,19 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "3bcae0ed-4308-4686-b85c-8595c6e916bc",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:05.442544Z",
+     "iopub.status.busy": "2024-04-19T17:42:05.442302Z",
+     "iopub.status.idle": "2024-04-19T17:42:05.948185Z",
+     "shell.execute_reply": "2024-04-19T17:42:05.947389Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x7fb6f8b361f0>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x7f8640f91340>"
       ]
      },
      "execution_count": 1,
@@ -65,11 +72,18 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "1dcaff2d-2d1b-4cc0-87d1-0f4f5de823ff",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:05.951993Z",
+     "iopub.status.busy": "2024-04-19T17:42:05.951230Z",
+     "iopub.status.idle": "2024-04-19T17:42:06.747817Z",
+     "shell.execute_reply": "2024-04-19T17:42:06.746915Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 956.385x618.722 with 1 Axes>"
       ]
@@ -88,21 +102,26 @@
    "id": "bd1617a1-e793-43a9-a24a-c81c18fd2a1e",
    "metadata": {},
    "source": [
-    "### Specify some observables\n",
-    "\n",
-    "Next, we specify a list of observables whose expectation values we would like to determine."
+    "### Specify an observable"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "id": "b791aa42-c485-453b-a110-3c790194adaf",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:06.750528Z",
+     "iopub.status.busy": "2024-04-19T17:42:06.750245Z",
+     "iopub.status.idle": "2024-04-19T17:42:06.754252Z",
+     "shell.execute_reply": "2024-04-19T17:42:06.753499Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit.quantum_info import PauliList\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
-    "observables_0 = PauliList([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])"
+    "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])"
    ]
   },
   {
@@ -123,11 +142,18 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "22f19d29-a182-4758-b5d0-6b66f9a946be",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:06.757184Z",
+     "iopub.status.busy": "2024-04-19T17:42:06.756691Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.216831Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.215960Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1290.83x702.333 with 1 Axes>"
       ]
@@ -163,19 +189,26 @@
    "id": "088431d9-de2f-4b5d-90d2-7e7a5947dcb5",
    "metadata": {},
    "source": [
-    "### Create observables to go with the new circuit\n",
+    "### Create observable to go with the new circuit\n",
     "\n",
-    "These observables correspond with `observables_0`, but we must account correctly for the extra qubit wire that has been added (i.e., we insert an \"I\" at index 4).  Note that in Qiskit, the string representation qubit-0 corresponds to the right-most Pauli character."
+    "This observable corresponds with `observable`, but we must account correctly for the extra qubit wire that has been added (i.e., we insert an \"I\" at index 4).  Note that in Qiskit, the string representation qubit-0 corresponds to the right-most Pauli character."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "id": "d33d5580-879f-466f-ac87-dcc6a19fbab6",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.220100Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.219501Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.224077Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.223355Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "observables_1 = PauliList([\"ZIIIIIII\", \"IIIIZIII\", \"IIIIIIIZ\"])"
+    "observable_expanded = SparsePauliOp([\"ZIIIIIII\", \"IIIIZIII\", \"IIIIIIIZ\"])"
    ]
   },
   {
@@ -192,13 +225,20 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "fc3738c7-2bb2-4d67-ae2b-7a3090b31e6a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.226529Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.226328Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.239655Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.238873Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.cutting import partition_problem\n",
     "\n",
     "partitioned_problem = partition_problem(\n",
-    "    circuit=qc_1, partition_labels=\"AAAABBBB\", observables=observables_1\n",
+    "    circuit=qc_1, partition_labels=\"AAAABBBB\", observables=observable_expanded.paulis\n",
     ")\n",
     "subcircuits = partitioned_problem.subcircuits\n",
     "subobservables = partitioned_problem.subobservables\n",
@@ -217,7 +257,14 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "9c53a862-f762-471a-bda7-b27e88292ac9",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.242366Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.241966Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.246915Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.246177Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -239,11 +286,18 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "69df912e-6709-45bb-8eb3-c66a70edefdc",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.249463Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.249031Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.535187Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.534423Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1058.03x367.889 with 1 Axes>"
       ]
@@ -261,11 +315,18 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "d851adcb-e524-48c8-8adc-0d1606613c8d",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.537882Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.537648Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.711037Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.710365Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 723.783x367.889 with 1 Axes>"
       ]
@@ -295,7 +356,14 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "7af74c54-3e68-4d58-a2e6-02bc212d911d",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.713923Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.713702Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.717904Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.716959Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -325,7 +393,14 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "d28a8b82-8405-47b2-8142-62d56266409a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.721136Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.720389Z",
+     "iopub.status.idle": "2024-04-19T17:42:08.196614Z",
+     "shell.execute_reply": "2024-04-19T17:42:08.195634Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.cutting import generate_cutting_experiments\n",
@@ -337,68 +412,120 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5327bee0-39eb-4da9-8316-a617e5a6ff01",
+   "id": "9f66e3eb-7cbc-45a9-8f40-9f92b61b6e65",
    "metadata": {},
    "source": [
-    "### Run the subexperiments using the Qiskit Sampler primitive"
+    "### Choose a backend\n",
+    "\n",
+    "Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "1bce51e7-0ae6-4626-b204-573a7c014955",
-   "metadata": {},
+   "id": "6944546f-e3e0-4863-9049-9feed1086e68",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:08.200171Z",
+     "iopub.status.busy": "2024-04-19T17:42:08.199914Z",
+     "iopub.status.idle": "2024-04-19T17:42:08.756774Z",
+     "shell.execute_reply": "2024-04-19T17:42:08.756066Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit_aer.primitives import Sampler\n",
+    "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n",
+    "\n",
+    "backend = FakeManilaV2()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdb47142-690c-4b1a-ad0b-85e1260a2cea",
+   "metadata": {},
+   "source": [
+    "### Prepare the subexperiments for the backend\n",
     "\n",
-    "# Set up a Qiskit Aer Sampler primitive for each circuit partition\n",
-    "samplers = {\n",
-    "    label: Sampler(run_options={\"shots\": 2**12}) for label in subexperiments.keys()\n",
-    "}\n",
+    "We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0229e6f2-a637-481d-ac76-84dfaadc264f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:08.761659Z",
+     "iopub.status.busy": "2024-04-19T17:42:08.760351Z",
+     "iopub.status.idle": "2024-04-19T17:42:11.493961Z",
+     "shell.execute_reply": "2024-04-19T17:42:11.493042Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
     "\n",
-    "# Retrieve results from each partition's subexperiments\n",
-    "results = {\n",
-    "    label: sampler.run(subexperiments[label]).result()\n",
-    "    for label, sampler in samplers.items()\n",
+    "# Transpile the subexperiments to ISA circuits\n",
+    "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
+    "isa_subexperiments = {\n",
+    "    label: pass_manager.run(partition_subexpts)\n",
+    "    for label, partition_subexpts in subexperiments.items()\n",
     "}"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "03bf7ba4-14c7-46de-901a-855456372d7b",
+   "id": "9578c64a-8087-4696-bb2b-ca53d45442ba",
    "metadata": {},
    "source": [
-    "To use the Qiskit Runtime Sampler, replace the code above with this commented block."
+    "### Run the subexperiments using the Qiskit Runtime Sampler primitive"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "d30a278d-0c8a-4c96-b7ca-718fa6f59991",
+   "execution_count": 14,
+   "id": "49cfcf6f-7ac3-4c1e-9dc7-ea79f011d37c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:11.497416Z",
+     "iopub.status.busy": "2024-04-19T17:42:11.496951Z",
+     "iopub.status.idle": "2024-04-19T17:42:21.708906Z",
+     "shell.execute_reply": "2024-04-19T17:42:21.708185Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/runner/work/circuit-knitting-toolbox/circuit-knitting-toolbox/.tox/docs/lib/python3.9/site-packages/qiskit_ibm_runtime/session.py:157: UserWarning: Session is not supported in local testing mode or when using a simulator.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit_ibm_runtime import SamplerV2, Batch\n",
+    "\n",
+    "# Set up a Qiskit Runtime Sampler primitive for each circuit partition\n",
+    "samplers = {label: SamplerV2(backend=backend) for label in subexperiments.keys()}\n",
+    "\n",
+    "# Submit each partition's subexperiments as a single batch\n",
+    "with Batch(backend=backend):\n",
+    "    jobs = {\n",
+    "        label: sampler.run(isa_subexperiments[label], shots=2**12)\n",
+    "        for label, sampler in samplers.items()\n",
+    "    }\n",
+    "\n",
+    "# Retrive results\n",
+    "results = {label: job.result() for label, job in jobs.items()}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03bf7ba4-14c7-46de-901a-855456372d7b",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# from qiskit_ibm_runtime import Session, Options, Sampler, QiskitRuntimeService\n",
-    "# from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
-    "#\n",
-    "# service = QiskitRuntimeService()\n",
-    "# backend = service.least_busy(operational=True, simulator=False)\n",
-    "#\n",
-    "# # Prepare transpiler for target backend\n",
-    "# pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
-    "#\n",
-    "# with Session(backend=backend) as session:\n",
-    "#     # Set up Qiskit Runtime Sampler primitives.\n",
-    "#     samplers = {\n",
-    "#         label: Sampler(Options(execution={\"shots\": 2**12})) for label in subexperiments.keys()\n",
-    "#     }\n",
-    "#\n",
-    "#     # Retrieve results from each subexperiment\n",
-    "#     results = {\n",
-    "#         label: sampler.run(pass_manager.run(subexperiments[label])).result()\n",
-    "#         for label, sampler in samplers.items()\n",
-    "#     }"
+    "To use the Qiskit Runtime Sampler, replace the code above with this commented block."
    ]
   },
   {
@@ -408,14 +535,21 @@
    "source": [
     "### Reconstruct the expectation values\n",
     "\n",
-    "Use the subexperiment results, subobservables, and sampling coefficients to reconstruct the expectation value of the original circuit."
+    "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "a1904c54-27fa-45e0-a9dd-727b4542db71",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:21.712202Z",
+     "iopub.status.busy": "2024-04-19T17:42:21.711788Z",
+     "iopub.status.idle": "2024-04-19T17:42:24.261549Z",
+     "shell.execute_reply": "2024-04-19T17:42:24.260798Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.cutting import reconstruct_expectation_values\n",
@@ -424,7 +558,8 @@
     "    results,\n",
     "    coefficients,\n",
     "    subobservables,\n",
-    ")"
+    ")\n",
+    "final_expval = np.dot(reconstructed_expvals, observable.coeffs)"
    ]
   },
   {
@@ -432,44 +567,43 @@
    "id": "cdc793f2-3e2b-417b-b863-7b9d57b86a8f",
    "metadata": {},
    "source": [
-    "### Compare the reconstructed expectation values with the exact expectation values from the original circuit"
+    "### Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "6e3d63e4-a510-4712-bc43-48df6e2f7ded",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:24.265047Z",
+     "iopub.status.busy": "2024-04-19T17:42:24.264536Z",
+     "iopub.status.idle": "2024-04-19T17:42:24.279626Z",
+     "shell.execute_reply": "2024-04-19T17:42:24.278941Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reconstructed expectation values: [0.15630245, 0.68548346, 0.69142044]\n",
-      "Exact expectation values: [0.1767767, 0.70710678, 0.70710678]\n",
-      "Errors in estimation: [-0.02047424, -0.02162333, -0.01568635]\n",
-      "Relative errors in estimation: [-0.11581981, -0.03058, -0.02218384]\n"
+      "Reconstructed expectation value: 1.54284137\n",
+      "Exact expectation value: 1.59099026\n",
+      "Error in estimation: -0.04814888\n",
+      "Relative error in estimation: -0.03026347\n"
      ]
     }
    ],
    "source": [
-    "from qiskit_aer.primitives import Estimator\n",
+    "from qiskit_aer.primitives import EstimatorV2\n",
     "\n",
-    "estimator = Estimator(run_options={\"shots\": None}, approximation=True)\n",
-    "exact_expvals = (\n",
-    "    estimator.run([qc_0] * len(observables_0), list(observables_0)).result().values\n",
-    ")\n",
-    "print(\n",
-    "    f\"Reconstructed expectation values: {[np.round(reconstructed_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Exact expectation values: {[np.round(exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Errors in estimation: {[np.round(reconstructed_expvals[i]-exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
+    "estimator = EstimatorV2()\n",
+    "exact_expval = estimator.run([(qc_0, observable)]).result()[0].data.evs\n",
+    "print(f\"Reconstructed expectation value: {np.real(np.round(final_expval, 8))}\")\n",
+    "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n",
+    "print(f\"Error in estimation: {np.real(np.round(final_expval-exact_expval, 8))}\")\n",
     "print(\n",
-    "    f\"Relative errors in estimation: {[np.round((reconstructed_expvals[i]-exact_expvals[i]) / exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
+    "    f\"Relative error in estimation: {np.real(np.round((final_expval-exact_expval) / exact_expval, 8))}\"\n",
     ")"
    ]
   }
@@ -490,7 +624,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/.doctrees/nbsphinx/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb b/.doctrees/nbsphinx/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb
new file mode 100644
index 000000000..47094b4a1
--- /dev/null
+++ b/.doctrees/nbsphinx/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb
@@ -0,0 +1,339 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Automatically find cuts using CKT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create a circuit and observables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:27.266446Z",
+     "iopub.status.busy": "2024-04-19T17:42:27.266196Z",
+     "iopub.status.idle": "2024-04-19T17:42:28.358961Z",
+     "shell.execute_reply": "2024-04-19T17:42:28.358112Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1300.22x494.978 with 1 Axes>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from qiskit.circuit.random import random_circuit\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "\n",
+    "circuit = random_circuit(7, 6, max_operands=2, seed=1242)\n",
+    "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])\n",
+    "\n",
+    "\n",
+    "circuit.draw(\"mpl\", scale=0.8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Find cut locations, given a maximum of 4 qubits per subcircuit. This circuit can be separated in two by making a single wire cut and cutting one `CRZGate`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:28.361713Z",
+     "iopub.status.busy": "2024-04-19T17:42:28.361425Z",
+     "iopub.status.idle": "2024-04-19T17:42:28.779585Z",
+     "shell.execute_reply": "2024-04-19T17:42:28.778903Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found solution using 2 cuts with a sampling overhead of 127.06026169907257.\n",
+      "Wire Cut at circuit instruction index 19\n",
+      "Gate Cut at circuit instruction index 28\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDAAAAGRCAYAAACT7EP6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACF00lEQVR4nOzdd1xV9R8G8OculiwB4SKg4MCJIubAPXKluWel+SstM2fTypGVpqlpappZaaU5Mk1zkIrbVFwgiII4AbmyZY87fn9Q6JW97jkXnvfr5Uvu96znXDjjfu453yPR6XQ6EBERERERERGJmFToAEREREREREREJWEBg4iIiIiIiIhEjwUMIiIiIiIiIhI9FjCIiIiIiIiISPRYwCAiIiIiIiIi0WMBg4iIiIiIiIhEjwUMIiIiIiIiIhI9FjCIiIiIiIiISPRYwCAiIiIiIiIi0WMBg4iIiIiIiIhEjwUMIiIiIiIiIhI9FjCIiIiIiIiISPRYwCAiIiIiIiIi0WMBg4iIiIiIiIhEjwUMIiIiIiIiIhI9FjCIiIiIiIiISPRYwCAiIiIiIiIi0WMBg4iIiIiIiIhEjwUMIiIiIiIiIhI9FjCIiIiIiIiISPRYwCAiIiIiIiIi0WMBg4iIiIiIiIhEjwUMIiIiIiIiIhI9FjCIiIiIiIiISPRYwCAiIiIiIiIi0WMBg4iIiIiIiIhEjwUMIiIiIiIiIhI9FjCIiIiIiIiISPRYwCAiIiIiIiIi0WMBg4iIiIiIiIhEjwUMIiIiIiIiIhI9FjCIiIiIiIiISPTkQgcgMhT/V5cg9Z5K6Biwclei989zKjQPsawLUDnrQ0REZAhiOX7y2EnGhNtN2YnlPQOM630rDRYwqMZIvadCcniU0DEqRXVaFyIiIkPh8ZOo7LjdlB3fs6rDW0iIiIiIiIiISPRYwCAiIiIiIiIi0WMBg4iIiIiIiIhEjwUMIiIiIiIiIhI9duJJ9Iwuq95GozE9AQBajQaZj5IRczYEVxZvRYYqUeB0REREVNV4LkBUdtxuyBB4BQZRIVTnQ7Gj1STseu4tnHp7FexbuqPH9+8KHYuIiIgMhOcCRGXH7YaqGgsYRIXQ5qiRGZeMDFUiHp2/gbAtR+HYrgkUluZCRyMiIiID4LkAUdlxu6GqxgIGUQnMnWrDfVBHaNUa6DRaoeMQERGRgfFcgKjsuN1QVWAfGESFUHZqgZcjfoVEKoXc3BQAELJ+H9SZ2QCAHhvfxcOTQQjfchQAYNfSA93WzcRffd6HJjtXsNxERERUOUo6F7BQ2uGF/Yuwv9+HyEpIgczcBEOOrsCx15ch+eYDIaMTCaak7abegPbwfmeU3jQ2nq4ImLcJYb8cNnheMj5GXcAICgrC/PnzceLECeh0OvTq1Qvr16+Hp6cnBg4ciO3btwsdkYxU3JVbODNzLWSmCrgP7oS6XVvh6tJt+cMD5m3CgL2f4/7BC8hOSoPvksm48PGPLF4QERFVEyWdC2SoEhG6YT/aLZyI09NWw/vd0bh/6AKLF1SjlbTdPDgUgAeHAvJf1+vfDj4fvYSI308IkJaMkdEWMPz9/TFo0CDUr18fc+fOhbm5OTZv3owBAwYgLS0N3t7eQkckI6bJykHqPRUAIHDZDli5K9Fh0ev4573vAOSdtFzfsB/PzRuP+KsReHwnBjFngoWMXGoyMxO0mjEcHkM6w8LZLm9d7z/C7V2ncOPHg0LHIxKltMg4hP16GHGXw6FVa2DdwBmer/RBHZ/GkEgkQscjoipQ0rkAANz48RAG+S1Fs0kvoP4LHbCv93tCxSUShdJsN/+xcLZDh8WTcPTlxdBk5hg6qmj1+e0TKCwtcGjoPOi0T269sfPywMD9i3Hq7dW4v/+cgAmFZZQFjLi4OIwZMwY+Pj44evQozM3zOoUZP348PDw8AIAFDKpUgct3YNipbxD26xEkBN0GANzc5IeBBxbDuXNL/DVgjsAJS893yWQoO7dEwLyfkHj9PhRW5rBv6YFaLg5CRyMSHZ1Oh6tLt+Pa6t2ATgdIAECC2ICbiNh+HC49vdF9wzswsbIQOioRVbHCzgV0Wi0uLtiM/rsX4thrX+VfJk9EeQrbbgAAEgm6rZ2J4LV/IunGfeECitCZWd9iyLEV8JoxDNdW/QEg7wvIbmtn4M7u0zW6eAEYaSeeS5cuRVJSEjZt2pRfvAAAGxsb+Pj4AGABgypX6l0VIo9cgs+ccU8adTqE/XIEUf5XkJ2QIly4MqrXvz1C1u3FA7+LSIuMRVLofUTsPIGglbuEjkYkOoHLd+LaN3/kFS8AQIcnPwOIPh6IYxOXQpurFiYgERlMoecCAFx6t0GGKhG1m9YTKBmReBW13bSeNQI5qRm4+dMhgZKJV2ZsMv557zu0nj0S9q0bAgDafvIKpCYKXJj7k8DphGeUBYzt27eja9eu8PT0LHS4k5MTlEolAECtVmPmzJmws7ODra0tXn/9dWRlZRkyLlUTIev2waWHN5S+LZ40arXQaXVFTyRCGbFJcOnZBia2lkJHIRK1jEdJecWLEqj+uY4HfgEljkdExu/ZcwHbpvVQr3977B8wB41f6g3Leo4CJyQSn2e3G8d2TdD4pd44O/tbgZOJ1wO/i4jYeQLd1s6AW9/n0GRCH5yethrqdH6ONbpbSFQqFaKjozFmzJgCw7RaLYKDg9GmTZv8tsWLF+P48eMIDg6GiYkJBg8ejA8++ACrV68u1fLUajVUKlWl5Sfh5JbyG9IzswrfmcZdCsNm55GVkiMqKqrC8yivf95dj27rZmFsyI9IDotC3JVwRPtfwQO/i+XOUtH1IRKjOz8eLvVj34I27IO8Db99JRK7yj4X8F36Bi4u2IwMVSKufrUdHRa9Dv/xX5YqB4+dZCwqc7sxsbZA1zUzcGbmWmQnpZU5h7FsNxU5V//Pxfmb8eKRZej50/u4tvIPxF0OL3cWsb5vSqUScnnZShJGV8BIT08HgEI7Tdu7dy9iY2P1bh/54Ycf8NVXX8HFxQUA8Omnn2LUqFFYuXIlZDJZictTqVRwc3OrnPAkqC/s+8BFYS10DISHh2N0Bf+mKrIusRfD8EfHt+HQpjEc23rCqWNz9Nj4HqKPXYX/q0vKPL/KWB8iMZph6wtvU+dSddIZc/EmjxVERqAyzwUav/w8suIfI8r/CgDg9u8n0XhcL9R7oQMeHLxQ7LQ8dpIxqcztpsmr/WDuaIv2CyfqtUf8fhKh3+8vdlpj2m4q4z1TZ2YjZP0++C6ZjKBV5b/VW8zvW2RkJFxdXcs0jdEVMNzc3CCTyXDy5Em99vv372P69OkAnvR/kZycjMjISL2Cho+PD1JTU3Hv3j00bNjQULGpmorYeQIRO08IHaPMdBot4i6FIe5SGK5v+AsNRnRFt7Uz4eTbHI/OhQodj0gUJCj900WkfBIJUY1za+tR3Np6VK/Nb/gCgdIQGYfgNXsQvGaP0DGMhu7fKzlKe0VoTWB0BQwTExNMmDABmzZtwpAhQzBw4EBERkZi48aNcHJyQnR0dH7BIjU1FQBga2ubP/1/P/83rCRKpRKRkZGVuQokkHOjlyD9rvC3A3l6eiJyZ8U64KnsdXl8KxoAYGZvU+ZpK2N9iMTo1pp9uP/LsZJHlAD2Tesj8hKPFURiV53OBYgMhdtN2YnlPQPE/b79129lWRhdAQMAVq9eDYVCgb179+LYsWPw9fXFnj178NlnnyEiIiK/c08rKysAwOPHj/PfnOTkZL1hJZHL5WW+rIXESaEQx5+7QlHxv6mKrEv/3Qtx98+ziA+6jayEx7B2d4bPRy8hOzkNqn9CypWF2whVR9ZThpWugKEDvCYN4nZAZASq07kAkaFwuyk7sbxngHG9b6Uhnne2DCwtLbFhwwZs2LBBrz0kJAReXl6QSvMermJraws3NzcEBgaiSZMmAICrV6/CysoK7u7uho5NJArRx66iwfCu8H5/DEwszZGZ8BiPzt/AmdnfIjuxdFcmEdUE1h7OaDyuF25tK76IYdPYBR7DuhgoFREREVHNZZQFjMIkJycjKioKAwcO1GufNGkSvvzyS3Tt2hUKhQKffvopJk6cWKoOPImqo+C1fyJ47Z9CxyAyCh2XTIY6Mxt3/zxb6HAbT1f0/W0uFBZmBk5GRERE1Z2x9rdXlapNASM4OBgA9DrsBICPP/4Y8fHxaNGiBbRaLUaOHImlS5cKkJCIiIyNzESBbutmocn4vghZvw9RRy8DABx8GqPZawPgPsgXMlOFwCmJiIiIaoZqX8CQy+VYvXo1Vq9eLUAqMhaNX+qNxmN7QafT4tyHG5F880H+MLe+z6HVjOHQ5KoR/usR3Nl9GgDQafkUWDesC01WDs6+ux4ZDxPQaHQPtH5nFNKj4wEAR15eBE1WjiDrRESVQyKRQNmpBazclfi97ZsAgJ4b30OtuvYCJyOiqmTpWgfd1s2CVq2GRCbD+TkbkXTjfv7wrmtnwKqeEyQyKW5u9sPt308WMzei6qukbUVmboIOn78Gy3pOkMqkOPrKYli61YHvsjeh0+qgU2tw9t31SHsQK+BakLGoNgWMqVOnYurUqULHICNkYmuJJq/2xYGBH8OqvhN8l0zG36MW5g2USND2k5exf8BH0GTnoP/uhYg8chnOnVtAk50Lv2HzYd+qAdp+8gpOv/0NACB8yxHeokFERGTk0mMScHDIXECng7JzS7SaMRwn31qZPzxwxU6k3lVBaiLHkGNf4+6fZ6H995GHRDVJSduK9zujcWfPGajOPuksPishBUdf+RK5qRlw6emN1rNH4uzsdULEJyNTbQoYROVVp00jqP65Dp1ag5TbD2FqZw1IJIBOBzM7K2TFp0CdkQUAeBzxEHV8GsO6QV0kBN0GACRcuwOnDk3z59doTE+49mmLB34XcX39PkHWiYiIiCpGp9Hm/2xiZY7E0Ht6w1P/fUSiNkcN6HTQ6XSGjEckGiVtK8rOLSAzlcP7nVF4ePoarq36A1kJKfnDtbkavXkQFUcqdAAioZnYWiLncXr+69y0TJhYWwDIqw6bOVjD3NEW8lpmcOrQDKa2lki6+QB1e3gDAFx6esPc3gYA8MAvAH92n42/Ry6E0rcFnLt4GXx9iIiIqHLYtXDHC38tQodFkxBzOrjQcVq+PRT3DpyHTq0xcDoi8ShuW7Fr7o7o44HwG/kp7L0aQOnbIn+YzMwE3u+PRugPBw0dmYwUCxhU4+U8ToeJda381wpLc+SkZOS/Pvfh9+j27Ux0Xz8byWGRyHiUiOhjV5Fy5yH6/7EQLr3aIPHf+/xyUjKg02qhzVXj/sELsPPyMPj6EBERUeVIvH4PB1/8BP4Tl6DD4tcLDPcY0hn2Xh64unS7AOmIxKO4bSUrMQXRJ4IAnQ4PTwahdvP6AACJTIpu62bi+vp9ev3PERWHBQyq8eKu3IJTx2aQyKSwclciOzEFeOoy0Efnb+DvUQtxcspKyC1MEXf5FgAgcPlO+I1YgMi/L0H1z3UAgMLKIn86pW9zpN6NMezKEBERUaWQmjy50zo3JQOaTP1Ouev2aI3G43rh9Iw1eucNRDVNSdvKo/M3YN+qAQDAvlUDpPx7ftx5xVt4eCIID/wuGi4sGT32gUE1Xk5yGm795o8Bez6HTqfF+Y9+gEtPb5jYWuLunjN4bsEE2Hs1gFatwZUvf4M2Vw1TOyv03PgetGoN0qPjceGTHwEALaa8CJce3tBptYgPvM0dMhERkZFybNcU3u+Nhk6jhUQiQcCnm/XOD7p+Mw0Zj5LQd9s8AMDJKSuRGZcsbGgiAZS0rVxevAWdl78FmZkJksMiEX3sKlx6esN9cCdYujnCY0hnJF6/i4D5m4VeFTICLGAQAQjfchThW47mv04KffLop0sLfykwfnZiKvxGLCjQHrhsBwKX7aiakERERGQwqrMh8HvqqQnP2tF6sgHTEIlXSdtKelQ8Do/9XK8t+nggtjR4uaqjUTXEW0iIiIiIiIiISPRYwCAiIiIiIiIi0WMBg4iIiIiIiIhEj31gUI1h5a6s0PRatQYpd/J6TbZu4AypXCZIjsqYR2WtS2VkISIiMhSxHD957CRjwu2m7MSUVUxZKoNEp+Nzn4hKI/1hAn5v+yYAYNTlDahV117gROVXndaFyFC43RAR9wNEZcfthioTbyEhIiIiIiIiItFjAYOIiIiIiIiIRI8FDCIiIiIiIiISPRYwiIiIiIiIiEj0WMAgIiIiIiIiItFjAYOIiIiIiIiIRI8FDCIiIiIiIiISPRYwiIiIiIiIiEj0WMAgIiIiIiIiItFjAYOIiIiIiIiIRI8FDCIiIiIiIiISPRYwiIiIiIiIiEj0WMAgIiIiIiIiItFjAYOIiIiIiIiIRI8FDCIiIiIiIiISPbnQAUi8/F9dgtR7KqFjAACs3JXo/fMcoWMQERGVmZiOp8aCx/2Sienvyph+X7MvANEZQqcAXCyAlR2ETkFkfFjAoCKl3lMhOTxK6BhERERGjcdTqgr8uyqf6AzgTqrQKYiovHgLCRERERERERGJHgsYRERERERERCR6vIVERB6n5iDs3mNkZquhkEvRwNUKSgcLoWMRERERERERCY4FDIGF3k7C+p038fc/Ubh1P6XA8LqOFujxnDPeHNkEXdsqIZFIBEhJREREREREJCwWMARy/2Eqpi76BwdPF9/50sPYDPx28DZ+O3gbrZvYYcO8zujQytFAKUuvy6q30WhMTwCAVqNB5qNkxJwNwZXFW5GhShQ4HREREVHNxfM0Iqou2AeGAH7eewsth+8psXjxrKCwRHSasB8ffXMRGo22itKVn+p8KHa0moRdz72FU2+vgn1Ld/T4/l2hYxERERHVeDxPI6LqgAUMA1u26RomzjuFtIzcck2v1eqw5MdrmPDJKdEVMbQ5amTGJSNDlYhH528gbMtROLZrAoWludDRiIiIiGo0nqcRUXXAAoYB/bLvFj5YebHYcWQyCVycLODiZAGZrOj+Ln47eBvvLLtQ2RErjblTbbgP6gitWgOdyAotRERERDUZz9OIyFixgGEgD2LSMO3LcyWOp3QwR9SRcYg6Mg5Kh+Ir4qt/C4X/+YeVFbHClJ1a4OWIX/HKna0YE7gRSt8WCN14AOrMbACAhdIOIy+th5m9NQBAZm6C4WfXwLZpPSFjExEREVV7JZ2n9dj4LjxfeT5/fLuWHhh6ahVkpgqhIhuV4MnuQkcgqhGMuoARFBSEIUOGwMbGBtbW1hg6dChiYmJgZWWFsWPHCh1Pz7TF55CaXr7bRooz6dPTyMnVVPp8yyPuyi3se/597B8wB4Ff/47Yi2G4unRb/vAMVSJCN+xHu4UTAQDe747G/UMXkHzzgUCJiYioKqm1QHAicCEOuPUY0OmETkRUc5V0nhYwbxO8pg+DqZ0VIJHAd8lkXPj4R2iyK//8lYiovIy2gOHv74+OHTsiLCwMc+fOxeLFixEVFYUBAwYgLS0N3t7eQkfMF3Y3GX+drJoP6fcepmH30XtVMu+y0mTlIPWeCslhkQhctgOpkbHosOh1vXFu/HgItp5uaDbpBdR/oQOCVvwuUFoiIqoq2Rrg+zBg4BHgf2eAt88B404CY08Afz1gIeNpTh2bodemDzHy4npMjNmFVrNGCB2JqqmSztMyVIm4vmE/nps3Hk3G98HjOzGIORMsYGLjEPnDbITO8kZu4kOEzvLGna/GCB2JqFozyseoxsXFYcyYMfDx8cHRo0dhbp53q8X48ePh4eEBAKIqYGzYdbNK579uxw2MHdCwSpdRHoHLd2DYqW8Q9usRJATdBgDotFpcXLAZ/XcvxLHXvsq/bJGIiKqHLDUw/TxwNRF4tienO6nAwkAgPAV4pwUgKbqrpxpDbmGG5FuRuLPnNNp/9j+h41ANUth52s1Nfhh4YDGcO7fEXwPmCJxQWNrsTMTsWoyk09uRkxAFqYk5TJUNYd9jPBxfnJE/ntuklQDybiFpvipQoLRENYdRXoGxdOlSJCUlYdOmTfnFCwCwsbGBj48PAHEVMP7+J7pK5382MBbp5XyqSVVKvatC5JFL8JkzTq/dpXcbZKgSUZt9XxARVTurQvOKFwDw7IUW/73edgc4VLYniVdb0ceu4sri33Bv3z/Q5ojvWE7VV6HnaTodwn45gij/K8hOSBEunAg8+O4tJB7/Ba4Tl6HF2lB4fnEcdV54G+r0ZKGjEdVoRlnA2L59O7p27QpPT89Chzs5OUGpVAIAdu7ciS5dusDS0hLu7u4GTJknLSMXN+8+rtJlaLU6BIYlVukyyitk3T649PCG0rcFAMC2aT3U698e+wfMQeOXesOynqPACYmIqLKk5AD7SnHHpATA1ju8lYRIaM+epwEAtFrotNw4ky/8Cadh78O241CYOnnAwqM1HHpPRN2x84WORlSjGd0tJCqVCtHR0RgzpuD9ZVqtFsHBwWjTpk1+W+3atTFt2jQ8evQIK1euLPPy1Go1VCpVufOG3E6B9pmDgEwmKfIJI85PtTsXMY4qPhMajf48/7l8F/XrVO43N7m56lKPe2bWt4W2x10Kw2bnkfmvfZe+gYsLNiNDlYirX21Hh0Wvw3/8l6XKEhUl7Nd1WY+S83+OiYmBmTZTuDAVVJ3WhchQuN2UzD/BAjlauxLH0wEIewwE3FbBxaz0xxpjVZbjKeURw3G/MGLaD1TFeVpFsojx91WY3FwnACU/WUVR2xkpV/xg1+0lyK1K3q+VPUcuoqIeVfp8xUhM2w2Ji1KphFxetpKE0RUw0tPTAQCSQm6c3bt3L2JjY/VuH+nTpw8A4M8//yzX8lQqFdzc3Mo1LQDAoiHQ8CO9pv8elVqSi9uGFtru2mcboh9l6LV9MOdjfDD5eLljFuYL+z5wUVhX2vwav/w8suIfI8r/CgDg9u8n0XhcL9R7oQMeHLxQ7LTh4eEYXZHfQyWoLTXH144vAADat2+PJCPe+VandSEyFG43JXMa+i5c/7e81OP3fnEE0m/+U4WJxKGyj6c1gRiO+4UR035ATH9XYv19Fab5mhCY12tR4nj1p/2AuyteQtCEOjB3a4FaTTrCpu0LsOkwpNDPIWUVHh4Ot34tKzwfYyCm7YbEJTIyEq6urmWaxugKGG5ubpDJZDh58qRe+/379zF9+nQA4ur/AloDfeuiFcejVItza+tR3Np6VK/Nb/gCgdIQEVFl02SU7Z55TWbNvseeSIwidp5AxM4TQscQnGWzzmi54TbSwwOQHnYOqddP4fbSkbBpOwANP9lXoIhh5tZcoKRENYvRFTBMTEwwYcIEbNq0CUOGDMHAgQMRGRmJjRs3wsnJCdHR0ZVawFAqlYiMjCz39MmpufAac0yvTRWfCdc+2wod39nBPP/Ki3bj/kRMfMEKpaqQtu2/rEZnb/ty5yzMudFLkH63/LfPVCZPT09E7vxJ0AxZj5JxZtCnAICAgACYOdkKmqciqtO6EBkKt5uSJeRIMTlEB22B54/ok0AHJxM1dp8+BGkNeBKJmI6nxkIMx/3CiGk/IKa/K7H+vgozPdQJkVmlG1cik8OyWSdYNusEp6HvIuHEFtxbOR5p10/BqmV3vXEbzz9Yphyenp74uwKfMYyJmLYbEpf/+q0sC6MrYADA6tWroVAosHfvXhw7dgy+vr7Ys2cPPvvsM0RERBTZuWd5yOXyMl/W8jRXAA1crXAnKjW/TaPRFbgFpDAx8ZmlGg8A+nVrCltr0/LGLJRCIZ4/D4WiYr+HypAufap/Emdn1KpbuQUjQ6pO60JkKNxuSuYKoFcicPRh8ePpIMG4xgrUcxN2v24oxR1P5RZmsPbIO4GTKuQwr2MLuxbuyE3PQuo9cXw4FYIYjvuFEdN+gOdp5aO4BaCUBYxnmbk2AwCoH8dWPIdCYTTvWUWJabsh4yeePV8ZWFpaYsOGDdiwYYNee0hICLy8vCCViuvhKj3bOesVMCqbd1O7Si9eEBERlccHXsDNx0BUetHjdHUCxngYLpOYObRuiP67F+a/bvbaADR7bQBU/1yH3wjeZkkklLCPu8Ou6zhYNHoOcps6yI6JQPSvH0NWyxZWXj2FjkdUYxllAaMwycnJiIqKwsCBA/XaNRoNcnNzkZubC51Oh6ysLEgkEpiaGu4D/5ujmuLHPeFVNv8po5pV2byJiIjKws4U2NQFWBECHHkIPP3QLEs5MNIdmNIUkIvruwbBqM5dr5SnQBBR5bLxGYDEU1vxcNt8aDJSILdxhFWLbnCfsQlyaweh4xHVWNXm9CE4OBhAwQ48f/31V5ibm2P06NF48OABzM3N0aRJE4Nma9eyDnxbO1bJvO1sTPHywIZVMu+ysG7gjAkPtqOOT2O9du93R2PkxfXo89sn+W0ycxO88NcivHTzZ3gM6WzoqEREVMVqmwJftAV+7vqk7aNWgF9fYFpzFi+IDK2o87T/9P9jIXyXvlGmaao75cg5aPLlabT+JRY+u7LQ6scH8HhnC8zrsbNOIiFVm1OIogoYEydOhE6n0/t37949g+f7bl5nyOWV31PZ6jkdYWlR8rOsq1rr2SOhOhdaoD3s18MFLoHVZqtx/LVlCN14wFDxiIhIALWfutixixNgVm2u+yQyLkWdpwGA6/NtkZtWsIP44qYhIhJKtSlgTJ06FTqdDh07dhQ6SqFaedrh0yk+JY733xNKXPtsK/RpI08b1rs+XnpB+KsvHNo0RmZsMjJiEgoMy4xNBrQ6vTadVovMuGTDhCMiIiKqwYo7T4NEgqb/64+bm/1KPw0RkYCqTQHDGHw8uTUmjyj+9pX/nlAS/SgDGo2uyPG6tHHCr4u6F3gGtRBazRyO4LV7hI5BRERERM8o7jyt0egeuH/wAjRZuaWehohISCxgGJBEIsF38zrj40mtUZG6w4jn3eG3vh9qieDWEdfePkgIuo3spDShoxARERHRU4o7T5OZKtBgeFdEbD9W6mmIiITGu1ENTCqVYNGM5zCwmxtem38aYfcel3pae1tTrJnji7EDGojiygsAsGvpDmWnFnBs1wS2TevBumFdHH99Wd6tI0REREQkmOLO0yzrOcLEphae//UjmNhawtzRFg1HdUetuvY8tyMi0WIBQyCdvJ0Qsns4DpyOxPodN3AsIAa5am2B8SQSoG1zB0wZ1RRj+zcQxVUXT7v2zW5c+2Y3AKDLqrcR9sth2LVwh0lnS9zdcwaerzyPhqO6w6aRC/rumI/TM9Yg81ESevzwHuxbekCdkQUHn8a4uGCzsCtCREREVM2UdJ62v/+HAAClbwt4DO2M27+fzJ/u6WlYvCAisWABQ0ByuRRDetbHkJ71kZ2jQUhEEs5eeYSZX50HAOxa0Qt9O7nAqpaJwElL58ysbwu0hW85ivAtRwu0n5i03BCRiIiIiAiFn6f9R3XuOlTnrpdpGiIiIbAPDJEwNZGhbXMHDH/ePb+tg5ej0RQviIiIiIiIiKoSCxhEREREREREJHosYBARERERERGR6LEPDCqSlbtS6Aj5xJSFiIiISGhiOjcSU5aSuFgInSCPWHIQGRsWMKhIvX+eI3QEIiIiIioEz9PKZ2UHoRMQUUXwFhIiIiIiIiIiEj0WMIiIiIiIiIhI9FjAICIiIiIiIiLRYwGDiIiIiIiIiESPBQwiIiIiIiIiEj0WMIiIiIiIiIhI9FjAICIiIiIiIiLRYwGDiIiIiIiIiESPBQwiIiIiIiIiEj0WMIiIiIiIiIhI9FjAICIiIiIiIiLRYwGDiIiIiIiIiESPBQwiIiIiIiIiEj0WMIiIiIiIiIhI9FjAICIiIiIiIiLRkwsdgMRr9gUgOkPoFHlcLICVHYROQVS9+b+6BKn3VELHKDUrdyV6/zxH6BhERIIQ0z67KvbHYlo/Y8Hjonjwc1TVYQGDihSdAdxJFToFERlK6j0VksOjhI5BRESlUN332dV9/ah64+eoqsNbSIiIiIiIiIhI9FjAICIiIiIiIiLRYwGDiIiIiIiIiESPBQwiIiIiIiIiEj0WMIiIiIiIqrEuq97GxJhdmBizCxOidmDU5Q3osno6LJR2QkcTrf5/LESn5VMKtFu61sHEmF1wbN9UgFRUHYV90gP31kwq0J796B4uD5EgLfSMAKnEiwUMIiIiIqJqTnU+FDtaTcKu597CqbdXwb6lO3p8/67QsYiIyoQFDCIiIiKiak6bo0ZmXDIyVIl4dP4GwrYchWO7JlBYmgsdjYio1FjAICIiIiKqQcydasN9UEdo1RroNFqh4xARlZpc6ABERERERFS1lJ1a4OWIXyGRSiE3NwUAhKzfB3VmNgCgx8Z38fBkEMK3HAUA2LX0QLd1M/FXn/ehyc4VLLfY9d/zGUwszSFRyBF74QbOf/QDdFoWhajy3V31KlKuHILcxhEt1oQIHUcwRn0FRlBQEIYMGQIbGxtYW1tj6NChiImJgZWVFcaOHSt0PCIiIiIiUYi7cgv7nn8f+wfMQeDXvyP2YhiuLt2WPzxg3iZ4TR8GUzsrQCKB75LJuPDxjyxelMB//JfY1+d97O0xG6b21nB/0VfoSFRNOTz/Ghov8BM6huCM9goMf39/DBo0CPXr18fcuXNhbm6OzZs3Y8CAAUhLS4O3t7fQEWs0bXYmYnYtRtLp7chJiILUxBymyoaw7zEeji/OEDoeUZnkpGbg9q6TiDpyGbnpWTCvY4uGI7vB9fm2kMplQserck4dm6HFm4Nh19Idlq51cGXpNlxb9YfQsYiIqAw0WTlIvacCAAQu2wErdyU6LHod/7z3HQAgQ5WI6xv247l54xF/NQKP78Qg5kywkJEFlZOSARPrWgXaTWzy2v4r7OSmZQIAJHIZZAo5dDqd4UJStSCzsIEm43GBdk16MgBAojADAFi17I7sR/cMmEycjLKAERcXhzFjxsDHxwdHjx6FuXle50Pjx4+Hh4cHALCAIbAH372F1ODjcJv0Dcw9WkOTkYKMO1eRE/dA6GhEZXL/UABOT/sG6oxsQAIAEkCnw/0D52Hl4Yznf5kDm0YuQsesUnILMyTfisSdPafR/rP/CR2HiIgqQeDyHRh26huE/XoECUG3AQA3N/lh4IHFcO7cEn8NmCNwQmE9joiG+4u+kEilereEOLRpBK1ag9S7Mflt/XZ9CvuWHojyv4L7+88LEZeMmJlrUySd/R06jQYS2ZMvxtJvBQBSGUydGwmYTnyM8haSpUuXIikpCZs2bcovXgCAjY0NfHx8ALCAIbTkC3/Cadj7sO04FKZOHrDwaA2H3hNRd+x8oaMRlVr0iUCcmLQc6sycvAYdgKe+WUm9GwO/EQuQHpMgTEADiT52FVcW/4Z7+/6BNoeXEhMRVQepd1WIPHIJPnPGPWnU6RD2yxFE+V9BdkKKcOFE4ObPfjCrY4POq96GfasGsKrvBI+hndHmg7GI2HEcOSkZ+eP+PfJT7PCeDJm5CZRdWgqYmoxRnQFToU5+hHur/4f0iMvIjrmNxFPb8HDrPDj0/h/klrZCRxQVoyxgbN++HV27doWnp2ehw52cnKBUKpGdnY3JkyejQYMGsLKygqenJ9asWWPgtDWTorYzUq74QZ2aKHQUonLR6XQImL8571LQYi4HzYxNRsi3fxouGBERUSUJWbcPLj28ofRt8aRRq4VOy9sg0qPicfDFT2BqUwu9f56DwcdWoNWM4QhZtw/n5mwsML4mKwcPDgWgXr92AqQlY2bqWB9Nlv4DTXoSbn/xIkJntkLMrsVwGvY+6k1ZJ3Q80TG6W0hUKhWio6MxZsyYAsO0Wi2Cg4PRpk0bAIBarYZSqcThw4fRoEEDXLt2Df369YOTkxNGjx5dquWp1WqoVKpKXYfixMRnPflZFQOozQy27Gfl5joBUJRr2vrTfsDdFS8haEIdmLu1QK0mHWHT9gXYdBgCiURSjiy5iIp6VK4slSXrUXL+zzExMTDTZgoXpoKq07pUlaTLEXh8K6pU44ZvOwblqz0g+7dXd2OVm6sWOkKZ5OaqERVVut9RZeB2U3bxOTIAzgDy3rNcE42wgQRibNuWGBh6+y4tMe0HyvJ3dWbWt4W2x10Kw2bnkZWSpbJ/X2LYbpJC78P/1SVFDldYWUBqIkd2QgokMinc+jwH1T/XDZhQH7cb8Sjr5ygLj9ZoNPevKsoi/OeooiiVSsjlZStJGF0BIz09HQAK/RC8d+9exMbG5t8+UqtWLXz++ef5w729vTF48GCcOXOm1AUMlUoFNze3igcvLXltoNkyAED7du0BdZLhlv2M5mtCYF6vRckjFsKyWWe03HAb6eEBSA87h9Trp3B76UjYtB2Ahp/sK3MRIzw8HG79hL0kr7bUHF87vgAAaN++PZKMeOdbndalqvSzaIyx1q1KNa4mIxtdm7dFpLpgB0zG5Av7PnBRWAsdo9TCw8Mx2oD7Z243Zaewd0Grn/JOptu3b4fchGiBEwnD2LYtMTD09l1aYtoPiOnvqip+X2Jav6KY2Fig5w/vQ6qQQyKTIuZUEMJ+PSxYHm434lGRz1FFub10FNJunIE6JR7XXnOFcuTHcHxhaonTieFzVFEiIyPh6upapmmMroDh5uYGmUyGkydP6rXfv38f06dPB1B0/xe5ubk4ffo03nvvvaqOSQAkMjksm3WCZbNOcBr6LhJObMG9leORdv0UrFp2FzoeUbGkZSyySVH2K4uIiIjEJmLnCUTsPCF0DKOQHhWP/f0/FDoG1RANP/xd6AiiYHQFDBMTE0yYMAGbNm3CkCFDMHDgQERGRmLjxo1wcnJCdHR0kQWMadOmwcrKChMmTCj18pRKJSIjIyspfcli4rPQfkJecSbgYgCcHYS7hWR6qBMis0oer7TMXJsBANSPY8s8raenJ/424O+hMFmPknFm0KcAgICAAJg52QqapyKq07pUlbhTIQh694dSjSuRy3A88DwUNgUft2ZMzo1egvS7hrtlrqI8PT0RufMngy2P203ZxefIMCkk7+eAgItwqKG3kBjbtiUGht6+S0tM+wEx/V1Vxe9LTOtnLLjdiEdlf46qCDF8jiqKUqks8zRGV8AAgNWrV0OhUGDv3r04duwYfH19sWfPHnz22WeIiIgotHPPd955B+fOncOxY8dgYmJS6mXJ5fIyX9ZSIfL0/B+dlc5wVQr3gUhxC0A5N7ywj7vDrus4WDR6DnKbOsiOiUD0rx9DVssWVl49y55FoTDs76EQ6dInT7xxdnZGrbr2AqapmOq0LlWl7mhn3Fq+BxmqhLynjxTDY0hneLRoYphgVUihKPyQILcwg7VH3gFGqpDDvI4t7Fq4Izc9C6n3hDu5VCgMu3/mdlN2ikwA/xYwnJ2d4WRe7OjVVlHbFhXN0Nt3aYlpPyCmv6uq+H2Jaf2MBbcb8ajI56jKJobPUZXJKPcMlpaW2LBhAzZs2KDXHhISAi8vL0il+g9XmTVrFvz9/XHs2DE4ODgYMmqNZeMzAImntuLhtvnQZKRAbuMIqxbd4D5jE+TW/B2Q+EnlMrT5YAzOzl4HSFB4EUMigczMBF7Thxk6nkE5tG6I/rsX5r9u9toANHttAFT/XIffiAUCJiMiIiKimsQoCxiFSU5ORlRUFAYOHKjXPmPGDBw7dgzHjx9HnTp1BEpX8yhHzoFy5ByhYxBVSOOxvZCdlIpLn/2qP+Dfgoailhl6/fwhajcRX4dZlUl17nql9FJPRERERFQR1aaAERwcDEC/A8/79+9jzZo1MDU1hYeHR357165dcejQIUNHJCIj1PKtIXDt5YOQ9XsRseMEAMDawxmNx/VC47G9YOZgI2xAIiIiIqIaoloXMOrXrw+droSb14mISmDbxA1tPhiXX8Do9/unNeL+TSIiqj4sXeug27pZ0KrVkMhkOD9nI5Ju3M8f3nXtDFjVc4JEJsXNzX64/fvJYuYmHOsGzhh6YiUODZ2HuCu39IZZ1nNE56+nQqqQ48GhAFz/bh9k5ibot3MBbBu74tyH3+Pu3rPFzt/U3hodF70OM3trqDNz4D/hS73hzScPhMewLtDmapAYfAcX5hbfaWbr2SNRt0draLJycWbWWmTEJJa4PKlCjm7fzoS5oy0kMikufPIjEq7dQevZI+HcxQsAYOWhRMi3e3Hjx4OlfetIhK6MskAtz/YAAMdBM1Hbt+BtyWGf9ICZS1PUn/pdfltWdDiuT2+BJl+ehmWTjgbLKwbVpoAxdepUTJ1a8nNwiYiIiIhqmvSYBBwcMhfQ6aDs3BKtZgzHybdW5g8PXLETqXdVkJrIMeTY17j751loc9UCJi5c69kjoToXWuiw5+aOx5Uvf0Pc5XD0370Q9w+cR3p0PI6/tgxNJvQt1fzbLXgVgct34HHEw0KHRx65jNCNBwAA3dfPhpNvczwqIo+tpysc2zfFoSHz4NytFXw+HIczs74tcXnOXb2Qk5qBE2+sgEObxmg1cwSOv74MQSt3IWjlLgDAi4e/wv0D50u1TiReJnXqocmiE0UOT764HzJzqwLtMTs/h1WL7lWYTLykJY9CRERERETGTKfRAv9emWxiZY7E0Ht6w1P/fWSpNkcN6HSivIrZoU1jZMYmIyMmodDhNo1dEHc5HAAQdfQKnDo2g06rRWZccqnmL5FKYdvEFV7ThqH/7oVo/FLvAuM8/fQtrVqd974Wwaljc0QeuQwAiDl1DfatGpRqean3VJCZKgAAJjYWyEp4rDedracrch6nI0OlfzUHGZ/cxIcI+7g77iwbi9zkWL1hOq0WcQe/RZ0X3tZrTw+7AIWtEiYO1efJImXBAgYRERERUQ1g18IdL/y1CB0WTULM6eBCx2n59lDcO3AeOrXGwOlK1mrmcASv3VPkcIlUkv9z9uN0mNYu+M11ccwcrGHX3B0h6/fh8NjP0XhsL1jVdyp0XMf2TWGhtENswM0i52dia4mcx2lP8sn0P3oVtby0qDjIzU0x7PQ36Pz1VNz4Qf82kQYjuuHOnjNlWjcSJ6/v76DJ4pOwbT8YUZve1RuWcOxn2PoOh1Rhptce8/siKEfU3IclsIBBRERERFQDJF6/h4MvfgL/iUvQYfHrBYZ7DOkMey8PXF26XYB0xXPt7YOEoNvITkorcpynLxoxsbZAdlJqmZaR8zgd6Q/jkRwWCW2OGo/Oh8K2kCeN2TR2wXNzx+PEm18XP7/kNJhY13qS75mrNYpaXqPRPZAWGYs9XWfi0OC56Py1/m3y9V/ogPv7z5Vp3Uic5NYOAIDaXUYj487V/HZtThYST26FQ+//6Y3/+NIBWDR6DnLrmtsXGwsYRERUabqsehsTY3ZhYswuTIjagVGXN6DL6umwUNoJHY2IqEaTmjzp+i43JQOazBy94XV7tEbjcb1wesYa/UqASNi1dIeyUwv0+e0TOHdrhXYLJ8Lc0VZvnMfhUXDwbgQgr+Dx6MKNIucnr2UGE2sLvTZNdi7So+Lzj1l2rRog5albRgCglosDunwzDafe/gbZiU8KJBZKO0ik+h+tHp0PhUuvNgAAZeeWSLh2p3TLk0iQ9e+8sx+nQ/FUTsf2TZF8Kwo5KRlFrhsZB01WOnSavCudUq+fgqlzo/xh2Y/uQpOejIjPByHq5w/w+PJBJBz7BRl3ApEWcgK3Pu2PlMAjiPpxNnITY4RaBUFUm048iYhIHFTnQ3Hyja8hkUlh5e6Ejosnocf37+Lg4E+EjkZEVGM5tmsK7/dGQ6fRQiKRIODTzXDp6Q0TW0vc3XMGXb+ZhoxHSei7bR4A4OSUlaXuO8IQrn2zG9e+2Q0gr1ge9sthZMYm663D5cVb0XnFW5DIZYj8+yLSHuT1KdDjh/dg39ID6owsOPg0xsUFm+ExtAvkZiYFnuIRsGAzuq2bCalcjqjjV/E4PArmdWzR/M1BuPzFFjw3dzzM7KzRZVVevwTBa/cg+ngguq2fhWOvLtErLCSHRyEh8DYG7P0cmmw1zs7O68Cz0egeSIuOh+psSKHLS4+MQ7d1s9B/90LIzU1xdem2/Hk2GN4Vd3bz9pHqICvqJu5/OxkyM0tI5ArUm7oBj6/4QZOaCLvuL6HZ15cAAKnBJ5B4ejvse00AADiPzjufuvfNRDj0nwKFnbNQqyAIFjCIiKhSaXPU+Se9GapEhG05io6LXofC0hy5aZnChiMiqqFUZ0PgdzakyOE7Wk82YJqKefpJHtHHA/N/Tr2ngt+IBQXGPzFpeYG22k3dELTqjwLtiSF34Tdcfx6Zccm4/MUWANB7cst/JHIZ0h7EFnpVROCKnQhcsVOvLWLniWKXp87MxrH/LS0wLwA4P2djoe1kfGo1aovmK6/otZk9dRXGf6y8esDKq0eBdveZm6sombixgEFERFXG3Kk23Ad1hFatKbandiIiIkMKmLep0ualU2twZubaSpsfERWNfWAQEVGlUnZqgZcjfsUrd7ZiTOBGKH1bIHTjAagzswHk3Sc88tJ6mNlbAwBk5iYYfnYNbJvWK3YYEREREdVsLGAQEVGlirtyC/uefx/7B8xB4Ne/I/ZimN79uxmqRIRu2I92CycCALzfHY37hy4g+eaDYocRERERUc3GW0ioSC4WJY9jKGLKQkTF02TlIPXfXtsDl+2AlbsSHRa9jn/e+y5/nBs/HsIgv6VoNukF1H+hA/b1fq9Uw4iIKI+Vu7JC02vVGqTcyXt6gXUDZ0jlMsGyGGqe1R3fM/EQ02cXMWWpDCxgUJFWdhA6ARFVB4HLd2DYqW8Q9usRJATdBgDotFpcXLAZ/XcvxLHXvsq/vaSkYURElKf3z3MqNH36wwT83vZNAEC/3z9Frbr2lRGr0lR0/YiExM9RVYe3kBARUZVKvatC5JFL8JkzTq/dpXcbZKgSUbuQ/i2KG0ZERERENRMLGEREVOVC1u2DSw9vKH1bAABsm9ZDvf7tsX/AHDR+qTcs6znmj1vcMCIiIiKquVjAICKiSnNm1rc4POazAu1xl8Kw2XkkVOeuAwB8l76Biws2I0OViKtfbUeHRa/nj1vcMCIiIiKquVjAICIig2r88vPIin+MKP8rAIDbv5+EopYZ6r3QodhhRERERFSzsRNPIiIyqFtbj+LW1qN6bX7DF+gNL2oYEREREdVcvAKDiIiIiIiIiESPBQwiIiIiIiIiEj0WMIiIiIiIiIhI9FjAICIiIiIiIiLRYwGDiIiIiIiIiESPBQwiIiIiIiIiEj0+RpWIiCqdracrfJe9CZ1WB51ag7Pvrkfag9j84d7vjkajsT3x+FYUjry0qFTTEBEREVHNxiswiIio0mUlpODoK1/Cb9h8hKzbi9azR+oND/v1MPxGLCjTNERERERUs7GAQURElS4rIQW5qRkAAG2uBjqNVm94ZmwyoNWVaRoiIiIiqtlYwCAioiojMzOB9/ujEfrDwSqdhoiIiIiqP/aBQUQ1hv+rS5B6T1WuabVqTf7Pf4/6FFK5rNw5rNyV6P3znHJPbywkMim6rZuJ6+v3IfnmgyqbhoiIiKgyVeScsboR23krCxhEVGOk3lMhOTyqwvNJuRNTCWmqv84r3sLDE0F44HexSqchIiIiqkyVdc5IlY8FDCIiqnQuPb3hPrgTLN0c4TGkMxKv30X08UCY2Fri7p4z8HzleTQc1R02jVzQd8d8nJ6xBnbN6xeYJmD+ZqFXhYiIiIhEggUMIiKqdNHHA7GlwctFDg/fchThW47qT/MoqdhpiIiIiKhmYyeeRERERERERCR6LGAQERERERERkejxFhIiomd0WfU2Go3pCQDQajTIfJSMmLMhuLJ4KzJUiQKnIyIiIiKqmXgFBhFRIVTnQ7Gj1STseu4tnHp7FexbuqPH9+8KHYuIiIiIqMZiAYOIqBDaHDUy45KRoUrEo/M3ELblKBzbNYHC0lzoaERERERENRILGEREJTB3qg33QR2hVWug02iFjkNEREREVCOxDwwiokIoO7XAyxG/QiKVQm5uCgAIWb8P6sxsAEC9Ae3h/c4ovWlsPF0RMG8Twn45bPC8RERERETVnVEXMIKCgjB//nycOHECOp0OvXr1wvr16+Hp6YmBAwdi+/btQkekakKn0+FxRHT+a61aI2AaMoS4K7dwZuZayEwVcB/cCXW7tsLVpdvyhz84FIAHhwLyX9fr3w4+H72EiN9PCJCWiIiqmk6nQ8rdmPzX2ly1gGmIjEfKXVX+z9xuqKKMtoDh7++PQYMGoX79+pg7dy7Mzc2xefNmDBgwAGlpafD29hY6IlUDOp0Ot38/iesb/kJS6P389gODPkKz/w1Ay6lDIDNVCJiQqoomKwep9/IOuIHLdsDKXYkOi17HP+99V2BcC2c7dFg8CUdfXgxNZo6hoxqES682aPvRS7Bp7IrM2CSE/ngQoRv2Cx2LiMgg7vx5BtfX70PCtTv5bftf+AhNX+0Lr2nDILcwFTAdkTjd3fcPQtbvRULg7fy2/QM+RJMJ/eA1YxgUFmYCpisfp47N0OLNwbBr6Q5L1zq4snQbrq36Q+hYNYpRFjDi4uIwZswY+Pj44OjRozA3z+tUb/z48fDw8AAAFjCownQ6HS4u2IzQjQcAif6wrPjHuPrVdsScCcbzWz7Ov8WAqq/A5Tsw7NQ3CPv1CBKCnhyIIZGg29qZCF77J5Ju3C96BkbMvnVD9N78IUK+24eTU1ehTpvG8F36BjSZObxdhoiqvStLtuHaN38UOBfITkpB0MpdiD4ZhH4750NRi508E/0ncMVOBC7fWXC7SU7DtW/+wMOTQei7cz5MrCyECVhOcgszJN+KxJ09p9H+s/8JHadGMspOPJcuXYqkpCRs2rQpv3gBADY2NvDx8QHAAgZV3J3dp/OKFwCge2bgv69V/1zHpS+2GDQXCSP1rgqRRy7BZ844vfbWs0YgJzUDN386JFCyqtfijUGID7yNK4t/w+Nb0YjYeQI3fjoEr2lDhY5GRFSl7h+8kFe8AIo8F4i/cgsXPvnJoLmIxCzyyKW84gVQ9HYTGIHzH/1g0FyVIfrYVVxZ/Bvu7fsH2pxcoePUSEZZwNi+fTu6du0KT0/PQoc7OTlBqVQCAKZOnQo3NzdYW1vDxcUFs2bNQk5O9bzEmyqPTqfD9Q1/FagaF+bWb/7ISUmv+lAkuJB1++DSwxtK3xYAAMd2TdD4pd44O/tbgZNVLcf2TRF9/KpeW/TxQFi6OcLC2U6gVEREVS90Y+lulbv9xylkxj+u4jRExiH/C8AS3P3zDDIeJVVxGqpujO4WEpVKhejoaIwZM6bAMK1Wi+DgYLRp0ya/bdq0aVi2bBlq1aqF+Ph4jBo1CosXL8ann35aquWp1WqoVKqSR6wkMfFZT35WxQBq47s3rDpIvx+LxOC7pRpXk5WDwG1/o+7A9lWcqvJkPUrO/zkmJgZm2kzhwhhQbik7jjozq/CCRNylMGx2HgkAMLG2QNc1M3Bm5lpkJ6WVOUdUVFSZpjGEot4fc0dbZMYl67Vlxib9O6w2MmISqzpaoQz9PtbU7aYi4nNkAJwB5L1nuSY1swPk0u576Akx7CezHiXj0fkbpRpXp9YgaMshuI7sUsWpKg/3aVQVsuNTEHM6uFTj6jRaBP56EPXGdq/iVGXH/fYTVbk/ViqVkMvLVpIwugJGenreN90SScGvxvfu3YvY2Fi920eaN2+e/7NOp4NUKsWtW7dKvTyVSgU3N7fyBy4reW2g2TIAQPt27QE1q5JCaKywx8f2PUo9/vx35uDvKaX/uxJabak5vnZ8AQDQvn17JNWQk5Yv7PvARWFdKfNq8mo/mDvaov3CiXrtEb+fROj3xX9jFx4ejtGG3K+UUmW+P4Zg6Pexpm43FaGwd0Grn/JOetq3b4fchOgSpqiejG3bEgMx7CfryW2w0OH5Uo+/ZO5n2Du7dAUPMeA+jaqCi9waXzj0KfX4yxcuxp73X6nCROXD/fYTVbk/joyMhKura5mmMboChpubG2QyGU6ePKnXfv/+fUyfPh1Awf4vlixZgi+++ALp6emwt7fHkiVLDBWXjFSmrmz3tGXpWKWtaYLX7EHwmj1CxzCIzNhkmNex1Wsz+/f1f1diEFHVkluYYdiZb3Dsf1/pdyRspOS1zDDinzU4PO4Lvad8iUlmGY/tZT13IKqOMrU8h6aqZXQFDBMTE0yYMAGbNm3CkCFDMHDgQERGRmLjxo1wcnJCdHR0gQLGnDlzMGfOHNy4cQNbt26Fs7NzqZenVCoRGRlZyWtRtJj4LLSfkFecCbgYAGcH3kIiBJ1Wi7PDvkBWTGLBzoeeJZXgx7N/wbSOjUGyVYasR8k4M+hTAEBAQADMnGwFzWMo50YvQfpdw90SVhRPT09E7hRfh29FvT+xATdRt4c3glbuym9z6emNtMhYwW4fAQz/PtbU7aYi4nNkmBSS93NAwEU41NBbSCpj3+M1bSgSgu4gIeg2bBrVxYuHl+HCvE24tfVo/jiWrnUw2H85Ar/+HaEb9kPp2wJ9d87H0VcW4+HJoPzxHLwb4YV9X+DElJV4cPBCmXJ4DO2MLqumYf8Lc/QKDxKZFC/sW4SsxBRIZVIoLC1waOg86LTa/HHsvDwwcP9inHp7Ne7vP4frG/aj3YJXcXjMZwWWI4b9pE6nw/mxS/N+dyWdC0iANf6/w8LVwSDZKgP3aVQVdDodAl5ZjtRb0SVvNwBW+v2G792dqj5YGYnlnFEMqnJ//F+/lWVhdAUMAFi9ejUUCgX27t2LY8eOwdfXF3v27MFnn32GiIiIIjv3bNasGVq3bo3x48fj+PHjpVqWXC4v82UtFSJ/0hmks9IZrspahls26fGa/CIuLvy5xPHcB3ZEwzYtDJCo8qRLnzy9x9nZGbXq2guYxnAUCnHs8hQKA+9XSqmo9+f69/sx8K9FaDNnHO7sOgmHNo3R7LUBuPhpydtHVTL0+1hTt5uKUGQC+LeA4ezsDKca+pTJiu57ZKYKNHm1L05PXwMAeBzxEJc++xXtF74K1dkQpN5TQSKVouu3MxAfdAehG/JuY1Odu47Q7/ej88qp2Nf7XWQnpUFubopu387E7V2niixeKH1boMs3b2NX+6kFht398yxcn2+Lbt/OxP7+H0KTnfdta+tZI2HpVgf+E76ERCbFkGMr4DVjGK6tynuCh8zMBN3WzsCd3adxf/85AEDEjuPwmTMOtk3ckBym/2WRWPaTGW8OxrkPvy9xPJeebeDZ0bvqA1Ui7tOoqmRNGYyz76wvcby63VqhSZe2BkhUdkXtt+UWZrD2yPvQLVXIYV7HFnYt3JGbnoXUe9Wz4CGW/fF/jPIpJJaWltiwYQNUKhVSU1Nx+PBh+Pr6IiQkBF5eXpBKi16t3NxchIeHGzAtGatmrw+Aa2+fYsexqu+EDosnGSgRkTASgm7j2P++gtvzbTH46Aq0+WAsrizdhrBfDgsdjahGcOnpDZmZid5VFDc3++HR+RvotnYGJDIpvGYMg62nG87MXKM37ZUl25CdmArfr94EALT//H+QyKS4MK/836ad/+gHKGqZwefjlwHkXdHhNWMYzs5eh6yEFGTGJuOf975D69kjYd+6IQCg7SevQGqiwIW5T5ablZCC2EthaDiiW7mzVLXGL/dG/UEd814U8WQyi7r26LRsiuFCEYlcozE94TG0c96LorYbpR06f12wSCp2Dq0bYvDR5Rh8dDkslHZo9toADD66HJ1XvCV0tBpDHF9HVoLk5GRERUVh4MCB+W2PHz/Gnj17MHToUNjY2CA4OBhffPEF+vXrJ2BSMhZShRw9f3ofV7/ajrBfDiM39UnnVhKZFO4v+qL956/B3MF4bh0hKq8o/yuI8r8idAyiGsnJtwUSQ+5Cp9HqtZ+dvQ5Djq9A1zUz4D6oI05PX1Pgti5trhqn3v4Ggw4tQdc10+ExtAv8hs+HOj0L5ZWbmoFT09eg/+8LoDobgufmjUf4Vn+9fcQDv4uI2HkC3dbOwKXPf0WTCX3gN3xBgeXGXbkFZeeW5c5S1aQyGbqvn43ARjtx86dDyEnJyB8mkUpRb0B7dPjiNVgo+Uhpov9IpFJ0XTsD1g3r4sYPB5Hz+MkV5pBKUK9fO3T44nWjvOpHde56/hPpSBjVpoARHJz3uJ6n+7+QSCTYsmUL3nnnHeTk5MDR0RHDhw/HwoULBUpJxkZmosBzc8ej9TujEHX0CrLikiG3NIdLT29YONYWOh5VgVfubEX81QgAQOgPB/DgUED+sK5rZ8CqnhMkMilubvbD7d9PwtbTFb7L3oROq4NOrcHZd9cj7UGsUPGJqBqyqudYaH8zmXHJuPzlNnRePgX39p/D3b1nC50+OSwS17/fj9YzRyBk/T7EXgyrcKbYCzcQvG4vev70PlLuxODSZ78UGOfi/M148cgy9PzpfVxb+QfiLhe8AjYjJhFW9R0rnKcqSeUy+Hw4Dq1mDEeU/xVkPkqC3MIUdXt4o5az8X0AIzIEqUyGNu+NgdfbQxHlfxWZsUmQm5ugbrfWqOViPH3FkPhU6wKGtbU1jh49WsQURKWnsDCDx+BOQscgA0iPjoffiAWFDgtcsROpd1WQmsgx5NjXuPvnWWQlpODoK18iNzUDLj290Xr2SJydvc7AqYmoOpOZmeh98/8fiUyKxmN7Ijc9E/ZeDSCvZVbolRXyWmZoMLQLctMz4diuCSRSqV7nmrVcHDD05Mon85VKITNV4OWIX/Pb0qLisbfHbL35Bi7fmVcUWfsnNFk5BZarzsxGyPp98F0yGUGrdhUYDgCa7BzIzExKfhNEQG5uCvdBvkLHIDIqedtNR6FjUDVSbQoYU6dOxdSpxncfFRGJi7lTbfTfvRCZj5JxYe6PyEpIyR+W+m9v1NocNaDTQafT6Q3X5moKXOJNRFRRWQkpMLW1LNDeetZIWDdwxl/9PkTfbXPRfuFE/PPedwXG67jodWjVGuwfMAcD/1qs17kmAGSoErHv+ffzX9fxaYy2n7yiV8zVqgs+6lCnznuqjFZT9NNldLl50xW1bzS1tdTbjxIRERXHKDvxJCKqKn90fBt+wxfgweGLaPfpq4WO0/Ltobh34Hz+yTuQ9w2p9/ujEfrDQUNFJaIaIiH4DmybuOm1ObRpjFYzh+Of9zcg5fZDnJ65Fo3G9oRrH/0e/esP7IAGw7vi9LTVeHwrGufn/ojWs0fCzssjfxydRovUe6r8fxkxidBpNHpt6VHxVbJuts3qIyHoTpXMm4iIqh8WMIiInpKdmAoAuLfvH9i19Cgw3GNIZ9h7eeDq0u35bRKZFN3WzcT19fuQfPOBwbISUc0QfewqrOo7weLfDu/k5qbotnYGbv/x5FGoj86FInTDfnRePgWm9tYAAHNHW/h+9SaCVv2B+MC8vn3u7DqFyL8voeuaGZCZKoRZoacoOzRD1NHLQscgIiIjwQIGEdG/5OamkPz7GGanjs0LPM+7bo/WaDyuF07PWAPodPntnVe8hYcngvDA76JB8xJRzfD4VjRizoag4cjuAIB2n02ERC7VeyQpAFxZug2Z8SnotCzvkaldvpmG1HuPcO2bP/TG++eDDTC1qZX/GFShKDu1gLyWGe7+9Y+gOYiIyHhUmz4wiIgqyqaxCzotn4Lc9CxoczU498EGuPT0homtJe7uOYOu30xDxqMk9N02DwBwcspK2LV0h/vgTrB0c4THkM5IvH4XAfM3C7siRFTtXF22A93Xz0Lo9/tx7v0NhY6jzVFjX+93818fGfdFoePlJKdhZ5s3ilyW6tx17Gpfun7FSnqcYMTOE4jYeaLQYS2nDkHw2j+hySzYASgREVFhWMAgIvpXwrU7+KvvB3ptT1+FsaP15ALTRB8PxJYGwn6LSUTVX+yFGwj6+ndY1XNEcniU0HEqTF7LDLGXwxH6/X6hoxARkRFhAYOIiIjICIRvqT6PhlenZ+HaysIfrUpERFQU9oFBRERERERERKLHAgYRERERERERiR5vISGiGsPKXSl0BADiyfEsseYqirHlJSIiIqKKYQGDiGqM3j/PETqCqPH9ISIiIiIx4y0kRERERERERCR6LGAQERERERERiUTfHfPRZdXbQscQJRYwiIiIiIiIiGoQqcI4e5MwztREREREREREItV0Yn80/V8/WNVXIic1A48u3MCJScsxMmAdwn/zx7VVf+SP22n5FFh7OMNvxAJ0WfU26nZrBQBoNKYnAMBv+AKozl0vdnkSmRStZo5Aw1HdUcvZHlmJKXhw8AIuzP0JADAxZhcuzP0JdXwaw/V5H0QfD4QmKyd/GU8LXL4TgSt2VtZbUalYwCAiIiIiIiKqJN7vjUaLKS/i8qKteHgyCPJaZnDt1aZU016YtwmW9Z2Q+SgJAfM2AQCyk9NKnK7z11Ph0qsNLi78GXEXw2Bmb406zzXRG6f1O6MQuHwHrn61HZBKkBWfgsuLtuYPd+v3HDp+ORmPLtwow9oaFgsYRERERERERJVAbm6KllOH4OpXO3Bzk19+e2Lw3VJNn5uaAW2OGpqsHGTGJZdqGit3JRqN7oHjk5bj/oHzAIDU+48Qd+WW3ngP/AL0Mv23PACwa+GOdp++igtzf0LMmeBSLVcI7AODiIiIiIiIqBLYNnGD3NwUD08GGWyZ9l4eAFDiMuMDIwptN3e0Re+f5+DWb/4I+/nvSs9XmVjAICIiIiIiIjIAnVYHSCR6bYbqUFOdkVWgTWZugt4/z0FCyF0ELPjZIDkqggUMIiIiIiIiokqQHB4FdWY26nZvXejwrPjHsHCqrddm19JD77U2Vw2JrPQf1RP+vT2lqGUWp+vq6ZDIZDj11ipApyvz9IbGPjCIiIiIiIiIKoE6IwvXN/wF7/dGQZOVg4engiAzM4Frbx8Er9mDh6evoemr/fDgUADSouLQZEJfWLo6IPGpjjpTH8TCuXMLWNV3Qk5qBnJSMqBTa4pcZuo9FW7/cQodl0yGzEyBuEvhMLG1hGO7Jrjxw8Eip/N+dzScO7fE4bGfQ2FpDoWlOQAgNz2r0Ks1xIAFDCIiIiIiIqJKcnXpdmQlpKDZ6wPQbuGryHmcjkfn857sEbz2T1i61kH372ZDq9YgbPPfuPfXOVh7OOdPf/27fajdrB4G+y+HopZ5qR6jembWt/B+ZxR8PhwHc6fayIpPwf0D54qdRtmpBUxrW+HFv7/Sa+djVImIiIiIiIhqiBs/HCz06gd1ehZOT19T7LRpD2LhN2x+mZanU2tw9avteY9ILcRm55EF2vxGLCjTMsSAfWAQERERERERkejxCgwiIiIiIiIikfKaMRytZgwrcvjWRuMNmEZYLGAQERERERERiVTYL4dxb98/QscQBRYwqMaYfQGIzhA6BeBiAazsIHQKcfJ/dQlS76mEjmH0rNyV6P3znEqZ1+DpR3A7KqVS5lURDV2tsW9NH6FjEJGRE8txpjL300RU/eUkpyHnqaeU1GQsYFCNEZ0B3EkVOgUVJ/WeCsnhUULHoKfcjkpB6O1koWMQEVUKHmeIiIwbO/EkIiIiIiIiItFjAYOIiIiIiIiIRI8FDCIiIiIiIiISPRYwiIiIiIiIiEj0WMAgIiIiIiIiItFjAYOIqBj9/1iITsunFGi3dK2DiTG74Ni+qQCpiIiIiIhqHhYwiIiIiIiIiEj0WMAgIiIiIiIiItFjAYOIiIiIiIiIRM+oCxhBQUEYMmQIbGxsYG1tjaFDhyImJgZWVlYYO3as0PGohgie7C50BCIiIiIiompPLnSA8vL398egQYNQv359zJ07F+bm5ti8eTMGDBiAtLQ0eHt7Cx2RiKjGOrpxABRyKXq8dgA63ZP2P795Hi6OFvAd/xfUal3RMyAiEpE+v30ChaUFDg2dB51Wm99u5+WBgfsX49Tbq3F//zkBExKJj06rRfTxQNzedRIZqiTIzU3g2tsHDUd1h4l1LaHjlZtLrzZo+9FLsGnsiszYJIT+eBChG/YLHavGMMoCRlxcHMaMGQMfHx8cPXoU5ubmAIDx48fDw8MDAFjAoCoX+cNspIYcR27iQ4TO8oZZ3SZo8MEOoWNRJctJySj0IGtik9emyc41dCSj8Orck7i2azg+fK0Vlvx4DQDwxsgm6NPRBT5j/mTxgoiMyplZ32LIsRXwmjEM11b9AQCQmZmg29oZuLP7NIsXRM9IjYyF//gvkRwWmdcgkQA6HaKPB+LSoq3osupteAzuJGzIcrBv3RC9N3+IkO/24eTUVajTpjF8l74BTWYOwn45LHS8GsEoCxhLly5FUlISNm3alF+8AAAbGxv4+PjA39+fBQwqt8tDJMUON3GsD6+N9+A2aSWAvFtImq8KNEAyEsLjiGi4v+gLiVSq962bQ5tG0Ko1SL0bI2A68Yp+lIG3vjiLXxd3h9/ZaGRkqfH1+x3w/tcBCLv3WOh4RERlkhmbjH/e+w7dN7yD6OOBSAi6jbafvAKpiQIX5v4kdDwiUcmKfwy/4QuQHhX3pPGpyzE1WTk4OWUlZKYK1OvXToCE5dfijUGID7yNK4t/AwA8vhUN2yZu8Jo2lAUMAzHKAsb27dvRtWtXeHp6FjrcyckJSqVSry0zMxNeXl5QqVRIS0szREwyUq02P/lAmnbzH9xZMgLNVl6BorZzXqNUJlAyEsLNn/3Q9LX+6Lzqbdz44QByHqfDoU0jtPlgLCJ2HEdOSobQEUVr59938WL3etj6ZXdkZKlx6rIK63bcEDoWEVG5PPC7iIidJ9Bt7Qxc+vxXNJnQB37DF0CdniV0NCJRub5hv37x4lk6HSCRIGD+Jrj1aQuJ1Hi6ZXRs3xS3fvPXa4s+HoiWU4fAwtkOGTGJAiWrOYyugKFSqRAdHY0xY8YUGKbVahEcHIw2bdoUGDZ//nzUr18fKpWqTMtTq9VlnqYiYuKfHARjVDGA2sxgy67ucnOdAChKHE9R+0nxS25pl/e/dR299orlyEVU1KNKmVd5ZT1Kzv85JiYGZtpM4cI8JTdXLXSEAtKj4nHwxU/g8+E49P55DhTWFki7/wgh6/Yh9IcDQscrVG6uGlFRUZUyL3VuxW6RmfblOUQfHQutVodB045UKEdlrVN5iXW7EbP4HBmAvOJvTEwMck00wgYSiBj3bWJXmfuxZ+dbERfnb8aLR5ah50/v49rKPxB3ObzcObhPo+pIm6PGzV//LnlEnQ5pD2IRtMsfDp2aVX2wMipqX2HuaIvMuGS9tszYpH+H1a6WBYyq3F8plUrI5WUrSRhdASM9PR0AIJEUvMx/7969iI2NLXD7yOXLl+Hn54cVK1Zg+PDhZVqeSqWCm5tbufOWmbw20GwZAKB9u/aAOslwy67mmq8JgXm9FkLHQHh4ONz6tRQ0Q22pOb52fAEA0L59eySJ5KTlC/s+cFFYCx2jgKTQ+/B/dYnQMUotPDwcoytrv9V4IWDmUu7JXxnYEBJIYGEmQ9vmDjh4OrJc8wkPD4eb27hy56gMYt1uxExh74JWP+Wd9LRv3w65CdECJxJGVe7bbD1d4bvsTei0OujUGpx9dz3SHsTqjdN17QxY1XOCRCbFzc1+uP37SVi61kG3dbOgVashkclwfs5GJN24X+yyJHIZhp1chVvb/BG89k+9Yc0nD4THsC7Q5mqQGHwn/7YKU3trdFz0OszsraHOzIH/hC9LtV6Vuh97SkV/F+rMbISs3wffJZMRtGpXuedTVetXFtynUVVwklliSZ1+pR5//msz8Ff6zSpMVD5iPScVQlXuryIjI+Hq6lqmaYyugOHm5gaZTIaTJ0/qtd+/fx/Tp08HoN+Bp1qtxuTJk/Htt99C+9T960REVLWaetjgq9ntMfOr82jewBY/fNoFXiN2IyE5W+hoRNVGVkIKjr7yJXJTM+DS0xutZ4/E2dnr9MYJXLETqXdVkJrIMeTY17j751mkxyTg4JC5gE4HZeeWaDVjOE6+tbLYZTUZ3wePIwovQkUeuYzQjXlXpXVfPxtOvs3x6Fwo2i14FYHLd+BxxMPKWWER0P37zaxOw/NKomdJUXxfcgXGL+RLaTHLjE2GeR1bvTazf1//dyUGVS2jK2CYmJhgwoQJ2LRpE4YMGYKBAwciMjISGzduhJOTE6Kjo/UKGMuWLUObNm3QrVs3nDhxoszLUyqViIws3zeG5RETn4X2E/KKMwEXA+DswFtIKsv0UCdEVsFtqmZuzcs0vqenJ/424N9UYbIeJePMoE8BAAEBATBzshU0z3/OjV6C9LuGu2WruvL09ETkzsrpVK73lDMIf5Be5unkcgm2fNkDRy9E44c/wmBqIkMfXxdsmN8ZI985Vub5eXp6wv8YtxtjE58jw6SQvJ8DAi7CoYbeQlKV+7ashJT8n7W5mkI/VKf+u2xtjhrQ6aDT6fTGM7EyR2LovWKXI7cwg0uvNrj/1zmYO9oWXMa9J+unVauh02ghkUph28QVXtOGwbKeI27vOlXg3vGiVOZ+7GliOc5U1fqVBfdpVBXUGdk41XcutKV8Stvcb5ZgbV+fKk5VdkXtK2IDbqJuD28ErXxyBZZLT2+kRcZWy9tHgKrdXz3bb2VpGF0BAwBWr14NhUKBvXv34tixY/D19cWePXvw2WefISIiIr9zz4iICHz33Xe4evVquZcll8vLfFlLhciffFBwVjrDVWm8z0gWG8UtAFVQwGg8/2DZcigUhv2bKkS69MnTe5ydnVGrrr2AaZ5QKIxylyQ6CkXl7bfkipL7jSnMZ1PbwtWpFgZMzbsPNjtHg1c+OoGA3wZj/IuN8OtfEWXOwe3G+CgyAfxbwHB2doaTebGjV1uG2LfJzEzg/f5onPtwY5HjtHx7KO4dOA+dOq+QZNfCHR2XTEatug44/vqyYuffcupghG48gFpKu2LHc2zfFBZKO8QG3IS5oy3smrvjzIy1SLkbg/67FkJ1NgSp90vuB6oy92PPzlcMqmr9yoL7NKoqD0f1QPiWkvu9MrWzQpuXB0BmWr5zjapU1L7i+vf7MfCvRWgzZxzu7DoJhzaN0ey1Abj46c8GTmg4YthfPc14unx9iqWlJTZs2ACVSoXU1FQcPnwYvr6+CAkJgZeXF6T/9mR75swZPHr0CJ6ennBwcMCQIUOQnp4OBwcHnDp1SuC1ICKqnjq3ccL7E70w6dPTiEt8UjUMCkvEgnVXsPrDjnBjcZao0khkUnRbNxPX1+9D8s0HhY7jMaQz7L08cHXp9vy2xOv3cPDFT+A/cQk6LH69yPmbOdjArqUHYk5dKzaHTWMXPDd3PE68+TUAIOdxOtIfxiM5LBLaHDUenQ+FbRNh+30goqrX8q3BkNcyA0q4PcT7nVGiLF4UJyHoNo797yu4Pd8Wg4+uQJsPxuLK0m18hKoBiaMMXQmSk5MRFRWFgQMH5reNHj0azz//fP7rc+fOYeLEiQgMDESdOnWEiElEVO2dvfoICp9NhQ5b8uM1LPmx+A9BRFQ2nVe8hYcngvDA72Khw+v2aI3G43rh6IQv8x5fCEBqIs+7pQRAbkoGNJk5AAB5LTNIZVK9R0TXblYPZvbW6PPbJ7BQ2kGqkCMh5C4engjKH6eWiwO6fDMNJ99ciezEVACAJjsX6VHxsFDaIUOVCLtWDRCxS78PM2MUsfMEInaeEDoGkWhZN3BGn9/mwn/84rx9iQSATn+c1u+OQtPXBgiSr6Ki/K8gyv+K0DFqrGpTwAgODgag34GnhYUFLCws8l/XqVMHEolEVJfAEBEREZWXS09vuA/uBEs3R3gM6YzE63cRMH8zXHp6w8TWEnf3nEHXb6Yh41ES+m6bBwA4OWUlbDxd4f3e6Ly+KiQSBHy6GQDgMbQL5GYmuPHjk9sjY04HI+Z03nlWo9E9YO5oi4cngmBexxbN3xyEy19swXNzx8PMzhpdVr0NAAheuwfRxwMRsGAzuq2bCalcjqjjV/E4XNhHhxKRYTi1b4rh/6xFxI7jCN96FCl3YgAADYZ3RYu3BsO+pYfACclYVesCxrN69OiBtLQ0AyWi6sDKqwfa7tWVPCJVOpm5CfrtXADbxq449+H3uLv3bIFxvN8djUZje+LxrSgceWlRqad7WqflU+D6fFtE/n0R5z78vtBxvKYNhXPXVpDKZbiydBtiA26W6dGAprUt0WX1dJhYWSA+MKLAfZLKTi3g89FL0Oaq8zq/mrYaOclP9lVdvpkG8zo2+es44vy3SI+OBwDc3XuWly0S1WDRxwOxpcHLhbb/Z0fryQWGZ8Ylw+9sSIH22k3dELTqjyKX9/SVB5lxybj8xRYAKPIJJokhd+E3fEGR8yOi6svM3hotpw6Bx9Au+L3tmwCAtp+8wv5WqEKqTQFj6tSpmDp1qtAxiKiSaLPVOP7aMjSZ0LfIccJ+PYyI30/Ad8nkMk33tMDlO3Hnj9PwGNq50OEuvdpAZm6Kw2M+02svy6MBvaYNw50/TuHun2fR9duZUPq2gOrc9fzhKfdU+Hvkp9Bk56LJhL5o9toABH39OwCgdrP6MLHW7y9Cm6uG3wh+ICCiyhcwr/Dbv4iIiMTAKDvxJKLqT6fVIjMuudhxMmOTAa3+FTKlme5pGariH3nl/qIv5Bam6LtzAbqsehvyWmZ6jwbsv3shGr/Uu9h5OHVohsgjlwEAkX4BcPLVf/RuxsMEaP593Jg2Vw2d9snjDVvPHolrq3frjS+RStFv16fo/fMcWLmX/fFTRERERETGiAUMIqJiWCjtoMvV4PDohUi8fg8tpwyGmYM17Jq7I2T9Phwe+zkaj+0Fq/pORc5DYWUOdXre0ziyH6fDtLZloeOZ2lujycR+uPWbPwBA6dsCj+88RNYzBZkDL36Mv0d+iuB1e9H567cqZ0WJiIiIiESOBQwiomJkJ6Xl30seffwqajevX+ZHA+amZUFuYQYAMLGuheykgn3xyC3M0GPDOzg/54e8K0sAeE0fiuvr9hbM9G8P/7EXbsC8jm3FVpCIiIiIyEiwgEFENYK8lhlMrC1KHvEZqnPXYd+6IQDAvnVDpNyN0Xs0IADYtWqAlHsqSGRSmDvaFpjHo/OhcO3dBgDg1vc5PDoXqjdcqpCjx8Z3cf27vxB/9VZ+XvM6tuj+3Wx0WT0N9q0aoMVbgyE1kec/M926gTNy0zLLvE5ERERERMao2nTiSUTVT48f3oN9Sw+oM7Lg4NMYFxfoPxrQ85Xn0XBUd9g0ckHfHfNxesYaZD5KKnS6wh4NCOT1MeHWvx3MHWzRd8d8HB77OcwdbPIfDRix4zg6r3gL/XbldbJ5esYaACj00YBWHko8N3c8jr++TG8Zwev2ous309Ds9ReQcO12fgeeXVZPx5kZa9B4XC/UadMIcrPBaPnWYEQfv4rgtX9iX5/3AQCWrnXg+9UbuL5+H8ydauP5Xz+COiMbkADn5mw0wG+CiIiIiEh4LGAQkWidmLS8QNvTjwYM33IU4VuOlmq6oh4NGLRyF4JW7tJre/rRgNocNU5PX1NgusIeDVinTWPc2naswLjZCSk4+sriAu1n/i2GhP1yuNhHoaZFxeU/QjXzURL+6vtBkeMSEREREVVXLGAQUY1giEcD3tl9usqXQURERERUU7EPDCIiIiIiIiISPRYwiIiIiIiIiEj0eAsJ1RguZX8ARZUQSw4xsnJXCh2hWqjM97Ghq3WlzasixJKDiIybWI4zYslBRIXjNvqE2N4LFjCoxljZQegEVJLeP88ROgI9Y9+aPkJHICKqNDzOEFFpcF8hXryFhIiIiIiIiIhEjwUMIiIiEtT333+PHj165P9zdnbGJ598UmT7086ePYtFi/IeM5yRkQFfX1/Y2tpi+/btBZaj0+kwefJkdOvWDf369UNkZCQAICAgIH8Zbdu2hY+PDwAgMTERr7zyShWvPREREZUWbyEhIiIiQb3xxht44403AAC3b9/G0KFD8d5776F27dqFtj9t6dKl2LQp7zHJpqam2LNnD7777rtCl7N3716Ympri1KlTuHz5MubMmYOtW7eiffv2OHHiBABg1apVyMzMBADY2dnBxsYGISEhaNmyZVWsOhEREZUBr8AgIiIiUcjNzcUrr7yC9evXo3bt2iW2p6Sk4PHjx7C3twcAyGQyKJVFdzYWHh6O5557DgDg4+OD06dPFxjnt99+w7hx4/JfDxgwALt27arwuhEREVHFsYBBREREojBnzhwMHDgQXbp0KVV7WFgYPDw8Sj1/Ly8v/P3339DpdPj7778RGxurNzw8PBwmJiZwd3fPb2vYsCGCg4PLvjJERERU6XgLCREREQnu4MGDCAoKwuHDh0vVXh4DBgzA+fPn0bNnT7Ru3RqtWrXSG75161a89NJLFV4OERERVQ0WMIiIiEhQMTExeP/993H06FFIpdIS2//j6emJO3fulGlZCxcuBAD4+/vD1NRUb9jOnTsL3FZy+/Zt9n9BREQkEixgEBERkaC++OILpKSk6PU90atXLzx69KjQ9vnz5wMAbGxsYGNjg4SEhPx+MEaMGIGrV6+iVq1auHDhAlauXAkAmDBhAr7++muMHDkScrkc9erVw5o1a/Lne+HCBTRo0AAODg562Q4dOoQpU6ZU2boTERFR6bGAQURERIL69ttv8e233xY5rDgffvghvvvuu/zHq/7xxx+FjvfLL78AQP7TRp7VoUMHHDhwQK8tMTERjx8/hpeXV7EZiIiIyDBYwCAiIiKj1aVLlwKde1YWOzs7bNmypUrmTURERGXHp5AQERERERERkeixgEFEREREREREoscCBhERERERERGJHgsYRERERERERCR6LGAQERERERERkejxKSRUpNkXgOgMoVPkcbEAVnYQOgUREREZM/9XlyD1nkroGLByV6L3z3OEjkFEZHRYwKAiRWcAd1KFTkFERERUOVLvqZAcHiV0DCIiKifeQkJEREREREREoscCBhERERERERGJHm8hISIiIiIiIhIBjUaLWw9SkJSSDQkkcLQ3g4eLFSQSidDRRIEFDCIiIiIiIiKBPE7Nwa/7I7Dd7w6u3khARpZab7iNlQnat3TAhBcbY2Qfd5iZ1tyP8TV3zYmIiIiIiIgEkpOrwZc/BGHZ5mCkZ6qLHO9xag6OnHuII+ceYvayC/hiWlu8MbJJjbwqg31gEBERERERERnQzbvJaP/SPny6/mqxxYtnxSdlYcrnZ9H3TT+o4jOqMKE4sYBBREREREREZCCBNxPQ5dX9CApLLPc8jp5/iK4TDyBKlV6JycSPBQwiIiIiIiIiA4hSpaPvm35ISM4uchyZTAIXJwu4OFlAJiv6NpGIBynoO8UP6Rm5VRFVlFjAIIMLnuwudAQiIiIiIiKD0ul0mPTpacQlZRU7ntLBHFFHxiHqyDgoHcyLHffGnWR8tPpSZcYUNaMuYAQFBWHIkCGwsbGBtbU1hg4dipiYGFhZWWHs2LFCxysznU6H8PuP819rNFoB0xAREVXM04exB2mATidcFiIiIqFtPXAbf/8TXenzXfNbKM4HxVb6fMXIaAsY/v7+6NixI8LCwjB37lwsXrwYUVFRGDBgANLS0uDt7S10xFLT6XT4Zd8t+Iz+E70nH8pv7zRhP778IQg5uRoB01WeyB9mI3SWN3ITHyJ0ljfufDVG6EhERFQFcjTAT+HAa2eetL11DnjlJHAwkoWMp7n0aoPBR5Zh/L1tGBmwDs3fHCR0JCpEn98+wQv7FkEi1T91tvPywPj721B/kK9AyYjIWOh0Oqz4JbjK5r9yS0iVzVtMjPIxqnFxcRgzZgx8fHxw9OhRmJvnXVYzfvx4eHh4AIDRFDB0Oh3eWXYBq7Zcx7NPwYmJz8DHqy/hWMBD/LWmj2if93t5SPGP7zFxrA+vjffgNmklgLxbSJqvCjRAMiIiMrQsDTDrAnApvuCw8BRg/tW8/2c2R4HjXk1j37ohem/+ECHf7cPJqatQp01j+C59A5rMHIT9cljoePSUM7O+xZBjK+A1YxiurfoDACAzM0G3tTNwZ/dp3N9/TuCERCR256/FIvBm+TvtLMlu/3uIicuAcx2LKluGGBjlFRhLly5FUlISNm3alF+8AAAbGxv4+PgAMJ4Cxpb9EVi15TqAgt9I/ff66PmHmLNKvPc1tdock/+vwZy8g3qzlVfy25ouvyhwQiIiMpTVoYUXLwDgv8PcltvA35V/Ba3RafHGIMQH3saVxb/h8a1oROw8gRs/HYLXtKFCR6NnZMYm45/3vkPr2SNh37ohAKDtJ69AaqLAhbk/CZyOiIzB32er9sCnVutwLOBhlS5DDIyygLF9+3Z07doVnp6ehQ53cnKCUqkEAEycOBEmJiawtLTM/+fn52fIuEXS6XRY+WtIqb6B+mF3GFLScqo+VDkoaivz/8kt7QAAcus6T9pt6gickIiIDCE1F9h7v+TxJAB+u1PlcUTPsX1TRB+/qtcWfTwQlm6OsHC2EygVFeWB30VE7DyBbmtnwK3vc2gyoQ9OT1sNdXrxnfEREQHA5dAiqvtGtgyhifOehGKoVCpER0djzJiC/SdotVoEBwejTZs2eu1vvPEG1q5dW67lqdVqqFSqck1bkojINFwt5WVE6Zlq/LwnEMN61q2SLIXJzXUCoDDY8oqTm5uLqKhHQseoNrIeJef/HBMTAzNtpnBhiIwEt5uSHUuwQLa25A/eOgChyUBAhAp1zdRVnktoubmFr6O5oy0y45L12jJjk/4dVhsZMVV3qbHY5eaqERUVVSXzrYiL8zfjxSPL0POn93Ft5R+Iuxxe7hxVsX5lwX0aGQr/1vIE39IvLshkkiKfMOL8VLtzMU8hUcVnQqN5chn/1dBHgu9bykKpVEIuL1tJwugKGOnp6QAASSGXLezduxexsbGVevuISqWCm5tbpc1Pj0VjoOGHpR59xuy5mJFwpGqyFKL5mhCY12thsOUVJzw8HG79Wgodo9qoLTXH144vAADat2+PpBp6ICEqC243JXMa+i5c/7e81OP3enEE0m/+U4WJxOEL+z5wUVgLHcOohIeHY3QVnH9V9HehzsxGyPp98F0yGUGrdpV7PlW1fmXBfRoZCv/W/tV0GaConf/yv0elluTitqFFDnPtsw3RjzLyX584eRZubkWPLzaRkZFwdXUt0zRGdwuJm5sbZDIZTp48qdd+//59TJ8+HUDB/i+2bt0KOzs7NGvWDIsWLYJaLZJve7RlvOSwrOOLlJlbc6EjEBFRFdBkppZpfG0Zx69uMmOTYV7HVq/N7N/X/12JQeKj+/cqDh0fd09EZaEzwJMldSL5nFuFjO4KDBMTE0yYMAGbNm3CkCFDMHDgQERGRmLjxo1wcnJCdHS0XgFjxowZ+Oqrr+Dg4IArV65g3LhxyMrKwueff16q5SmVSkRGRlbJumg0OnR5/RSi47JKfKScTCrB+eM/QWlvViVZCjM91AmRVVAzaTz/YJmn8fT0xN9V9HuoibIeJePMoE8BAAEBATBzshU0D5Ex4HZTsvgcGd4I0UGL4jt3kkAHRxMNdp8+CGkNeBLJudFLkH634O2osQE3UbeHN4JWPvkm36WnN9IiY2v07SNA3nE/cmfld45Z1O/C0Kpq/cqC+zQyFP6t5Rn70UWcDXqyb1fFZ8K1z7ZCx3V2MM+/8qLduD8RE1/4VSuqZ9pfGdMfX057p3ICG8B//VaWhdEVMABg9erVUCgU2Lt3L44dOwZfX1/s2bMHn332GSIiIvQ69/zvqSQA8Nxzz2HhwoVYsGBBqQsYcrm8zJe1lMX0l7zw4aqSn9Ixoo87nmvdqMpyFEZxC4BILvpQKBRV+nuoadKlT91X5+yMWnXtBUxDZBy43ZTMFUCPBOBYTPHj6SDBuEZy1HOrGft1haLw063r3+/HwL8Woc2ccbiz6yQc2jRGs9cG4OKnPxs4ofgoFFVz/lXU78LQqmr9yoL7NDIU/q3l6dTmoV4BQ6PR6d3+UZSY+MxSjQcA3dvVF3zfUtWM7hYSALC0tMSGDRugUqmQmpqKw4cPw9fXFyEhIfDy8oJUWvRqSaVS6Eq63MGAZo1vgb6dXIodp6GrFdbM8TVQIiIiovL7wAuoW8Ij6Ds5AmMbGCaPmCUE3cax/30Ft+fbYvDRFWjzwVhcWboNYb8cFjoaERFVsh7tnKt8Gd3alv2KBmMjjjJ0JUhOTkZUVBQGDhyo175jxw70798f1tbWCA4OxsKFCzFq1CiBUhZkopBh3+o++Hj1JXy/6ybSMp7ctySXSTCijztWf+gLR/uie58lIiISCwczYFMXYHlI3pUYT3WODgs5MKI+MLUZIDfKr1AqX5T/FUT5XxE6BpVBxM4TiNh5QugYRGRk+nVyQT3nWngQk14l8+/V3hme7jZVMm8xqTYFjODgYAAFO/Bct24dpkyZgtzcXDg7O2P8+PH46KOPBEhYNFMTGVa81wGfvtUG+09GIjYxE1a1TDCgiyuc65TwNZbIWHn1QNu94rnChYiIDM/eDPjyOSAuC/gnFshQA3amQFenvCIGERFRTSOTSTFtbHN8sLLk7gPKY/pLNeNBCdXmNKKoAsazTysRM6taJhj3QkOhYxAREVWKOmbAkHpCpyAiIhKHma+0wK/7IxB8q3KfNPVi93oY0rN+pc5TrKrNBZxTp06FTqdDx44dhY5CREREREREpMdEIcPmz7vBRFH8x/D/nlDi2mdbgSeNPMve1hTfzesEiaQGPNYL1aiAQURERERERCRmPs0dsGNZT8hlRRcc/ntCSfSjDGg0Rd+eb22pwKF1/VDXsVZVRBUlFjCIiIiIiIiIDGRoL3fsW90Hdjam5Z6He11LHP/hBbRrWacSk4kfCxhEREREREREBjSgqxtC/xyBYb3L3nfF1DHNELx7OHyaO1RBMnGrNp14EhERERERERkLJ3tz7F75PK7eiMf6nTex4+87SEnLLXTcOrXN8OrgxpgyuikaulkbOKl4sIBBREREREREJJA2zRzw/YIu+G5eZ9y6/xhHzz/EtC/PAQC+n98ZfTu5oJ6zZY3pqLM4vIWEiIiIiIiISGBSqQRNPGz1Hok6oIsb6te1YvHiXyxgEBEREREREZHosYBBRERERERERKLHPjCoSC4WQid4QkxZiIiIysLKXSl0BKNTVe+ZWH4XYslBRGRsWMCgIq3sIHQCIiIi49f75zlCR6B/8XdBRGTceAsJEREREREREYkeCxhEREREREREJHosYBARERERERGR6LGAQURERERERESixwIGEREREREREYkeCxhEREREREREJHosYBARERERERGR6LGAQURERERERESixwIGEREREREREYkeCxhEREREREREJHosYBARERERERGR6LGAQURERERERESixwIGEREREREREYkeCxhEREREREREJHosYBARERERERGR6MmFDkBkKLMvANEZQqcAXCyAlR2ETkFU8/i/ugSp91Tlnl6r1uT//PeoTyGVy8o9Lyt3JXr/PKfc0xMR1UQV3Y9XN4Y6lojl+MljJwEsYFANEp0B3EkVOgURCSX1ngrJ4VGVMq+UOzGVMh8iIiq9ytyPU+nx+EliwltIiIiIiIiIiEj0WMAgIiIiIiIiItFjAYOIiIiIiIiIRI8FDCIiIiIiIiISPXbiSURE9Iwuq95GozE9AQBajQaZj5IRczYEVxZvRYYqUeB0RERE4sTjJ1U1XoFBRERUCNX5UOxoNQm7nnsLp95eBfuW7ujx/btCxyIiIhI1Hj+pKrGAQUREVAhtjhqZccnIUCXi0fkbCNtyFI7tmkBhaS50NCIiItHi8ZOqEgsYREREJTB3qg33QR2hVWug02iFjkNERGQUePykysY+MIiIiAqh7NQCL0f8ColUCrm5KQAgZP0+qDOzAQD1BrSH9zuj9Kax8XRFwLxNCPvlsMHzEhERiUFJx88eG9/Fw5NBCN9yFABg19ID3dbNxF993ocmO1ew3GQcjPoKjKCgIAwZMgQ2NjawtrbG0KFDERMTAysrK4wdO1boeEREZMTirtzCvuffx/4BcxD49e+IvRiGq0u35Q9/cCgA+/q8n/8vcMVOpN5TIeL3E8KFJiIi0RoZsA6tZo0QOkaVK+n4GTBvE7ymD4OpnRUgkcB3yWRc+PhHFi+oVIy2gOHv74+OHTsiLCwMc+fOxeLFixEVFYUBAwYgLS0N3t7eQkekakKbnYnorfMQMqUxrowyR+DLdrjxbjvE/rVa6Gjlkv4wAde/25f/+sqS35B4/Z5wgYhESpOVg9R7KiSHRSJw2Q6kRsaiw6LXCx3XwtkOHRZPwskpq6DJzDFwUiKissmITULoDwfyX19etAXxgRECJqoY09qWaDv3FQw7/Q3G3/0NY4J/RP89n6HhqO6QyEr3cafT8ino/8fCKs3514A5CN2wv0qXIQYlHT8zVIm4vmE/nps3Hk3G98HjOzGIORMsYGJxCbmViPnfXs5/vXTTNTyISRMwkbgY5S0kcXFxGDNmDHx8fHD06FGYm+d1CDN+/Hh4eHgAAAsYVGkefPcWUoOPw23SNzD3aA1NRgoy7lxFTtwDoaOViU6nQ+Dynbi26g/otE/uQbz9+0nc/v0k6r/QAV3WTIfCwkzAlETiFbh8B4ad+gZhvx5BQtDtJwMkEnRbOxPBa/9E0o37wgUkIiqBTqdDyLd/4sqSbXr9EdzZfRp3dp+Ga28fdFs/CyZWFgKmLBuLuvZ4Ye8X0Ko1CFy2Awkhd6HNVcPxuSZoMWUwkkLvi+aLmuyElGKHSxVyaHPVBkpjOIUdP29u8sPAA4vh3Lkl/howR+CE4pCVrcZr809j26E7eu1rt4Vi3fZQvDfRC1/ObAepVCJQQnEwyiswli5diqSkJGzatCm/eAEANjY28PHxAcACBlWe5At/wmnY+7DtOBSmTh6w8GgNh94TUXfsfKGjlUngip0I+vp3veLF0+4fvIATk5ZDq9EYOBmRcUi9q0LkkUvwmTNOr731rBHISc3AzZ8OCZSMiKh0rq/fh8uLthbZmWKU/xX4v7oEmhzjuZTf98vJkJnI8VffD3Bn92k8Do9C6l0Vbv9+En/1+wApd2LQ/4+F6LR8it50rWaNwMiAdQAA73dHw/Pl56Hs1AITY3ZhYswuNBrdo9jlOnfxwvj72yAzNwEAyEwVGH/3NwzY+/mTcbq1wvj72yD/98uhZ28hGRmwDm0+HIuOX07C2OubMODPvGntWzVAn+3z8HLErxgb8iN6/vg+ark6VPi9Ekqhx0+dDmG/HEGU/5USCzs1gVarw7gPTxQoXuQP1wFfbQrGhysvGjiZ+BhlAWP79u3o2rUrPD09Cx3u5OQEpVKZ//rAgQPw8fFBrVq1oFQqsWzZMkNFpWpAUdsZKVf8oE5NFDpKuWWoEnFt5R8ljhd9PBBRR68YIBGRcQpZtw8uPbyh9G0BAHBs1wSNX+qNs7O/FTgZEVHxshJTceWpfgiK8uhcKO7vP2+ARBVnYmsJl95tcGOTH3JTMwoM16k1+R1HFidk/T7c3n0asRfDsKPVJOxoNQl39/1T7DSxl8IArQ5OHZoBABzbNUVueiYcWjfK77jSuUtLxAfehjojq8j5NHv9BWTFp+Dgix/jzOxvYePpiv67FyLuUhj29/8QfiMXQqfRou/2+ZCZKkpcF7F69vgJANBqodPqhAslIv4XHuLPYyVfxbnil2DciarZBR+ju4VEpVIhOjoaY8aMKTBMq9UiODgYbdq0yW87fPgw3njjDfzyyy/o3r07MjIy8OBB6S/9V6vVUKlUlZKdhJWb6wSg7Dv++tN+wN0VLyFoQh2Yu7VArSYdYdP2Bdh0GAKJpOyXcOXm5iIq6lGZp6uIOxv9irzy4llB3+2FtIVzFSciMrzcMlyWe2ZW4QWJuEth2Ow8EgBgYm2Brmtm4MzMtchOKtu9qbm5akRFRZVpGiKiirj/6zFoc0q3Hwz6fh9M2ntUcaKye3Y/bu2uhFQmQ3J4xfan6owsaDKzoc1VIzMuuVTTaLJyEHflFpy7eOHhiSAou7TEg78vwfE5Tzh1bIbo44Fw7uyFhyeDip1PfOBtBK7Ymf+6y6q3EXXkCgKXP2k7Ne0bvHTzZ7j09MYDvyffwBvqWFLZx8+K5Kiux84Vm6+WajydDlj+00V8/FqTKk5kGEqlEnJ52UoSRlfASE9PB4BCPzju3bsXsbGxerePzJs3D/PmzUPv3r0BANbW1mjZsmWpl6dSqeDm5lax0CQKzdeEwLxei5JHfIZls85oueE20sMDkB52DqnXT+H20pGwaTsADT/ZV+YiRnh4ONz6lf5vsDLMsu2EVqbKUmV98M81DObfPFVDX9j3gYvCutLm1+TVfjB3tEX7hRP12iN+P4nQ74vvpC08PByjuZ0RkQG9ZdMB7c1dSzVuQuBtUZ7/FtiPl+OLpMoUczYEbn2fAwA4d26JGz8dgiY7F8rOLRF7MQz2rRrg8qItxc4jPvCW3mt770awdlfi5Yhf9dplpgpYeeh/wWSoY0llHz/Lq1ofO5suBxS2JY+n02H9Zj+sX/B8lUcyhMjISLi6lm6/9B+jK2C4ublBJpPh5MmTeu3379/H9OnTATzp/yI9PR0XL17EgAED0LRpUyQlJaFDhw745ptv8jv7JCoNiUwOy2adYNmsE5yGvouEE1twb+V4pF0/BauW3YWOVyJpGQ7wUtTsjoGISit4zR4Er9kjdAwiolKRSiTQ6XSl+jLDWM4EUu7GQKvRwNbTFQ8OXihyPJ1WW6DYIZXLKrz8mLMhaP3OSNRycYB9qwZQnQmBNicXXtOH49GFG9DmqvNuNSmGOkP/FheJVILbu04ieO2fBcbNTkqtcGYxidh5AhE7TwgdQyRK27ODDpAYZS8QlcboChgmJiaYMGECNm3ahCFDhmDgwIGIjIzExo0b4eTkhOjo6PwCRlJSEnQ6Hf744w/4+fnB0dERs2bNwvDhw3HlypVS7cCVSiUiIyOreK3IEKaHOiGy6FsQy8TMNe9+R/Xj2DJP6+npib8N/DcVtmI3IrefKnlECeDUsiEif+bfPFU/50YvQfpdcdwS6OnpicidPwkdg4hqkIh1B3Bv05GSR5QA1h7OiLwkvnOBZ/fjOclpiD4WiGb/648bPx4q0A+GRC6DTCFHVnwKLJR2esPsvRrovdbmqkv9yNX/xF+5BU12LlrPHomUuypkxiUj5ux1dF8/G/Vf6IDYS+Glvm3nPwlBt1G7eX2k3iv5eGWoY4lYjp/V+dg56sMAXAhOQok9gkikeGlELyydMd0Qsarc0/1WlpbRFTAAYPXq1VAoFNi7dy+OHTsGX19f7NmzB5999hkiIiLyO/e0srICAMycORPu7u4AgMWLF6NOnTqIjIxEvXr1SlyWXC4v82UtJE6KWwDKUcAI+7g77LqOg0Wj5yC3qYPsmAhE//oxZLVsYeXVs+w5FAqD/01ZThlWugKGDvCaNJB/81QtKRTiOeQpFDy2EJFh2UwZinubj+bdRF8cHdDitRdEuY8qbD9+/qONeGHvF3jx76W4umwHEq/fgzZHjTptG6PlW0NwZuZaPDx9Db5LJqP+IF8khtyF+6COcOrQDDkp6fnzSX0QC/cXfWHr6YrM+MfITcsssfigzVUj9mIYGo3ugbBfDgPIK6ok3YxEwxHd9Pq2KK1rq3dj0MEl6PrtTNzYeABZCSmwdKuDegPaI3TjAaQ9ePLlmaGOJWI5flbnY+eMl7Px0pwTpRr3nYlt4WrET6WpKHH8NZaRpaUlNmzYgA0bNui1h4SEwMvLC1JpXvXUxsYG9evXL1dHi0T/sfEZgMRTW/Fw23xoMlIgt3GEVYtucJ+xCXJr49h52DZxg8ewLri750yx49k0coHHkC4GSkVERESGYlXPCY3H9cKt3/yLHc+ynmOJjxAVk/ToeOzr9wG83h4K73dHw9LFATlpmXh8Kwoh6/ci6eYDJIVFonZTN3T8chJkCjlu7z6N0B8PotGoJ7cB39p2DM6dW+KFvxbBxLoWzsxcW6rbG1RnQ+DSvTVizobkt8WcDYa9lwdizoQUM2XhHt+KxoEXP4HPh+PQZ9tcyEwVyFAlIuZsCHJSCj5phaqHEX3c0eonO1wLL/6ph0N61kPb5sbx+aOqSHS6ksqwxiE5ORm1a9fGm2++ie+++y6/fcmSJfjtt99w4MAB1KlTB7Nnz8bFixdx6dIlAdOSEEYfB+6I4NbBBlbAzrJfuFFh6sxsnHxrJSL/fuZvXwJAl1e86Lt9Hmq51OydIlVff3afVeGe6iuLracrhp5cJXQMIqphNDm5OD1jDe7tfeYRof+eC1jWc0LfHfNg7V72y7oNQUz7cTEw1LFELO97dT92xsRloP9bf+NaeOJ/m6Sefp1csOvr3rC0MN7H6VYGo7wCozDBwcEAoPcEEgD44IMPkJSUBB8fH2i1WnTp0gW7d+8WICGRsOTmpuj10weIPhGEm5v9EHc5HDq1BjaNXOA5oS88BnfKf245ERERVT8yEwW6r58Nz5efx81NfngUcBO6XDWsPJzR5JXn4TG8KxQWZkLHJKqRnOtY4MLWF7HryD18t/MGQu8kQyaVoF3LOpg6phkGdHGFrIz9tFRH1b6AIZVKsXTpUixdulSAVETiIpFK4dqrDVx7tRE6CpEo2Xq6wnfZm9BpddCpNTj77nq9+43/0/+PhXgcEY1zH34PmbkJ+u1cANvGrjj34fe4u/esAMmJiEpHIpGgbtdWqNu1ldBRRK3B8K7w/eqNIof/2X020qPjDZhI3Eo6fsrMTdDh89dgWc8JUpkUR19ZDNsmbnhu3ngAgNzSDBKJBH/1/UCoVRAFM1M5XhnUCK8MaiR0FNGqNgWMqVOnYurUqULHICIiI5aVkIKjr3yJ3NQMuPT0RuvZI3F29jq9cVyfb4vctMz819psNY6/tgxNJvQ1dFwiIqoiD/6+iLgrt4ocnqEqvq+Cmqak46f3O6NxZ88ZqJ7qKyQ+MAJ+IxYAAJpPHgiZmYnBc5PxqTYFDCIioorKSkjJ/1mbq4FOo9UfQSJB0//1x40fDqBe//YAAJ1Wi8y4ZAOmJCKiqqZOz0JquvCPDjUWJR0/lZ1bQGYqh/c7o/Dw9DVcW/WH3nCPYV1w8o2vDZKVjBtvoiEiInqGzMwE3u+PRugPB/XaG43ugfsHL0CTlStQMiIiIvEq6vhp19wd0ccD4TfyU9h7NYDSt0X+MOsGztDmqpEWFWfouGSEWMAgIiJ6ikQmRbd1M3F9/T4k33yQ3y4zVaDB8K6I2H5MwHRERETiVNTxEwCyElMQfSII0Onw8GQQajevnz/s/+3cvUtVcRjA8ScCiTIKFBGk8N5wKiRryktU1NBkEQRBtbW4B9EQDUEt9h80BLoFNdQQQWkgGUhog0RE2ZulIBJJURDchkQSKyfveczPZztv8JwznB984Zzysb3x6uZgrcdlhRIwAOA3lavd8WHgaby9O7xgf/3WpqjbtCEO9Z6P3RdORcvBjth2fF9BUwJALn9bPyMiph4/i4b2ckRENLSX4/P4x/ljrV2d8fr2o0XXwJ/4BwYAzGk5sDNauzqjfktTlI5UYmZsPCb6R6Nuc32M3xqMO4fPRURE857tUTpaiZc3HkZExP5rZ6NhRyl+fP0WjbvaYvji9QLvAgBqa6n188nlvqj0dMfadXXx6fm7mHgwEhERjR1tMftmKr7PzBZ8B6wUAgYAzJnoH42+8sklz5scGovJobH57YEzPcs5FgCkttT6+eX9dNw7cWnR/umRF3H/9JXlHI3/jE9IAAAAgPQEDAAAACA9AQMAAABIzz8wWDVa1hc9wS9Z5oDVZmNrc9EjzMs0C8BK4d25UK2eR5bnnmUOirWmWq1Wix4CAAAA4F98QgIAAACkJ2AAAAAA6QkYAAAAQHoCBgAAAJCegAEAAACkJ2AAAAAA6QkYAAAAQHoCBgAAAJCegAEAAACkJ2AAAAAA6QkYAAAAQHoCBgAAAJCegAEAAACkJ2AAAAAA6QkYAAAAQHoCBgAAAJCegAEAAACkJ2AAAAAA6QkYAAAAQHoCBgAAAJCegAEAAACkJ2AAAAAA6f0ECqr0qcsChncAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1367.11x494.978 with 1 Axes>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from circuit_knitting.cutting.automated_cut_finding import (\n",
+    "    find_cuts,\n",
+    "    OptimizationParameters,\n",
+    "    DeviceConstraints,\n",
+    ")\n",
+    "\n",
+    "# Specify settings for the cut-finding optimizer\n",
+    "optimization_settings = OptimizationParameters(seed=111)\n",
+    "\n",
+    "# Specify the size of the QPUs available\n",
+    "device_constraints = DeviceConstraints(qubits_per_subcircuit=4)\n",
+    "\n",
+    "cut_circuit, metadata = find_cuts(circuit, optimization_settings, device_constraints)\n",
+    "print(\n",
+    "    f'Found solution using {len(metadata[\"cuts\"])} cuts with a sampling '\n",
+    "    f'overhead of {metadata[\"sampling_overhead\"]}.'\n",
+    ")\n",
+    "for cut in metadata[\"cuts\"]:\n",
+    "    print(f\"{cut[0]} at circuit instruction index {cut[1]}\")\n",
+    "cut_circuit.draw(\"mpl\", scale=0.8, fold=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Add ancillas for wire cuts and expand the observables to account for ancilla qubits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:28.782250Z",
+     "iopub.status.busy": "2024-04-19T17:42:28.781889Z",
+     "iopub.status.idle": "2024-04-19T17:42:29.150532Z",
+     "shell.execute_reply": "2024-04-19T17:42:29.149699Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1367.11x561.867 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from circuit_knitting.cutting import cut_wires, expand_observables\n",
+    "\n",
+    "qc_w_ancilla = cut_wires(cut_circuit)\n",
+    "observables_expanded = expand_observables(observable.paulis, circuit, qc_w_ancilla)\n",
+    "qc_w_ancilla.draw(\"mpl\", scale=0.8, fold=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:29.153658Z",
+     "iopub.status.busy": "2024-04-19T17:42:29.153186Z",
+     "iopub.status.idle": "2024-04-19T17:42:29.169241Z",
+     "shell.execute_reply": "2024-04-19T17:42:29.168559Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sampling overhead: 127.06026169907257\n"
+     ]
+    }
+   ],
+   "source": [
+    "from circuit_knitting.cutting import partition_problem\n",
+    "\n",
+    "partitioned_problem = partition_problem(\n",
+    "    circuit=qc_w_ancilla, observables=observables_expanded\n",
+    ")\n",
+    "subcircuits = partitioned_problem.subcircuits\n",
+    "subobservables = partitioned_problem.subobservables\n",
+    "print(\n",
+    "    f\"Sampling overhead: {np.prod([basis.overhead for basis in partitioned_problem.bases])}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:29.172554Z",
+     "iopub.status.busy": "2024-04-19T17:42:29.172099Z",
+     "iopub.status.idle": "2024-04-19T17:42:29.177041Z",
+     "shell.execute_reply": "2024-04-19T17:42:29.176349Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: PauliList(['IIII', 'IZII', 'IIIZ']),\n",
+       " 1: PauliList(['ZIII', 'IIII', 'IIII'])}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subobservables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:29.179712Z",
+     "iopub.status.busy": "2024-04-19T17:42:29.179108Z",
+     "iopub.status.idle": "2024-04-19T17:42:29.400997Z",
+     "shell.execute_reply": "2024-04-19T17:42:29.400316Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwYAAAD2CAYAAABsvJBQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABQHklEQVR4nO3dd1gUV9sG8HsXdinSRKSIKFiwoohRUYM1GkuiaFSMJur7BX3tJSTRGLuGaBKjYgtqXmtii7HEFhVL1KhYwFAURQEBQVBEgWVpu98fxtUNbRHYYdn7d11cF3vOmdlnmNlhnp0z54iUSqUSRERERESk18RCB0BERERERMJjYkBEREREREwMiIiIiIiIiQEREREREYGJARERERERgYkBERERERGBiQEREREREYGJARERERERgYkBERERERGBiQEREREREYGJARERERERgYkBERERERGBiQEREREREYGJARERERERgYkBERERERGBiQEREREREYGJARERERERgYkBERERERGBiQEREREREYGJARERERERgYkBERERERGBiQEREREREYGJARERERERgYkBERERERGBiQEREREREYGJARERERERgYkBERERERGBiQEREREREYGJARERERERgYkBERERERGBiQEREREREYGJARERERERATAUOgCqHoJGL0VGbLLQYQAAzJ3t0XPrLKHDqDK4b0hbqsqxxuNMODwGqi7uG9IEEwOqEBmxyUi/kyB0GFQE7hvSFh5rxGOg6uK+IU2wKxERERERETExICIiIiIiJgZERERERAQmBkREREREBD58TAJ4e+UkNPLpDgBQFBQg+1E6ki6G44b/z5AlpwkcHRFVNp4DiMcAUdXEOwYkiOTLkdjdyhe/vjUBf05aiVotndFtg5/QYRGRlvAcQDwGiKoeJgYkCEVuPrJT0yFLTsOjy7cQteMUbNs1gcTMROjQiEgLeA4gHgNEVQ8TAxKciV1NOL/nCUV+AZQFCqHDISIt4zmAeAwQVQ3V/hkDhUKBVatWITAwELGxsahduzaGDRuGRYsWoUaNGkKHp7fsO7XAyOjtEInFMDQxAgCErz+E/OwcAEC3jX54eO4m7uw4BQCwbumCLuum4fden6MgJ0+wuImoYpR2DjC1t0a/w1/j8LszIX/yHAYmUgw8tRynP/kO6bcfCBk6VZDSjoF6fdvD/dOhastYutZF8NzNiNp2QuvxEumDap8YzJgxAwEBARg0aBD8/Pxw69YtBAQEICQkBKdOnYJYzJsmQki9cRcXpq2BgZEEzgM6oY5XK4Qs26mqD567GX0PLkbc0SvIeZqJjkvH4srsn5gUEL0mX5aDJ+ExUOTlw6xubZjXtxM6JI2Vdg6QJachMvAw2i0cg/OTA+DuNwxxx64wKahGSjsGHhwLxoNjwarX9fq0g8eXIxC996wA0RLph2qdGERERGD16tUYPHgw9u3bpyp3cXHB1KlTsWvXLowYMULACPVXgTwXGbHJAIDQ73bD3NkeHb7+BH999iOAFxcFEYGH8dbcj/E4JBrP7ich6UKYkCHrJQNjKVpNHQyXgZ1h6mD9Yr/FPcK9X//ErZ+OCh2e3pKnZeDvlb/i7q4zyMuQqcrtO7WA2+RBcOzuLlxwGirtHAAAt346hveOL0Mz336o368DDvX8TKhwqRJocgy8ZOpgjQ7+vjg10h8F2bnaDlWv9frlK0jMTHHMey6UilfdvKzdXND/sD/+nBSAuMOXBIyQKpLOfl1+8+ZNDBw4EJaWlrCwsIC3tzeSkpJgbm6O4cOHAwB27twJpVKJ6dOnqy07duxYmJqaYseOHQJETkUJ/X43Gvl0R63WDVVltzcfh1UTJ7hN9sbVhVsFjE5/dVw6Fg2HdsW1xdtwoOsMHB+yALc3H4fUwlTo0PSWLOUpjrw3G5Ebj6glBQCQ/FcETo78Gre3/iFQdG+uqHOAUqHA1flb0GHx/+Ha4u2qLiZUPRV1DAAARCJ0WTMNYWsO4OmtOGGC02MXpq+FRQN7uE0dpCozMJaiy5qpuP/beSYF1YxOJgZBQUHw9PREVFQU5syZA39/fyQkJKBv377IzMyEu7s7AODq1asQi8Vo37692vLGxsZwd3fH1atXBYieipIRk4z4k9fgMevDV4VKJaK2nURC0A3kPHkuXHB6rF6f9ghfdxAPjl9FZnwKnkbGIXrPWdxc8avQoemtPyetQkZMUgktlLj85SY8Do3WWkwVochzAADHnm0gS05Dzab1BIqMtKW4Y6D19A+QmyHD7f8dEygy/Zadko6/PvsRrWcMUSVtbb/6CGKpBFfm/E/g6Kii6VxikJqaCh8fH3h4eCAkJASff/45Jk+ejKCgIDx48KLv6cvE4OHDh7CxsYGRkVGh9Tg6OuLx48fIzeUtyaoifN0hOHZzh33HFq8KFQooFUrhgtJzspSncOzeBlIrM6FDIQBpEbFIvhBeciMlAKVSJ7t6/fscYNW0Hur1aY/DfWeh8YieMKtnK3CEVNn+fQzYtmuCxiN64uKMtQJHpt8eHL+K6D1n0WXNVDj1fgtNRvXC+ckByM+SCx0aVTCde8Zg2bJlePr0KTZv3gwTk1djHVtaWsLDwwNBQUGqxEAmkxWZFAAv7hq8bCOVSisktvz8fCQnJ1fIunRNXl6+xm0vTC/6BJ96LQpbHIZUSCwJCQnlXk91UZZ9829/+a1Hl3XTMTz8J6RHJSD1xh0kBt3Ag+NvdreN+6Z87m7T/BvT+wcuor7fQIgNDSoxInWaHmuangM6LhuHq/O3QJachpBvd6HD158g6ONvNIqDx5kwKvIYkFqYwmv1VFyYtgY5TzPLHAePAXXl+V8AAFfnbcH7J79D9/99jr9X7EPq9TtvHAf3jXbY29vD0LBsl/o6lxjs2rULXl5ecHV1LbLezs4O9vb2AABTU1OkpKQU2U4ul6vavJSfnw8/Pz9s374dCoUCH3zwAdauXatKIkqTnJwMJyensmxOtbGkVi84SiyEDgMAcOfOHQzT0/1QlPLsm5SrUdjnOQk2bRrDtq0r7Dybo9vGz5B4OgRBo5eWeX3cN+XzfxZt4WXqrFFbZX4BmjdojCyl9kbyqsjzQOOR70D++BkSgm4AAO7tPYfGH/ZAvX4d8ODolRKX5XEmnIo8BpqMfhcmtlZov3CMWnn03nOI3HC4xGV5DBRW3n2Tn52D8PWH0HHpWNxc+ebdSblvtCc+Ph5169Yt0zI6lRgkJycjMTERPj4+heoUCgXCwsLQpk0bVVmdOnUQGRmJnJycQncOEhMTYWNjo3a3wN/fH2fOnEFYWBikUikGDBiAL774AgEBAZW3UVSi6D1nEb3nrNBh6DVlgQKp16KQei0KEYG/o8EHXuiyZhrsOjbHo0uRQoenV7LLcJGvUCqRoyyoxGgq192fT+Huz6fUyo4Pni9QNCSEsNX7EbZ6v9Bh0GuU/9x14CR01ZdOJQZZWVkAAJFIVKju4MGDSElJUXUjAoB27drhxIkTCA4OhpeXl6pcLpcjNDQUXbp0UVvHpk2b8O2338LR0REAsGDBAgwdOhQrVqyAgUHpt+Pt7e0RHx//Jpum8y4NW4qsmKrRjcrV1RXxe/hA1EsVvW+e3U0EABjXsizzstw35ZN2/S5ujNesr7WtVwvErFhVyRGpqyrnAR5nwuExUHVx3+iflz1oykKnEgMnJycYGBjg3LlzauVxcXGYMmUKAKglBj4+PvD398fKlSvVEoONGzdCJpNh5MiRqrL09HTEx8erLe/h4YGMjAzExsaiYcN/DZ9WBENDwzLfsqkuJJKqcyhJJPq7H4pSnn3T57eFiDlwEY9v3oP8yTNYODvA48sRyEnPRPJfpTwEW0ws3DdvztHREfd/OIj0O6X3z3Uf7631v3VVOQ/wOBMOj4Gqi/uGNFE1jhINSaVSjBo1Cps3b8bAgQPRv39/xMfHY+PGjbCzs0NiYqLahb2bmxsmTZqENWvWYPDgwejXr59q5uOuXbuqTW6WkZEBALCyslKVvfz9ZR2Rvkk8HYIGg73g/rkPpGYmyH7yDI8u38KFGWuRk8bPhbaJRCJ0/XEGjnnPRe5zWRENACiB5mP7w7FHm8L1REREJdCpxAAAAgICIJFIcPDgQZw+fRodO3bE/v37sWjRIkRHRxd6KHnlypVwdnbGhg0bcOTIEdjY2GDKlClYtGgRxOJXo7Wam5sDAJ49e6a69ZKenq5WR6RvwtYcQNiaA0KHQa+p2aw++v3uj+B5m/Hw3E21OpPaNdFy0kA0H9u/yC6XRETlwef+qj+dSwzMzMwQGBiIwMBAtfLw8HC4ubmpXewDgIGBAfz8/ODn51fieq2srODk5ITQ0FA0adIEABASEgJzc3M4OztX6DYQEZWHlWtd9N41F8mXI3F80DwAQOcVk9DwAy+Iq0h3ASIi0j3V4j9Ieno6EhIS0L9//3Ktx9fXF9988w28vLwgkUiwYMECjBkzRqMHj6l4jUf0ROPhPaBUKnBp5kak336gqnPq/RZaTR2Mgrx83Nl+Evd/Ow8A6PT9eFg0rIMCeS4u+q2H7OETNBrWDa0/HYqsxMcAgJMjv0aBnBPUkf4yr2en+r1Ol1Y6lRSY1a2NLuumQ5GfD5GBAS7P2oint+JU9V5rpsK8nh1EBmLc3nIc9/aeK2FtpCtK2+8GJlJ0WPx/MKtnB7GBGKc+8oeZU210/O6/UCqUUOYX4KLfemQ+KHoociIqH935L1KCsLAwAOoPHr+J2bNn4/Hjx2jRogUUCgWGDBmCZcuWVUCE+ktqZYYmo3vjSP/ZMK9vh45Lx+KPoQtfVIpEaPvVSBzu+yUKcnLR57eFiD95HQ6dW6AgJw/HB81DrVYN0Parj3B+0ovRVe7sOMmuLUTVQFbSExwdOAdQKmHfuSVaTR2McxNWqOpDl+9BRkwyxFJDDDz9A2IOXISinBM0kfBK2+/unw7D/f0XkHzx1eAG8ifPceqjb5CXIYNjd3e0njEEF2esEyJ8omqPicFrDA0NERAQwHkLKlDtNo2Q/FcElPkFeH7vIYysLQCRCFAqYWxtDvnj58iXvZhs7ln0Q9T2aAyLBnXw5OY9AMCTv+/DrkNT1foa+XRH3V5t8eD4VUSsPyTINhFR+b0+DrrU3ARpkbFq9Rn/DKuoyM0HlEoolUpthkeVpLT9bt+5BQyMDOH+6VA8PP83/l65D/Inz1X1irwCjqFPVInEpTep+iZOnAilUglPT0+hQ6F/kVqZIfdZlup1XmY2pBYvZpuWP3kOYxsLmNhawbCGMew6NIORlRme3n6AOt3cAQCO3d1h8s94+Q+OB+NA1xn4Y8hC2HdsAYe33bS+PURUcaxbOKPf71+jw9e+SDofVmSblpO8EXvkMpT5ujtZG6krab9bN3dG4plQHB+yALXcGsC+YwtVnYGxFO6fD0PkpqPaDplIb1SLxICqrtxnWZBa1FC9lpiZqA2zeGnmBnRZOw1d189AelQ8ZI/SkHg6BM/vP0SffQvh2KMN0v7pf5r7XAalQgFFXj7ijl6BtZuL1reHiCpOWkQsjr7/FYLGLEUH/08K1bsM7Ixabi4IWbZLgOiospS03+Vpz5F49iagVOLhuZuo2bw+AEBkIEaXddMQsf6Q2nNqRFSxmBhQpUq9cRd2ns0gMhDD3NkeOWnPgde6BDy6fAt/DF2Ic+NXwNDUCKnX7wIAQr/fg+MfzEf8H9eQ/FcEAEBibqpazr5jc2TEJGl3Y4iowoilr3qy5j2XoSBbfSCBOt1ao/GHPXB+6mq1cwbpttL2+6PLt1CrVQMAQK1WDfD8n/N85+UT8PDsTTw4flV7wRLpoWrxjAFVXbnpmbj7SxD67l8MpVKBy19ugmN3d0itzBCz/wLemj8KtdwaQJFfgBvf/AJFXj6MrM3RfeNnUOQXICvxMa589RMAoMX49+HYzR1KhQKPQ+/xHwSRDrNt1xTunw2DskABkUiE4AVb1M4NXqsmQ/boKXrvnAsAODd+BbJT04UNmsqttP1+3X8HOn8/AQbGUqRHxSPxdAgcu7vDeUAnmDnZwmVgZ6RFxCB43hahN4WoWmJiQJXuzo5TuLPjlOr108hXQ9NdW7itUPuctAwc/2B+ofLQ73Yj9LvdlRMkEWlV8sVwHH9t5Jl/2916rBajIW0pbb9nJTzGieGL1coSz4RiR4ORlR0aEYFdiYiIiIiICEwMiIiIiIgI7EpEFcTc2b5cyyvyC/D8/ouHzCwaOEBs+OazTZc3luqG+4a0paocazzOhFOevz3PNZWrqvxNqkocVDSRkrPGUBWQ9fAJ9rb9LwBg6PVA1KhTS+CI6CXum6qruu2b6rY9VDbc/0TCY1ciIiIiIiJiYkBEREREREwMiIiIiIgITAyIiIiIiAhMDIiIiIiICEwMiIiIiIgITAyIiIiIiAhMDIiIiIiICEwMiIiIiIgITAyIiIiIiAhMDIiIiIiICEwMiIiIiIgITAyIiIiIiAhMDIiIiIiICEwMiIiIiIgIgKHQAeizGVeARJnQUQCOpsCKDkJHQVRY0OilyIhNFjoMjZk726Pn1llCh0EVoCodezyu9E9VOv50QWV9RvTxOo2JgYASZcD9DKGjIKq6MmKTkX4nQegwSA/x2CMh8firGvTxOo1diYiIiIiIiIkBERERERExMSAiIiIiIjAxICIiIiIiMDEgIiIiIiIwMSAiIiqXt1dOwpikXzEm6VeMStiNodcD8XbAFJjaWwsdGlGZ9dm3EJ2+H1+o3KxubYxJ+hW27ZsKEFXVF/VVN8Su9i1UnvMoFtcHipAZeUGAqMqOiQEREVE5JV+OxO5Wvvj1rQn4c9JK1GrpjG4b/IQOi4ioTJgYEBERlZMiNx/ZqemQJafh0eVbiNpxCrbtmkBiZiJ0aEREGtOLxEChUGDFihVo2rQpjI2N4eTkBD8/P2RlZQkdGhERVTMmdjXh/J4nFPkFUBYohA6HiEhjejHz8YwZMxAQEIBBgwbBz88Pt27dQkBAAEJCQnDq1CmIxXqRHxERUSWx79QCI6O3QyQWw9DECAAQvv4Q8rNzAADdNvrh4bmbuLPjFADAuqULuqybht97fY6CnDzB4iZ6U332L4LUzAQiiSFSrtzC5S83QalgIlyamJWj8fzGMRha2qLF6nChwymk2icGERERWL16NQYPHox9+/apyl1cXDB16lTs2rULI0aMEDBCqk6UCgWSLoQjevcZZD18AgNjKRy7tkYjn24wqmkudHhEVElSb9zFhWlrYGAkgfOATqjj1Qohy3aq6oPnbkbfg4sRd/QKcp5mouPSsbgy+ycmBaSzgj7+BnmZ2QCAbps+g/P7HRFz8KLAUVV9Nu/8H+zen4aYlaOEDqVIOv1V+c2bNzFw4EBYWlrCwsIC3t7eSEpKgrm5OYYPHw4A2LlzJ5RKJaZPn6627NixY2FqaoodO3YIEHn5KHKykfjzXISPb4wbQ00QOtIat/zaIeX3AKFD02tZSU/w+7szccJnEe7/dh6PrtzCw7OhuLpwK3a3GYfoPWeFDrHasPNshh6bZ2LI1fUYk/QrWk3/QOiQSM8VyHOREZuM9Kh4hH63GxnxKejw9SeqellyGiICD+OtuR+jyce98Ox+EpIuhAkYMVHRcp/LILWoUahcavmi7GUy+zIpEBkawEBiCKVSqb0gqyADU0sUyJ4VKi/ISgcAiCTGAADzll1hYFZ1RyzT2cQgKCgInp6eiIqKwpw5c+Dv74+EhAT07dsXmZmZcHd3BwBcvXoVYrEY7du3V1ve2NgY7u7uuHr1qgDRl8+DHycg7cw21B3zHVqsiYTrkjOo3W8S8v85+Ej7ctIz8ceQBUgLj3lV+NpJUpGbjwvT1vDblApiaGqM9LvxuLZ4O2SPngodDlEhod/vRiOf7qjVuqGq7Pbm47Bq4gS3yd64unCrgNERFe9ZdCJqtWoA0b+6Wdu0aQRFfgEyYpJUZe/+ugAfhv8PeZnZiDt8WduhVinGdZtCdu86lAUFauVZd4MBsQGMHBoJFFnZ6GRikJqaCh8fH3h4eCAkJASff/45Jk+ejKCgIDx48AAAVInBw4cPYWNjAyMjo0LrcXR0xOPHj5Gbm6vN8Mst/coB2A36HFae3jCyc4GpS2vY9ByDOsPnCR2a3rq9+Tie308qvoFSCYiA4HlboMjL115g1VTi6RDc8P8FsYf+giKXXTGo6smISUb8yWvwmPXhq0KlElHbTiIh6AZynjwXLjiiEtzeehzGtS3ReeUk1GrVAOb17eDi3RltvhiO6N1nkPtcpmr7x5AF2O0+FgYmUti/3VLAqIVXu+9E5Kc/QmzAf5AVfR05SfeQ9udOPPx5Lmx6/geGZlZCh6gRnXzGYNmyZXj69Ck2b94ME5NXQ8FZWlrCw8MDQUFBqsRAJpMVmRQAL+4avGwjlUrLHVd+fj6Sk5M1bp+XZwdAUub3kdR0wPMbx2HdZQQMzct/OyovLw8JCY/KvZ7ykD9KV/2elJQEY0W2cMGUkbJAgcjNxwARgJLupCqB7JSnCNn5B+x6tNZWeOUm5L7J07EkKi8vHwkJCVp7P13+3BSlKm1PRRx74esOof/vX8O+YwskX4p4UahQQKkoW5cLbR9XQqlK+19oQp77shIe4+j7X8Fj5ofouXUWJBamyIx7hPB1hxC56Uih9gXyXDw4Fox677ZD0p9/CxBx5X1GynKdZmRbH02W/YWHP8/BvSXvo0D2DFL7BrAb9Dns3p9Wzjje7DrN3t4ehoZlu9TXycRg165d8PLygqura5H1dnZ2sLe3BwCYmpoiJSWlyHZyuVzV5qU9e/YgICAAoaGhsLGxQWxsrMZxJScnw8nJSeP2zVeHw6ReC43bv1R/8ibELB+Bm6Nqw8SpBWo08YRl236w7DAQIpGozOu7c+cOnN4VNtOvKTbBD7b9AADt27fHUx36h2AlNsYK2/4at186aRZ+zax6IxEUR8h9s6RWLzhKLLT2fuV1584dDCvDOaC8dPlzU5SqtD1lOfYuTF9bZHnqtShscRhS7li0fVwJpSrtf6EJfe57GhmHoNFLi62XmJtCLDVEzpPnEBmI4dTrLST/FaHFCNVV1mekrNdppi6t0WjO7xUex5tep8XHx6Nu3bplWkbnEoPk5GQkJibCx8enUJ1CoUBYWBjatGmjKqtTpw4iIyORk5NT6M5BYmIibGxs1O4W1KxZE5MnT8ajR4+wYsWKytuQcjBr1hktA+8h604wsqIuISPiT9xbNgSWbfui4VeH3ig5oDcnRtn+3mLuHyIi0mFSS1N03/Q5xBJDiAzESPrzJqK2nxA6LJ1wb9lQZN66gPznj/H3/9WF/ZDZsO03UeiwVHQuMXg5KVlRF78HDx5ESkqKqhsRALRr1w4nTpxAcHAwvLy8VOVyuRyhoaHo0qWL2jp69eoFADhw4ECZY7O3t0d8fLzG7adE2iFeXua3AQCIDAxh1qwTzJp1gp23H56c3YHYFR8jM+JPmLfsWqZ1ubq64o8yxF0Z5I/SceG9BQCA4OBgGNtZCRpPWShy8/Hnu3OQn6nZzvz0m3lYPtCzkqOqOELum0vDliIrRvPueUJzdXVF/J7/ae39dPlzU5SqtD2VdexF7zlb5hHKtH1cCaUq7X+hVfVzX1bCYxzuM1PoMFQq6zNSnuu04jScubfMy7zpddrL3jNloXOJgZOTEwwMDHDu3Dm18ri4OEyZMgUA1BIDHx8f+Pv7Y+XKlWqJwcaNGyGTyTBy5MgKi83Q0LBMt2wkdwFU0AFnXLcZACD/WdHdpkqMQyIp862mipYlfvWsiIODA2rUqSVgNGXnOuIdRG44XGo7iZkJPP7zHiSmxlqIqmIIuW8kEt06RUkkZTsHlJeuf27+rSptT1U69rR9XAmlKu1/oVWl408XVNZnpCKv08pDm9dpOnfkSaVSjBo1Cps3b8bAgQPRv39/xMfHY+PGjbCzs0NiYqJaYuDm5oZJkyZhzZo1GDx4MPr166ea+bhr1646OblZ1OyusPb6EKaN3oKhZW3kJEUjcftsGNSwgrlbd6HD00vNffsjes9Z5D7LLPEBZLepg3UqKaiqDE2NYeHy4psQscQQJrWtYN3CGXlZcmTEVt1v2YiIiKoynUsMACAgIAASiQQHDx7E6dOn0bFjR+zfvx+LFi1CdHR0oYeSV65cCWdnZ2zYsAFHjhyBjY0NpkyZgkWLFkEs1r0RWy09+iLtz5/xcOc8FMiew9DSFuYtusB56mYYWtgIHZ5eMnOqjd475+DkyK+Rk5ZR5AhFLScOhNtkbyHCq3ZsWjdEn98Wql43+7++aPZ/fZH8VwSOfzBfwMiIiIh0l04mBmZmZggMDERgYKBaeXh4ONzc3Apd7BsYGMDPzw9+fn7aDLPS2A+ZBfshs4QOg/7Fxr0RBl9cjeg9Z3Fn+0k8i04EADgP6ISWEwbAxl03JjfRBcmXIipktBciIiJ6RScTg6Kkp6cjISEB/ftrPmxkUQoKCpCXl4e8vDwolUrI5XKIRKJi50Igep2RlRlajHsPzu91xN62/wUAtJs/Wq/7yhLpA7O6tdFl3XQo8vMhMjDA5Vkb8fRWnKrea81UmNezg8hAjNtbjuPe3nMlrI2oMIsGDvA+uwLHvOci9cZdtTqzerbo/MNEiCWGeHAsGBE/HoKBiRTv7pkPq8Z1cWnmBsQcvFji+o1qWcDz609gXMsC+dm5CBr1jVp987H94TLobSjyCpAWdh9X5pT8sG/rGUNQp1trFMjzcGH6GsiS0kp9P7HEEF3WToOJrRVEBmJc+eonPPn7PlrPGAKHt90AAOYu9ghfexC3fjqq6Z+u0t0Yaooaru0BALbvTUPNjoMKtYn6qhuMHZui/sQfVWXyxDuImNICTb45D7MmVWNQkmqTGISFhQFQf/D4TWzfvh3/+c9/VK9NTExQv379Ms1nQERE+iUr6QmODpwDKJWw79wSraYOxrkJr4a8Dl2+BxkxyRBLDTHw9A+IOXCRs6BTmbSeMQTJlyKLrHtrzse48c0vSL1+B31+W4i4I5eRlfgYZ/7vOzQZ1Vuj9bebPxqh3+/Gs+iHRdbHn7yOyI0vJjjrun4G7Do2x6Ni4rFyrQvb9k1xbOBcOHRpBY+ZHxaa76Oo93PwckNuhgxnxy2HTZvGaDXtA5z55DvcXPErbq74FQDw/olvEXfkskbbpC3S2vXQ5OuzxdanXz0MAxPzQuVJexbDvEXZRpKsbLrXwb4YFZUYjBkzBkqlUu2HSQEREZVEWaAAlC8eLJKamyAtMlatPuOfoScVufnAP/9biDRl06YxslPSIUt6UmS9ZWNHpF6/AwBIOHUDdp7NoFQokJ2artH6RWIxrJrUhdvkQejz20I0HtGzUJvXB3ZQ5Oe/OOaLYefZHPEnrwMAkv78G7VaNdDo/TJik2Fg9GKmYamlKeRPnqktZ+VaF7nPsiBLVr/7ILS8tIeImt0V978bjrx09dEhlQoFUo+uRe1+k9TKs6KuQGJlD6lN1RpxrNokBhMnToRSqYSnZ9W4FUNERPrFuoUz+v3+NTp87Yuk82FFtmk5yRuxRy5DmV+g5ehIl7WaNhhha/YXWy8Sv5rbKedZFoxqFv52uiTGNhawbu6M8PWHcGL4YjQe3gPm9e2KbGvbvilM7a2REny72PVJrcxejNL3Mj4D9cvN4t4vMyEVhiZGGHR+FTr/MBG3Nql3F2rwQRfc33+hTNumDW4b7qOJ/zlYtR+AhM3qz7M+Ob0VVh0HQyxRH5Ewae/XsP+g6j0vWm0SAyIiIiGlRcTi6PtfIWjMUnTw/6RQvcvAzqjl5oKQZbsEiI50Vd2eHnhy8x5ynmYW2+b1G1BSC1PkPM0o03vkPstC1sPHSI+KhyI3H48uR8KqiVOhdpaNHfHWnI9x9r8/lLy+9ExILWq8iu9fdxeKe79Gw7ohMz4F+72m4diAOej8g/qMwPX7dUDc4Utl2jZteDkiZM23h0F2P0RVrsiVI+3cz7Dp+R+19s+uHXkx5LxF1Xv+kIkBEVV7b6+chDFJv2JM0q8YlbAbQ68H4u2AKTC1txY6NKomxNJXj+zlPZehIDtXrb5Ot9Zo/GEPnJ+6Wv0qjqgU1i2dYd+pBXr98hUcurRCu4VjYGJrpdbm2Z0E1ch3dXt64NGVW8Wuz7CGMaQWpmplBTl5yEp4rDonWrdqgOf/mhOmhqMN3l41GX9OWvViWO5/mNpbQ/Sv0SAfXY6EY482AAD7zi3x5O/7mr2fSAT5P+vOeZYFyWtx2rZvivS7Cch9Lit224RQIM+CsuDFHcCMiD9h5PBqBMKcRzEoyEpH9OL3kLD1Czy7fhRPTm+D7H4oMsPP4u6CPngeehIJP81AXlqSUJugpto8fExEVJLky5E4N+4HiAzEMHe2g6e/L7pt8MPRAV8JHRpVA7btmsL9s2FQFiggEokQvGALHLu7Q2plhpj9F+C1ajJkj56i9865AIBz41do3P+b9Nvfq37D36t+A/DiS46obSeQnZKudnxd9/8ZnZdPgMjQAPF/XEXmgxf93Ltt+gy1WrogXyaHjUdjXJ2/BS7eb8PQWFpoVJ/g+VvQZd00iA0NkXAmBM/uJMCkthWa//c9XF+yA2/N+RjG1hZ4e+WLvvJha/Yj8UwouqyfjtOjl6pdsKffScCT0Hvoe3AxCnLycXHGiwePGw3rhszEx0i+GF7k+2XFp6LLuuno89tCGJoYIWTZTtU6Gwz2wv3fql43InnCbcStHQsDYzOIDCWoNzEQz24cR0FGGqy7jkCzH64BADLCziLt/C7U6jEKAOAw7MX/nthVY2DTZzwk1g5CbYIaJgZEpBcUufmqCzFZchqidpyC59efQGJmgrzMbGGDI52XfDEcxy+GF1u/u/VYLUZD1dXrI/sknglV/Z4Rm1zk5I5nfb8vVFazqRNurtxXqDwtPAbHB6uvIzs1HdeX7AAAtVG2XhIZGiDzQUqR3+KHLt+D0OV71Mqi95wt8f3ys3Nw+j/LCq0LAC7P2lhkudBqNGqL5ituqJUZOxSet8jcrRvM3boVKneetqWSInszTAyISO+Y2NWE83ueUOQXlDiyBhFRdRM8d3OFrUuZX4AL09ZU2PpIeHzGgIj0gn2nFhgZvR0f3f8ZPqEbYd+xBSI3HkF+dg6AF/1kh1xbD+NaFgAAAxMpBl9cDaum9UqsIyIiqi6YGBCRXki9cReH3vkch/vOQugPe5FyNUqt/6osOQ2RgYfRbuEYAIC73zDEHbuC9NsPSqwjIiKqLtiVSECOpqW30YaqEgdRZSqQ56om6An9bjfMne3R4etP8Ndnr6anv/XTMbx3fBma+fZD/X4dcKjnZxrVUfVj7mxfruUV+QV4fv/FKCMWDRwgNjQQLBbSPdznZVNZf6+qcn2kzTiYGAhoRQehIyDSX6Hf78agP1chavtJPLl5D8CLGSqvzt+CPr8txOn/+1bVzai0Oqp+em4t38RDWQ+fYG/b/wIA3t27ADXqVL3xyqnqKu/xRxVDH6/T2JWIiPRSRkwy4k9eg8esD9XKHXu2gSw5DTWLeH6gpDoiIiJdx8SAiPRW+LpDcOzmDvuOLQAAVk3roV6f9jjcdxYaj+gJs3q2qrYl1REREVUHTAyIqNq7MH0tTvgsKlSeei0KWxyGIPlSBACg47JxuDp/C2TJaQj5dhc6fP2Jqm1JdURERNUBEwMiIgCNR74D+eNnSAh6MVHNvb3nIKlhjHr9OpRYR0REVF3w4WMiIgB3fz6Fuz+fUit7fVbOkuqIiIiqA94xICIiIiIiJgZERERERMTEgIiIiIiIwMSAiIiIiIjAxICIiIiIiMDEgIiIiIiIwMSAiIiIiIjAeQyISE9YudZFx+/+C6VCCWV+AS76rUfmgxRVvbvfMDQa3h3P7ibg5IivNVqGiEjXvPPOO6hbty62bNkidCjFksvlmDBhAkJDQxEREYF69eohOjpa6LD0Au8YEJFekD95jlMffYPjg+YhfN1BtJ4xRK0+avsJHP9gfpmWISKiwnJzc8u1fEFBAaRSKcaNG4fhw4dXUFSkCSYGRKQX5E+eIy9DBgBQ5BVAWaBQq89OSQcUyjItQ0QkhLVr16J58+YwMjKCra0tPvjgAwCAs7MzlixZotbW19cX3bp1AwCMGTMGQUFB2Lp1K0QiEUQiEc6ePVvq++Xn52PhwoVo2LAhjIyM4OjoiClTpqjqRSIRAgICMGLECFhaWuLjjz/GmDFjVO/x+s+CBQtKfb8aNWogMDAQEyZMQIMGDTT+u1D5sSsRURGCRi9FRmzyGy+vyC9Q/f7H0AUQGxq80XrMne3Rc+usN46DCjMwlsL982G4NHNjpS5DRFQZ5s+fj+XLl2Pp0qXo3bs3MjMzcezYMY2WXbVqFe7fvw8HBwesWrUKAGBtbV3qcp988gmOHTuG5cuXo1OnTkhNTcWlS5fU2ixcuBALFy7E4sWLoVAoYGtri6VLl6rqDx06hIkTJ8LLy6sMW0vaxsSAqAgZsclIv5NQIet6fj+pQtZD5ScyEKPLummIWH8I6bcfVNoyRESVISsrC99++y0WL16MyZMnq8o9PDw0Wt7S0hJSqRQmJiawt7fXaJno6Ghs27YNe/fuxZAhL7pTNmzYEJ6enmrtvL291WJ6+X4AEBoaik8//RQBAQHo2bOnRu9LwmBXIiLSG52XT8DDszfx4PjVSl2GiKgyREREQC6Xo3fv3lp7zxs3bgBAqe/Zvn37IsuTkpLw/vvvw9fXFxMnTqzw+Khi8Y4BEekFx+7ucB7QCWZOtnAZ2BlpETFIPBMKqZUZYvZfgOtH76Dh0K6wbOSI3rvn4fzU1bBuXr/QMsHztgi9KURERRKLxVAq1Z+VysvL08p716hRo1CZTCbDgAED0KZNG/zwww9aiYPKh4kBEemFxDOh2NFgZLH1d3acwp0dp9SXefS0xGWIiLSpefPmMDY2xokTJ9CqVatC9ba2tnj48KFaWUhIiNpzBFKpFAUFBf9etFgvuymdOHFC1ZVIE0qlEqNGjUJ+fj527twJsZidVHQBEwMiIiIiHWBmZgY/Pz8sWLAAJiYm6NWrF7Kzs3H06FF8+eWXeOedd7Bu3ToMGjQI9evXx48//oi4uDi1xMDFxQVnzpzBvXv3YGlpCUtLS0gkkmLfs1GjRhg5ciQmTpwIuVyOjh07Ii0tDX/99RemTZtW7HILFy7E6dOncfLkSWRkZCAjI0O1DWZmZqVua2RkJHJzc5GcnIzc3FyEhoYCeJEcSaVSDf9iVFZMDIjK4e2Vk9DIpzsAQFFQgOxH6Ui6GI4b/j9DlpwmcHRERFTdLF68GLVr10ZAQABmzJiBmjVrokuXLgCAmTNnIi4uDj4+PpBIJJg4cSKGDh2qNjmYn58fwsLC0Lp1a2RlZeHMmTOq4UyLs3nzZixatAhz5szBw4cPYWtrW+rdg7Nnz+Lp06d466231Mrnz5+v0ZCl/fr1Q1xcnOp1mzZtAAAxMTFwdnYudXl6M0wMiMop+XIkzo37ASIDMcyd7eDp74tuG/xwdMBXQodGRETVjEgkwrRp04r8tt7c3Bzbt28vcfkGDRrgzz//LNN7SiQSLF68GIsXLy6y/t/PNQDQaH6EksTGxpZreXoz7PBFVE6K3Hxkp6ZDlpyGR5dvIWrHKdi2awKJmYnQoRERERFpjIkBUQUysasJ5/c8ocjnLLlERFT1+fv7q/r9F/VTGUp6P39//0p5T9KMXnQlUigUWLVqFQIDAxEbG4vatWtj2LBhWLRoUZHDaxGVhX2nFhgZvR0isRiGJkYAgPD1h5CfnQMAqNe3Pdw/Haq2jKVrXQTP3YyobSe0Hi8REdFL48ePR9OmTYutv3r11Rwuubm52LJlC8aMGVPqA8Dt2rUrtu7lg8RF0WQmZqo8epEYzJgxAwEBARg0aBD8/Pxw69YtBAQEICQkBKdOneIQWlVAfnau6vespCeoUaeWgNGUTeqNu7gwbQ0MjCRwHtAJdbxaIWTZTlX9g2PBeHAsWPW6Xp928PhyBKL3nhUgWqKqKzMhVfV7QY52xl6nqkOpeHWXNf1uAkwdrCESiQSMSD9YW1vDyclJo7a5ubnYtGkTRowYUa6RgRo1avTGy1LlqvaJQUREBFavXo3Bgwdj3759qnIXFxdMnToVu3btwogRIwSMUL/JnzzH36v24e7O06qyo+/NhkOXVmg1ZRAc3nYTMDrNFMhzkRGbDAAI/W43zJ3t0eHrT/DXZz8WamvqYI0O/r44NdIfBa8lQ1R2jj3aoO2XI2DZuC6yU54i8qejiAw8LHRY9AbiT11H+NoDeHT5lqrscN+ZcP24F1pNGQSpBe/sVmdKhQK3Nx9HxIZXn9+TwxfDokEdNPPti6aj34WIX+ARaYVOf9Ju3ryJgQMHwtLSEhYWFvD29kZSUhLMzc0xfPhwAMDOnTuhVCoxffp0tWXHjh0LU1NT7NixQ4DICQBkj57iyHuzEbnxCPIys9Xqkv78G3/4LMLdXaeLWbrqCv1+Nxr5dEet1g3VK0QidFkzDWFrDuDprbiiFyaN1GrdED23zETCmRAc6vUZQr/fg7azRqDJqN5Ch0ZlFLnxCII+/kYtKQCA3OdZCF9zAEcHzoE8LUOg6KiyKQoKcG7iSlyZ8z9kxqeo1T2PeYgrs3/C+ckBancTiKjy6GxiEBQUBE9PT0RFRWHOnDnw9/dHQkIC+vbti8zMTLi7uwN40TdOLBajffv2assbGxvD3d1dre8cade5CStU37QXSQn85fcjnoTHaC+oCpARk4z4k9fgMetDtfLW0z9AboYMt/93TKDIqo8W497D49B7uOH/C57dTUT0nrO49b9jcJvsLXRoVAaPLkcieN5moKjeIv+Mfph+Ox4XZ6zValykPeHrDiH24F8vXvx7xMt/Xt/ffwERP/6u1bioaIaGhhgwYAAMDat9hxO9pZOJQWpqKnx8fODh4YGQkBB8/vnnmDx5MoKCgvDgwQMAUCUGDx8+hI2NDYyMjAqtx9HREY8fP0ZuLrt0aNuTv+/j0aXIkhsplapbzLomfN0hOHZzh33HFgAA23ZN0HhET17gVBDb9k2ReCZErSzxTCjMnGxh6sAH13RF5KajL34pPAS6mvgT1/A8JqnyAyKtUuTl49amo0Unhq8TiRC56SgU+QVaiYuKZ2xsjDlz5sDY2FjoUKiS6GTKt2zZMjx9+hSbN2+GicmrseItLS3h4eGBoKAgVWIgk8mKTAoAqA5smUxWIdNr5+fnIzm5hG/ASeXONs2/Nb+37xzqTeuv1T6meXn5GrW7ML3oC/3Ua1HY4vBiVkiphSm8Vk/FhWlrkPM0s8xxJCQklGmZiiZ/lK76PSkpCcaK7OIbV7Di9oOJrRWyU9PVyrJTnv5TVxOyJGFmndb2/hJy35RXQXYO4o5d0bh96NajaOD7biVGVLF0ed9oy5MrUarPbYmUSsiSniD8yJ+wbtu48gPTQ1lZWRq1y8nJQUBAAKZOnVrstdVLQv/vIsDe3r7Md3d0MjHYtWsXvLy84OrqWmS9nZ0d7O3tAQCmpqZISUkpsp1cLle1AV4c8C/vPKSmpsLBwQFTpkzBlClTNIorOTlZ4yf79Z2v5VvobFJfo7aKnHy4OjeEXKnZxXpFWFKrFxwlFhWyriaj34WJrRXaLxyjVh699xwiN5T8sOydO3cwTOBjqqbYBD/Y9gMAtG/fHk+1eIFTkftBG7S9v4TcN+X1euya2PDDavy80LcSI6pYurxvtKWDcV2Mt+qgcXvf4R/jqjyxEiPSX76+mn22cnNzcfToUdjY2JT6heqmTZsqIjQqh/j4eNStW7dMy+hcYpCcnIzExET4+PgUqlMoFAgLC0ObNm1UZXXq1EFkZCRycnIKZbeJiYlqB3d+fj7s7e1x4sQJNGjQAH///Tfeffdd2NnZYdiwYZW7YXpGrtD8Il+hVCJHi0lBRQtbvR9hq/cLHUa1kp2SDpPaVmplxv+81ugbSBJcWRN9bX4xQNpR5mOgDP83iOjN6Fxi8PJ2V1FjGx88eBApKSmqbkTAiwk2Tpw4geDgYHh5eanK5XI5QkND0aVLF1VZjRo1sHjxYtVrd3d3DBgwABcuXNAoMbC3t0d8fPybbJbeeXIlCiGT12vU1q6bGx58v6qSI1J3adhSZMUI3y3M1dUV8Xv+J2gM8kfpuPDeAgBAcHAwjO2stPbexe2HlODbqNPNHTdX/Koqc+zujsz4FMG6EQHa319C7puKcO2/q5F+455GbRf+GoiVLTS7y1gV6Pq+0YZ8WQ7O95mHgn8mgyyJQQ1j/H7+LxgYl7/bLxUWHR2tUbusrCxs27YNw4YNK3WC2Pnz51dEaFQOL3vPlIXOJQZOTk4wMDDAuXPn1Mrj4uJUXX5eTwx8fHzg7++PlStXqiUGGzduhEwmw8iRI4t9r7y8PJw/fx6fffaZRrEZGhqW+ZaNvnKsUwf3fjiI5zEPS33wsM2EQaij5b+rRFI1PhoSifDHVJb41XM8Dg4OWp18rrj9ELHhMPr//jXazPoQ9389B5s2jdHs//ri6oKtWoutKNreX0Lum4qQP8EbZ8cuL7VdrdYN0bx3J52a7ErX9422PBrRE7d+OlpquyYj30H9Rg20EJF+SkrS7OF+iUQCX19fWFlZldqVSOj/XfRmdG5UIqlUilGjRuHatWsYOHAgNmzYgLlz56JDhw6oVevFiff1xMDNzQ2TJk3Cb7/9hsGDB2PTpk3w8/PDp59+iq5du5Y4udnkyZNhbm6OUaNGVfZm6R2RWIyugTNgWKOYkQ3++f/fYvz7qNO1tfYCI53w5OY9nP7Pt3B6py0GnFqONl8Mx41lOxG17YTQoVEZ1O/vicYjer54Ucw1v1FNM3RZM1WnkgLSnMesD1HLreQL/lqtG6LNF4W7D5P2SaVSjBs3rkIGbKGqqWp8LVpGAQEBkEgkOHjwIE6fPo2OHTti//79WLRoEaKjows9lLxy5Uo4Oztjw4YNOHLkCGxsbDBlyhQsWrQI4mJGuvn0009x6dIlnD59mh+ASlKrpQv6H/oawfM2I+lCuFqdSe2acJvijWafaP5wIumXhKAbSAi6IXQYVA4ikQidvvsvzJ3tEfHjIeT8ayIzx+7u6LDkE1g0cBAoQqpsEjMTvLtvAa4u2Ip7v/4JRW6eqk5sJEGjIV3RbsFoSGqYlLAW0pbs7Gx88cUX+Pbbb9VGhaTqQycTAzMzMwQGBiIwMFCtPDw8HG5uboUu9g0MDODn5wc/Pz+N1j99+nQEBQXh9OnTsLGxqbC4qbCazerj3b0LkH4nAY8uR6IgNw/m9ezg2N0d4irSnYeIKo9ILEarKYPQYtx7SAi6gayHj2FgJIWDlxssnMveP5Z0j9TcFJ2XT0Dbrz5CwqnryH2WBallDTj1agujmuZCh0evKSgowJUrV1BQwDklqqtqc+WVnp6OhIQE9O/fv1zrmTp1Kk6fPo0zZ86gdu3aFRQdlcbKtS6sXHWvP+JH93/G45AXD21FbjqCB8eCVXVea6bCvJ4dRAZi3N5yHPf2noOVa110/O6/UCqUUOYX4KLfemQ+KHo4XSJ9YmAkQf1+mg9dSdWPsbU5Gg3rJnQYRHqt2iQGYWFhANSfLyiruLg4rF69GkZGRnBxcVGVe3l54dgxzSfkIv2RlfgYxz8oeuSF0OV7kBGTDLHUEANP/4CYAxchf/Icpz76BnkZMjh2d0frGUNwccY6LUdNREREVBgTg9fUr18fSmUpQ+QQvcbErib6/LYQ2Y/ScWXOT5A/ea6qy/hnmE1Fbj6gVEKpVKrVK/IKoCxQaD1mIiKiN2FkZITZs2eXOusx6S6dG5WoOBMnToRSqYSnp6fQoZAe2ec5CccHz8eDE1fRbsHoItu0nOSN2COXocx/1SfTwFgK98+HIXJT6cP0ERERVQUSiQTe3t6QSCRCh0KVpNokBkRCeDmKSuyhv2Dd0qVQvcvAzqjl5oKQZbtUZSIDMbqsm4aI9YeQfvuB1mIlIiIqD5lMBh8fH8hkMqFDoUrCxIDoDRmaGEH0zwhYdp7NkRGrPkNvnW6t0fjDHjg/dTXwWhe1zssn4OHZm3hw/KpW4yUiIioPhUKBmJgYKBTsBltdVZtnDIi0zbKxIzp9Px55WXIo8gpw6YtAOHZ3h9TKDDH7L8Br1WTIHj1F751zAQDnxq+AdUtnOA/oBDMnW7gM7Iy0iBgEz9si7IYQERERgYkB0Rt78vd9/N77C7Wy1+8a7G49ttAyiWdCsaPByEqPjYiIiKis2JWIiIiIiEplbGyMVatWwdjYWOhQqJLwjgERERERlcrQ0BAdO3YUOgyqREwMiIpg7mwvdAgAqk4cQtG17de1eImIyiIzMxPvv/8+fv/9d5iZmQkdDlUCJgZERei5dZbQIRC4H4iIqpqsrCyhQ6BKxGcMiIiIiIiIiQERERERETExICIiIiINmJiYYOfOnTAxMRE6FKokTAyIiIiIqFRisRh2dnYQi3n5WF1xzxIRERFRqbKystCjRw8+gFyNMTEgIiIiIiImBkRERERExHkMiIiIiPRau3btNGqXk5OD+fPno1OnTjAyMqrkqEgITAyIiIiIqFRGRkZYsGCB0GFQJWJXIiIiIiIiYmJARERERERMDIiIiIiICEwMiIiIiIgITAyIiIhIQ++88w7GjBkjdBhadeXKFXTq1AnGxsZwcHDAl19+iYKCAqHDIqoUTAyIiIiIihAfH49evXqhSZMmuH79OtavX4/AwEB89dVXQodGVCk4XClViBlXgESZ0FG84GgKrOggdBREpQsavRQZsclvvLwi/9W3ln8MXQCxocEbr8vc2R49t8564+VJd6xduxZr167FvXv3YGlpCS8vL+zbtw/Ozs7w9fXFnDlzVG19fX0RHR2Ns2fPYsyYMQgKCgIAbN26FQBw5swZdOvWrcT3c3Z2xscff4zHjx9j586dkEqlmDdvHsaOHYvPPvsMO3bsgKmpKb788ktMnjxZtVxSUhJmzJiB48ePIycnBx06dMD333+Pt956CwqFAs7Ozhg/fjxmz56tWiYnJwf29vb47rvv4OvrCwBYvXo11q5di9jYWDg5OWHMmDGYOXMmDA1LvwRav349LCws8NNPP0EsFqNFixZITEzEF198gblz56JGjRoa/92JdAETA6oQiTLgfobQURDplozYZKTfSaiQdT2/n1Qh66Hqbf78+Vi+fDmWLl2K3r17IzMzE8eOHdNo2VWrVuH+/ftwcHDAqlWrAADW1tYaLbt69WrMmzcP165dw65duzBlyhQcPXoU77zzDq5evYq9e/di6tSp6NGjB5o3bw6lUglvb2/k5OTg8OHDsLS0xJIlS9CrVy/cvXsXNjY2+Oijj7B9+3a1xODgwYOQy+UYOnQoAGDBggXYvHkzVq5cCXd3d9y6dQvjx4+HXC7H4sWLS4374sWL6N27N8TiVx0s+vTpg8mTJyMkJARvv/22RttPpCvYlYiIiEgPZGVl4dtvv8WCBQswefJkuLq6wsPDQ+NuMZaWlpBKpTAxMYG9vT3s7e0hlUo1WrZbt2749NNP0ahRI8yePRvm5uYwMDBQlc2cOROWlpY4ffo0AOD06dMIDg7GL7/8grfffhtubm7Ytm0bjI2NsW7dOgDAqFGjcPv2bVy9elX1Ptu2bYO3tzcsLS0hk8nw7bffIjAwEIMGDYKLiwv69euHJUuWYPXq1RrFnZSUBHt7e7Wyl6+TkpiMU/XDOwZERER6ICIiAnK5HL1799b6e7du3Vr1u1gsRu3atdGqVSu1MltbW6SkpKhirVWrFpo3b65qY2RkhA4dOiAiIgIA0LRpU7Rv3x7bt29Hu3btkJKSgj/++AOHDh1SrSM7OxsffPABRCKRaj0FBQWQy+VITU1F7dq1K3W7iXQNEwMiIiKCWCyGUqlUK8vLy6uQdUskErXXIpGoyDKFQlGm9Y4aNQoLFy7E8uXL8csvv8DGxkaV+Lxc1969e+Hq6lpoWU26QTk4OCA5Wf05oEePHqnqiKobdiUiIiLSA82bN4exsTFOnDhRZL2trS0ePnyoVhYSEqL2WiqVamWozhYtWuDJkyeIjIxUleXk5ODKlSto2bKlquzDDz/Es2fPcPz4cWzbtg0jR46EgYGBah3Gxsa4f/8+GjVqVOjnZbuSdO7cGSdPnlRLWI4fPw5TU1O0adOmAreYqGpgYkBERKQHzMzM4OfnhwULFmDt2rW4c+cObt68iW+++QbAizkKdu/ejRMnTiAqKgozZsxAXFyc2jpcXFxw/fp13Lt3D48fP66wOwr/1qNHD7Rv3x4jRozAxYsXER4ejlGjRkEul2PChAmqdtbW1ujfvz/mzZuHkJAQjB49Wm17Z8+ejdmzZ2Pt2rWIiopCREQEdu3ahZkzZ2oUx4QJE/Ds2TOMHTsWEREROHToEObOnYspU6ZwRCKqltiViIhIB7y9chIa+XQHACgKCpD9KB1JF8Nxw/9nyJLTBI6OdMXixYtRu3ZtBAQEYMaMGahZsya6dOkCAJg5cybi4uLg4+MDiUSCiRMnYujQoYiOjlYt7+fnh7CwMLRu3RpZWVkaDVf6JkQiEQ4cOIAZM2agf//+yMnJQfv27XHy5EnY2NiotR09ejS8vb3h7u4ONzc3tbq5c+fCwcEBa9asgZ+fH0xMTODq6qrxJG1OTk44ceIEPv30U7Rt2xZWVlYYN24clixZUlGbSlSliJT/7lBI9AaGnak6w5U2MAf2dBc6iuoj6+ET7G37XwDA0OuBqFGnlsARVR8Huk7XeLjSt1dOgll9O5wb9wNEBmKYO9vB098XeZlyHB1Q/smWrFzrwvvcynKvh17g54aIdBG7EhER6QhFbj6yU9MhS07Do8u3ELXjFGzbNYHEzETo0IiIqBrQi65ECoUCq1atQmBgIGJjY1G7dm0MGzYMixYtYh9BItJJJnY14fyeJxT5BVAWlG0kF6KK4u/vD39//2LrMzMzVb+/Pt9AcXJzc7FlyxaMGTNGozkS2rVrp1mgRTh//jz69u1bbP2xY8fg5eX1xusn0kV6kRjMmDEDAQEBGDRoEPz8/HDr1i0EBAQgJCQEp06dUpvRkIioqrLv1AIjo7dDJBbD0MQIABC+/hDys3MAAPX6tof7p0PVlrF0rYvguZsRta3okWiIymP8+PEYNmxYha0vNzcXmzZtwogRIzSePO1NvfXWWwgNDS223tHRsVLfn6gqqvaJQUREBFavXo3Bgwdj3759qnIXFxdMnToVu3btwogRIwSMkKjqkj95jttb/1C9vrpoG5p/0he132qiNmEQaUfqjbu4MG0NDIwkcB7QCXW8WiFk2U5V/YNjwXhwLFj1ul6fdvD4cgSi954VIFr9JU/LUEvEri7Ygmaf9INt+6bV7nNjbW2t0XwAVZGJiQkaNWokdBhEVYpOf1V+8+ZNDBw4EJaWlrCwsIC3tzeSkpJgbm6O4cOHAwB27twJpVKJ6dOnqy07duxYmJqaYseOHQJETq9T5GQj8ee5CB/fGDeGmiB0pDVu+bVDyu8BQoem125vOY49HuMQFvCbqiz24EUcHTAHf3ywAPK0KvK0uR4pkOciIzYZ6VHxCP1uNzLiU9Dh60+KbGvqYI0O/r44N34lCrJztRyp/orafhJ72ozF36tefREV+/slHPOei2OD5kH++JmA0RERlUxnE4OgoCB4enoiKioKc+bMgb+/PxISEtC3b19kZmbC3d0dwIs+jWKxGO3bt1db3tjYGO7u7hr1eaTK9eDHCUg7sw11x3yHFmsi4brkDGr3m4T8rHShQ9NbUdtP4vKXm6DIyy+yPvlSBE5+uBj5shwtR0avC/1+Nxr5dEet1g3VK0QidFkzDWFrDuDprbiiF6YKd3fXaVz6IrDYz03KlVv4Y/hi5GVlazky3WFoaIgBAwbA0LDad2ggqpJ08pOXmpoKHx8feHh44NSpUzAxeTEix8cffwwXFxcAUCUGDx8+hI2NDYyMjAqtx9HREX/99Rdyc3MrvS8jFS/9ygHUGbkEVp7eqjJTl9bCBaTn8mRyXFu8DRABKGEw4yd/30f03rNoOvpdrcVG6jJikhF/8ho8Zn2Ikx++Gle99fQPkJshw+3/HRMwOv2Sn52Dq/O3lPq5eRoRi7s7T6O5b39thaZTjI2NMWfOHKHDINJbOpkYLFu2DE+fPsXmzZtVSQEAWFpawsPDA0FBQarEQCaTFZkUAC9OQC/bVERikJ+fj+Tk5HKvRxfl5dkBkLzRspKaDnh+4zisu4yAoXn5+6rm5eUhIeFRudejrxIPXEJehgbfaIqAsA2/w6xni8oPqprKK+ab5bIIX3cI/X//GvYdWyD5UgRs2zVB4xE98Xvvz8scS0KCZnMqUGEPDwcj97ms9IYiIHzjYZi/26raPW9QmqysrFLb5OTkICAgAFOnTi32f/freMwSFc/e3r7Md990MjHYtWsXvLy84OrqWmS9nZ0d7O3tAQCmpqZISUkpsp1cLle1eWnixIn4/fff8ezZM5ibm2Po0KH49ttvNUockpOT4eTkVNbNqRaarw6HSb03u0CsP3kTYpaPwM1RtWHi1AI1mnjCsm0/WHYY+Eb/OO/cuQOnd1u+USwEjLbwQDdTl9IbKoGs+8lo4FQfeeBwmW9iSa1ecJRYaNT2wvS1RZanXovCFochAACphSm8Vk/FhWlrkPM0s8j2xblz5w6G6en5qyJ8ZO6OnjUalt5QCcgepMK1fgPIleVPDHWJr69vqW1yc3Nx9OhR2NjYaPR/d9OmTRURGlG1FB8fj7p165ZpGZ17xiA5ORmJiYlo27ZtoTqFQoGwsDDV3QIAqFOnDh4/foycnMJ9oRMTEwudfCZPnozbt2/j+fPnuHnzJm7evFniGM1UfmbNOqNl4D008T+PWj1GIy/9Ee4tG4J7Xw8AJ+bWvrKeFAxEOncaqbaajH4XJrZWaL9wDAac/E7103zce0KHVu2Jy/glhhj6dbeAiHSDzt0xeHkrsqhvkg8ePIiUlBS1xKBdu3Y4ceIEgoOD1SYqkcvlCA0NRZcuXdTW0bx5c9XvSqUSYrEYd+/e1Sg2e3t7xMfHl2Vzqo0pkXaIl7/58iIDQ5g16wSzZp1g5+2HJ2d3IHbFx8iM+BPmLbuWaV2urq74Q0/3Q0WI3X4a0QGHSm8oAqS1LHAn+L7edYmoKJeGLUVWTMV1PwxbvR9hq/e/0bKurq6I3/O/CotF3zz45SzurDhQekMRILEyw+0r0RDp2Rw60dHRpbbJysrCtm3bMGzYMI0mIJ0/f35FhEZULb3sPVMWOpcYODk5wcDAAOfOnVMrj4uLw5QpUwBALTHw8fGBv78/Vq5cqZYYbNy4ETKZDCNHjiz0HkuXLsWSJUuQlZWFWrVqYenSpRrFZmhoWOZbNtWF5C6AciQG/2ZctxkAIP9Z0d3ASoxFItHb/VARao0diPvrj0CRV1ByQyXQ4j999bb7XEWQSKrOKVgi0d/zV0Ww8R2I6LWHocgtpXuQEmg+pg+c6tXTTmBVSFJSUqltJBIJfH19YWVlpVFXIh6zRBVL576ukEqlGDVqFK5du4aBAwdiw4YNmDt3Ljp06IBatWoBUE8M3NzcMGnSJPz2228YPHgwNm3aBD8/P3z66afo2rVrkZObzZo1C5mZmYiMjMT48ePh4OCgrc3TS1GzuyL12I/IunsNOSlxeH4zCA9+nAiDGlYwd+sudHh6x8TGEs00GDHFxNYKrqN6ayEioqrP2NocLca9X3IjEWBsY4kmYziSV3GkUinGjRvHkQKJBKJziQEABAQEYNy4cbhy5Qr8/Pxw5coV7N+/H3Xq1IGpqWmhh5JXrlyJ77//HhEREZg0aRJ27dqFKVOm4PDhwxCXcCu3WbNmaN26NT7++OPK3iS9ZunRF2l//ozoxf0QMbEJYgP+A+M6jdFk6UUYWtgIHZ5eavvVSDQe0bNwxT9dhkztrdF7z3yY2FhqOTKiqsvjyw/RpKhk+Z+edia1rdB791yY2tbUbmA6JDs7G1OmTEF2Nud6IBKCSFmNnu50cnKCo6MjLl++XGHr/OWXX/D5558jMTGxwtZZHQ07A9yvIhPhNjAH9vBGQ7kplUqkXLmF21v/QNKFcBTk5MGsni1cR/ZEwyFdITU3LX0lVKIDXacj/U7VGG7RyrUuvM+tFDoMnadUKpFyNQpRW47j4YUwFMjzYOZUG64jeqLh0K6QWpTeb7660mRC0czMTPTo0QOnT5+GmZlZqe3btWtXEaER0T+qTgfXckpPT0dCQgL693/zSWOePXuG/fv3w9vbG5aWlggLC8OSJUvw7ru87Uv6RyQSwc6zOew8m5femCqNlWtddPzuv1AqlFDmF+Ci33pkPij87E2ffQvxLDoRl2ZugIGJFO/umQ+rxnVxaeYGxBy8KEDk+kkkEsGufVPYtW8qdChERGWmk12JihIWFgZA/fmCshKJRNixYwcaNGgAc3NzeHt7o1+/fli9enUFRUlEVDbyJ89x6qNvcHzQPISvO4jWM4YUalP3nbbIy3zV9UKRk48z//cdIjce0WaoRESk46rNHYOKSAwsLCxw6tSpCoqIiKj85E+eq35X5BVAWfCvyeREIjT9Tx/c2nQE9fq0BwAoFQpkp6ZrMUqiimFkZITZs2drNOsxEVW8anPHYOLEiVAqlfD09BQ6FCKiCmdgLIX758MQuemoWnmjYd0Qd/QKCuR5AkVGVHEkEgm8vb0hkUiEDoVIL1WbxICIqLoSGYjRZd00RKw/hPTbD1TlBkYSNBjshehdpwWMjqjiyGQy+Pj4QCaTCR0KkV6qNl2JiIiqq87LJ+Dh2Zt4cFx9VBezeraQWtbAO9u/hNTKDCa2Vmg4tCvu7T1XzJqIqjaFQoGYmBgoFIrSGxNRhWNiQERUhTl2d4fzgE4wc7KFy8DOSIuIQeKZUEitzBCz/wIO95kJALDv2AIu3p1VSUG3TZ+hVksX5MvksPFojKvztwi4FUREpAuYGBARVWGJZ0Kxo8HIUtslX4pA8qUI1euzvt9XZlhERFQN8RkDIiIiqhKMjY2xatUqGBsbCx0KkV7iHQMiIiKqEgwNDdGxY0ehwyDSW7xjQERERFVCZmYmunfvjszMTKFDIdJLvGNAFcLRVOgIXqlKsRCVxNzZXugQVKpSLKTfsrKyhA6BSG8xMaAKsaKD0BEQ6Z6eW2cJHQIREZEKuxIRERERERETAyIiIqoaTExMsHPnTpiYmAgdCpFeYmJAREREVYJYLIadnR3EYl6eEAmBnzwiIiKqErKystCjRw8+gEwkECYGRERERETExICIiIiIiJgYEBERERERAJFSqVQKHQQRERGRUqlERkYGzM3NIRKJhA6HSO8wMSAiIiIiInYlIiIiIiIiJgZERERERAQmBkREREREBCYGREREREQEJgZERERERAQmBkREREREBCYGREREREQEJgZERERERAQmBkREREREBCYGREREREQEJgZERERERAQmBkREREREBCYGREREREQE4P8BK0zHs9vKHGAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 980.198x294.311 with 1 Axes>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subcircuits[0].draw(\"mpl\", style=\"iqp\", scale=0.8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:29.403930Z",
+     "iopub.status.busy": "2024-04-19T17:42:29.403727Z",
+     "iopub.status.idle": "2024-04-19T17:42:29.616812Z",
+     "shell.execute_reply": "2024-04-19T17:42:29.615970Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1114.14x294.311 with 1 Axes>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subcircuits[1].draw(\"mpl\", style=\"iqp\", scale=0.8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Generate the experiments to run on the backend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:29.619427Z",
+     "iopub.status.busy": "2024-04-19T17:42:29.619202Z",
+     "iopub.status.idle": "2024-04-19T17:42:30.174075Z",
+     "shell.execute_reply": "2024-04-19T17:42:30.173155Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "96 total subexperiments to run on backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from circuit_knitting.cutting import generate_cutting_experiments\n",
+    "\n",
+    "subexperiments, coefficients = generate_cutting_experiments(\n",
+    "    circuits=subcircuits, observables=subobservables, num_samples=1_000\n",
+    ")\n",
+    "print(\n",
+    "    f\"{len(subexperiments[0]) + len(subexperiments[1])} total subexperiments to run on backend.\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_2_0.png b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_2_0.png
index 0d635601d..d49c4986b 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_2_0.png and b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_2_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_32_0.png b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_32_0.png
index f2a13e3c2..90ac1b06b 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_32_0.png and b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_32_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_8_0.png b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_8_0.png
index 784789bb0..d9bdfd5ae 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_8_0.png and b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_8_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_9_0.png b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_9_0.png
index 256498ef2..7cacf7ce8 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_9_0.png and b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_9_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_10_0.png b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_10_0.png
index 0d7abb483..3943c69fa 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_10_0.png and b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_10_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_11_0.png b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_11_0.png
index 7be97850a..d27616e59 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_11_0.png and b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_11_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_27_0.png b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_27_0.png
index b27d0714d..09efb0656 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_27_0.png and b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_27_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_2_0.png b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_2_0.png
index 67a2e122f..573a064e3 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_2_0.png and b/.doctrees/nbsphinx/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_2_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_2_0.png b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_2_0.png
index b2f80061c..f26a08566 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_2_0.png and b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_2_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_4_0.png b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_4_0.png
index 6e654e029..710d4d946 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_4_0.png and b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_4_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_5_0.png b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_5_0.png
index f569c52e5..88f9d4eb5 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_5_0.png and b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_5_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_17_0.png b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_17_0.png
index a69dffe64..0c3d42d93 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_17_0.png and b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_17_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_18_0.png b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_18_0.png
index 0eccde94a..7970c279a 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_18_0.png and b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_18_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_3_0.png b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_3_0.png
index 6c277e118..3e1178a76 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_3_0.png and b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_3_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_5_0.png b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_5_0.png
index b03613853..57088d947 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_5_0.png and b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_5_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_9_0.png b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_9_0.png
index 346b5f5d3..904e45520 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_9_0.png and b/.doctrees/nbsphinx/circuit_cutting_how-tos_how_to_specify_cut_wires_9_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_10_0.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_10_0.png
index 7df0fce6b..7f4e2c680 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_10_0.png and b/.doctrees/nbsphinx/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_10_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_2_0.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_2_0.png
index c11497274..70a0005a7 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_2_0.png and b/.doctrees/nbsphinx/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_2_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_9_0.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_9_0.png
index 7b07a2352..d8a5ba43d 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_9_0.png and b/.doctrees/nbsphinx/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_9_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_11_0.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_11_0.png
index 612b45ae2..4a7d6beb3 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_11_0.png and b/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_11_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_13_0.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_13_0.png
deleted file mode 100644
index c4ee933e7..000000000
Binary files a/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_13_0.png and /dev/null differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_19_1.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_17_1.png
similarity index 99%
rename from .doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_19_1.png
rename to .doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_17_1.png
index 86cb3a01b..1a126dd89 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_19_1.png and b/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_17_1.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_2_0.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_2_0.png
index 688437746..5302f00d1 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_2_0.png and b/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_2_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_9_0.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_9_0.png
new file mode 100644
index 000000000..b962c8cc2
Binary files /dev/null and b/.doctrees/nbsphinx/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_9_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_14_0.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_14_0.png
index 7c2ed74b1..3746dde06 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_14_0.png and b/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_14_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_15_0.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_15_0.png
index 0eccde94a..a8ecc1494 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_15_0.png and b/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_15_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_3_0.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_3_0.png
index b03613853..57088d947 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_3_0.png and b/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_3_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_7_0.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_7_0.png
index e08d03211..a207e727a 100644
Binary files a/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_7_0.png and b/.doctrees/nbsphinx/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_7_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_04_automatic_cut_finding_10_0.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_04_automatic_cut_finding_10_0.png
new file mode 100644
index 000000000..95c3e0bf0
Binary files /dev/null and b/.doctrees/nbsphinx/circuit_cutting_tutorials_04_automatic_cut_finding_10_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_04_automatic_cut_finding_11_0.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_04_automatic_cut_finding_11_0.png
new file mode 100644
index 000000000..7ebcdacb4
Binary files /dev/null and b/.doctrees/nbsphinx/circuit_cutting_tutorials_04_automatic_cut_finding_11_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_04_automatic_cut_finding_2_0.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_04_automatic_cut_finding_2_0.png
new file mode 100644
index 000000000..ff7f56b48
Binary files /dev/null and b/.doctrees/nbsphinx/circuit_cutting_tutorials_04_automatic_cut_finding_2_0.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_04_automatic_cut_finding_4_1.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_04_automatic_cut_finding_4_1.png
new file mode 100644
index 000000000..453636452
Binary files /dev/null and b/.doctrees/nbsphinx/circuit_cutting_tutorials_04_automatic_cut_finding_4_1.png differ
diff --git a/.doctrees/nbsphinx/circuit_cutting_tutorials_04_automatic_cut_finding_6_0.png b/.doctrees/nbsphinx/circuit_cutting_tutorials_04_automatic_cut_finding_6_0.png
new file mode 100644
index 000000000..0335cc64e
Binary files /dev/null and b/.doctrees/nbsphinx/circuit_cutting_tutorials_04_automatic_cut_finding_6_0.png differ
diff --git a/.doctrees/release-notes.doctree b/.doctrees/release-notes.doctree
index 74ddea59d..ab4f599c5 100644
Binary files a/.doctrees/release-notes.doctree and b/.doctrees/release-notes.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.cutting.DeviceConstraints.doctree b/.doctrees/stubs/circuit_knitting.cutting.DeviceConstraints.doctree
new file mode 100644
index 000000000..11c90f7c2
Binary files /dev/null and b/.doctrees/stubs/circuit_knitting.cutting.DeviceConstraints.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.cutting.OptimizationParameters.doctree b/.doctrees/stubs/circuit_knitting.cutting.OptimizationParameters.doctree
new file mode 100644
index 000000000..a8a968485
Binary files /dev/null and b/.doctrees/stubs/circuit_knitting.cutting.OptimizationParameters.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.cutting.cutqc.build.doctree b/.doctrees/stubs/circuit_knitting.cutting.cutqc.build.doctree
index 29fcccfd6..d245b53b4 100644
Binary files a/.doctrees/stubs/circuit_knitting.cutting.cutqc.build.doctree and b/.doctrees/stubs/circuit_knitting.cutting.cutqc.build.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires.doctree b/.doctrees/stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires.doctree
index 78a95072c..94d819483 100644
Binary files a/.doctrees/stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires.doctree and b/.doctrees/stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits.doctree b/.doctrees/stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits.doctree
index 0d10ac635..21cdc9c07 100644
Binary files a/.doctrees/stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits.doctree and b/.doctrees/stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.cutting.cutqc.generate_summation_terms.doctree b/.doctrees/stubs/circuit_knitting.cutting.cutqc.generate_summation_terms.doctree
index 62e1bf477..6baef7d55 100644
Binary files a/.doctrees/stubs/circuit_knitting.cutting.cutqc.generate_summation_terms.doctree and b/.doctrees/stubs/circuit_knitting.cutting.cutqc.generate_summation_terms.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.doctree b/.doctrees/stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.doctree
index d8635fef4..6067c895b 100644
Binary files a/.doctrees/stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.doctree and b/.doctrees/stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances.doctree b/.doctrees/stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances.doctree
index f023c0207..bef5aaf28 100644
Binary files a/.doctrees/stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances.doctree and b/.doctrees/stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.cutting.cutqc.verify.doctree b/.doctrees/stubs/circuit_knitting.cutting.cutqc.verify.doctree
index ac7a0ede2..08f21ee6c 100644
Binary files a/.doctrees/stubs/circuit_knitting.cutting.cutqc.verify.doctree and b/.doctrees/stubs/circuit_knitting.cutting.cutqc.verify.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.cutting.find_cuts.doctree b/.doctrees/stubs/circuit_knitting.cutting.find_cuts.doctree
new file mode 100644
index 000000000..d37e723f9
Binary files /dev/null and b/.doctrees/stubs/circuit_knitting.cutting.find_cuts.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.cutting.qpd.WeightType.doctree b/.doctrees/stubs/circuit_knitting.cutting.qpd.WeightType.doctree
index dada0ce84..12abc6c54 100644
Binary files a/.doctrees/stubs/circuit_knitting.cutting.qpd.WeightType.doctree and b/.doctrees/stubs/circuit_knitting.cutting.qpd.WeightType.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.doctree b/.doctrees/stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.doctree
index 2c8c4f7f6..5a7ac15f9 100644
Binary files a/.doctrees/stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.doctree and b/.doctrees/stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.doctree b/.doctrees/stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.doctree
index f13474875..e2788101f 100644
Binary files a/.doctrees/stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.doctree and b/.doctrees/stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction.doctree b/.doctrees/stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction.doctree
index dc22e1973..8f6de2ff6 100644
Binary files a/.doctrees/stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction.doctree and b/.doctrees/stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.cutting.reconstruct_expectation_values.doctree b/.doctrees/stubs/circuit_knitting.cutting.reconstruct_expectation_values.doctree
index cce2523a8..ece973e22 100644
Binary files a/.doctrees/stubs/circuit_knitting.cutting.reconstruct_expectation_values.doctree and b/.doctrees/stubs/circuit_knitting.cutting.reconstruct_expectation_values.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.utils.conversion.dict_to_array.doctree b/.doctrees/stubs/circuit_knitting.utils.conversion.dict_to_array.doctree
index 3ec17963e..4925c3cdd 100644
Binary files a/.doctrees/stubs/circuit_knitting.utils.conversion.dict_to_array.doctree and b/.doctrees/stubs/circuit_knitting.utils.conversion.dict_to_array.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.utils.conversion.naive_probability_distribution.doctree b/.doctrees/stubs/circuit_knitting.utils.conversion.naive_probability_distribution.doctree
index 2339fbc15..f1c0df887 100644
Binary files a/.doctrees/stubs/circuit_knitting.utils.conversion.naive_probability_distribution.doctree and b/.doctrees/stubs/circuit_knitting.utils.conversion.naive_probability_distribution.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.utils.conversion.nearest_probability_distribution.doctree b/.doctrees/stubs/circuit_knitting.utils.conversion.nearest_probability_distribution.doctree
index 86250c8f8..c82c99bc7 100644
Binary files a/.doctrees/stubs/circuit_knitting.utils.conversion.nearest_probability_distribution.doctree and b/.doctrees/stubs/circuit_knitting.utils.conversion.nearest_probability_distribution.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.utils.conversion.quasi_to_real.doctree b/.doctrees/stubs/circuit_knitting.utils.conversion.quasi_to_real.doctree
index ddca0593f..b7333173a 100644
Binary files a/.doctrees/stubs/circuit_knitting.utils.conversion.quasi_to_real.doctree and b/.doctrees/stubs/circuit_knitting.utils.conversion.quasi_to_real.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.utils.metrics.HOP.doctree b/.doctrees/stubs/circuit_knitting.utils.metrics.HOP.doctree
index d7e6a2fa2..21e8a2170 100644
Binary files a/.doctrees/stubs/circuit_knitting.utils.metrics.HOP.doctree and b/.doctrees/stubs/circuit_knitting.utils.metrics.HOP.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.utils.metrics.MAPE.doctree b/.doctrees/stubs/circuit_knitting.utils.metrics.MAPE.doctree
index d6ab61baa..9e72487e0 100644
Binary files a/.doctrees/stubs/circuit_knitting.utils.metrics.MAPE.doctree and b/.doctrees/stubs/circuit_knitting.utils.metrics.MAPE.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.utils.metrics.MSE.doctree b/.doctrees/stubs/circuit_knitting.utils.metrics.MSE.doctree
index 6105a3751..35727bc1b 100644
Binary files a/.doctrees/stubs/circuit_knitting.utils.metrics.MSE.doctree and b/.doctrees/stubs/circuit_knitting.utils.metrics.MSE.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.utils.metrics.chi2_distance.doctree b/.doctrees/stubs/circuit_knitting.utils.metrics.chi2_distance.doctree
index b163c4b7e..1604d5bb9 100644
Binary files a/.doctrees/stubs/circuit_knitting.utils.metrics.chi2_distance.doctree and b/.doctrees/stubs/circuit_knitting.utils.metrics.chi2_distance.doctree differ
diff --git a/.doctrees/stubs/circuit_knitting.utils.metrics.cross_entropy.doctree b/.doctrees/stubs/circuit_knitting.utils.metrics.cross_entropy.doctree
index ab1424a6d..0a7416675 100644
Binary files a/.doctrees/stubs/circuit_knitting.utils.metrics.cross_entropy.doctree and b/.doctrees/stubs/circuit_knitting.utils.metrics.cross_entropy.doctree differ
diff --git a/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_2_0.png b/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_2_0.png
index 0d635601d..d49c4986b 100644
Binary files a/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_2_0.png and b/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_2_0.png differ
diff --git a/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_32_0.png b/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_32_0.png
index f2a13e3c2..90ac1b06b 100644
Binary files a/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_32_0.png and b/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_32_0.png differ
diff --git a/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_8_0.png b/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_8_0.png
index 784789bb0..d9bdfd5ae 100644
Binary files a/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_8_0.png and b/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_8_0.png differ
diff --git a/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_9_0.png b/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_9_0.png
index 256498ef2..7cacf7ce8 100644
Binary files a/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_9_0.png and b/_images/circuit_cutting_cutqc_tutorials_tutorial_1_automatic_cut_finding_9_0.png differ
diff --git a/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_10_0.png b/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_10_0.png
index 0d7abb483..3943c69fa 100644
Binary files a/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_10_0.png and b/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_10_0.png differ
diff --git a/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_11_0.png b/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_11_0.png
index 7be97850a..d27616e59 100644
Binary files a/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_11_0.png and b/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_11_0.png differ
diff --git a/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_27_0.png b/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_27_0.png
index b27d0714d..09efb0656 100644
Binary files a/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_27_0.png and b/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_27_0.png differ
diff --git a/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_2_0.png b/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_2_0.png
index 67a2e122f..573a064e3 100644
Binary files a/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_2_0.png and b/_images/circuit_cutting_cutqc_tutorials_tutorial_2_manual_cutting_2_0.png differ
diff --git a/_images/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_2_0.png b/_images/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_2_0.png
index b2f80061c..f26a08566 100644
Binary files a/_images/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_2_0.png and b/_images/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_2_0.png differ
diff --git a/_images/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_4_0.png b/_images/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_4_0.png
index 6e654e029..710d4d946 100644
Binary files a/_images/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_4_0.png and b/_images/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_4_0.png differ
diff --git a/_images/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_5_0.png b/_images/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_5_0.png
index f569c52e5..88f9d4eb5 100644
Binary files a/_images/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_5_0.png and b/_images/circuit_cutting_how-tos_how_to_generate_exact_sampling_coefficients_5_0.png differ
diff --git a/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_17_0.png b/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_17_0.png
index a69dffe64..0c3d42d93 100644
Binary files a/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_17_0.png and b/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_17_0.png differ
diff --git a/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_18_0.png b/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_18_0.png
index 0eccde94a..7970c279a 100644
Binary files a/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_18_0.png and b/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_18_0.png differ
diff --git a/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_3_0.png b/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_3_0.png
index 6c277e118..3e1178a76 100644
Binary files a/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_3_0.png and b/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_3_0.png differ
diff --git a/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_5_0.png b/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_5_0.png
index b03613853..57088d947 100644
Binary files a/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_5_0.png and b/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_5_0.png differ
diff --git a/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_9_0.png b/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_9_0.png
index 346b5f5d3..904e45520 100644
Binary files a/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_9_0.png and b/_images/circuit_cutting_how-tos_how_to_specify_cut_wires_9_0.png differ
diff --git a/_images/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_10_0.png b/_images/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_10_0.png
index 7df0fce6b..7f4e2c680 100644
Binary files a/_images/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_10_0.png and b/_images/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_10_0.png differ
diff --git a/_images/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_2_0.png b/_images/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_2_0.png
index c11497274..70a0005a7 100644
Binary files a/_images/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_2_0.png and b/_images/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_2_0.png differ
diff --git a/_images/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_9_0.png b/_images/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_9_0.png
index 7b07a2352..d8a5ba43d 100644
Binary files a/_images/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_9_0.png and b/_images/circuit_cutting_tutorials_01_gate_cutting_to_reduce_circuit_width_9_0.png differ
diff --git a/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_11_0.png b/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_11_0.png
index 612b45ae2..4a7d6beb3 100644
Binary files a/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_11_0.png and b/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_11_0.png differ
diff --git a/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_13_0.png b/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_13_0.png
deleted file mode 100644
index c4ee933e7..000000000
Binary files a/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_13_0.png and /dev/null differ
diff --git a/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_19_1.png b/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_17_1.png
similarity index 99%
rename from _images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_19_1.png
rename to _images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_17_1.png
index 86cb3a01b..1a126dd89 100644
Binary files a/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_19_1.png and b/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_17_1.png differ
diff --git a/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_2_0.png b/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_2_0.png
index 688437746..5302f00d1 100644
Binary files a/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_2_0.png and b/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_2_0.png differ
diff --git a/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_9_0.png b/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_9_0.png
new file mode 100644
index 000000000..b962c8cc2
Binary files /dev/null and b/_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_9_0.png differ
diff --git a/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_14_0.png b/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_14_0.png
index 7c2ed74b1..3746dde06 100644
Binary files a/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_14_0.png and b/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_14_0.png differ
diff --git a/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_15_0.png b/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_15_0.png
index 0eccde94a..a8ecc1494 100644
Binary files a/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_15_0.png and b/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_15_0.png differ
diff --git a/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_3_0.png b/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_3_0.png
index b03613853..57088d947 100644
Binary files a/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_3_0.png and b/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_3_0.png differ
diff --git a/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_7_0.png b/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_7_0.png
index e08d03211..a207e727a 100644
Binary files a/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_7_0.png and b/_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_7_0.png differ
diff --git a/_images/circuit_cutting_tutorials_04_automatic_cut_finding_10_0.png b/_images/circuit_cutting_tutorials_04_automatic_cut_finding_10_0.png
new file mode 100644
index 000000000..95c3e0bf0
Binary files /dev/null and b/_images/circuit_cutting_tutorials_04_automatic_cut_finding_10_0.png differ
diff --git a/_images/circuit_cutting_tutorials_04_automatic_cut_finding_11_0.png b/_images/circuit_cutting_tutorials_04_automatic_cut_finding_11_0.png
new file mode 100644
index 000000000..7ebcdacb4
Binary files /dev/null and b/_images/circuit_cutting_tutorials_04_automatic_cut_finding_11_0.png differ
diff --git a/_images/circuit_cutting_tutorials_04_automatic_cut_finding_2_0.png b/_images/circuit_cutting_tutorials_04_automatic_cut_finding_2_0.png
new file mode 100644
index 000000000..ff7f56b48
Binary files /dev/null and b/_images/circuit_cutting_tutorials_04_automatic_cut_finding_2_0.png differ
diff --git a/_images/circuit_cutting_tutorials_04_automatic_cut_finding_4_1.png b/_images/circuit_cutting_tutorials_04_automatic_cut_finding_4_1.png
new file mode 100644
index 000000000..453636452
Binary files /dev/null and b/_images/circuit_cutting_tutorials_04_automatic_cut_finding_4_1.png differ
diff --git a/_images/circuit_cutting_tutorials_04_automatic_cut_finding_6_0.png b/_images/circuit_cutting_tutorials_04_automatic_cut_finding_6_0.png
new file mode 100644
index 000000000..0335cc64e
Binary files /dev/null and b/_images/circuit_cutting_tutorials_04_automatic_cut_finding_6_0.png differ
diff --git a/_images/circuit_knitting-cutting-instructions-Move-1.svg b/_images/circuit_knitting-cutting-instructions-Move-1.svg
index c7c398eee..7efc7db40 100644
--- a/_images/circuit_knitting-cutting-instructions-Move-1.svg
+++ b/_images/circuit_knitting-cutting-instructions-Move-1.svg
@@ -6,7 +6,7 @@
   <rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
    <cc:Work>
     <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
-    <dc:date>2024-04-17T21:19:25.611736</dc:date>
+    <dc:date>2024-04-19T17:42:05.219728</dc:date>
     <dc:format>image/svg+xml</dc:format>
     <dc:creator>
      <cc:Agent>
@@ -32,22 +32,22 @@ z
    <g id="line2d_1">
     <path d="M 122.178864 68.868799 
 L 451.29226 68.868799 
-" clip-path="url(#p4e778baf8d)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
    </g>
    <g id="line2d_2">
     <path d="M 122.178864 115.222798 
 L 451.29226 115.222798 
-" clip-path="url(#p4e778baf8d)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
    </g>
    <g id="line2d_3">
     <path d="M 122.178864 161.576798 
 L 451.29226 161.576798 
-" clip-path="url(#p4e778baf8d)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
    </g>
    <g id="line2d_4">
     <path d="M 122.178864 207.930797 
 L 451.29226 207.930797 
-" clip-path="url(#p4e778baf8d)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
    </g>
    <g id="patch_2">
     <path d="M 117.543464 45.691799 
@@ -55,7 +55,7 @@ L -1041.306521 45.691799
 L -1041.306521 277.461796 
 L 117.543464 277.461796 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #ffffff; stroke: #ffffff; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #ffffff; stroke: #ffffff; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_3">
     <path d="M 129.018397 130.287848 
@@ -63,7 +63,7 @@ L 165.401651 130.287848
 L 165.401651 53.803749 
 L 129.018397 53.803749 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_4">
     <path d="M 132.144974 222.995847 
@@ -71,7 +71,7 @@ L 162.275073 222.995847
 L 162.275073 192.865747 
 L 132.144974 192.865747 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_5">
     <path d="M 193.237227 176.641848 
@@ -79,7 +79,7 @@ L 240.244818 176.641848
 L 240.244818 100.157749 
 L 193.237227 100.157749 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_6">
     <path d="M 201.675973 83.933849 
@@ -87,7 +87,7 @@ L 231.806073 83.933849
 L 231.806073 53.803749 
 L 201.675973 53.803749 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #33b1ff; stroke: #33b1ff; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #33b1ff; stroke: #33b1ff; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_7">
     <path d="M 268.080395 222.995847 
@@ -95,7 +95,7 @@ L 304.463649 222.995847
 L 304.463649 146.511748 
 L 268.080395 146.511748 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_8">
     <path d="M 332.299225 176.641848 
@@ -103,7 +103,7 @@ L 379.306816 176.641848
 L 379.306816 100.157749 
 L 332.299225 100.157749 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_9">
     <path d="M 340.737971 222.995847 
@@ -111,7 +111,7 @@ L 370.868071 222.995847
 L 370.868071 192.865747 
 L 340.737971 192.865747 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_10">
     <path d="M 407.142393 130.287848 
@@ -119,10 +119,10 @@ L 443.525647 130.287848
 L 443.525647 53.803749 
 L 407.142393 53.803749 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="text_1">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- ${q}_{0}$ -->
      <g transform="translate(94.870564 73.319776) scale(0.1625 -0.1625)">
       <defs>
@@ -184,7 +184,7 @@ z
     </g>
    </g>
    <g id="text_2">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- ${q}_{1}$ -->
      <g transform="translate(94.870564 119.673775) scale(0.1625 -0.1625)">
       <defs>
@@ -209,7 +209,7 @@ z
     </g>
    </g>
    <g id="text_3">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- ${q}_{2}$ -->
      <g transform="translate(94.870564 166.027774) scale(0.1625 -0.1625)">
       <defs>
@@ -244,7 +244,7 @@ z
     </g>
    </g>
    <g id="text_4">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- ${q}_{3}$ -->
      <g transform="translate(94.870564 212.381774) scale(0.1625 -0.1625)">
       <defs>
@@ -287,7 +287,7 @@ z
     </g>
    </g>
    <g id="text_5">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 0 -->
      <g style="fill: #ffffff" transform="translate(132.263177 72.455987) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-30"/>
@@ -295,7 +295,7 @@ z
     </g>
    </g>
    <g id="text_6">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 1 -->
      <g style="fill: #ffffff" transform="translate(132.263177 118.809986) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-31"/>
@@ -303,7 +303,7 @@ z
     </g>
    </g>
    <g id="text_7">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\pi$/4 -->
      <g style="fill: #ffffff" transform="translate(145.988964 102.133479) scale(0.08 -0.08)">
       <defs>
@@ -364,7 +364,7 @@ z
     </g>
    </g>
    <g id="text_8">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\mathrm{R_{YY}}$ -->
      <g style="fill: #ffffff" transform="translate(141.973964 88.216346) scale(0.13 -0.13)">
       <defs>
@@ -416,7 +416,7 @@ z
     </g>
    </g>
    <g id="text_9">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\pi$/4 -->
      <g style="fill: #ffffff" transform="translate(140.890024 219.177327) scale(0.08 -0.08)">
       <use xlink:href="#DejaVuSans-Oblique-3c0" transform="translate(0 0.09375)"/>
@@ -426,7 +426,7 @@ z
     </g>
    </g>
    <g id="text_10">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\mathrm{R_X}$ -->
      <g style="fill: #ffffff" transform="translate(139.280024 206.99847) scale(0.13 -0.13)">
       <defs>
@@ -452,7 +452,7 @@ z
     </g>
    </g>
    <g id="text_11">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 0 -->
      <g style="fill: #ffffff" transform="translate(196.482007 118.809986) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-30"/>
@@ -460,7 +460,7 @@ z
     </g>
    </g>
    <g id="text_12">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 1 -->
      <g style="fill: #ffffff" transform="translate(196.482007 165.163985) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-31"/>
@@ -468,7 +468,7 @@ z
     </g>
    </g>
    <g id="text_13">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- Move -->
      <g style="fill: #ffffff" transform="translate(204.407775 141.986986) scale(0.13 -0.13)">
       <defs>
@@ -553,7 +553,7 @@ z
     </g>
    </g>
    <g id="text_14">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\pi$/4 -->
      <g transform="translate(210.421023 80.115329) scale(0.08 -0.08)">
       <use xlink:href="#DejaVuSans-Oblique-3c0" transform="translate(0 0.09375)"/>
@@ -563,7 +563,7 @@ z
     </g>
    </g>
    <g id="text_15">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\mathrm{R_Z}$ -->
      <g transform="translate(208.811023 67.936472) scale(0.13 -0.13)">
       <defs>
@@ -587,7 +587,7 @@ z
     </g>
    </g>
    <g id="text_16">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 0 -->
      <g style="fill: #ffffff" transform="translate(271.325175 165.163985) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-30"/>
@@ -595,7 +595,7 @@ z
     </g>
    </g>
    <g id="text_17">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 1 -->
      <g style="fill: #ffffff" transform="translate(271.325175 211.517985) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-31"/>
@@ -603,7 +603,7 @@ z
     </g>
    </g>
    <g id="text_18">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\pi$/4 -->
      <g style="fill: #ffffff" transform="translate(285.050962 194.841477) scale(0.08 -0.08)">
       <use xlink:href="#DejaVuSans-Oblique-3c0" transform="translate(0 0.09375)"/>
@@ -613,7 +613,7 @@ z
     </g>
    </g>
    <g id="text_19">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\mathrm{R_{YY}}$ -->
      <g style="fill: #ffffff" transform="translate(281.035962 180.924345) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-52" transform="translate(0 0.09375)"/>
@@ -623,7 +623,7 @@ z
     </g>
    </g>
    <g id="text_20">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 0 -->
      <g style="fill: #ffffff" transform="translate(335.544005 165.163985) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-30"/>
@@ -631,7 +631,7 @@ z
     </g>
    </g>
    <g id="text_21">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 1 -->
      <g style="fill: #ffffff" transform="translate(335.544005 118.809986) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-31"/>
@@ -639,7 +639,7 @@ z
     </g>
    </g>
    <g id="text_22">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- Move -->
      <g style="fill: #ffffff" transform="translate(343.469773 141.986986) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-4d"/>
@@ -650,7 +650,7 @@ z
     </g>
    </g>
    <g id="text_23">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\pi$/4 -->
      <g style="fill: #ffffff" transform="translate(349.483021 219.177327) scale(0.08 -0.08)">
       <use xlink:href="#DejaVuSans-Oblique-3c0" transform="translate(0 0.09375)"/>
@@ -660,7 +660,7 @@ z
     </g>
    </g>
    <g id="text_24">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\mathrm{R_X}$ -->
      <g style="fill: #ffffff" transform="translate(347.873021 206.99847) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-52" transform="translate(0 0.09375)"/>
@@ -669,7 +669,7 @@ z
     </g>
    </g>
    <g id="text_25">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 0 -->
      <g style="fill: #ffffff" transform="translate(410.387173 72.455987) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-30"/>
@@ -677,7 +677,7 @@ z
     </g>
    </g>
    <g id="text_26">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 1 -->
      <g style="fill: #ffffff" transform="translate(410.387173 118.809986) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-31"/>
@@ -685,7 +685,7 @@ z
     </g>
    </g>
    <g id="text_27">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\pi$/4 -->
      <g style="fill: #ffffff" transform="translate(424.11296 102.133479) scale(0.08 -0.08)">
       <use xlink:href="#DejaVuSans-Oblique-3c0" transform="translate(0 0.09375)"/>
@@ -695,7 +695,7 @@ z
     </g>
    </g>
    <g id="text_28">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\mathrm{R_{YY}}$ -->
      <g style="fill: #ffffff" transform="translate(420.09796 88.216346) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-52" transform="translate(0 0.09375)"/>
@@ -707,7 +707,7 @@ z
   </g>
  </g>
  <defs>
-  <clipPath id="p4e778baf8d">
+  <clipPath id="p581eb3dbb7">
    <rect x="64.769668" y="31.7856" width="391.157992" height="203.957597"/>
   </clipPath>
  </defs>
diff --git a/_images/index-1.svg b/_images/index-1.svg
index 258c0f17c..58fd40ebb 100644
--- a/_images/index-1.svg
+++ b/_images/index-1.svg
@@ -6,7 +6,7 @@
   <rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
    <cc:Work>
     <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
-    <dc:date>2024-04-17T21:19:29.289216</dc:date>
+    <dc:date>2024-04-19T17:42:25.566851</dc:date>
     <dc:format>image/svg+xml</dc:format>
     <dc:creator>
      <cc:Agent>
@@ -243,7 +243,7 @@ L 64.7424 233.736932
 L 61.1712 233.682238 
 L 57.6 233.664 
 z
-" clip-path="url(#p2864c6c48a)" style="fill: #57ffff"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: #57ffff"/>
    </g>
    <g id="PolyCollection_2">
     <path d="M 57.6 233.664 
@@ -451,7 +451,7 @@ L 64.7424 233.664
 L 61.1712 233.664 
 L 57.6 233.664 
 z
-" clip-path="url(#p2864c6c48a)" style="fill: #2b568c"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: #2b568c"/>
    </g>
    <g id="PolyCollection_3">
     <path d="M 57.6 233.664 
@@ -659,7 +659,7 @@ L 64.7424 231.343262
 L 61.1712 232.503058 
 L 57.6 233.664 
 z
-" clip-path="url(#p2864c6c48a)" style="fill: #007da3"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: #007da3"/>
    </g>
    <g id="PolyCollection_4">
     <path d="M 57.6 233.664 
@@ -867,7 +867,7 @@ L 64.7424 229.022525
 L 61.1712 231.342117 
 L 57.6 233.664 
 z
-" clip-path="url(#p2864c6c48a)" style="fill: #ffa502"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: #ffa502"/>
    </g>
    <g id="PolyCollection_5">
     <path d="M 57.6 233.664 
@@ -1075,7 +1075,7 @@ L 64.7424 226.701787
 L 61.1712 230.181175 
 L 57.6 233.664 
 z
-" clip-path="url(#p2864c6c48a)" style="fill: #7abaff"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: #7abaff"/>
    </g>
    <g id="PolyCollection_6">
     <path d="M 57.6 233.664 
@@ -1283,18 +1283,18 @@ L 64.7424 224.38105
 L 61.1712 229.020233 
 L 57.6 233.664 
 z
-" clip-path="url(#p2864c6c48a)" style="fill: #f2cc86"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: #f2cc86"/>
    </g>
    <g id="matplotlib.axis_1">
     <g id="xtick_1">
      <g id="line2d_1">
       <defs>
-       <path id="mf5119957ef" d="M 0 0 
+       <path id="mb2d40fea38" d="M 0 0 
 L 0 3.5 
 " style="stroke: #000000; stroke-width: 0.8"/>
       </defs>
       <g>
-       <use xlink:href="#mf5119957ef" x="57.6" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#mb2d40fea38" x="57.6" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_1">
@@ -1330,7 +1330,7 @@ z
     <g id="xtick_2">
      <g id="line2d_2">
       <g>
-       <use xlink:href="#mf5119957ef" x="146.88" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#mb2d40fea38" x="146.88" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_2">
@@ -1396,7 +1396,7 @@ z
     <g id="xtick_3">
      <g id="line2d_3">
       <g>
-       <use xlink:href="#mf5119957ef" x="236.16" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#mb2d40fea38" x="236.16" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_3">
@@ -1437,7 +1437,7 @@ z
     <g id="xtick_4">
      <g id="line2d_4">
       <g>
-       <use xlink:href="#mf5119957ef" x="325.44" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#mb2d40fea38" x="325.44" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_4">
@@ -1487,7 +1487,7 @@ z
     <g id="xtick_5">
      <g id="line2d_5">
       <g>
-       <use xlink:href="#mf5119957ef" x="414.72" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#mb2d40fea38" x="414.72" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_5">
@@ -1815,12 +1815,12 @@ z
     <g id="ytick_1">
      <g id="line2d_6">
       <defs>
-       <path id="mf02425d1ee" d="M 0 0 
+       <path id="m18cd9b8d97" d="M 0 0 
 L -3.5 0 
 " style="stroke: #000000; stroke-width: 0.8"/>
       </defs>
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_7">
@@ -1844,7 +1844,7 @@ z
     <g id="ytick_2">
      <g id="line2d_7">
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="270.624" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="270.624" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_8">
@@ -1886,7 +1886,7 @@ z
     <g id="ytick_3">
      <g id="line2d_8">
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="233.664" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="233.664" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_9">
@@ -1917,7 +1917,7 @@ z
     <g id="ytick_4">
      <g id="line2d_9">
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="196.704" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="196.704" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_10">
@@ -1932,7 +1932,7 @@ z
     <g id="ytick_5">
      <g id="line2d_10">
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="159.744" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="159.744" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_11">
@@ -1947,7 +1947,7 @@ z
     <g id="ytick_6">
      <g id="line2d_11">
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="122.784" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="122.784" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_12">
@@ -1962,7 +1962,7 @@ z
     <g id="ytick_7">
      <g id="line2d_12">
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="85.824" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="85.824" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_13">
@@ -1977,7 +1977,7 @@ z
     <g id="ytick_8">
      <g id="line2d_13">
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="48.864" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="48.864" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_14">
@@ -2311,17 +2311,17 @@ z
    <g id="line2d_14">
     <path d="M 236.16 307.584 
 L 236.16 41.472 
-" clip-path="url(#p2864c6c48a)" style="fill: none; stroke-dasharray: 5.55,2.4; stroke-dashoffset: 0; stroke: #aaaaaa; stroke-width: 1.5"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: none; stroke-dasharray: 5.55,2.4; stroke-dashoffset: 0; stroke: #aaaaaa; stroke-width: 1.5"/>
    </g>
    <g id="line2d_15">
     <path d="M 146.88 307.584 
 L 146.88 41.472 
-" clip-path="url(#p2864c6c48a)" style="fill: none; stroke-dasharray: 1.5,2.475; stroke-dashoffset: 0; stroke: #aaaaaa; stroke-width: 1.5"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: none; stroke-dasharray: 1.5,2.475; stroke-dashoffset: 0; stroke: #aaaaaa; stroke-width: 1.5"/>
    </g>
    <g id="line2d_16">
     <path d="M 57.6 233.664 
 L 414.72 233.664 
-" clip-path="url(#p2864c6c48a)" style="fill: none; stroke: #aaaaaa; stroke-width: 1.5; stroke-linecap: square"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: none; stroke: #aaaaaa; stroke-width: 1.5; stroke-linecap: square"/>
    </g>
    <g id="patch_3">
     <path d="M 57.6 307.584 
@@ -2858,7 +2858,7 @@ z
   </g>
  </g>
  <defs>
-  <clipPath id="p2864c6c48a">
+  <clipPath id="pa23f73a4d8">
    <rect x="57.6" y="41.472" width="357.12" height="266.112"/>
   </clipPath>
  </defs>
diff --git a/_modules/circuit_knitting/cutting/automated_cut_finding.html b/_modules/circuit_knitting/cutting/automated_cut_finding.html
new file mode 100644
index 000000000..4548092e4
--- /dev/null
+++ b/_modules/circuit_knitting/cutting/automated_cut_finding.html
@@ -0,0 +1,519 @@
+<!doctype html>
+<html class="no-js" lang="en">
+  <head><meta charset="utf-8"/>
+    <meta name="viewport" content="width=device-width,initial-scale=1"/>
+    <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
+
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.automated_cut_finding - Circuit Knitting Toolbox 0.7.0</title>
+      <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-ecosystem.css?v=745c5aa7" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/jupyter-sphinx.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/thebelab.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo-extensions.css?v=36a5483c" />
+    
+    
+
+
+<style>
+  body {
+    --color-code-background: #ffffff;
+  --color-code-foreground: black;
+  
+  }
+  @media not print {
+    body[data-theme="dark"] {
+      --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+    }
+    @media (prefers-color-scheme: dark) {
+      body:not([data-theme="light"]) {
+        --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+      }
+    }
+  }
+</style><link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin><link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&family=IBM+Plex+Sans:ital,wght@0,400;0,600;1,400;1,600&display=swap" rel="stylesheet">
+<script src="../../../_static/js/web-components/top-nav-bar.js"></script></head>
+  <body>
+    
+    <script>document.body.dataset.theme = "light";</script>
+    
+<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
+  <symbol id="svg-toc" viewBox="0 0 24 24">
+    <title>Contents</title>
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32">
+      <defs><style>.cls-1{fill:none;}</style></defs>
+      <path d="M28,4H4A2,2,0,0,0,2,6V26a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V6A2,2,0,0,0,28,4ZM4,6H20V26H4ZM28,26H22V6h6Z"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-menu" viewBox="0 0 24 24">
+    <title>Menu</title>
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32">
+      <defs><style>.cls-1{fill:none;}</style></defs>
+      <rect x="4" y="6" width="24" height="2"/>
+      <rect x="4" y="24" width="24" height="2"/>
+      <rect x="4" y="12" width="24" height="2"/>
+      <rect x="4" y="18" width="24" height="2"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
+    <title>Expand</title>
+    <svg version="1.1" id="icon" xmlns="http://www.w3.org/2000/svg" x="0px" y="0px"
+         viewBox="0 0 32 32" style="enable-background:new 0 0 32 32;" xml:space="preserve"><polygon points="22,16 12,26 10.6,24.6 19.2,16 10.6,7.4 12,6 " stroke="currentColor"/>
+      <rect id="_x3C_Transparent_Rectangle_x3E_" fill="none" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-new-tab" viewBox="0 0 32 32">
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 32 32">
+      <defs>
+        <style>
+          .cls-1 {
+            fill: none;
+          }
+        </style>
+      </defs>
+      <path fill="#6929C4" d="M26,28H6a2.0027,2.0027,0,0,1-2-2V6A2.0027,2.0027,0,0,1,6,4H16V6H6V26H26V16h2V26A2.0027,2.0027,0,0,1,26,28Z"/>
+      <polygon fill="#6929C4" points="20 2 20 4 26.586 4 18 12.586 19.414 14 28 5.414 28 12 30 12 30 2 20 2"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-sun" viewBox="0 0 24 24">
+    <title>Light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      <circle cx="12" cy="12" r="5"></circle>
+      <line x1="12" y1="1" x2="12" y2="3"></line>
+      <line x1="12" y1="21" x2="12" y2="23"></line>
+      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
+      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
+      <line x1="1" y1="12" x2="3" y2="12"></line>
+      <line x1="21" y1="12" x2="23" y2="12"></line>
+      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
+      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon" viewBox="0 0 24 24">
+    <title>Dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
+    </svg>
+  </symbol>
+  <symbol id="svg-sun-half" viewBox="0 0 24 24">
+    <title>Auto light/dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+      <circle cx="12" cy="12" r="9" />
+      <path d="M13 12h5" />
+      <path d="M13 15h4" />
+      <path d="M13 18h1" />
+      <path d="M13 9h4" />
+      <path d="M13 6h1" />
+    </svg>
+  </symbol>
+</svg>
+
+<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
+<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
+<label class="overlay sidebar-overlay" for="__navigation">
+  <div class="visually-hidden">Hide navigation sidebar</div>
+</label>
+<label class="overlay toc-overlay" for="__toc">
+  <div class="visually-hidden">Hide table of contents sidebar</div>
+</label>
+
+
+
+<div class="page">
+  <header class="mobile-header">
+    <div class="header-left">
+      <label class="nav-overlay-icon" for="__navigation">
+        <div class="visually-hidden">Toggle site navigation sidebar</div>
+        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
+      </label>
+    </div>
+    <div class="header-center">
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
+    </div>
+    <div class="header-right">
+      <div class="theme-toggle-container theme-toggle-header">
+        <button class="theme-toggle">
+          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+        </button>
+      </div>
+      <label class="toc-overlay-icon toc-header-icon no-toc" for="__toc">
+        <div class="visually-hidden">Toggle table of contents sidebar</div>
+        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+      </label>
+    </div>
+  </header>
+  <aside class="sidebar-drawer">
+    <div class="sidebar-container">
+      
+      <div class="sidebar-sticky"><div class="sidebar-brand">
+  <a href="https://www.qiskit.org/ecosystem">
+    <div class="sidebar-logo-container">
+      <img class="sidebar-logo" src="../../../_static/images/ecosystem-logo.svg" alt="Qiskit Ecosystem logo"/>
+    </div>
+  </a>
+  
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
+</div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
+  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
+  <input type="hidden" name="check_keywords" value="yes">
+  <input type="hidden" name="area" value="default">
+</form>
+<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
+  <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../index.html">About Circuit Knitting Toolbox</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../install.html">Installation Instructions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Circuit Cutting</span></p>
+<ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../../../circuit_cutting/tutorials/index.html">Cutting Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of Cutting Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../../../circuit_cutting/how-tos/index.html">Cutting How-To Guides</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Cutting How-To Guides</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.html">How to generate exact quasiprobability distributions from Sampler</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.html">How to generate exact sampling coefficients</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/how-tos/how_to_specify_cut_wires.html">How to place wire cuts using a single-qubit <code class="docutils literal notranslate"><span class="pre">CutWire</span></code> instruction</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../../../circuit_cutting/cutqc/index.html">CutQC (legacy circuit cutting implementation)</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of CutQC (legacy circuit cutting implementation)</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.html">CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.html">CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">References</span></p>
+<ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../../../apidocs/index.html">API References</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of API References</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../apidocs/circuit_cutting.html">Circuit Cutting</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Circuit Cutting</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.cut_wires.html">circuit_knitting.cutting.cut_wires</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.expand_observables.html">circuit_knitting.cutting.expand_observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.partition_circuit_qubits.html">circuit_knitting.cutting.partition_circuit_qubits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.partition_problem.html">circuit_knitting.cutting.partition_problem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.cut_gates.html">circuit_knitting.cutting.cut_gates</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.generate_cutting_experiments.html">circuit_knitting.cutting.generate_cutting_experiments</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.reconstruct_expectation_values.html">circuit_knitting.cutting.reconstruct_expectation_values</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.TwoQubitQPDGate.html">circuit_knitting.cutting.qpd.TwoQubitQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.WeightType.html">circuit_knitting.cutting.qpd.WeightType</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.html">circuit_knitting.cutting.qpd.generate_qpd_weights</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.html">circuit_knitting.cutting.qpd.decompose_qpd_instructions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction.html">circuit_knitting.cutting.qpd.qpdbasis_from_instruction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances.html">circuit_knitting.cutting.cutqc.run_subcircuit_instances</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.cutqc.generate_summation_terms.html">circuit_knitting.cutting.cutqc.generate_summation_terms</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.cutqc.build.html">circuit_knitting.cutting.cutqc.build</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.cutqc.verify.html">circuit_knitting.cutting.cutqc.verify</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires.html">circuit_knitting.cutting.cutqc.cut_circuit_wires</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits.html">circuit_knitting.cutting.cutqc.evaluate_subcircuits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.html">circuit_knitting.cutting.cutqc.reconstruct_full_distribution</a></li>
+</ul>
+</li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../apidocs/utils.html">Utilities</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of Utilities</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.bitwise.bit_count.html">circuit_knitting.utils.bitwise.bit_count</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.conversion.quasi_to_real.html">circuit_knitting.utils.conversion.quasi_to_real</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.conversion.nearest_probability_distribution.html">circuit_knitting.utils.conversion.nearest_probability_distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.conversion.naive_probability_distribution.html">circuit_knitting.utils.conversion.naive_probability_distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.conversion.dict_to_array.html">circuit_knitting.utils.conversion.dict_to_array</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.iteration.unique_by_id.html">circuit_knitting.utils.iteration.unique_by_id</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.iteration.unique_by_eq.html">circuit_knitting.utils.iteration.unique_by_eq</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.metrics.chi2_distance.html">circuit_knitting.utils.metrics.chi2_distance</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.metrics.MSE.html">circuit_knitting.utils.metrics.MSE</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.metrics.MAPE.html">circuit_knitting.utils.metrics.MAPE</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.metrics.cross_entropy.html">circuit_knitting.utils.metrics.cross_entropy</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.metrics.HOP.html">circuit_knitting.utils.metrics.HOP</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.html">circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.observable_grouping.CommutingObservableGroup.html">circuit_knitting.utils.observable_grouping.CommutingObservableGroup</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.observable_grouping.ObservableCollection.html">circuit_knitting.utils.observable_grouping.ObservableCollection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.simulation.simulate_statevector_outcomes.html">circuit_knitting.utils.simulation.simulate_statevector_outcomes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.simulation.ExactSampler.html">circuit_knitting.utils.simulation.ExactSampler</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.transforms.separate_circuit.html">circuit_knitting.utils.transforms.separate_circuit</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.transforms.SeparatedCircuits.html">circuit_knitting.utils.transforms.SeparatedCircuits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.transpiler_passes.RemoveFinalReset.html">circuit_knitting.utils.transpiler_passes.RemoveFinalReset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.utils.transpiler_passes.ConsolidateResets.html">circuit_knitting.utils.transpiler_passes.ConsolidateResets</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../release-notes.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference external" href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox">GitHub</a></li>
+</ul>
+
+</div></div>
+      </div>
+      
+    </div>
+  </aside>
+  <div class="main">
+    <div class="content">
+      <div class="article-container">
+        <a href="#" class="back-to-top muted-link">
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
+            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
+          </svg>
+          <span>Back to top</span>
+        </a>
+        <div class="content-icon-container">
+          <div class="theme-toggle-container theme-toggle-content">
+            <button class="theme-toggle">
+              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+            </button>
+          </div>
+          <label class="toc-overlay-icon toc-content-icon no-toc" for="__toc">
+            <div class="visually-hidden">Toggle table of contents sidebar</div>
+            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+          </label>
+        </div>
+        <article role="main">
+          <h1>Source code for circuit_knitting.cutting.automated_cut_finding</h1><div class="highlight"><pre>
+<span></span><span class="c1"># This code is a Qiskit project.</span>
+
+<span class="c1"># (C) Copyright IBM 2024.</span>
+
+<span class="c1"># This code is licensed under the Apache License, Version 2.0. You may</span>
+<span class="c1"># obtain a copy of this license in the LICENSE.txt file in the root directory</span>
+<span class="c1"># of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.</span>
+<span class="c1"># Any modifications or derivative works of this code must retain this</span>
+<span class="c1"># copyright notice, and modified files need to carry a notice indicating</span>
+<span class="c1"># that they have been altered from the originals.</span>
+
+<span class="sd">&quot;&quot;&quot;Function for automatically finding locations for gate and wire cuts.&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">cast</span><span class="p">,</span> <span class="n">Any</span>
+<span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">dataclass</span>
+
+<span class="kn">from</span> <span class="nn">qiskit.circuit</span> <span class="kn">import</span> <span class="n">QuantumCircuit</span><span class="p">,</span> <span class="n">CircuitInstruction</span>
+
+<span class="kn">from</span> <span class="nn">.instructions</span> <span class="kn">import</span> <span class="n">CutWire</span>
+<span class="kn">from</span> <span class="nn">.cutting_decomposition</span> <span class="kn">import</span> <span class="n">cut_gates</span>
+<span class="kn">from</span> <span class="nn">.cut_finding.optimization_settings</span> <span class="kn">import</span> <span class="n">OptimizationSettings</span>
+<span class="kn">from</span> <span class="nn">.cut_finding.disjoint_subcircuits_state</span> <span class="kn">import</span> <span class="n">DisjointSubcircuitsState</span>
+<span class="kn">from</span> <span class="nn">.cut_finding.circuit_interface</span> <span class="kn">import</span> <span class="n">SimpleGateList</span>
+<span class="kn">from</span> <span class="nn">.cut_finding.lo_cuts_optimizer</span> <span class="kn">import</span> <span class="n">LOCutsOptimizer</span>
+<span class="kn">from</span> <span class="nn">.cut_finding.cco_utils</span> <span class="kn">import</span> <span class="n">qc_to_cco_circuit</span>
+
+
+<div class="viewcode-block" id="find_cuts">
+<a class="viewcode-back" href="../../../stubs/circuit_knitting.cutting.find_cuts.html#circuit_knitting.cutting.find_cuts">[docs]</a>
+<span class="k">def</span> <span class="nf">find_cuts</span><span class="p">(</span>
+    <span class="n">circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span>
+    <span class="n">optimization</span><span class="p">:</span> <span class="n">OptimizationParameters</span><span class="p">,</span>
+    <span class="n">constraints</span><span class="p">:</span> <span class="n">DeviceConstraints</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">QuantumCircuit</span><span class="p">,</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Find cut locations in a circuit, given optimization parameters and cutting constraints.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        circuit: The circuit to cut. The input circuit may not contain gates acting</span>
+<span class="sd">            on more than two qubits.</span>
+<span class="sd">        optimization: Options for controlling optimizer behavior. Currently, the optimal</span>
+<span class="sd">            cuts are chosen using Dijkstra&#39;s best-first search algorithm.</span>
+<span class="sd">        constraints: Constraints on how the circuit may be partitioned</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        A circuit containing :class:`.BaseQPDGate` instances. The subcircuits</span>
+<span class="sd">        resulting from cutting these gates will be runnable on the devices meeting</span>
+<span class="sd">        the ``constraints``.</span>
+
+<span class="sd">        A metadata dictionary:</span>
+<span class="sd">            - cuts: A list of length-2 tuples describing each cut in the output circuit.</span>
+<span class="sd">              The tuples are formatted as ``(cut_type: str, cut_id: int)``. The</span>
+<span class="sd">              cut ID is the index of the cut gate or wire in the output circuit&#39;s</span>
+<span class="sd">              ``data`` field.</span>
+<span class="sd">            - sampling_overhead: The sampling overhead incurred from cutting the specified</span>
+<span class="sd">              gates and wires.</span>
+
+<span class="sd">    Raises:</span>
+<span class="sd">        ValueError: The input circuit contains a gate acting on more than 2 qubits.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">circuit_cco</span> <span class="o">=</span> <span class="n">qc_to_cco_circuit</span><span class="p">(</span><span class="n">circuit</span><span class="p">)</span>
+    <span class="n">interface</span> <span class="o">=</span> <span class="n">SimpleGateList</span><span class="p">(</span><span class="n">circuit_cco</span><span class="p">)</span>
+
+    <span class="n">opt_settings</span> <span class="o">=</span> <span class="n">OptimizationSettings</span><span class="p">(</span>
+        <span class="n">seed</span><span class="o">=</span><span class="n">optimization</span><span class="o">.</span><span class="n">seed</span><span class="p">,</span>
+        <span class="n">max_gamma</span><span class="o">=</span><span class="n">optimization</span><span class="o">.</span><span class="n">max_gamma</span><span class="p">,</span>
+        <span class="n">max_backjumps</span><span class="o">=</span><span class="n">optimization</span><span class="o">.</span><span class="n">max_backjumps</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Hard-code the optimizer to an LO-only optimizer</span>
+    <span class="n">optimizer</span> <span class="o">=</span> <span class="n">LOCutsOptimizer</span><span class="p">(</span><span class="n">interface</span><span class="p">,</span> <span class="n">opt_settings</span><span class="p">,</span> <span class="n">constraints</span><span class="p">)</span>
+
+    <span class="c1"># Find cut locations</span>
+    <span class="n">opt_out</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">optimize</span><span class="p">()</span>
+
+    <span class="n">wire_cut_actions</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">gate_ids</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="n">opt_out</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">DisjointSubcircuitsState</span><span class="p">,</span> <span class="n">opt_out</span><span class="p">)</span>
+    <span class="n">opt_out</span><span class="o">.</span><span class="n">actions</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">opt_out</span><span class="o">.</span><span class="n">actions</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">action</span> <span class="ow">in</span> <span class="n">opt_out</span><span class="o">.</span><span class="n">actions</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">action</span><span class="o">.</span><span class="n">action</span><span class="o">.</span><span class="n">get_name</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;CutTwoQubitGate&quot;</span><span class="p">:</span>
+            <span class="n">gate_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">action</span><span class="o">.</span><span class="n">gate_spec</span><span class="o">.</span><span class="n">instruction_id</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># The cut-finding optimizer currently only supports 4 cutting</span>
+            <span class="c1"># actions: {CutTwoQubitGate + these 3 wire cut types}</span>
+            <span class="k">assert</span> <span class="n">action</span><span class="o">.</span><span class="n">action</span><span class="o">.</span><span class="n">get_name</span><span class="p">()</span> <span class="ow">in</span> <span class="p">(</span>
+                <span class="s2">&quot;CutLeftWire&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;CutRightWire&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;CutBothWires&quot;</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">wire_cut_actions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">action</span><span class="p">)</span>
+
+    <span class="c1"># First, replace all gates to cut with BaseQPDGate instances.</span>
+    <span class="c1"># This assumes each gate to cut is replaced 1-to-1 with a QPD gate.</span>
+    <span class="c1"># This may not hold in the future as we stop treating gate cuts individually.</span>
+    <span class="n">circ_out</span> <span class="o">=</span> <span class="n">cut_gates</span><span class="p">(</span><span class="n">circuit</span><span class="p">,</span> <span class="n">gate_ids</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="c1"># Insert all the wire cuts</span>
+    <span class="n">counter</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">action</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">wire_cut_actions</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">a</span><span class="p">:</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]):</span>
+        <span class="n">inst_id</span> <span class="o">=</span> <span class="n">action</span><span class="o">.</span><span class="n">gate_spec</span><span class="o">.</span><span class="n">instruction_id</span>
+        <span class="c1"># action.args[0][0] will be either 1 (control) or 2 (target)</span>
+        <span class="n">qubit_id</span> <span class="o">=</span> <span class="n">action</span><span class="o">.</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span>
+        <span class="n">circ_out</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span>
+            <span class="n">inst_id</span> <span class="o">+</span> <span class="n">counter</span><span class="p">,</span>
+            <span class="n">CircuitInstruction</span><span class="p">(</span><span class="n">CutWire</span><span class="p">(),</span> <span class="p">[</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">inst_id</span><span class="p">]</span><span class="o">.</span><span class="n">qubits</span><span class="p">[</span><span class="n">qubit_id</span><span class="p">]],</span> <span class="p">[]),</span>
+        <span class="p">)</span>
+        <span class="n">counter</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="n">action</span><span class="o">.</span><span class="n">action</span><span class="o">.</span><span class="n">get_name</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;CutBothWires&quot;</span><span class="p">:</span>  <span class="c1"># pragma: no cover</span>
+            <span class="c1"># There should be two wires specified in the action in this case</span>
+            <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">action</span><span class="o">.</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span>
+            <span class="n">qubit_id2</span> <span class="o">=</span> <span class="n">action</span><span class="o">.</span><span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span>
+            <span class="n">circ_out</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span>
+                <span class="n">inst_id</span> <span class="o">+</span> <span class="n">counter</span><span class="p">,</span>
+                <span class="n">CircuitInstruction</span><span class="p">(</span>
+                    <span class="n">CutWire</span><span class="p">(),</span> <span class="p">[</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">inst_id</span><span class="p">]</span><span class="o">.</span><span class="n">qubits</span><span class="p">[</span><span class="n">qubit_id2</span><span class="p">]],</span> <span class="p">[]</span>
+                <span class="p">),</span>
+            <span class="p">)</span>
+            <span class="n">counter</span> <span class="o">+=</span> <span class="mi">1</span>
+
+    <span class="c1"># Return metadata describing the cut scheme</span>
+    <span class="n">metadata</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;cuts&quot;</span><span class="p">:</span> <span class="p">[]}</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">inst</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">circ_out</span><span class="o">.</span><span class="n">data</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">inst</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;qpd_2q&quot;</span><span class="p">:</span>
+            <span class="n">metadata</span><span class="p">[</span><span class="s2">&quot;cuts&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="s2">&quot;Gate Cut&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">))</span>
+        <span class="k">elif</span> <span class="n">inst</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;cut_wire&quot;</span><span class="p">:</span>
+            <span class="n">metadata</span><span class="p">[</span><span class="s2">&quot;cuts&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="s2">&quot;Wire Cut&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">))</span>
+    <span class="n">metadata</span><span class="p">[</span><span class="s2">&quot;sampling_overhead&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">opt_out</span><span class="o">.</span><span class="n">upper_bound_gamma</span><span class="p">()</span> <span class="o">**</span> <span class="mi">2</span>
+
+    <span class="k">return</span> <span class="n">circ_out</span><span class="p">,</span> <span class="n">metadata</span></div>
+
+
+
+<div class="viewcode-block" id="OptimizationParameters">
+<a class="viewcode-back" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html#circuit_knitting.cutting.OptimizationParameters">[docs]</a>
+<span class="nd">@dataclass</span>
+<span class="k">class</span> <span class="nc">OptimizationParameters</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Specify parameters that control the optimization.&quot;&quot;&quot;</span>
+
+    <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="n">OptimizationSettings</span><span class="p">()</span><span class="o">.</span><span class="n">seed</span>
+    <span class="n">max_gamma</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">OptimizationSettings</span><span class="p">()</span><span class="o">.</span><span class="n">max_gamma</span>
+    <span class="n">max_backjumps</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">OptimizationSettings</span><span class="p">()</span><span class="o">.</span><span class="n">max_backjumps</span></div>
+
+
+
+<div class="viewcode-block" id="DeviceConstraints">
+<a class="viewcode-back" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html#circuit_knitting.cutting.DeviceConstraints">[docs]</a>
+<span class="nd">@dataclass</span>
+<span class="k">class</span> <span class="nc">DeviceConstraints</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Specify the constraints (qubits per subcircuit) that must be respected.&quot;&quot;&quot;</span>
+
+    <span class="n">qubits_per_subcircuit</span><span class="p">:</span> <span class="nb">int</span>
+
+    <span class="k">def</span> <span class="nf">__post_init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Post-init method for data class.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">qubits_per_subcircuit</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;qubits_per_subcircuit must be a positive definite integer.&quot;</span>
+            <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">get_qpu_width</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the number of qubits per subcircuit.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">qubits_per_subcircuit</span></div>
+
+</pre></div>
+        </article>
+      </div>
+      <footer>
+        <div class="related-pages">
+          
+          
+        </div>
+        <div class="bottom-of-page">
+          <div class="left-details">
+            <div class="copyright">
+                Copyright &#169; 2023
+            </div>
+            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
+            
+            <a href="https://github.com/pradyunsg/furo">Furo</a>
+            
+          </div>
+          <div class="right-details">
+            <div class="icons">
+              <a class="muted-link " href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox" aria-label="GitHub">
+                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
+                </svg>
+            </a>
+              
+            </div>
+          </div>
+        </div>
+        
+      </footer>
+    </div>
+    <aside class="toc-drawer no-toc">
+      
+      
+      
+    </aside>
+  </div>
+</div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
+    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
+    <script src="../../../_static/doctools.js?v=9a2dae69"></script>
+    <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="../../../_static/thebelab-helper.js"></script>
+    <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
+    <script src="../../../_static/copybutton.js?v=f281be69"></script>
+    <script src="../../../_static/scripts/qiskit-sphinx-theme.js?v=4d77b8ca"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/@jupyter-widgets/html-manager@^1.0.1/dist/embed-amd.js"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    </body>
+</html>
\ No newline at end of file
diff --git a/_modules/circuit_knitting/cutting/cutqc/wire_cutting.html b/_modules/circuit_knitting/cutting/cutqc/wire_cutting.html
index 39b9dbd3d..0b7b888ff 100644
--- a/_modules/circuit_knitting/cutting/cutqc/wire_cutting.html
+++ b/_modules/circuit_knitting/cutting/cutqc/wire_cutting.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../../genindex.html" /><link rel="search" title="Search" href="../../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cutqc.wire_cutting - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cutqc.wire_cutting - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -318,6 +322,7 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting</h1><div class="
 <span class="kn">from</span> <span class="nn">qiskit.dagcircuit</span> <span class="kn">import</span> <span class="n">DAGCircuit</span><span class="p">,</span> <span class="n">DAGOpNode</span>
 <span class="kn">from</span> <span class="nn">qiskit.converters</span> <span class="kn">import</span> <span class="n">circuit_to_dag</span><span class="p">,</span> <span class="n">dag_to_circuit</span>
 <span class="kn">from</span> <span class="nn">qiskit_ibm_runtime</span> <span class="kn">import</span> <span class="n">Options</span><span class="p">,</span> <span class="n">QiskitRuntimeService</span>
+<span class="kn">from</span> <span class="nn">qiskit.utils.deprecation</span> <span class="kn">import</span> <span class="n">deprecate_func</span>
 
 <span class="kn">from</span> <span class="nn">.wire_cutting_evaluation</span> <span class="kn">import</span> <span class="n">run_subcircuit_instances</span>
 <span class="kn">from</span> <span class="nn">.wire_cutting_post_processing</span> <span class="kn">import</span> <span class="n">generate_summation_terms</span><span class="p">,</span> <span class="n">build</span>
@@ -331,6 +336,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting</h1><div class="
 
 <div class="viewcode-block" id="cut_circuit_wires">
 <a class="viewcode-back" href="../../../../stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires.html#circuit_knitting.cutting.cutqc.cut_circuit_wires">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">cut_circuit_wires</span><span class="p">(</span>
     <span class="n">circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span>
     <span class="n">method</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
@@ -368,7 +379,7 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting</h1><div class="
     <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;automatic&quot;</span><span class="p">:</span>
         <span class="k">if</span> <span class="n">max_subcircuit_width</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s2">&quot;The max_subcircuit_width argument must be set if using automatic cut finding.&quot;</span>
+                <span class="s2">&quot;The max_subcircuit_width argument must be set if using automatic cut-finding.&quot;</span>
             <span class="p">)</span>
         <span class="n">cuts</span> <span class="o">=</span> <span class="n">find_wire_cuts</span><span class="p">(</span>
             <span class="n">circuit</span><span class="o">=</span><span class="n">circuit</span><span class="p">,</span>
@@ -398,6 +409,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting</h1><div class="
 
 <div class="viewcode-block" id="evaluate_subcircuits">
 <a class="viewcode-back" href="../../../../stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits.html#circuit_knitting.cutting.cutqc.evaluate_subcircuits">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">evaluate_subcircuits</span><span class="p">(</span>
     <span class="n">cuts</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span>
     <span class="n">service</span><span class="p">:</span> <span class="n">QiskitRuntimeService</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -459,6 +476,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting</h1><div class="
 
 <div class="viewcode-block" id="reconstruct_full_distribution">
 <a class="viewcode-back" href="../../../../stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.html#circuit_knitting.cutting.cutqc.reconstruct_full_distribution">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">reconstruct_full_distribution</span><span class="p">(</span>
     <span class="n">circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span>
     <span class="n">subcircuit_instance_probabilities</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]],</span>
@@ -502,6 +525,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting</h1><div class="
 
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">create_dd_bin</span><span class="p">(</span>
     <span class="n">subcircuit_instance_probabilities</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]],</span>
     <span class="n">cuts</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span>
@@ -541,6 +570,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting</h1><div class="
     <span class="p">)</span>
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">reconstruct_dd_full_distribution</span><span class="p">(</span>
     <span class="n">circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span>
     <span class="n">cuts</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span>
@@ -691,6 +726,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting</h1><div class="
 
 
 <span class="nd">@no_type_check</span>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">find_wire_cuts</span><span class="p">(</span>
     <span class="n">circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span>
     <span class="n">max_subcircuit_width</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -714,7 +755,7 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting</h1><div class="
 <span class="sd">        max_subcircuit_cuts: Max number of cuts for a subcircuit</span>
 <span class="sd">        max_subcircuit_size: The maximum number of two qubit gates in each</span>
 <span class="sd">            subcircuit</span>
-<span class="sd">        verbose: Whether to print information about the cut finding or not</span>
+<span class="sd">        verbose: Whether to print information about the cut-finding or not</span>
 
 <span class="sd">    Returns:</span>
 <span class="sd">        The solution found for the cuts</span>
@@ -735,7 +776,7 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting</h1><div class="
             <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
                 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%d</span><span class="s2"> subcircuits : IMPOSSIBLE&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">num_subcircuit</span><span class="p">))</span>
             <span class="k">continue</span>
-        <span class="n">kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>  <span class="c1"># pylint: disable=use-dict-literal</span>
             <span class="n">n_vertices</span><span class="o">=</span><span class="n">n_vertices</span><span class="p">,</span>
             <span class="n">edges</span><span class="o">=</span><span class="n">edges</span><span class="p">,</span>
             <span class="n">vertex_ids</span><span class="o">=</span><span class="n">vertex_ids</span><span class="p">,</span>
@@ -795,7 +836,7 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting</h1><div class="
 
         <span class="k">if</span> <span class="n">best_mip_model</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s2">&quot;Something went wrong during cut finding. The best MIP model object was never instantiated.&quot;</span>
+                <span class="s2">&quot;Something went wrong during cut-finding. The best MIP model object was never instantiated.&quot;</span>
             <span class="p">)</span>
         <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Model objective value = </span><span class="si">%.2e</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">best_mip_model</span><span class="o">.</span><span class="n">objective</span><span class="p">),</span> <span class="n">flush</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
         <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;MIP runtime:&quot;</span><span class="p">,</span> <span class="n">best_mip_model</span><span class="o">.</span><span class="n">runtime</span><span class="p">,</span> <span class="n">flush</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@@ -808,6 +849,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting</h1><div class="
     <span class="k">return</span> <span class="n">cut_solution</span>
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">cut_circuit_wire</span><span class="p">(</span>
     <span class="n">circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span> <span class="n">subcircuit_vertices</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]],</span> <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
@@ -1368,7 +1415,7 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting</h1><div class="
   </div>
 </div><script src="../../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/cutting/cutqc/wire_cutting_evaluation.html b/_modules/circuit_knitting/cutting/cutqc/wire_cutting_evaluation.html
index b9b0c58c7..45553ac2f 100644
--- a/_modules/circuit_knitting/cutting/cutqc/wire_cutting_evaluation.html
+++ b/_modules/circuit_knitting/cutting/cutqc/wire_cutting_evaluation.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../../genindex.html" /><link rel="search" title="Search" href="../../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cutqc.wire_cutting_evaluation - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cutqc.wire_cutting_evaluation - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -318,14 +322,22 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_evaluation</h1><
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 <span class="kn">from</span> <span class="nn">qiskit</span> <span class="kn">import</span> <span class="n">QuantumCircuit</span>
+<span class="kn">from</span> <span class="nn">qiskit.utils.deprecation</span> <span class="kn">import</span> <span class="n">deprecate_func</span>
 <span class="kn">from</span> <span class="nn">qiskit.converters</span> <span class="kn">import</span> <span class="n">circuit_to_dag</span><span class="p">,</span> <span class="n">dag_to_circuit</span>
 <span class="kn">from</span> <span class="nn">qiskit.circuit.library.standard_gates</span> <span class="kn">import</span> <span class="n">HGate</span><span class="p">,</span> <span class="n">SGate</span><span class="p">,</span> <span class="n">SdgGate</span><span class="p">,</span> <span class="n">XGate</span>
 <span class="kn">from</span> <span class="nn">qiskit.primitives</span> <span class="kn">import</span> <span class="n">BaseSampler</span><span class="p">,</span> <span class="n">Sampler</span> <span class="k">as</span> <span class="n">TestSampler</span>
+<span class="kn">from</span> <span class="nn">qiskit.transpiler.preset_passmanagers</span> <span class="kn">import</span> <span class="n">generate_preset_pass_manager</span>
 <span class="kn">from</span> <span class="nn">qiskit_ibm_runtime</span> <span class="kn">import</span> <span class="n">QiskitRuntimeService</span><span class="p">,</span> <span class="n">Sampler</span><span class="p">,</span> <span class="n">Session</span><span class="p">,</span> <span class="n">Options</span>
 
 
 <div class="viewcode-block" id="run_subcircuit_instances">
 <a class="viewcode-back" href="../../../../stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances.html#circuit_knitting.cutting.cutqc.run_subcircuit_instances">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">run_subcircuit_instances</span><span class="p">(</span>
     <span class="n">subcircuits</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">QuantumCircuit</span><span class="p">],</span>
     <span class="n">subcircuit_instances</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="o">...</span><span class="p">]],</span> <span class="nb">int</span><span class="p">]],</span>
@@ -402,6 +414,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_evaluation</h1><
 
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">mutate_measurement_basis</span><span class="p">(</span><span class="n">meas</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="o">...</span><span class="p">]]:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Change of basis for all identity measurements.</span>
@@ -429,6 +447,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_evaluation</h1><
         <span class="k">return</span> <span class="n">mutated_meas_out</span>
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">modify_subcircuit_instance</span><span class="p">(</span>
     <span class="n">subcircuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span> <span class="n">init</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">meas</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">QuantumCircuit</span><span class="p">:</span>
@@ -509,6 +533,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_evaluation</h1><
     <span class="k">return</span> <span class="n">subcircuit_instance_circuit</span>
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">run_subcircuits_using_sampler</span><span class="p">(</span>
     <span class="n">subcircuits</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">QuantumCircuit</span><span class="p">],</span>
     <span class="n">sampler</span><span class="p">:</span> <span class="n">BaseSampler</span><span class="p">,</span>
@@ -541,6 +571,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_evaluation</h1><
     <span class="k">return</span> <span class="n">all_probabilities_out</span>
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">run_subcircuits</span><span class="p">(</span>
     <span class="n">subcircuits</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">QuantumCircuit</span><span class="p">],</span>
     <span class="n">service</span><span class="p">:</span> <span class="n">QiskitRuntimeService</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -562,12 +598,24 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_evaluation</h1><
     <span class="k">if</span> <span class="n">service</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">session</span> <span class="o">=</span> <span class="n">Session</span><span class="p">(</span><span class="n">service</span><span class="o">=</span><span class="n">service</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="n">backend_name</span><span class="p">)</span>
         <span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="p">(</span><span class="n">session</span><span class="o">=</span><span class="n">session</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="n">options</span><span class="p">)</span>
+        <span class="c1"># Transpile circuits</span>
+        <span class="n">backend</span> <span class="o">=</span> <span class="n">service</span><span class="o">.</span><span class="n">backend</span><span class="p">(</span><span class="n">session</span><span class="o">.</span><span class="n">backend</span><span class="p">())</span>
+        <span class="n">pass_manager</span> <span class="o">=</span> <span class="n">generate_preset_pass_manager</span><span class="p">(</span>
+            <span class="n">optimization_level</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="n">backend</span>
+        <span class="p">)</span>
+        <span class="n">subcircuits</span> <span class="o">=</span> <span class="n">pass_manager</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">subcircuits</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">sampler</span> <span class="o">=</span> <span class="n">TestSampler</span><span class="p">(</span><span class="n">options</span><span class="o">=</span><span class="n">options</span><span class="p">)</span>
 
     <span class="k">return</span> <span class="n">run_subcircuits_using_sampler</span><span class="p">(</span><span class="n">subcircuits</span><span class="p">,</span> <span class="n">sampler</span><span class="p">)</span>
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">measure_prob</span><span class="p">(</span><span class="n">unmeasured_prob</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">meas</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Compute the effective probability distribution from the subcircuit distribution.</span>
@@ -591,6 +639,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_evaluation</h1><
         <span class="k">return</span> <span class="n">measured_prob</span>
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">measure_state</span><span class="p">(</span><span class="n">full_state</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">meas</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Compute the corresponding effective_state for the given full_state.</span>
@@ -735,7 +789,7 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_evaluation</h1><
   </div>
 </div><script src="../../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/cutting/cutqc/wire_cutting_post_processing.html b/_modules/circuit_knitting/cutting/cutqc/wire_cutting_post_processing.html
index bbe343cd9..0faa3d90c 100644
--- a/_modules/circuit_knitting/cutting/cutqc/wire_cutting_post_processing.html
+++ b/_modules/circuit_knitting/cutting/cutqc/wire_cutting_post_processing.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../../genindex.html" /><link rel="search" title="Search" href="../../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cutqc.wire_cutting_post_processing - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cutqc.wire_cutting_post_processing - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -316,9 +320,16 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_post_processing<
 
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">from</span> <span class="nn">qiskit</span> <span class="kn">import</span> <span class="n">QuantumCircuit</span>
+<span class="kn">from</span> <span class="nn">qiskit.utils.deprecation</span> <span class="kn">import</span> <span class="n">deprecate_func</span>
 <span class="kn">from</span> <span class="nn">qiskit.circuit</span> <span class="kn">import</span> <span class="n">Qubit</span>
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">get_cut_qubit_pairs</span><span class="p">(</span>
     <span class="n">complete_path_map</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Qubit</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span> <span class="o">|</span> <span class="n">Qubit</span><span class="p">]]]</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]]:</span>
@@ -346,6 +357,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_post_processing<
     <span class="k">return</span> <span class="n">O_rho_pairs</span>
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">get_label</span><span class="p">(</span><span class="n">label_idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">num_cuts</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the basis label for each cut point.</span>
@@ -367,6 +384,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_post_processing<
     <span class="k">return</span> <span class="n">label</span>
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">attribute_label</span><span class="p">(</span>
     <span class="n">label</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
     <span class="n">O_rho_pairs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]],</span>
@@ -404,6 +427,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_post_processing<
     <span class="k">return</span> <span class="n">subcircuit_label</span>
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">fill_label</span><span class="p">(</span>
     <span class="n">subcircuit_idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
     <span class="n">subcircuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span>
@@ -442,6 +471,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_post_processing<
     <span class="k">return</span> <span class="n">init</span><span class="p">,</span> <span class="n">meas</span>
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">get_init_meas</span><span class="p">(</span>
     <span class="n">init_label</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="n">meas_label</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">]]]:</span>
@@ -483,6 +518,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_post_processing<
     <span class="k">return</span> <span class="n">subcircuit_init_meas</span>
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">convert_to_physical_init</span><span class="p">(</span><span class="n">init</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">]]:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Convert the initial measurements to the physical representations.</span>
@@ -521,6 +562,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_post_processing<
 
 <div class="viewcode-block" id="generate_summation_terms">
 <a class="viewcode-back" href="../../../../stubs/circuit_knitting.cutting.cutqc.generate_summation_terms.html#circuit_knitting.cutting.cutqc.generate_summation_terms">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">generate_summation_terms</span><span class="p">(</span>
     <span class="n">subcircuits</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">QuantumCircuit</span><span class="p">],</span>
     <span class="n">complete_path_map</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Qubit</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span> <span class="o">|</span> <span class="n">Qubit</span><span class="p">]]],</span>
@@ -633,6 +680,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_post_processing<
 
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">naive_compute</span><span class="p">(</span>
     <span class="n">subcircuit_order</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span>
     <span class="n">summation_terms</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]],</span>
@@ -681,6 +734,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_post_processing<
 
 <div class="viewcode-block" id="build">
 <a class="viewcode-back" href="../../../../stubs/circuit_knitting.cutting.cutqc.build.html#circuit_knitting.cutting.cutqc.build">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">build</span><span class="p">(</span>
     <span class="n">summation_terms</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]],</span>
     <span class="n">subcircuit_entry_probs</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]],</span>
@@ -738,6 +797,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_post_processing<
 
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">find_process_jobs</span><span class="p">(</span>
     <span class="n">jobs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">],</span> <span class="n">rank</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">num_workers</span><span class="p">:</span> <span class="nb">int</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]:</span>
@@ -802,7 +867,7 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_post_processing<
   </div>
 </div><script src="../../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/cutting/cutqc/wire_cutting_verification.html b/_modules/circuit_knitting/cutting/cutqc/wire_cutting_verification.html
index d3cdac5bf..8312fee21 100644
--- a/_modules/circuit_knitting/cutting/cutqc/wire_cutting_verification.html
+++ b/_modules/circuit_knitting/cutting/cutqc/wire_cutting_verification.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../../genindex.html" /><link rel="search" title="Search" href="../../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cutqc.wire_cutting_verification - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cutqc.wire_cutting_verification - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -319,6 +323,7 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_verification</h1
 <span class="kn">from</span> <span class="nn">qiskit.circuit</span> <span class="kn">import</span> <span class="n">Qubit</span>
 <span class="kn">from</span> <span class="nn">qiskit.quantum_info</span> <span class="kn">import</span> <span class="n">Statevector</span>
 <span class="kn">from</span> <span class="nn">qiskit_aer</span> <span class="kn">import</span> <span class="n">Aer</span>
+<span class="kn">from</span> <span class="nn">qiskit.utils.deprecation</span> <span class="kn">import</span> <span class="n">deprecate_func</span>
 
 <span class="kn">from</span> <span class="nn">...utils.conversion</span> <span class="kn">import</span> <span class="n">quasi_to_real</span>
 <span class="kn">from</span> <span class="nn">...utils.metrics</span> <span class="kn">import</span> <span class="p">(</span>
@@ -332,6 +337,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_verification</h1
 
 <div class="viewcode-block" id="verify">
 <a class="viewcode-back" href="../../../../stubs/circuit_knitting.cutting.cutqc.verify.html#circuit_knitting.cutting.cutqc.verify">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">verify</span><span class="p">(</span>
     <span class="n">full_circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span>
     <span class="n">reconstructed_output</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
@@ -374,6 +385,12 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_verification</h1
 
 
 
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+    <span class="n">additional_msg</span><span class="o">=</span><span class="s2">&quot;Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. &quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">generate_reconstructed_output</span><span class="p">(</span>
     <span class="n">full_circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span>
     <span class="n">subcircuits</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">QuantumCircuit</span><span class="p">],</span>
@@ -496,7 +513,7 @@ <h1>Source code for circuit_knitting.cutting.cutqc.wire_cutting_verification</h1
   </div>
 </div><script src="../../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/cutting/cutting_decomposition.html b/_modules/circuit_knitting/cutting/cutting_decomposition.html
index 0afcc6bd8..624f9a313 100644
--- a/_modules/circuit_knitting/cutting/cutting_decomposition.html
+++ b/_modules/circuit_knitting/cutting/cutting_decomposition.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cutting_decomposition - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cutting_decomposition - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -609,7 +613,7 @@ <h1>Source code for circuit_knitting.cutting.cutting_decomposition</h1><div clas
   </div>
 </div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/cutting/cutting_experiments.html b/_modules/circuit_knitting/cutting/cutting_experiments.html
index 14c8195ef..ca83a544b 100644
--- a/_modules/circuit_knitting/cutting/cutting_experiments.html
+++ b/_modules/circuit_knitting/cutting/cutting_experiments.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cutting_experiments - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cutting_experiments - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -729,7 +733,7 @@ <h1>Source code for circuit_knitting.cutting.cutting_experiments</h1><div class=
   </div>
 </div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/cutting/cutting_reconstruction.html b/_modules/circuit_knitting/cutting/cutting_reconstruction.html
index 3ba6996d2..52b6aa65f 100644
--- a/_modules/circuit_knitting/cutting/cutting_reconstruction.html
+++ b/_modules/circuit_knitting/cutting/cutting_reconstruction.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cutting_reconstruction - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cutting_reconstruction - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -314,7 +318,10 @@ <h1>Source code for circuit_knitting.cutting.cutting_reconstruction</h1><div cla
 
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">from</span> <span class="nn">qiskit.quantum_info</span> <span class="kn">import</span> <span class="n">PauliList</span>
-<span class="kn">from</span> <span class="nn">qiskit.primitives</span> <span class="kn">import</span> <span class="n">SamplerResult</span>
+<span class="kn">from</span> <span class="nn">qiskit.primitives</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">SamplerResult</span><span class="p">,</span>  <span class="c1"># for SamplerV1</span>
+    <span class="n">PrimitiveResult</span><span class="p">,</span>  <span class="c1"># for SamplerV2</span>
+<span class="p">)</span>
 
 <span class="kn">from</span> <span class="nn">..utils.observable_grouping</span> <span class="kn">import</span> <span class="n">CommutingObservableGroup</span><span class="p">,</span> <span class="n">ObservableCollection</span>
 <span class="kn">from</span> <span class="nn">..utils.bitwise</span> <span class="kn">import</span> <span class="n">bit_count</span>
@@ -326,7 +333,11 @@ <h1>Source code for circuit_knitting.cutting.cutting_reconstruction</h1><div cla
 <div class="viewcode-block" id="reconstruct_expectation_values">
 <a class="viewcode-back" href="../../../stubs/circuit_knitting.cutting.reconstruct_expectation_values.html#circuit_knitting.cutting.reconstruct_expectation_values">[docs]</a>
 <span class="k">def</span> <span class="nf">reconstruct_expectation_values</span><span class="p">(</span>
-    <span class="n">results</span><span class="p">:</span> <span class="n">SamplerResult</span> <span class="o">|</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Hashable</span><span class="p">,</span> <span class="n">SamplerResult</span><span class="p">],</span>
+    <span class="n">results</span><span class="p">:</span> <span class="p">(</span>
+        <span class="n">SamplerResult</span>
+        <span class="o">|</span> <span class="n">PrimitiveResult</span>
+        <span class="o">|</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Hashable</span><span class="p">,</span> <span class="n">SamplerResult</span> <span class="o">|</span> <span class="n">PrimitiveResult</span><span class="p">]</span>
+    <span class="p">),</span>
     <span class="n">coefficients</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">WeightType</span><span class="p">]],</span>
     <span class="n">observables</span><span class="p">:</span> <span class="n">PauliList</span> <span class="o">|</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Hashable</span><span class="p">,</span> <span class="n">PauliList</span><span class="p">],</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]:</span>
@@ -367,9 +378,11 @@ <h1>Source code for circuit_knitting.cutting.cutting_reconstruction</h1><div cla
 <span class="sd">        ValueError: ``observables`` and ``results`` are of incompatible types.</span>
 <span class="sd">        ValueError: An input observable has a phase not equal to 1.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">observables</span><span class="p">,</span> <span class="n">PauliList</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="n">SamplerResult</span><span class="p">):</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">observables</span><span class="p">,</span> <span class="n">PauliList</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span>
+        <span class="n">results</span><span class="p">,</span> <span class="p">(</span><span class="n">SamplerResult</span><span class="p">,</span> <span class="n">PrimitiveResult</span><span class="p">)</span>
+    <span class="p">):</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-            <span class="s2">&quot;If observables is a PauliList, results must be a SamplerResult instance.&quot;</span>
+            <span class="s2">&quot;If observables is a PauliList, results must be a SamplerResult or PrimitiveResult instance.&quot;</span>
         <span class="p">)</span>
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">observables</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
@@ -383,7 +396,7 @@ <h1>Source code for circuit_knitting.cutting.cutting_reconstruction</h1><div cla
         <span class="n">subobservables_by_subsystem</span> <span class="o">=</span> <span class="n">decompose_observables</span><span class="p">(</span>
             <span class="n">observables</span><span class="p">,</span> <span class="s2">&quot;A&quot;</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">observables</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
         <span class="p">)</span>
-        <span class="n">results_dict</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Hashable</span><span class="p">,</span> <span class="n">SamplerResult</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;A&quot;</span><span class="p">:</span> <span class="n">results</span><span class="p">}</span>
+        <span class="n">results_dict</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Hashable</span><span class="p">,</span> <span class="n">SamplerResult</span> <span class="o">|</span> <span class="n">PrimitiveResult</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;A&quot;</span><span class="p">:</span> <span class="n">results</span><span class="p">}</span>
         <span class="n">expvals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">observables</span><span class="p">))</span>
 
     <span class="k">else</span><span class="p">:</span>
@@ -406,10 +419,28 @@ <h1>Source code for circuit_knitting.cutting.cutting_reconstruction</h1><div cla
             <span class="n">subsystem_expvals</span> <span class="o">=</span> <span class="p">[</span>
                 <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cog</span><span class="o">.</span><span class="n">commuting_observables</span><span class="p">))</span> <span class="k">for</span> <span class="n">cog</span> <span class="ow">in</span> <span class="n">so</span><span class="o">.</span><span class="n">groups</span>
             <span class="p">]</span>
+            <span class="n">current_result</span> <span class="o">=</span> <span class="n">results_dict</span><span class="p">[</span><span class="n">label</span><span class="p">]</span>
             <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">cog</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">so</span><span class="o">.</span><span class="n">groups</span><span class="p">):</span>
-                <span class="n">quasi_probs</span> <span class="o">=</span> <span class="n">results_dict</span><span class="p">[</span><span class="n">label</span><span class="p">]</span><span class="o">.</span><span class="n">quasi_dists</span><span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">so</span><span class="o">.</span><span class="n">groups</span><span class="p">)</span> <span class="o">+</span> <span class="n">k</span><span class="p">]</span>
-                <span class="k">for</span> <span class="n">outcome</span><span class="p">,</span> <span class="n">quasi_prob</span> <span class="ow">in</span> <span class="n">quasi_probs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-                    <span class="n">subsystem_expvals</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+=</span> <span class="n">quasi_prob</span> <span class="o">*</span> <span class="n">_process_outcome</span><span class="p">(</span><span class="n">cog</span><span class="p">,</span> <span class="n">outcome</span><span class="p">)</span>
+                <span class="n">idx</span> <span class="o">=</span> <span class="n">i</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">so</span><span class="o">.</span><span class="n">groups</span><span class="p">)</span> <span class="o">+</span> <span class="n">k</span>
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">current_result</span><span class="p">,</span> <span class="n">SamplerResult</span><span class="p">):</span>
+                    <span class="c1"># SamplerV1 provides a SamplerResult</span>
+                    <span class="n">quasi_probs</span> <span class="o">=</span> <span class="n">current_result</span><span class="o">.</span><span class="n">quasi_dists</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+                    <span class="k">for</span> <span class="n">outcome</span><span class="p">,</span> <span class="n">quasi_prob</span> <span class="ow">in</span> <span class="n">quasi_probs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                        <span class="n">subsystem_expvals</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+=</span> <span class="n">quasi_prob</span> <span class="o">*</span> <span class="n">_process_outcome</span><span class="p">(</span>
+                            <span class="n">cog</span><span class="p">,</span> <span class="n">outcome</span>
+                        <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="c1"># SamplerV2 provides a PrimitiveResult</span>
+                    <span class="n">data_pub</span> <span class="o">=</span> <span class="n">current_result</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">data</span>
+                    <span class="n">obs_array</span> <span class="o">=</span> <span class="n">data_pub</span><span class="o">.</span><span class="n">observable_measurements</span><span class="o">.</span><span class="n">array</span>
+                    <span class="n">qpd_array</span> <span class="o">=</span> <span class="n">data_pub</span><span class="o">.</span><span class="n">qpd_measurements</span><span class="o">.</span><span class="n">array</span>
+                    <span class="n">shots</span> <span class="o">=</span> <span class="n">qpd_array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">shots</span><span class="p">):</span>
+                        <span class="n">obs_outcomes</span> <span class="o">=</span> <span class="nb">int</span><span class="o">.</span><span class="n">from_bytes</span><span class="p">(</span><span class="n">obs_array</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="s2">&quot;big&quot;</span><span class="p">)</span>
+                        <span class="n">qpd_outcomes</span> <span class="o">=</span> <span class="nb">int</span><span class="o">.</span><span class="n">from_bytes</span><span class="p">(</span><span class="n">qpd_array</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="s2">&quot;big&quot;</span><span class="p">)</span>
+                        <span class="n">subsystem_expvals</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">shots</span><span class="p">)</span> <span class="o">*</span> <span class="n">_process_outcome_v2</span><span class="p">(</span>
+                            <span class="n">cog</span><span class="p">,</span> <span class="n">obs_outcomes</span><span class="p">,</span> <span class="n">qpd_outcomes</span>
+                        <span class="p">)</span>
 
             <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">subobservable</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">subobservables_by_subsystem</span><span class="p">[</span><span class="n">label</span><span class="p">]):</span>
                 <span class="n">current_expvals</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">*=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span>
@@ -440,19 +471,38 @@ <h1>Source code for circuit_knitting.cutting.cutting_reconstruction</h1><div cla
     <span class="n">num_meas_bits</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">_get_pauli_indices</span><span class="p">(</span><span class="n">cog</span><span class="p">))</span>
 
     <span class="n">outcome</span> <span class="o">=</span> <span class="n">_outcome_to_int</span><span class="p">(</span><span class="n">outcome</span><span class="p">)</span>
-    <span class="n">meas_outcomes</span> <span class="o">=</span> <span class="n">outcome</span> <span class="o">&amp;</span> <span class="p">((</span><span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="n">num_meas_bits</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">obs_outcomes</span> <span class="o">=</span> <span class="n">outcome</span> <span class="o">&amp;</span> <span class="p">((</span><span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="n">num_meas_bits</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
     <span class="n">qpd_outcomes</span> <span class="o">=</span> <span class="n">outcome</span> <span class="o">&gt;&gt;</span> <span class="n">num_meas_bits</span>
 
+    <span class="k">return</span> <span class="n">_process_outcome_v2</span><span class="p">(</span><span class="n">cog</span><span class="p">,</span> <span class="n">obs_outcomes</span><span class="p">,</span> <span class="n">qpd_outcomes</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_process_outcome_v2</span><span class="p">(</span>
+    <span class="n">cog</span><span class="p">:</span> <span class="n">CommutingObservableGroup</span><span class="p">,</span> <span class="n">obs_outcomes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">qpd_outcomes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="o">/</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">typing</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Process a single outcome of a QPD experiment with observables.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        cog: The observable set being measured by the current experiment</span>
+<span class="sd">        obs_outcomes: An integer containing the outcome bits of the ``observable_measurements`` register</span>
+<span class="sd">        qpd_outcomes: An integer containing the outcome bits of the ``qpd_measurements`` register</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        A 1D array of the observable measurements.  The elements of</span>
+<span class="sd">        this vector correspond to the elements of ``cog.commuting_observables``,</span>
+<span class="sd">        and each result will be either +1 or -1.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
     <span class="c1"># qpd_factor will be -1 or +1, depending on the overall parity of qpd</span>
     <span class="c1"># measurements.</span>
     <span class="n">qpd_factor</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">bit_count</span><span class="p">(</span><span class="n">qpd_outcomes</span><span class="p">)</span> <span class="o">&amp;</span> <span class="mi">1</span><span class="p">)</span>
 
     <span class="n">rv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cog</span><span class="o">.</span><span class="n">pauli_bitmasks</span><span class="p">))</span>
     <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">mask</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">cog</span><span class="o">.</span><span class="n">pauli_bitmasks</span><span class="p">):</span>
-        <span class="c1"># meas will be -1 or +1, depending on the measurement</span>
+        <span class="c1"># obs will be -1 or +1, depending on the measurement</span>
         <span class="c1"># of the current operator.</span>
-        <span class="n">meas</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">bit_count</span><span class="p">(</span><span class="n">meas_outcomes</span> <span class="o">&amp;</span> <span class="n">mask</span><span class="p">)</span> <span class="o">&amp;</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="n">rv</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">qpd_factor</span> <span class="o">*</span> <span class="n">meas</span>
+        <span class="n">obs</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">bit_count</span><span class="p">(</span><span class="n">obs_outcomes</span> <span class="o">&amp;</span> <span class="n">mask</span><span class="p">)</span> <span class="o">&amp;</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">rv</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">qpd_factor</span> <span class="o">*</span> <span class="n">obs</span>
 
     <span class="k">return</span> <span class="n">rv</span>
 
@@ -505,7 +555,7 @@ <h1>Source code for circuit_knitting.cutting.cutting_reconstruction</h1><div cla
   </div>
 </div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/cutting/instructions/cut_wire.html b/_modules/circuit_knitting/cutting/instructions/cut_wire.html
index 4cb9bc023..d9aa4a26d 100644
--- a/_modules/circuit_knitting/cutting/instructions/cut_wire.html
+++ b/_modules/circuit_knitting/cutting/instructions/cut_wire.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../../genindex.html" /><link rel="search" title="Search" href="../../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.instructions.cut_wire - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.instructions.cut_wire - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -366,7 +370,7 @@ <h1>Source code for circuit_knitting.cutting.instructions.cut_wire</h1><div clas
   </div>
 </div><script src="../../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/cutting/instructions/move.html b/_modules/circuit_knitting/cutting/instructions/move.html
index 4100ac060..2752a214a 100644
--- a/_modules/circuit_knitting/cutting/instructions/move.html
+++ b/_modules/circuit_knitting/cutting/instructions/move.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../../genindex.html" /><link rel="search" title="Search" href="../../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.instructions.move - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.instructions.move - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -420,7 +424,7 @@ <h1>Source code for circuit_knitting.cutting.instructions.move</h1><div class="h
   </div>
 </div><script src="../../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/cutting/qpd/decompose.html b/_modules/circuit_knitting/cutting/qpd/decompose.html
new file mode 100644
index 000000000..a7c01981d
--- /dev/null
+++ b/_modules/circuit_knitting/cutting/qpd/decompose.html
@@ -0,0 +1,590 @@
+<!doctype html>
+<html class="no-js" lang="en">
+  <head><meta charset="utf-8"/>
+    <meta name="viewport" content="width=device-width,initial-scale=1"/>
+    <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../../genindex.html" /><link rel="search" title="Search" href="../../../../search.html" />
+
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.qpd.decompose - Circuit Knitting Toolbox 0.7.0</title>
+      <link rel="stylesheet" type="text/css" href="../../../../_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css?v=82a976d7" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/styles/qiskit-ecosystem.css?v=745c5aa7" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/plot_directive.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/jupyter-sphinx.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/thebelab.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/styles/furo-extensions.css?v=36a5483c" />
+    
+    
+
+
+<style>
+  body {
+    --color-code-background: #ffffff;
+  --color-code-foreground: black;
+  
+  }
+  @media not print {
+    body[data-theme="dark"] {
+      --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+    }
+    @media (prefers-color-scheme: dark) {
+      body:not([data-theme="light"]) {
+        --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+      }
+    }
+  }
+</style><link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin><link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&family=IBM+Plex+Sans:ital,wght@0,400;0,600;1,400;1,600&display=swap" rel="stylesheet">
+<script src="../../../../_static/js/web-components/top-nav-bar.js"></script></head>
+  <body>
+    
+    <script>document.body.dataset.theme = "light";</script>
+    
+<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
+  <symbol id="svg-toc" viewBox="0 0 24 24">
+    <title>Contents</title>
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32">
+      <defs><style>.cls-1{fill:none;}</style></defs>
+      <path d="M28,4H4A2,2,0,0,0,2,6V26a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V6A2,2,0,0,0,28,4ZM4,6H20V26H4ZM28,26H22V6h6Z"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-menu" viewBox="0 0 24 24">
+    <title>Menu</title>
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32">
+      <defs><style>.cls-1{fill:none;}</style></defs>
+      <rect x="4" y="6" width="24" height="2"/>
+      <rect x="4" y="24" width="24" height="2"/>
+      <rect x="4" y="12" width="24" height="2"/>
+      <rect x="4" y="18" width="24" height="2"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
+    <title>Expand</title>
+    <svg version="1.1" id="icon" xmlns="http://www.w3.org/2000/svg" x="0px" y="0px"
+         viewBox="0 0 32 32" style="enable-background:new 0 0 32 32;" xml:space="preserve"><polygon points="22,16 12,26 10.6,24.6 19.2,16 10.6,7.4 12,6 " stroke="currentColor"/>
+      <rect id="_x3C_Transparent_Rectangle_x3E_" fill="none" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-new-tab" viewBox="0 0 32 32">
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 32 32">
+      <defs>
+        <style>
+          .cls-1 {
+            fill: none;
+          }
+        </style>
+      </defs>
+      <path fill="#6929C4" d="M26,28H6a2.0027,2.0027,0,0,1-2-2V6A2.0027,2.0027,0,0,1,6,4H16V6H6V26H26V16h2V26A2.0027,2.0027,0,0,1,26,28Z"/>
+      <polygon fill="#6929C4" points="20 2 20 4 26.586 4 18 12.586 19.414 14 28 5.414 28 12 30 12 30 2 20 2"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-sun" viewBox="0 0 24 24">
+    <title>Light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      <circle cx="12" cy="12" r="5"></circle>
+      <line x1="12" y1="1" x2="12" y2="3"></line>
+      <line x1="12" y1="21" x2="12" y2="23"></line>
+      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
+      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
+      <line x1="1" y1="12" x2="3" y2="12"></line>
+      <line x1="21" y1="12" x2="23" y2="12"></line>
+      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
+      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon" viewBox="0 0 24 24">
+    <title>Dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
+    </svg>
+  </symbol>
+  <symbol id="svg-sun-half" viewBox="0 0 24 24">
+    <title>Auto light/dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+      <circle cx="12" cy="12" r="9" />
+      <path d="M13 12h5" />
+      <path d="M13 15h4" />
+      <path d="M13 18h1" />
+      <path d="M13 9h4" />
+      <path d="M13 6h1" />
+    </svg>
+  </symbol>
+</svg>
+
+<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
+<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
+<label class="overlay sidebar-overlay" for="__navigation">
+  <div class="visually-hidden">Hide navigation sidebar</div>
+</label>
+<label class="overlay toc-overlay" for="__toc">
+  <div class="visually-hidden">Hide table of contents sidebar</div>
+</label>
+
+
+
+<div class="page">
+  <header class="mobile-header">
+    <div class="header-left">
+      <label class="nav-overlay-icon" for="__navigation">
+        <div class="visually-hidden">Toggle site navigation sidebar</div>
+        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
+      </label>
+    </div>
+    <div class="header-center">
+      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
+    </div>
+    <div class="header-right">
+      <div class="theme-toggle-container theme-toggle-header">
+        <button class="theme-toggle">
+          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+        </button>
+      </div>
+      <label class="toc-overlay-icon toc-header-icon no-toc" for="__toc">
+        <div class="visually-hidden">Toggle table of contents sidebar</div>
+        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+      </label>
+    </div>
+  </header>
+  <aside class="sidebar-drawer">
+    <div class="sidebar-container">
+      
+      <div class="sidebar-sticky"><div class="sidebar-brand">
+  <a href="https://www.qiskit.org/ecosystem">
+    <div class="sidebar-logo-container">
+      <img class="sidebar-logo" src="../../../../_static/images/ecosystem-logo.svg" alt="Qiskit Ecosystem logo"/>
+    </div>
+  </a>
+  
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
+</div><form class="sidebar-search-container" method="get" action="../../../../search.html" role="search">
+  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
+  <input type="hidden" name="check_keywords" value="yes">
+  <input type="hidden" name="area" value="default">
+</form>
+<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
+  <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../index.html">About Circuit Knitting Toolbox</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../install.html">Installation Instructions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Circuit Cutting</span></p>
+<ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../../../../circuit_cutting/tutorials/index.html">Cutting Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of Cutting Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../../../../circuit_cutting/how-tos/index.html">Cutting How-To Guides</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Cutting How-To Guides</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.html">How to generate exact quasiprobability distributions from Sampler</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.html">How to generate exact sampling coefficients</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/how-tos/how_to_specify_cut_wires.html">How to place wire cuts using a single-qubit <code class="docutils literal notranslate"><span class="pre">CutWire</span></code> instruction</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../../../../circuit_cutting/cutqc/index.html">CutQC (legacy circuit cutting implementation)</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of CutQC (legacy circuit cutting implementation)</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.html">CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.html">CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">References</span></p>
+<ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../../../../apidocs/index.html">API References</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of API References</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../apidocs/circuit_cutting.html">Circuit Cutting</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Circuit Cutting</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cut_wires.html">circuit_knitting.cutting.cut_wires</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.expand_observables.html">circuit_knitting.cutting.expand_observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.partition_circuit_qubits.html">circuit_knitting.cutting.partition_circuit_qubits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.partition_problem.html">circuit_knitting.cutting.partition_problem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cut_gates.html">circuit_knitting.cutting.cut_gates</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.generate_cutting_experiments.html">circuit_knitting.cutting.generate_cutting_experiments</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.reconstruct_expectation_values.html">circuit_knitting.cutting.reconstruct_expectation_values</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.TwoQubitQPDGate.html">circuit_knitting.cutting.qpd.TwoQubitQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.WeightType.html">circuit_knitting.cutting.qpd.WeightType</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.html">circuit_knitting.cutting.qpd.generate_qpd_weights</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.html">circuit_knitting.cutting.qpd.decompose_qpd_instructions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction.html">circuit_knitting.cutting.qpd.qpdbasis_from_instruction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances.html">circuit_knitting.cutting.cutqc.run_subcircuit_instances</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cutqc.generate_summation_terms.html">circuit_knitting.cutting.cutqc.generate_summation_terms</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cutqc.build.html">circuit_knitting.cutting.cutqc.build</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cutqc.verify.html">circuit_knitting.cutting.cutqc.verify</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires.html">circuit_knitting.cutting.cutqc.cut_circuit_wires</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits.html">circuit_knitting.cutting.cutqc.evaluate_subcircuits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.html">circuit_knitting.cutting.cutqc.reconstruct_full_distribution</a></li>
+</ul>
+</li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../apidocs/utils.html">Utilities</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of Utilities</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.bitwise.bit_count.html">circuit_knitting.utils.bitwise.bit_count</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.conversion.quasi_to_real.html">circuit_knitting.utils.conversion.quasi_to_real</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.conversion.nearest_probability_distribution.html">circuit_knitting.utils.conversion.nearest_probability_distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.conversion.naive_probability_distribution.html">circuit_knitting.utils.conversion.naive_probability_distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.conversion.dict_to_array.html">circuit_knitting.utils.conversion.dict_to_array</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.iteration.unique_by_id.html">circuit_knitting.utils.iteration.unique_by_id</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.iteration.unique_by_eq.html">circuit_knitting.utils.iteration.unique_by_eq</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.metrics.chi2_distance.html">circuit_knitting.utils.metrics.chi2_distance</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.metrics.MSE.html">circuit_knitting.utils.metrics.MSE</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.metrics.MAPE.html">circuit_knitting.utils.metrics.MAPE</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.metrics.cross_entropy.html">circuit_knitting.utils.metrics.cross_entropy</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.metrics.HOP.html">circuit_knitting.utils.metrics.HOP</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.html">circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.observable_grouping.CommutingObservableGroup.html">circuit_knitting.utils.observable_grouping.CommutingObservableGroup</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.observable_grouping.ObservableCollection.html">circuit_knitting.utils.observable_grouping.ObservableCollection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.simulation.simulate_statevector_outcomes.html">circuit_knitting.utils.simulation.simulate_statevector_outcomes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.simulation.ExactSampler.html">circuit_knitting.utils.simulation.ExactSampler</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.transforms.separate_circuit.html">circuit_knitting.utils.transforms.separate_circuit</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.transforms.SeparatedCircuits.html">circuit_knitting.utils.transforms.SeparatedCircuits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.transpiler_passes.RemoveFinalReset.html">circuit_knitting.utils.transpiler_passes.RemoveFinalReset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.transpiler_passes.ConsolidateResets.html">circuit_knitting.utils.transpiler_passes.ConsolidateResets</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../release-notes.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference external" href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox">GitHub</a></li>
+</ul>
+
+</div></div>
+      </div>
+      
+    </div>
+  </aside>
+  <div class="main">
+    <div class="content">
+      <div class="article-container">
+        <a href="#" class="back-to-top muted-link">
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
+            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
+          </svg>
+          <span>Back to top</span>
+        </a>
+        <div class="content-icon-container">
+          <div class="theme-toggle-container theme-toggle-content">
+            <button class="theme-toggle">
+              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+            </button>
+          </div>
+          <label class="toc-overlay-icon toc-content-icon no-toc" for="__toc">
+            <div class="visually-hidden">Toggle table of contents sidebar</div>
+            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+          </label>
+        </div>
+        <article role="main">
+          <h1>Source code for circuit_knitting.cutting.qpd.decompose</h1><div class="highlight"><pre>
+<span></span><span class="c1"># This code is a Qiskit project.</span>
+
+<span class="c1"># (C) Copyright IBM 2024.</span>
+
+<span class="c1"># This code is licensed under the Apache License, Version 2.0. You may</span>
+<span class="c1"># obtain a copy of this license in the LICENSE.txt file in the root directory</span>
+<span class="c1"># of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.</span>
+<span class="c1"># Any modifications or derivative works of this code must retain this</span>
+<span class="c1"># copyright notice, and modified files need to carry a notice indicating</span>
+<span class="c1"># that they have been altered from the originals.</span>
+
+<span class="sd">&quot;&quot;&quot;Function to replace all QPD instructions in the circuit with local Qiskit operations and measurements.&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Sequence</span>
+
+<span class="kn">from</span> <span class="nn">qiskit.circuit</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">QuantumCircuit</span><span class="p">,</span>
+    <span class="n">ClassicalRegister</span><span class="p">,</span>
+    <span class="n">CircuitInstruction</span><span class="p">,</span>
+    <span class="n">Measure</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="kn">from</span> <span class="nn">.instructions</span> <span class="kn">import</span> <span class="n">BaseQPDGate</span><span class="p">,</span> <span class="n">TwoQubitQPDGate</span>
+
+
+<div class="viewcode-block" id="decompose_qpd_instructions">
+<a class="viewcode-back" href="../../../../stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.html#circuit_knitting.cutting.qpd.decompose_qpd_instructions">[docs]</a>
+<span class="k">def</span> <span class="nf">decompose_qpd_instructions</span><span class="p">(</span>
+    <span class="n">circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span>
+    <span class="n">instruction_ids</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]],</span>
+    <span class="n">map_ids</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="o">*</span><span class="p">,</span>
+    <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">QuantumCircuit</span><span class="p">:</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Replace all QPD instructions in the circuit with local Qiskit operations and measurements.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        circuit: The circuit containing QPD instructions</span>
+<span class="sd">        instruction_ids: A 2D sequence, such that each inner sequence corresponds to indices</span>
+<span class="sd">            of instructions comprising one decomposition in the circuit. The elements within a</span>
+<span class="sd">            common sequence belong to a common decomposition and should be sampled together.</span>
+<span class="sd">        map_ids: Indices to a specific linear mapping to be applied to the decompositions</span>
+<span class="sd">            in the circuit. If no map IDs are provided, the circuit will be decomposed randomly</span>
+<span class="sd">            according to the decompositions&#39; joint probability distribution.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        Circuit which has had all its :class:`BaseQPDGate` instances decomposed into local operations.</span>
+
+<span class="sd">        The circuit will contain a new, final classical register to contain the QPD measurement</span>
+<span class="sd">        outcomes (accessible at ``retval.cregs[-1]``).</span>
+
+<span class="sd">    Raises:</span>
+<span class="sd">        ValueError: An index in ``instruction_ids`` corresponds to a gate which is not a</span>
+<span class="sd">            :class:`BaseQPDGate` instance.</span>
+<span class="sd">        ValueError: A list within instruction_ids is not length 1 or 2.</span>
+<span class="sd">        ValueError: The total number of indices in ``instruction_ids`` does not equal the number</span>
+<span class="sd">            of :class:`BaseQPDGate` instances in the circuit.</span>
+<span class="sd">        ValueError: Gates within the same decomposition hold different QPD bases.</span>
+<span class="sd">        ValueError: Length of ``map_ids`` does not equal the number of decompositions in the circuit.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">_validate_qpd_instructions</span><span class="p">(</span><span class="n">circuit</span><span class="p">,</span> <span class="n">instruction_ids</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">inplace</span><span class="p">:</span>
+        <span class="n">circuit</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>  <span class="c1"># pragma: no cover</span>
+
+    <span class="k">if</span> <span class="n">map_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">instruction_ids</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">map_ids</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;The number of map IDs (</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">map_ids</span><span class="p">)</span><span class="si">}</span><span class="s2">) must equal the number of &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;decompositions in the circuit (</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">instruction_ids</span><span class="p">)</span><span class="si">}</span><span class="s2">).&quot;</span>
+            <span class="p">)</span>
+        <span class="c1"># If mapping is specified, set each gate&#39;s mapping</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">decomp_gate_ids</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">instruction_ids</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">gate_id</span> <span class="ow">in</span> <span class="n">decomp_gate_ids</span><span class="p">:</span>
+                <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">gate_id</span><span class="p">]</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">basis_id</span> <span class="o">=</span> <span class="n">map_ids</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+    <span class="c1"># Convert all instances of BaseQPDGate in the circuit to Qiskit instructions</span>
+    <span class="n">_decompose_qpd_instructions</span><span class="p">(</span><span class="n">circuit</span><span class="p">,</span> <span class="n">instruction_ids</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">circuit</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">_validate_qpd_instructions</span><span class="p">(</span>
+    <span class="n">circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span> <span class="n">instruction_ids</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Ensure the indices in instruction_ids correctly describe all the decompositions in the circuit.&quot;&quot;&quot;</span>
+    <span class="c1"># Make sure all instruction_ids correspond to QPDGates, and make sure each QPDGate in a given decomposition has</span>
+    <span class="c1"># an equivalent QPDBasis to its sibling QPDGates</span>
+    <span class="k">for</span> <span class="n">decomp_ids</span> <span class="ow">in</span> <span class="n">instruction_ids</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">decomp_ids</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Each decomposition must contain either one or two elements. Found a &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;decomposition with (</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">decomp_ids</span><span class="p">)</span><span class="si">}</span><span class="s2">) elements.&quot;</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">decomp_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">BaseQPDGate</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;A circuit data index (</span><span class="si">{</span><span class="n">decomp_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">) corresponds to a non-QPDGate &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">decomp_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">).&quot;</span>
+            <span class="p">)</span>
+        <span class="n">compare_basis</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">decomp_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">basis</span>
+        <span class="k">for</span> <span class="n">gate_id</span> <span class="ow">in</span> <span class="n">decomp_ids</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">gate_id</span><span class="p">]</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">BaseQPDGate</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;A circuit data index (</span><span class="si">{</span><span class="n">gate_id</span><span class="si">}</span><span class="s2">) corresponds to a non-QPDGate &quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">gate_id</span><span class="p">]</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">).&quot;</span>
+                <span class="p">)</span>
+            <span class="n">tmp_basis</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">gate_id</span><span class="p">]</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">basis</span>
+            <span class="k">if</span> <span class="n">compare_basis</span> <span class="o">!=</span> <span class="n">tmp_basis</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Gates within the same decomposition must share an equivalent QPDBasis.&quot;</span>
+                <span class="p">)</span>
+
+    <span class="c1"># Make sure the total number of QPD gate indices equals the number of QPDGates in the circuit</span>
+    <span class="n">num_qpd_gates</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">instruction_ids</span><span class="p">)</span>
+    <span class="n">qpd_gate_total</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">inst</span> <span class="ow">in</span> <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inst</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">BaseQPDGate</span><span class="p">):</span>
+            <span class="n">qpd_gate_total</span> <span class="o">+=</span> <span class="mi">1</span>
+    <span class="k">if</span> <span class="n">qpd_gate_total</span> <span class="o">!=</span> <span class="n">num_qpd_gates</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;The total number of QPDGates specified in instruction_ids (</span><span class="si">{</span><span class="n">num_qpd_gates</span><span class="si">}</span><span class="s2">) &quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;does not equal the number of QPDGates in the circuit (</span><span class="si">{</span><span class="n">qpd_gate_total</span><span class="si">}</span><span class="s2">).&quot;</span>
+        <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_decompose_qpd_measurements</span><span class="p">(</span>
+    <span class="n">circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span> <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">QuantumCircuit</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create mid-circuit measurements.</span>
+
+<span class="sd">    Convert all QPDMeasure instances to Measure instructions. Add any newly created</span>
+<span class="sd">    classical bits to a new &quot;qpd_measurements&quot; register.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">inplace</span><span class="p">:</span>
+        <span class="n">circuit</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>  <span class="c1"># pragma: no cover</span>
+
+    <span class="c1"># Loop through the decomposed circuit to find QPDMeasure markers so we can</span>
+    <span class="c1"># replace them with measurement instructions.  We can&#39;t use `_ids`</span>
+    <span class="c1"># here because it refers to old indices, before the decomposition.</span>
+    <span class="n">qpd_measure_ids</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">i</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">instruction</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">instruction</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">name</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;qpd_measure&quot;</span>
+    <span class="p">]</span>
+
+    <span class="c1"># Create a classical register for the qpd measurement results.  This is</span>
+    <span class="c1"># partly for convenience, partly to work around</span>
+    <span class="c1"># https://github.com/Qiskit/qiskit-aer/issues/1660.</span>
+    <span class="n">reg</span> <span class="o">=</span> <span class="n">ClassicalRegister</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">qpd_measure_ids</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;qpd_measurements&quot;</span><span class="p">)</span>
+    <span class="n">circuit</span><span class="o">.</span><span class="n">add_register</span><span class="p">(</span><span class="n">reg</span><span class="p">)</span>
+
+    <span class="c1"># Place the measurement instructions</span>
+    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">qpd_measure_ids</span><span class="p">):</span>
+        <span class="n">gate</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="n">inst</span> <span class="o">=</span> <span class="n">CircuitInstruction</span><span class="p">(</span>
+            <span class="n">operation</span><span class="o">=</span><span class="n">Measure</span><span class="p">(),</span> <span class="n">qubits</span><span class="o">=</span><span class="p">[</span><span class="n">gate</span><span class="o">.</span><span class="n">qubits</span><span class="p">],</span> <span class="n">clbits</span><span class="o">=</span><span class="p">[</span><span class="n">reg</span><span class="p">[</span><span class="n">idx</span><span class="p">]]</span>
+        <span class="p">)</span>
+        <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">inst</span>
+
+    <span class="c1"># If the user wants to access the qpd register, it will be the final</span>
+    <span class="c1"># classical register of the returned circuit.</span>
+    <span class="k">assert</span> <span class="n">circuit</span><span class="o">.</span><span class="n">cregs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="ow">is</span> <span class="n">reg</span>
+
+    <span class="k">return</span> <span class="n">circuit</span>
+
+
+<span class="k">def</span> <span class="nf">_decompose_qpd_instructions</span><span class="p">(</span>
+    <span class="n">circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span>
+    <span class="n">instruction_ids</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]],</span>
+    <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">QuantumCircuit</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Decompose all BaseQPDGate instances, ignoring QPDMeasure().&quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">inplace</span><span class="p">:</span>
+        <span class="n">circuit</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>  <span class="c1"># pragma: no cover</span>
+
+    <span class="c1"># Decompose any 2q QPDGates into single qubit QPDGates</span>
+    <span class="n">qpdgate_ids_2q</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">decomp</span> <span class="ow">in</span> <span class="n">instruction_ids</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">decomp</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">continue</span>  <span class="c1"># pragma: no cover</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">decomp</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">TwoQubitQPDGate</span><span class="p">):</span>
+            <span class="n">qpdgate_ids_2q</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">decomp</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+    <span class="n">qpdgate_ids_2q</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">qpdgate_ids_2q</span><span class="p">)</span>
+    <span class="n">data_id_offset</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">qpdgate_ids_2q</span><span class="p">:</span>
+        <span class="n">inst</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="n">data_id_offset</span><span class="p">]</span>
+        <span class="n">qpdcirc_2q_decomp</span> <span class="o">=</span> <span class="n">inst</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">definition</span>
+        <span class="n">inst1</span> <span class="o">=</span> <span class="n">CircuitInstruction</span><span class="p">(</span>
+            <span class="n">qpdcirc_2q_decomp</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">qubits</span><span class="o">=</span><span class="p">[</span><span class="n">inst</span><span class="o">.</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+        <span class="p">)</span>
+        <span class="n">inst2</span> <span class="o">=</span> <span class="n">CircuitInstruction</span><span class="p">(</span>
+            <span class="n">qpdcirc_2q_decomp</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">qubits</span><span class="o">=</span><span class="p">[</span><span class="n">inst</span><span class="o">.</span><span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
+        <span class="p">)</span>
+        <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="n">data_id_offset</span><span class="p">]</span> <span class="o">=</span> <span class="n">inst1</span>
+        <span class="n">data_id_offset</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="n">data_id_offset</span><span class="p">,</span> <span class="n">inst2</span><span class="p">)</span>
+
+    <span class="c1"># Decompose all the QPDGates (should all be single qubit now) into Qiskit operations</span>
+    <span class="n">new_instruction_ids</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">inst</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inst</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">BaseQPDGate</span><span class="p">):</span>
+            <span class="n">new_instruction_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+    <span class="n">data_id_offset</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">new_instruction_ids</span><span class="p">:</span>
+        <span class="n">inst</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="n">data_id_offset</span><span class="p">]</span>
+        <span class="n">qubits</span> <span class="o">=</span> <span class="n">inst</span><span class="o">.</span><span class="n">qubits</span>
+        <span class="c1"># All gates in decomposition should be local</span>
+        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span>
+        <span class="c1"># Gather instructions with which we will replace the QPDGate</span>
+        <span class="n">tmp_data</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">inst</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">definition</span><span class="o">.</span><span class="n">data</span><span class="p">:</span>
+            <span class="c1"># Can ignore clbits here, as QPDGates don&#39;t use clbits directly</span>
+            <span class="k">assert</span> <span class="n">data</span><span class="o">.</span><span class="n">clbits</span> <span class="o">==</span> <span class="p">()</span>
+            <span class="n">tmp_data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">CircuitInstruction</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">qubits</span><span class="o">=</span><span class="p">[</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">]]))</span>
+        <span class="c1"># Replace QPDGate with local operations</span>
+        <span class="k">if</span> <span class="n">tmp_data</span><span class="p">:</span>
+            <span class="c1"># Overwrite the QPDGate with first instruction</span>
+            <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="n">data_id_offset</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="c1"># Append remaining instructions immediately after original QPDGate position</span>
+            <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">tmp_data</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
+                <span class="n">data_id_offset</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="n">data_id_offset</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
+
+        <span class="c1"># If QPDGate decomposes to an identity operation, just delete it</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">del</span> <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="n">data_id_offset</span><span class="p">]</span>
+            <span class="n">data_id_offset</span> <span class="o">-=</span> <span class="mi">1</span>
+
+    <span class="n">_decompose_qpd_measurements</span><span class="p">(</span><span class="n">circuit</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">circuit</span>
+</pre></div>
+        </article>
+      </div>
+      <footer>
+        <div class="related-pages">
+          
+          
+        </div>
+        <div class="bottom-of-page">
+          <div class="left-details">
+            <div class="copyright">
+                Copyright &#169; 2023
+            </div>
+            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
+            
+            <a href="https://github.com/pradyunsg/furo">Furo</a>
+            
+          </div>
+          <div class="right-details">
+            <div class="icons">
+              <a class="muted-link " href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox" aria-label="GitHub">
+                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
+                </svg>
+            </a>
+              
+            </div>
+          </div>
+        </div>
+        
+      </footer>
+    </div>
+    <aside class="toc-drawer no-toc">
+      
+      
+      
+    </aside>
+  </div>
+</div><script src="../../../../_static/jquery.js?v=5d32c60e"></script>
+    <script src="../../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+    <script src="../../../../_static/documentation_options.js?v=fe7df9b0"></script>
+    <script src="../../../../_static/doctools.js?v=9a2dae69"></script>
+    <script src="../../../../_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="../../../../_static/thebelab-helper.js"></script>
+    <script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
+    <script src="../../../../_static/copybutton.js?v=f281be69"></script>
+    <script src="../../../../_static/scripts/qiskit-sphinx-theme.js?v=4d77b8ca"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/@jupyter-widgets/html-manager@^1.0.1/dist/embed-amd.js"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    </body>
+</html>
\ No newline at end of file
diff --git a/_modules/circuit_knitting/cutting/qpd/qpd.html b/_modules/circuit_knitting/cutting/qpd/decompositions.html
similarity index 57%
rename from _modules/circuit_knitting/cutting/qpd/qpd.html
rename to _modules/circuit_knitting/cutting/qpd/decompositions.html
index 8b9eb1ee4..16712d4f4 100644
--- a/_modules/circuit_knitting/cutting/qpd/qpd.html
+++ b/_modules/circuit_knitting/cutting/qpd/decompositions.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../../genindex.html" /><link rel="search" title="Search" href="../../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.qpd.qpd - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.qpd.decompositions - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -294,10 +298,10 @@
           </label>
         </div>
         <article role="main">
-          <h1>Source code for circuit_knitting.cutting.qpd.qpd</h1><div class="highlight"><pre>
+          <h1>Source code for circuit_knitting.cutting.qpd.decompositions</h1><div class="highlight"><pre>
 <span></span><span class="c1"># This code is a Qiskit project.</span>
 
-<span class="c1"># (C) Copyright IBM 2023.</span>
+<span class="c1"># (C) Copyright IBM 2024.</span>
 
 <span class="c1"># This code is licensed under the Apache License, Version 2.0. You may</span>
 <span class="c1"># obtain a copy of this license in the LICENSE.txt file in the root directory</span>
@@ -306,27 +310,17 @@ <h1>Source code for circuit_knitting.cutting.qpd.qpd</h1><div class="highlight">
 <span class="c1"># copyright notice, and modified files need to carry a notice indicating</span>
 <span class="c1"># that they have been altered from the originals.</span>
 
-<span class="sd">&quot;&quot;&quot;Functions for conducting quasiprobability decomposition.&quot;&quot;&quot;</span>
+<span class="sd">&quot;&quot;&quot;Instruction to :class:`.QPDBasis` decompositions.&quot;&quot;&quot;</span>
 
 <span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
 
 <span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Callable</span>
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">Counter</span>
-<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
-<span class="kn">import</span> <span class="nn">itertools</span>
-<span class="kn">import</span> <span class="nn">logging</span>
-<span class="kn">import</span> <span class="nn">math</span>
 
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">numpy.typing</span> <span class="k">as</span> <span class="nn">npt</span>
 <span class="kn">from</span> <span class="nn">qiskit.circuit</span> <span class="kn">import</span> <span class="p">(</span>
-    <span class="n">QuantumCircuit</span><span class="p">,</span>
     <span class="n">Gate</span><span class="p">,</span>
     <span class="n">ControlledGate</span><span class="p">,</span>
     <span class="n">Instruction</span><span class="p">,</span>
-    <span class="n">ClassicalRegister</span><span class="p">,</span>
-    <span class="n">CircuitInstruction</span><span class="p">,</span>
-    <span class="n">Measure</span><span class="p">,</span>
     <span class="n">Reset</span><span class="p">,</span>
 <span class="p">)</span>
 <span class="kn">from</span> <span class="nn">qiskit.circuit.library</span> <span class="kn">import</span> <span class="n">UnitaryGate</span>
@@ -364,476 +358,12 @@ <h1>Source code for circuit_knitting.cutting.qpd.qpd</h1><div class="highlight">
     <span class="n">iSwapGate</span><span class="p">,</span>
     <span class="n">DCXGate</span><span class="p">,</span>
 <span class="p">)</span>
-<span class="kn">from</span> <span class="nn">qiskit.synthesis.two_qubit.two_qubit_decompose</span> <span class="kn">import</span> <span class="n">TwoQubitWeylDecomposition</span>
+<span class="kn">from</span> <span class="nn">qiskit.synthesis</span> <span class="kn">import</span> <span class="n">TwoQubitWeylDecomposition</span>
 
 <span class="kn">from</span> <span class="nn">.qpd_basis</span> <span class="kn">import</span> <span class="n">QPDBasis</span>
-<span class="kn">from</span> <span class="nn">.instructions</span> <span class="kn">import</span> <span class="n">BaseQPDGate</span><span class="p">,</span> <span class="n">TwoQubitQPDGate</span><span class="p">,</span> <span class="n">QPDMeasure</span>
+<span class="kn">from</span> <span class="nn">.instructions</span> <span class="kn">import</span> <span class="n">QPDMeasure</span>
 <span class="kn">from</span> <span class="nn">..instructions</span> <span class="kn">import</span> <span class="n">Move</span>
-<span class="kn">from</span> <span class="nn">...utils.iteration</span> <span class="kn">import</span> <span class="n">unique_by_id</span><span class="p">,</span> <span class="n">strict_zip</span>
-
-
-<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
-
-
-<span class="c1"># math.sin(math.pi) is just above 1e-16, so at 1e-14, we are well above that.</span>
-<span class="c1"># Numbers like this can often come up in the QPD coefficients.</span>
-<span class="n">_NONZERO_ATOL</span> <span class="o">=</span> <span class="mf">1e-14</span>
-
-
-<div class="viewcode-block" id="WeightType">
-<a class="viewcode-back" href="../../../../stubs/circuit_knitting.cutting.qpd.WeightType.html#circuit_knitting.cutting.qpd.WeightType">[docs]</a>
-<span class="k">class</span> <span class="nc">WeightType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Type of weight associated with a QPD sample.&quot;&quot;&quot;</span>
-
-    <span class="c1">#: A weight given in proportion to its exact weight</span>
-    <span class="n">EXACT</span> <span class="o">=</span> <span class="mi">1</span>
-
-    <span class="c1">#: A weight that was determined through some sampling procedure</span>
-    <span class="n">SAMPLED</span> <span class="o">=</span> <span class="mi">2</span></div>
-
-
-
-<span class="k">def</span> <span class="nf">_min_filter_nonzero</span><span class="p">(</span><span class="n">vals</span><span class="p">:</span> <span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">],</span> <span class="o">*</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">_NONZERO_ATOL</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">vals</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">logical_not</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">vals</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">))])</span>
-
-
-<span class="k">def</span> <span class="nf">__update_running_product_after_increment</span><span class="p">(</span>
-    <span class="n">running_product</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span>
-    <span class="n">state</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span>
-    <span class="n">coeff_probabilities</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">]],</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Update the ``running_product`` list after the ``state`` has been incremented.</span>
-
-<span class="sd">    This snippet is used twice in</span>
-<span class="sd">    :func:`_generate_exact_weights_and_conditional_probabilities_assume_sorted`;</span>
-<span class="sd">    hence, we write it only once here.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="n">prev</span> <span class="o">=</span> <span class="n">running_product</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>
-    <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-        <span class="n">prev</span> <span class="o">=</span> <span class="mf">1.0</span>
-    <span class="n">running_product</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">prev</span> <span class="o">*</span> <span class="n">coeff_probabilities</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">state</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">][</span><span class="n">state</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
-
-
-<span class="k">def</span> <span class="nf">_generate_exact_weights_and_conditional_probabilities_assume_sorted</span><span class="p">(</span>
-    <span class="n">coeff_probabilities</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">]],</span> <span class="n">threshold</span><span class="p">:</span> <span class="nb">float</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Determine all exact weights above ``threshold`` and the conditional probabilities necessary to sample efficiently from all other weights.</span>
-
-<span class="sd">    Each yielded element will be a 2-tuple, the first element of which will be</span>
-<span class="sd">    a ``state``, represented by a tuple of ``int``\ s.</span>
-
-<span class="sd">    If ``state`` contains the same number of elements as</span>
-<span class="sd">    ``coeff_probabilities``, then the second element will be a ``float``,</span>
-<span class="sd">    greater than or equal to ``threshold``, that contains the exact probability</span>
-<span class="sd">    of this ``state``.</span>
-
-<span class="sd">    If, on the other hand, ``state`` contains fewer elements than</span>
-<span class="sd">    ``coeff_probabilities``, then the second element is a 1D ``np.ndarray`` of</span>
-<span class="sd">    the conditional probabilities that can be used to sample the remaining</span>
-<span class="sd">    weights that have not been evaluated exactly (i.e., are below</span>
-<span class="sd">    ``threshold``).  These will all be normalized *except* the</span>
-<span class="sd">    top-level one, which is given by ``state == ()``.</span>
-
-<span class="sd">    If there are no exact weights below a given level, then the conditional</span>
-<span class="sd">    probabilities at that level are equivalent to the corresponding element of</span>
-<span class="sd">    `coeff_probabilities``, and this generator does not yield them.  Skipping</span>
-<span class="sd">    redundant conditional probabilities, like this, allows for efficient</span>
-<span class="sd">    sampling without traversing the entire tree, which is desirable since the</span>
-<span class="sd">    tree grows exponentially with the number of cut gates.  By contrast, the</span>
-<span class="sd">    performance with the provided strategy is no worse than linear in</span>
-<span class="sd">    ``threshold``, in both time and memory.</span>
-
-<span class="sd">    This function assumes each element of ``coeff_probabilities`` contains</span>
-<span class="sd">    non-negative numbers, ordered largest to smallest.  For a generalization</span>
-<span class="sd">    that allows ``coeff_probabilities`` to be provided in any order, see</span>
-<span class="sd">    :func:`_generate_exact_weights_and_conditional_probabilities`.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">coeff_probabilities</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
-
-    <span class="n">next_pop</span> <span class="o">=</span> <span class="kc">False</span>  <span class="c1"># Stores whether the next move is a pop or not</span>
-    <span class="n">state</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">running_product</span> <span class="o">=</span> <span class="p">[</span><span class="n">coeff_probabilities</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
-    <span class="n">running_conditional_probabilities</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">next_pop</span><span class="p">:</span>
-            <span class="c1"># Pop</span>
-            <span class="n">state</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">state</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">running_conditional_probabilities</span><span class="p">):</span>
-                <span class="c1"># There were some exact weights found below us, so we need to</span>
-                <span class="c1"># yield the conditional probabilities, unless all probabilities</span>
-                <span class="c1"># at this level are zero *and* it&#39;s not the top level.</span>
-                <span class="n">current_condprobs</span> <span class="o">=</span> <span class="n">running_conditional_probabilities</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
-                <span class="n">current_condprobs</span><span class="p">[</span>
-                    <span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">current_condprobs</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">_NONZERO_ATOL</span><span class="p">)</span>
-                <span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="n">state</span><span class="p">:</span>
-                    <span class="c1"># Don&#39;t normalize the top-level conditional probabilities.</span>
-                    <span class="k">yield</span> <span class="p">(),</span> <span class="n">current_condprobs</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">current_condprobs</span><span class="p">)</span>
-                    <span class="k">if</span> <span class="n">norm</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                        <span class="c1"># Some, but not all, weights below us are exact.  If,</span>
-                        <span class="c1"># instead, _all_ weights had been exact, we could have</span>
-                        <span class="c1"># skipped this yield, as there is zero probability of</span>
-                        <span class="c1"># reaching this partial state when sampling.</span>
-                        <span class="k">yield</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">state</span><span class="p">),</span> <span class="n">current_condprobs</span> <span class="o">/</span> <span class="n">norm</span>
-                    <span class="c1"># Update the factor one level up from the popped one.</span>
-                    <span class="n">running_conditional_probabilities</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">state</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span> <span class="o">*=</span> <span class="n">norm</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">state</span><span class="p">:</span>
-                <span class="c1"># We&#39;re all done</span>
-                <span class="k">return</span>
-            <span class="n">running_product</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
-            <span class="c1"># Increment the state counter.</span>
-            <span class="n">state</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
-            <span class="k">if</span> <span class="n">state</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">coeff_probabilities</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">state</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]):</span>
-                <span class="c1"># Increment successful (no overflow).  We don&#39;t need to pop again.</span>
-                <span class="n">next_pop</span> <span class="o">=</span> <span class="kc">False</span>
-                <span class="n">__update_running_product_after_increment</span><span class="p">(</span>
-                    <span class="n">running_product</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">coeff_probabilities</span>
-                <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">running_product</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">threshold</span><span class="p">:</span>
-                <span class="n">next_pop</span> <span class="o">=</span> <span class="kc">True</span>
-            <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">state</span><span class="p">)</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">coeff_probabilities</span><span class="p">):</span>
-                <span class="c1"># Append 0 to work toward a &quot;full&quot; `state`</span>
-                <span class="n">running_product</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                    <span class="n">running_product</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">coeff_probabilities</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">state</span><span class="p">)][</span><span class="mi">0</span><span class="p">]</span>
-                <span class="p">)</span>
-                <span class="n">state</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="c1"># `state` is full.  Yield first.</span>
-                <span class="k">yield</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">state</span><span class="p">),</span> <span class="n">running_product</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-                <span class="c1"># Since we found something exact, we need to update running_conditional_probabilities.</span>
-                <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">running_conditional_probabilities</span><span class="p">)</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">coeff_probabilities</span><span class="p">):</span>
-                    <span class="n">running_conditional_probabilities</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                        <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-                            <span class="n">coeff_probabilities</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">running_conditional_probabilities</span><span class="p">)],</span>
-                            <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span>
-                        <span class="p">)</span>
-                    <span class="p">)</span>
-                <span class="c1"># It&#39;s exact, so we want no probability of sampling it going forward.</span>
-                <span class="n">running_conditional_probabilities</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">state</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span> <span class="o">=</span> <span class="mi">0</span>
-                <span class="c1"># Increment the state counter.</span>
-                <span class="n">state</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
-                <span class="k">if</span> <span class="n">state</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">coeff_probabilities</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
-                    <span class="c1"># The state counter has overflowed, so our next move should be a</span>
-                    <span class="c1"># pop.</span>
-                    <span class="n">next_pop</span> <span class="o">=</span> <span class="kc">True</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">__update_running_product_after_increment</span><span class="p">(</span>
-                        <span class="n">running_product</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">coeff_probabilities</span>
-                    <span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_invert_permutation</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
-    <span class="c1"># https://stackoverflow.com/questions/11649577/how-to-invert-a-permutation-array-in-numpy</span>
-    <span class="n">s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">p</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="n">s</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">s</span>
-
-
-<span class="k">def</span> <span class="nf">_generate_exact_weights_and_conditional_probabilities</span><span class="p">(</span>
-    <span class="n">coeff_probabilities</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">]],</span> <span class="n">threshold</span><span class="p">:</span> <span class="nb">float</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Generate exact weights and conditional probabilities.</span>
-
-<span class="sd">    This is identical in behavior to</span>
-<span class="sd">    :func:`_generate_exact_weights_and_conditional_probabilities_assume_sorted`,</span>
-<span class="sd">    except it makes no assumption on the order of the coefficients.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">permutations</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">cp</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">cp</span> <span class="ow">in</span> <span class="n">coeff_probabilities</span><span class="p">]</span>
-    <span class="n">sorted_coeff_probabilities</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="n">cp</span><span class="p">[</span><span class="n">permutation</span><span class="p">]</span> <span class="k">for</span> <span class="n">cp</span><span class="p">,</span> <span class="n">permutation</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">coeff_probabilities</span><span class="p">,</span> <span class="n">permutations</span><span class="p">)</span>
-    <span class="p">]</span>
-    <span class="n">ipermutations</span> <span class="o">=</span> <span class="p">[</span><span class="n">_invert_permutation</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">permutations</span><span class="p">]</span>
-    <span class="k">for</span> <span class="p">(</span>
-        <span class="n">coeff_indices</span><span class="p">,</span>
-        <span class="n">probability</span><span class="p">,</span>
-    <span class="p">)</span> <span class="ow">in</span> <span class="n">_generate_exact_weights_and_conditional_probabilities_assume_sorted</span><span class="p">(</span>
-        <span class="n">sorted_coeff_probabilities</span><span class="p">,</span> <span class="n">threshold</span>
-    <span class="p">):</span>
-        <span class="n">orig_coeff_indices</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span>
-            <span class="n">perm</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">perm</span><span class="p">,</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">permutations</span><span class="p">,</span> <span class="n">coeff_indices</span><span class="p">)</span>
-        <span class="p">)</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">coeff_indices</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">sorted_coeff_probabilities</span><span class="p">):</span>
-            <span class="c1"># In this case, `probability` is actually a vector of conditional</span>
-            <span class="c1"># probabilities, so apply the inverse permutation.</span>
-            <span class="n">probability</span> <span class="o">=</span> <span class="n">probability</span><span class="p">[</span><span class="n">ipermutations</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">coeff_indices</span><span class="p">)]]</span>
-        <span class="k">yield</span> <span class="n">orig_coeff_indices</span><span class="p">,</span> <span class="n">probability</span>
-
-
-<div class="viewcode-block" id="generate_qpd_weights">
-<a class="viewcode-back" href="../../../../stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.html#circuit_knitting.cutting.qpd.generate_qpd_weights">[docs]</a>
-<span class="k">def</span> <span class="nf">generate_qpd_weights</span><span class="p">(</span>
-    <span class="n">qpd_bases</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">QPDBasis</span><span class="p">],</span> <span class="n">num_samples</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">1000</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">WeightType</span><span class="p">]]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Generate weights from the joint quasiprobability distribution.</span>
-
-<span class="sd">    Each weight whose absolute value is above a threshold of ``1 /</span>
-<span class="sd">    num_samples`` will be evaluated exactly. The remaining weights -- those in</span>
-<span class="sd">    the tail of the distribution -- will then be sampled from, resulting in at</span>
-<span class="sd">    most ``num_samples`` unique weights.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        qpd_bases: The :class:`QPDBasis` objects from which to generate weights</span>
-<span class="sd">        num_samples: Controls the number of weights to generate</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">        A mapping from a given decomposition to its weight.</span>
-<span class="sd">        Keys are tuples of indices -- one index per input :class:`QPDBasis`. The indices</span>
-<span class="sd">        correspond to a specific decomposition mapping in the basis.</span>
-
-<span class="sd">        Values are tuples.  The first element is a number corresponding to the</span>
-<span class="sd">        weight of the contribution.  The second element is the :class:`WeightType`,</span>
-<span class="sd">        either ``EXACT`` or ``SAMPLED``.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">independent_probabilities</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">basis</span><span class="o">.</span><span class="n">probabilities</span><span class="p">)</span> <span class="k">for</span> <span class="n">basis</span> <span class="ow">in</span> <span class="n">qpd_bases</span><span class="p">]</span>
-    <span class="c1"># In Python 3.7 and higher, dicts are guaranteed to remember their</span>
-    <span class="c1"># insertion order.  For user convenience, we sort by exact weights first,</span>
-    <span class="c1"># then sampled weights.  Within each, values are sorted largest to</span>
-    <span class="c1"># smallest.</span>
-    <span class="n">lst</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span>
-        <span class="n">_generate_qpd_weights</span><span class="p">(</span><span class="n">independent_probabilities</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span>
-        <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">((</span><span class="n">v</span> <span class="o">:=</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="o">-</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="n">lst</span><span class="p">)</span></div>
-
-
-
-<span class="k">def</span> <span class="nf">_generate_qpd_weights</span><span class="p">(</span>
-    <span class="n">independent_probabilities</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">]],</span>
-    <span class="n">num_samples</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">_samples_multiplier</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">WeightType</span><span class="p">]]:</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">num_samples</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;num_samples must be at least 1.&quot;</span><span class="p">)</span>
-
-    <span class="n">retval</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">WeightType</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
-
-    <span class="n">threshold</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">num_samples</span>
-
-    <span class="c1"># Determine if the smallest *nonzero* probability is above the threshold implied by</span>
-    <span class="c1"># num_samples.  If so, then we can evaluate all weights exactly.</span>
-    <span class="n">smallest_probability</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span>
-        <span class="p">[</span><span class="n">_min_filter_nonzero</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span> <span class="k">for</span> <span class="n">probs</span> <span class="ow">in</span> <span class="n">independent_probabilities</span><span class="p">]</span>
-    <span class="p">)</span>
-    <span class="k">if</span> <span class="n">smallest_probability</span> <span class="o">&gt;=</span> <span class="n">threshold</span><span class="p">:</span>
-        <span class="c1"># All weights exactly</span>
-        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;All exact weights&quot;</span><span class="p">)</span>
-        <span class="n">multiplier</span> <span class="o">=</span> <span class="n">num_samples</span> <span class="k">if</span> <span class="n">math</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">num_samples</span><span class="p">)</span> <span class="k">else</span> <span class="mf">1.0</span>
-        <span class="k">for</span> <span class="n">map_ids</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span>
-            <span class="o">*</span><span class="p">[</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">probs</span><span class="p">))</span> <span class="k">for</span> <span class="n">probs</span> <span class="ow">in</span> <span class="n">independent_probabilities</span><span class="p">]</span>
-        <span class="p">):</span>
-            <span class="n">probability</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span>
-                <span class="p">[</span>
-                    <span class="n">probs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-                    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">probs</span> <span class="ow">in</span> <span class="n">strict_zip</span><span class="p">(</span><span class="n">map_ids</span><span class="p">,</span> <span class="n">independent_probabilities</span><span class="p">)</span>
-                <span class="p">]</span>
-            <span class="p">)</span>
-            <span class="k">if</span> <span class="n">probability</span> <span class="o">&lt;</span> <span class="n">_NONZERO_ATOL</span><span class="p">:</span>
-                <span class="k">continue</span>
-            <span class="n">retval</span><span class="p">[</span><span class="n">map_ids</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">multiplier</span> <span class="o">*</span> <span class="n">probability</span><span class="p">,</span> <span class="n">WeightType</span><span class="o">.</span><span class="n">EXACT</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">retval</span>
-
-    <span class="n">conditional_probabilities</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="n">weight_to_sample</span> <span class="o">=</span> <span class="mf">1.0</span>
-
-    <span class="n">largest_probability</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span>
-        <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span> <span class="k">for</span> <span class="n">probs</span> <span class="ow">in</span> <span class="n">independent_probabilities</span><span class="p">]</span>
-    <span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">largest_probability</span> <span class="o">&gt;=</span> <span class="n">threshold</span><span class="p">:</span>
-        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;No exact weights&quot;</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Some exact weights&quot;</span><span class="p">)</span>
-        <span class="k">for</span> <span class="p">(</span>
-            <span class="n">map_ids</span><span class="p">,</span>
-            <span class="n">probability</span><span class="p">,</span>
-        <span class="p">)</span> <span class="ow">in</span> <span class="n">_generate_exact_weights_and_conditional_probabilities</span><span class="p">(</span>
-            <span class="n">independent_probabilities</span><span class="p">,</span> <span class="n">threshold</span>
-        <span class="p">):</span>
-            <span class="c1"># As described in the docstring to</span>
-            <span class="c1"># _generate_exact_weights_and_conditional_probabilities_assume_sorted,</span>
-            <span class="c1"># there are two possible pieces of information that might be</span>
-            <span class="c1"># yielded by the generator.  The following branch distinguishes</span>
-            <span class="c1"># between them.</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">map_ids</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">independent_probabilities</span><span class="p">):</span>
-                <span class="c1"># The generator produced a state together with its exact probability.</span>
-                <span class="n">weight</span> <span class="o">=</span> <span class="n">probability</span> <span class="o">*</span> <span class="n">num_samples</span>
-                <span class="n">retval</span><span class="p">[</span><span class="n">map_ids</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">WeightType</span><span class="o">.</span><span class="n">EXACT</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="c1"># The generator produced a partial state along with a vector of</span>
-                <span class="c1"># conditional probabilities.</span>
-                <span class="n">conditional_probabilities</span><span class="p">[</span><span class="n">map_ids</span><span class="p">]</span> <span class="o">=</span> <span class="n">probability</span>
-                <span class="k">if</span> <span class="n">map_ids</span> <span class="o">==</span> <span class="p">():</span>
-                    <span class="n">weight_to_sample</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">probability</span><span class="p">)</span>
-                    <span class="n">conditional_probabilities</span><span class="p">[</span><span class="n">map_ids</span><span class="p">]</span> <span class="o">/=</span> <span class="n">weight_to_sample</span>
-
-    <span class="c1"># Loop through each gate and sample from the remainder of the distribution.</span>
-
-    <span class="c1"># Start by rescaling.</span>
-    <span class="n">weight_to_sample</span> <span class="o">*=</span> <span class="n">num_samples</span>
-    <span class="c1"># The following variable, `samples_needed`, must be integer and at least 1.</span>
-    <span class="c1"># `_samples_multiplier` will typically be 1, but we set it higher in</span>
-    <span class="c1"># testing to collect additional statistics, faster.</span>
-    <span class="n">samples_needed</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">weight_to_sample</span><span class="p">)</span> <span class="o">*</span> <span class="n">_samples_multiplier</span>
-    <span class="c1"># At the time of writing, the below assert should never fail.  But if</span>
-    <span class="c1"># future code changes result in inputs where it _may_ fail, then the only</span>
-    <span class="c1"># thing that should be needed if this is reached is to return `retval` in</span>
-    <span class="c1"># this case, since presumably it must contain all weights as exact weights.</span>
-    <span class="k">assert</span> <span class="n">samples_needed</span> <span class="o">&gt;=</span> <span class="mi">1</span>
-    <span class="n">single_sample_weight</span> <span class="o">=</span> <span class="n">weight_to_sample</span> <span class="o">/</span> <span class="n">samples_needed</span>
-
-    <span class="c1"># Figure out if we&#39;ve reached the special case where everything except</span>
-    <span class="c1"># *one* weight has been calculated exactly, so there&#39;s only one thing left</span>
-    <span class="c1"># to sample.  If that&#39;s the case, then we can calculate that one remaining</span>
-    <span class="c1"># weight exactly as well and skip sampling.</span>
-    <span class="k">if</span> <span class="n">conditional_probabilities</span><span class="p">:</span>
-        <span class="n">running_state</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="p">()</span>
-        <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">running_state</span><span class="p">)</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">independent_probabilities</span><span class="p">):</span>
-            <span class="c1"># If it&#39;s missing from `conditional_probabilities`, that just means</span>
-            <span class="c1"># to use the corresponding entry in `independent_probabilities`.</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="n">probs</span> <span class="o">=</span> <span class="n">conditional_probabilities</span><span class="p">[</span><span class="n">running_state</span><span class="p">]</span>
-            <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-                <span class="n">probs</span> <span class="o">=</span> <span class="n">independent_probabilities</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">running_state</span><span class="p">)]</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">flatnonzero</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span>
-            <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="k">break</span>
-            <span class="n">running_state</span> <span class="o">+=</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">assert</span> <span class="n">running_state</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">retval</span>
-            <span class="n">retval</span><span class="p">[</span><span class="n">running_state</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">weight_to_sample</span><span class="p">,</span> <span class="n">WeightType</span><span class="o">.</span><span class="n">EXACT</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">retval</span>
-
-    <span class="c1"># Form the joint samples, collecting them into a dict with counts for each.</span>
-    <span class="n">random_samples</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="n">_populate_samples</span><span class="p">(</span>
-        <span class="n">random_samples</span><span class="p">,</span>
-        <span class="n">samples_needed</span><span class="p">,</span>
-        <span class="n">independent_probabilities</span><span class="p">,</span>
-        <span class="n">conditional_probabilities</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="c1"># Insert the samples into the dict we are about to return.</span>
-    <span class="k">for</span> <span class="n">outcome</span><span class="p">,</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">random_samples</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="k">assert</span> <span class="n">outcome</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">retval</span>
-        <span class="n">retval</span><span class="p">[</span><span class="n">outcome</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">count</span> <span class="o">*</span> <span class="n">single_sample_weight</span><span class="p">,</span> <span class="n">WeightType</span><span class="o">.</span><span class="n">SAMPLED</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">retval</span>
-
-
-<span class="k">def</span> <span class="nf">_populate_samples</span><span class="p">(</span>
-    <span class="n">random_samples</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">int</span><span class="p">],</span>
-    <span class="n">num_desired</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-    <span class="n">independent_probabilities</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">,</span>
-    <span class="n">conditional_probabilities</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">]],</span>
-    <span class="n">running_state</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="p">(),</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Generate random samples from the conditional probabilitity distributions.</span>
-
-<span class="sd">    Items get populated into the ``random_samples`` dict, rather than returned.</span>
-
-<span class="sd">    This function is designed to call itself recursively.  The number of</span>
-<span class="sd">    elements in ``running_state`` will match the recursion depth.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">running_state</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">conditional_probabilities</span><span class="p">:</span>
-        <span class="c1"># Everything below us is sampled, so we can sample directly from the</span>
-        <span class="c1"># remaining independent probability distributions.</span>
-        <span class="n">samples_by_decomp</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">probs</span> <span class="ow">in</span> <span class="n">independent_probabilities</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">running_state</span><span class="p">)</span> <span class="p">:]:</span>
-            <span class="n">samples_by_decomp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">probs</span><span class="p">)),</span> <span class="n">num_desired</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">probs</span><span class="p">)</span>
-            <span class="p">)</span>
-        <span class="k">for</span> <span class="n">outcome</span><span class="p">,</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">Counter</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">samples_by_decomp</span><span class="p">))</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="k">assert</span> <span class="p">(</span><span class="n">running_state</span> <span class="o">+</span> <span class="n">outcome</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">random_samples</span>
-            <span class="n">random_samples</span><span class="p">[</span><span class="n">running_state</span> <span class="o">+</span> <span class="n">outcome</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>
-        <span class="k">return</span>
-
-    <span class="c1"># There are some exact weight(s) below us, so we must consider the</span>
-    <span class="c1"># conditional probabilities at the current level.</span>
-    <span class="n">probs</span> <span class="o">=</span> <span class="n">conditional_probabilities</span><span class="p">[</span><span class="n">running_state</span><span class="p">]</span>
-    <span class="n">current_outcomes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">probs</span><span class="p">)),</span> <span class="n">num_desired</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">probs</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">current_outcome</span><span class="p">,</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">Counter</span><span class="p">(</span><span class="n">current_outcomes</span><span class="p">)</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="n">outcome</span> <span class="o">=</span> <span class="n">running_state</span> <span class="o">+</span> <span class="p">(</span><span class="n">current_outcome</span><span class="p">,)</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">outcome</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">independent_probabilities</span><span class="p">):</span>
-            <span class="c1"># It&#39;s a full one</span>
-            <span class="k">assert</span> <span class="n">outcome</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">random_samples</span>
-            <span class="n">random_samples</span><span class="p">[</span><span class="n">outcome</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Recurse</span>
-            <span class="n">_populate_samples</span><span class="p">(</span>
-                <span class="n">random_samples</span><span class="p">,</span>
-                <span class="n">count</span><span class="p">,</span>
-                <span class="n">independent_probabilities</span><span class="p">,</span>
-                <span class="n">conditional_probabilities</span><span class="p">,</span>
-                <span class="n">outcome</span><span class="p">,</span>
-            <span class="p">)</span>
-
-
-<div class="viewcode-block" id="decompose_qpd_instructions">
-<a class="viewcode-back" href="../../../../stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.html#circuit_knitting.cutting.qpd.decompose_qpd_instructions">[docs]</a>
-<span class="k">def</span> <span class="nf">decompose_qpd_instructions</span><span class="p">(</span>
-    <span class="n">circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span>
-    <span class="n">instruction_ids</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]],</span>
-    <span class="n">map_ids</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">QuantumCircuit</span><span class="p">:</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Replace all QPD instructions in the circuit with local Qiskit operations and measurements.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        circuit: The circuit containing QPD instructions</span>
-<span class="sd">        instruction_ids: A 2D sequence, such that each inner sequence corresponds to indices</span>
-<span class="sd">            of instructions comprising one decomposition in the circuit. The elements within a</span>
-<span class="sd">            common sequence belong to a common decomposition and should be sampled together.</span>
-<span class="sd">        map_ids: Indices to a specific linear mapping to be applied to the decompositions</span>
-<span class="sd">            in the circuit. If no map IDs are provided, the circuit will be decomposed randomly</span>
-<span class="sd">            according to the decompositions&#39; joint probability distribution.</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">        Circuit which has had all its :class:`BaseQPDGate` instances decomposed into local operations.</span>
-
-<span class="sd">        The circuit will contain a new, final classical register to contain the QPD measurement</span>
-<span class="sd">        outcomes (accessible at ``retval.cregs[-1]``).</span>
-
-<span class="sd">    Raises:</span>
-<span class="sd">        ValueError: An index in ``instruction_ids`` corresponds to a gate which is not a</span>
-<span class="sd">            :class:`BaseQPDGate` instance.</span>
-<span class="sd">        ValueError: A list within instruction_ids is not length 1 or 2.</span>
-<span class="sd">        ValueError: The total number of indices in ``instruction_ids`` does not equal the number</span>
-<span class="sd">            of :class:`BaseQPDGate` instances in the circuit.</span>
-<span class="sd">        ValueError: Gates within the same decomposition hold different QPD bases.</span>
-<span class="sd">        ValueError: Length of ``map_ids`` does not equal the number of decompositions in the circuit.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">_validate_qpd_instructions</span><span class="p">(</span><span class="n">circuit</span><span class="p">,</span> <span class="n">instruction_ids</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">inplace</span><span class="p">:</span>
-        <span class="n">circuit</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>  <span class="c1"># pragma: no cover</span>
-
-    <span class="k">if</span> <span class="n">map_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">instruction_ids</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">map_ids</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s2">&quot;The number of map IDs (</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">map_ids</span><span class="p">)</span><span class="si">}</span><span class="s2">) must equal the number of &quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;decompositions in the circuit (</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">instruction_ids</span><span class="p">)</span><span class="si">}</span><span class="s2">).&quot;</span>
-            <span class="p">)</span>
-        <span class="c1"># If mapping is specified, set each gate&#39;s mapping</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">decomp_gate_ids</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">instruction_ids</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">gate_id</span> <span class="ow">in</span> <span class="n">decomp_gate_ids</span><span class="p">:</span>
-                <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">gate_id</span><span class="p">]</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">basis_id</span> <span class="o">=</span> <span class="n">map_ids</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-
-    <span class="c1"># Convert all instances of BaseQPDGate in the circuit to Qiskit instructions</span>
-    <span class="n">_decompose_qpd_instructions</span><span class="p">(</span><span class="n">circuit</span><span class="p">,</span> <span class="n">instruction_ids</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">circuit</span></div>
-
+<span class="kn">from</span> <span class="nn">...utils.iteration</span> <span class="kn">import</span> <span class="n">unique_by_id</span>
 
 
 <span class="n">_qpdbasis_from_instruction_funcs</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">Instruction</span><span class="p">],</span> <span class="n">QPDBasis</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
@@ -1317,159 +847,6 @@ <h1>Source code for circuit_knitting.cutting.qpd.qpd</h1><div class="highlight">
     <span class="p">)</span>
     <span class="n">maps</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">maps1</span><span class="p">,</span> <span class="n">maps2</span><span class="p">))</span>
     <span class="k">return</span> <span class="n">QPDBasis</span><span class="p">(</span><span class="n">maps</span><span class="p">,</span> <span class="n">coeffs</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_validate_qpd_instructions</span><span class="p">(</span>
-    <span class="n">circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span> <span class="n">instruction_ids</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Ensure the indices in instruction_ids correctly describe all the decompositions in the circuit.&quot;&quot;&quot;</span>
-    <span class="c1"># Make sure all instruction_ids correspond to QPDGates, and make sure each QPDGate in a given decomposition has</span>
-    <span class="c1"># an equivalent QPDBasis to its sibling QPDGates</span>
-    <span class="k">for</span> <span class="n">decomp_ids</span> <span class="ow">in</span> <span class="n">instruction_ids</span><span class="p">:</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">decomp_ids</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s2">&quot;Each decomposition must contain either one or two elements. Found a &quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;decomposition with (</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">decomp_ids</span><span class="p">)</span><span class="si">}</span><span class="s2">) elements.&quot;</span>
-            <span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">decomp_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">BaseQPDGate</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s2">&quot;A circuit data index (</span><span class="si">{</span><span class="n">decomp_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">) corresponds to a non-QPDGate &quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">decomp_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">).&quot;</span>
-            <span class="p">)</span>
-        <span class="n">compare_basis</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">decomp_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">basis</span>
-        <span class="k">for</span> <span class="n">gate_id</span> <span class="ow">in</span> <span class="n">decomp_ids</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">gate_id</span><span class="p">]</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">BaseQPDGate</span><span class="p">):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                    <span class="sa">f</span><span class="s2">&quot;A circuit data index (</span><span class="si">{</span><span class="n">gate_id</span><span class="si">}</span><span class="s2">) corresponds to a non-QPDGate &quot;</span>
-                    <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">gate_id</span><span class="p">]</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">).&quot;</span>
-                <span class="p">)</span>
-            <span class="n">tmp_basis</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">gate_id</span><span class="p">]</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">basis</span>
-            <span class="k">if</span> <span class="n">compare_basis</span> <span class="o">!=</span> <span class="n">tmp_basis</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                    <span class="s2">&quot;Gates within the same decomposition must share an equivalent QPDBasis.&quot;</span>
-                <span class="p">)</span>
-
-    <span class="c1"># Make sure the total number of QPD gate indices equals the number of QPDGates in the circuit</span>
-    <span class="n">num_qpd_gates</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">instruction_ids</span><span class="p">)</span>
-    <span class="n">qpd_gate_total</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">for</span> <span class="n">inst</span> <span class="ow">in</span> <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">:</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inst</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">BaseQPDGate</span><span class="p">):</span>
-            <span class="n">qpd_gate_total</span> <span class="o">+=</span> <span class="mi">1</span>
-    <span class="k">if</span> <span class="n">qpd_gate_total</span> <span class="o">!=</span> <span class="n">num_qpd_gates</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-            <span class="sa">f</span><span class="s2">&quot;The total number of QPDGates specified in instruction_ids (</span><span class="si">{</span><span class="n">num_qpd_gates</span><span class="si">}</span><span class="s2">) &quot;</span>
-            <span class="sa">f</span><span class="s2">&quot;does not equal the number of QPDGates in the circuit (</span><span class="si">{</span><span class="n">qpd_gate_total</span><span class="si">}</span><span class="s2">).&quot;</span>
-        <span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_decompose_qpd_measurements</span><span class="p">(</span>
-    <span class="n">circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span> <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">QuantumCircuit</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Create mid-circuit measurements.</span>
-
-<span class="sd">    Convert all QPDMeasure instances to Measure instructions. Add any newly created</span>
-<span class="sd">    classical bits to a new &quot;qpd_measurements&quot; register.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">inplace</span><span class="p">:</span>
-        <span class="n">circuit</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>  <span class="c1"># pragma: no cover</span>
-
-    <span class="c1"># Loop through the decomposed circuit to find QPDMeasure markers so we can</span>
-    <span class="c1"># replace them with measurement instructions.  We can&#39;t use `_ids`</span>
-    <span class="c1"># here because it refers to old indices, before the decomposition.</span>
-    <span class="n">qpd_measure_ids</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="n">i</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">instruction</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">instruction</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">name</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;qpd_measure&quot;</span>
-    <span class="p">]</span>
-
-    <span class="c1"># Create a classical register for the qpd measurement results.  This is</span>
-    <span class="c1"># partly for convenience, partly to work around</span>
-    <span class="c1"># https://github.com/Qiskit/qiskit-aer/issues/1660.</span>
-    <span class="n">reg</span> <span class="o">=</span> <span class="n">ClassicalRegister</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">qpd_measure_ids</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;qpd_measurements&quot;</span><span class="p">)</span>
-    <span class="n">circuit</span><span class="o">.</span><span class="n">add_register</span><span class="p">(</span><span class="n">reg</span><span class="p">)</span>
-
-    <span class="c1"># Place the measurement instructions</span>
-    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">qpd_measure_ids</span><span class="p">):</span>
-        <span class="n">gate</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-        <span class="n">inst</span> <span class="o">=</span> <span class="n">CircuitInstruction</span><span class="p">(</span>
-            <span class="n">operation</span><span class="o">=</span><span class="n">Measure</span><span class="p">(),</span> <span class="n">qubits</span><span class="o">=</span><span class="p">[</span><span class="n">gate</span><span class="o">.</span><span class="n">qubits</span><span class="p">],</span> <span class="n">clbits</span><span class="o">=</span><span class="p">[</span><span class="n">reg</span><span class="p">[</span><span class="n">idx</span><span class="p">]]</span>
-        <span class="p">)</span>
-        <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">inst</span>
-
-    <span class="c1"># If the user wants to access the qpd register, it will be the final</span>
-    <span class="c1"># classical register of the returned circuit.</span>
-    <span class="k">assert</span> <span class="n">circuit</span><span class="o">.</span><span class="n">cregs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="ow">is</span> <span class="n">reg</span>
-
-    <span class="k">return</span> <span class="n">circuit</span>
-
-
-<span class="k">def</span> <span class="nf">_decompose_qpd_instructions</span><span class="p">(</span>
-    <span class="n">circuit</span><span class="p">:</span> <span class="n">QuantumCircuit</span><span class="p">,</span>
-    <span class="n">instruction_ids</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]],</span>
-    <span class="n">inplace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">QuantumCircuit</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Decompose all BaseQPDGate instances, ignoring QPDMeasure().&quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">inplace</span><span class="p">:</span>
-        <span class="n">circuit</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>  <span class="c1"># pragma: no cover</span>
-
-    <span class="c1"># Decompose any 2q QPDGates into single qubit QPDGates</span>
-    <span class="n">qpdgate_ids_2q</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">decomp</span> <span class="ow">in</span> <span class="n">instruction_ids</span><span class="p">:</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">decomp</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">continue</span>  <span class="c1"># pragma: no cover</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">decomp</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">TwoQubitQPDGate</span><span class="p">):</span>
-            <span class="n">qpdgate_ids_2q</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">decomp</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-    <span class="n">qpdgate_ids_2q</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">qpdgate_ids_2q</span><span class="p">)</span>
-    <span class="n">data_id_offset</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">qpdgate_ids_2q</span><span class="p">:</span>
-        <span class="n">inst</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="n">data_id_offset</span><span class="p">]</span>
-        <span class="n">qpdcirc_2q_decomp</span> <span class="o">=</span> <span class="n">inst</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">definition</span>
-        <span class="n">inst1</span> <span class="o">=</span> <span class="n">CircuitInstruction</span><span class="p">(</span>
-            <span class="n">qpdcirc_2q_decomp</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">qubits</span><span class="o">=</span><span class="p">[</span><span class="n">inst</span><span class="o">.</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
-        <span class="p">)</span>
-        <span class="n">inst2</span> <span class="o">=</span> <span class="n">CircuitInstruction</span><span class="p">(</span>
-            <span class="n">qpdcirc_2q_decomp</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">qubits</span><span class="o">=</span><span class="p">[</span><span class="n">inst</span><span class="o">.</span><span class="n">qubits</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
-        <span class="p">)</span>
-        <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="n">data_id_offset</span><span class="p">]</span> <span class="o">=</span> <span class="n">inst1</span>
-        <span class="n">data_id_offset</span> <span class="o">+=</span> <span class="mi">1</span>
-        <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="n">data_id_offset</span><span class="p">,</span> <span class="n">inst2</span><span class="p">)</span>
-
-    <span class="c1"># Decompose all the QPDGates (should all be single qubit now) into Qiskit operations</span>
-    <span class="n">new_instruction_ids</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">inst</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">):</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inst</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">BaseQPDGate</span><span class="p">):</span>
-            <span class="n">new_instruction_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-    <span class="n">data_id_offset</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">new_instruction_ids</span><span class="p">:</span>
-        <span class="n">inst</span> <span class="o">=</span> <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="n">data_id_offset</span><span class="p">]</span>
-        <span class="n">qubits</span> <span class="o">=</span> <span class="n">inst</span><span class="o">.</span><span class="n">qubits</span>
-        <span class="c1"># All gates in decomposition should be local</span>
-        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">qubits</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span>
-        <span class="c1"># Gather instructions with which we will replace the QPDGate</span>
-        <span class="n">tmp_data</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">inst</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">definition</span><span class="o">.</span><span class="n">data</span><span class="p">:</span>
-            <span class="c1"># Can ignore clbits here, as QPDGates don&#39;t use clbits directly</span>
-            <span class="k">assert</span> <span class="n">data</span><span class="o">.</span><span class="n">clbits</span> <span class="o">==</span> <span class="p">()</span>
-            <span class="n">tmp_data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">CircuitInstruction</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">operation</span><span class="p">,</span> <span class="n">qubits</span><span class="o">=</span><span class="p">[</span><span class="n">qubits</span><span class="p">[</span><span class="mi">0</span><span class="p">]]))</span>
-        <span class="c1"># Replace QPDGate with local operations</span>
-        <span class="k">if</span> <span class="n">tmp_data</span><span class="p">:</span>
-            <span class="c1"># Overwrite the QPDGate with first instruction</span>
-            <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="n">data_id_offset</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="c1"># Append remaining instructions immediately after original QPDGate position</span>
-            <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">tmp_data</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
-                <span class="n">data_id_offset</span> <span class="o">+=</span> <span class="mi">1</span>
-                <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="n">data_id_offset</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
-
-        <span class="c1"># If QPDGate decomposes to an identity operation, just delete it</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">del</span> <span class="n">circuit</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="n">data_id_offset</span><span class="p">]</span>
-            <span class="n">data_id_offset</span> <span class="o">-=</span> <span class="mi">1</span>
-
-    <span class="n">_decompose_qpd_measurements</span><span class="p">(</span><span class="n">circuit</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">circuit</span>
 </pre></div>
         </article>
       </div>
@@ -1510,7 +887,7 @@ <h1>Source code for circuit_knitting.cutting.qpd.qpd</h1><div class="highlight">
   </div>
 </div><script src="../../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/cutting/qpd/instructions/qpd_gate.html b/_modules/circuit_knitting/cutting/qpd/instructions/qpd_gate.html
index 612b559da..d74b45aca 100644
--- a/_modules/circuit_knitting/cutting/qpd/instructions/qpd_gate.html
+++ b/_modules/circuit_knitting/cutting/qpd/instructions/qpd_gate.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../../../genindex.html" /><link rel="search" title="Search" href="../../../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.qpd.instructions.qpd_gate - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.qpd.instructions.qpd_gate - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -555,7 +559,7 @@ <h1>Source code for circuit_knitting.cutting.qpd.instructions.qpd_gate</h1><div
   </div>
 </div><script src="../../../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/cutting/qpd/qpd_basis.html b/_modules/circuit_knitting/cutting/qpd/qpd_basis.html
index 74418bddd..b3515eecd 100644
--- a/_modules/circuit_knitting/cutting/qpd/qpd_basis.html
+++ b/_modules/circuit_knitting/cutting/qpd/qpd_basis.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../../genindex.html" /><link rel="search" title="Search" href="../../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.qpd.qpd_basis - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.qpd.qpd_basis - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -418,7 +422,7 @@ <h1>Source code for circuit_knitting.cutting.qpd.qpd_basis</h1><div class="highl
 <span class="sd">        Generate a :class:`.QPDBasis` object, given a supported operation.</span>
 
 <span class="sd">        This static method is provided for convenience; it simply</span>
-<span class="sd">        calls :func:`~qpd.qpd.qpdbasis_from_instruction` under the hood.</span>
+<span class="sd">        calls :func:`~qpd.decompositions.qpdbasis_from_instruction` under the hood.</span>
 
 <span class="sd">        Args:</span>
 <span class="sd">            gate: The instruction from which to instantiate a decomposition</span>
@@ -427,7 +431,7 @@ <h1>Source code for circuit_knitting.cutting.qpd.qpd_basis</h1><div class="highl
 <span class="sd">            The newly-instantiated :class:`QPDBasis` object</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="c1"># pylint: disable=cyclic-import</span>
-        <span class="kn">from</span> <span class="nn">.qpd</span> <span class="kn">import</span> <span class="n">qpdbasis_from_instruction</span>
+        <span class="kn">from</span> <span class="nn">.decompositions</span> <span class="kn">import</span> <span class="n">qpdbasis_from_instruction</span>
 
         <span class="k">return</span> <span class="n">qpdbasis_from_instruction</span><span class="p">(</span><span class="n">gate</span><span class="p">)</span>
 
@@ -483,7 +487,7 @@ <h1>Source code for circuit_knitting.cutting.qpd.qpd_basis</h1><div class="highl
   </div>
 </div><script src="../../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/cutting/qpd/weights.html b/_modules/circuit_knitting/cutting/qpd/weights.html
new file mode 100644
index 000000000..7f5b7d2a0
--- /dev/null
+++ b/_modules/circuit_knitting/cutting/qpd/weights.html
@@ -0,0 +1,785 @@
+<!doctype html>
+<html class="no-js" lang="en">
+  <head><meta charset="utf-8"/>
+    <meta name="viewport" content="width=device-width,initial-scale=1"/>
+    <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../../genindex.html" /><link rel="search" title="Search" href="../../../../search.html" />
+
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.qpd.weights - Circuit Knitting Toolbox 0.7.0</title>
+      <link rel="stylesheet" type="text/css" href="../../../../_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css?v=82a976d7" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/styles/qiskit-ecosystem.css?v=745c5aa7" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/plot_directive.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/jupyter-sphinx.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/thebelab.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/styles/furo-extensions.css?v=36a5483c" />
+    
+    
+
+
+<style>
+  body {
+    --color-code-background: #ffffff;
+  --color-code-foreground: black;
+  
+  }
+  @media not print {
+    body[data-theme="dark"] {
+      --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+    }
+    @media (prefers-color-scheme: dark) {
+      body:not([data-theme="light"]) {
+        --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+      }
+    }
+  }
+</style><link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin><link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&family=IBM+Plex+Sans:ital,wght@0,400;0,600;1,400;1,600&display=swap" rel="stylesheet">
+<script src="../../../../_static/js/web-components/top-nav-bar.js"></script></head>
+  <body>
+    
+    <script>document.body.dataset.theme = "light";</script>
+    
+<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
+  <symbol id="svg-toc" viewBox="0 0 24 24">
+    <title>Contents</title>
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32">
+      <defs><style>.cls-1{fill:none;}</style></defs>
+      <path d="M28,4H4A2,2,0,0,0,2,6V26a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V6A2,2,0,0,0,28,4ZM4,6H20V26H4ZM28,26H22V6h6Z"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-menu" viewBox="0 0 24 24">
+    <title>Menu</title>
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32">
+      <defs><style>.cls-1{fill:none;}</style></defs>
+      <rect x="4" y="6" width="24" height="2"/>
+      <rect x="4" y="24" width="24" height="2"/>
+      <rect x="4" y="12" width="24" height="2"/>
+      <rect x="4" y="18" width="24" height="2"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
+    <title>Expand</title>
+    <svg version="1.1" id="icon" xmlns="http://www.w3.org/2000/svg" x="0px" y="0px"
+         viewBox="0 0 32 32" style="enable-background:new 0 0 32 32;" xml:space="preserve"><polygon points="22,16 12,26 10.6,24.6 19.2,16 10.6,7.4 12,6 " stroke="currentColor"/>
+      <rect id="_x3C_Transparent_Rectangle_x3E_" fill="none" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-new-tab" viewBox="0 0 32 32">
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 32 32">
+      <defs>
+        <style>
+          .cls-1 {
+            fill: none;
+          }
+        </style>
+      </defs>
+      <path fill="#6929C4" d="M26,28H6a2.0027,2.0027,0,0,1-2-2V6A2.0027,2.0027,0,0,1,6,4H16V6H6V26H26V16h2V26A2.0027,2.0027,0,0,1,26,28Z"/>
+      <polygon fill="#6929C4" points="20 2 20 4 26.586 4 18 12.586 19.414 14 28 5.414 28 12 30 12 30 2 20 2"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-sun" viewBox="0 0 24 24">
+    <title>Light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      <circle cx="12" cy="12" r="5"></circle>
+      <line x1="12" y1="1" x2="12" y2="3"></line>
+      <line x1="12" y1="21" x2="12" y2="23"></line>
+      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
+      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
+      <line x1="1" y1="12" x2="3" y2="12"></line>
+      <line x1="21" y1="12" x2="23" y2="12"></line>
+      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
+      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon" viewBox="0 0 24 24">
+    <title>Dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
+    </svg>
+  </symbol>
+  <symbol id="svg-sun-half" viewBox="0 0 24 24">
+    <title>Auto light/dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+      <circle cx="12" cy="12" r="9" />
+      <path d="M13 12h5" />
+      <path d="M13 15h4" />
+      <path d="M13 18h1" />
+      <path d="M13 9h4" />
+      <path d="M13 6h1" />
+    </svg>
+  </symbol>
+</svg>
+
+<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
+<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
+<label class="overlay sidebar-overlay" for="__navigation">
+  <div class="visually-hidden">Hide navigation sidebar</div>
+</label>
+<label class="overlay toc-overlay" for="__toc">
+  <div class="visually-hidden">Hide table of contents sidebar</div>
+</label>
+
+
+
+<div class="page">
+  <header class="mobile-header">
+    <div class="header-left">
+      <label class="nav-overlay-icon" for="__navigation">
+        <div class="visually-hidden">Toggle site navigation sidebar</div>
+        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
+      </label>
+    </div>
+    <div class="header-center">
+      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
+    </div>
+    <div class="header-right">
+      <div class="theme-toggle-container theme-toggle-header">
+        <button class="theme-toggle">
+          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+        </button>
+      </div>
+      <label class="toc-overlay-icon toc-header-icon no-toc" for="__toc">
+        <div class="visually-hidden">Toggle table of contents sidebar</div>
+        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+      </label>
+    </div>
+  </header>
+  <aside class="sidebar-drawer">
+    <div class="sidebar-container">
+      
+      <div class="sidebar-sticky"><div class="sidebar-brand">
+  <a href="https://www.qiskit.org/ecosystem">
+    <div class="sidebar-logo-container">
+      <img class="sidebar-logo" src="../../../../_static/images/ecosystem-logo.svg" alt="Qiskit Ecosystem logo"/>
+    </div>
+  </a>
+  
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
+</div><form class="sidebar-search-container" method="get" action="../../../../search.html" role="search">
+  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
+  <input type="hidden" name="check_keywords" value="yes">
+  <input type="hidden" name="area" value="default">
+</form>
+<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
+  <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../index.html">About Circuit Knitting Toolbox</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../install.html">Installation Instructions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Circuit Cutting</span></p>
+<ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../../../../circuit_cutting/tutorials/index.html">Cutting Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of Cutting Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../../../../circuit_cutting/how-tos/index.html">Cutting How-To Guides</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Cutting How-To Guides</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.html">How to generate exact quasiprobability distributions from Sampler</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.html">How to generate exact sampling coefficients</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/how-tos/how_to_specify_cut_wires.html">How to place wire cuts using a single-qubit <code class="docutils literal notranslate"><span class="pre">CutWire</span></code> instruction</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../../../../circuit_cutting/cutqc/index.html">CutQC (legacy circuit cutting implementation)</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of CutQC (legacy circuit cutting implementation)</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.html">CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.html">CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">References</span></p>
+<ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../../../../apidocs/index.html">API References</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of API References</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../apidocs/circuit_cutting.html">Circuit Cutting</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Circuit Cutting</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cut_wires.html">circuit_knitting.cutting.cut_wires</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.expand_observables.html">circuit_knitting.cutting.expand_observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.partition_circuit_qubits.html">circuit_knitting.cutting.partition_circuit_qubits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.partition_problem.html">circuit_knitting.cutting.partition_problem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cut_gates.html">circuit_knitting.cutting.cut_gates</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.generate_cutting_experiments.html">circuit_knitting.cutting.generate_cutting_experiments</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.reconstruct_expectation_values.html">circuit_knitting.cutting.reconstruct_expectation_values</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.TwoQubitQPDGate.html">circuit_knitting.cutting.qpd.TwoQubitQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.WeightType.html">circuit_knitting.cutting.qpd.WeightType</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.html">circuit_knitting.cutting.qpd.generate_qpd_weights</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.html">circuit_knitting.cutting.qpd.decompose_qpd_instructions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction.html">circuit_knitting.cutting.qpd.qpdbasis_from_instruction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances.html">circuit_knitting.cutting.cutqc.run_subcircuit_instances</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cutqc.generate_summation_terms.html">circuit_knitting.cutting.cutqc.generate_summation_terms</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cutqc.build.html">circuit_knitting.cutting.cutqc.build</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cutqc.verify.html">circuit_knitting.cutting.cutqc.verify</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires.html">circuit_knitting.cutting.cutqc.cut_circuit_wires</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits.html">circuit_knitting.cutting.cutqc.evaluate_subcircuits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.html">circuit_knitting.cutting.cutqc.reconstruct_full_distribution</a></li>
+</ul>
+</li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../../../apidocs/utils.html">Utilities</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of Utilities</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.bitwise.bit_count.html">circuit_knitting.utils.bitwise.bit_count</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.conversion.quasi_to_real.html">circuit_knitting.utils.conversion.quasi_to_real</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.conversion.nearest_probability_distribution.html">circuit_knitting.utils.conversion.nearest_probability_distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.conversion.naive_probability_distribution.html">circuit_knitting.utils.conversion.naive_probability_distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.conversion.dict_to_array.html">circuit_knitting.utils.conversion.dict_to_array</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.iteration.unique_by_id.html">circuit_knitting.utils.iteration.unique_by_id</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.iteration.unique_by_eq.html">circuit_knitting.utils.iteration.unique_by_eq</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.metrics.chi2_distance.html">circuit_knitting.utils.metrics.chi2_distance</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.metrics.MSE.html">circuit_knitting.utils.metrics.MSE</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.metrics.MAPE.html">circuit_knitting.utils.metrics.MAPE</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.metrics.cross_entropy.html">circuit_knitting.utils.metrics.cross_entropy</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.metrics.HOP.html">circuit_knitting.utils.metrics.HOP</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.html">circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.observable_grouping.CommutingObservableGroup.html">circuit_knitting.utils.observable_grouping.CommutingObservableGroup</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.observable_grouping.ObservableCollection.html">circuit_knitting.utils.observable_grouping.ObservableCollection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.simulation.simulate_statevector_outcomes.html">circuit_knitting.utils.simulation.simulate_statevector_outcomes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.simulation.ExactSampler.html">circuit_knitting.utils.simulation.ExactSampler</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.transforms.separate_circuit.html">circuit_knitting.utils.transforms.separate_circuit</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.transforms.SeparatedCircuits.html">circuit_knitting.utils.transforms.SeparatedCircuits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.transpiler_passes.RemoveFinalReset.html">circuit_knitting.utils.transpiler_passes.RemoveFinalReset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.utils.transpiler_passes.ConsolidateResets.html">circuit_knitting.utils.transpiler_passes.ConsolidateResets</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../release-notes.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference external" href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox">GitHub</a></li>
+</ul>
+
+</div></div>
+      </div>
+      
+    </div>
+  </aside>
+  <div class="main">
+    <div class="content">
+      <div class="article-container">
+        <a href="#" class="back-to-top muted-link">
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
+            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
+          </svg>
+          <span>Back to top</span>
+        </a>
+        <div class="content-icon-container">
+          <div class="theme-toggle-container theme-toggle-content">
+            <button class="theme-toggle">
+              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+            </button>
+          </div>
+          <label class="toc-overlay-icon toc-content-icon no-toc" for="__toc">
+            <div class="visually-hidden">Toggle table of contents sidebar</div>
+            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+          </label>
+        </div>
+        <article role="main">
+          <h1>Source code for circuit_knitting.cutting.qpd.weights</h1><div class="highlight"><pre>
+<span></span><span class="c1"># This code is a Qiskit project.</span>
+
+<span class="c1"># (C) Copyright IBM 2024.</span>
+
+<span class="c1"># This code is licensed under the Apache License, Version 2.0. You may</span>
+<span class="c1"># obtain a copy of this license in the LICENSE.txt file in the root directory</span>
+<span class="c1"># of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.</span>
+<span class="c1"># Any modifications or derivative works of this code must retain this</span>
+<span class="c1"># copyright notice, and modified files need to carry a notice indicating</span>
+<span class="c1"># that they have been altered from the originals.</span>
+
+<span class="sd">&quot;&quot;&quot;Code for generating quasiprobability weights.&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Sequence</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">Counter</span>
+<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
+<span class="kn">import</span> <span class="nn">itertools</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">import</span> <span class="nn">math</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">numpy.typing</span> <span class="k">as</span> <span class="nn">npt</span>
+
+<span class="kn">from</span> <span class="nn">.qpd_basis</span> <span class="kn">import</span> <span class="n">QPDBasis</span>
+<span class="kn">from</span> <span class="nn">...utils.iteration</span> <span class="kn">import</span> <span class="n">strict_zip</span>
+
+
+<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<span class="c1"># math.sin(math.pi) is just above 1e-16, so at 1e-14, we are well above that.</span>
+<span class="c1"># Numbers like this can often come up in the QPD coefficients.</span>
+<span class="n">_NONZERO_ATOL</span> <span class="o">=</span> <span class="mf">1e-14</span>
+
+
+<div class="viewcode-block" id="WeightType">
+<a class="viewcode-back" href="../../../../stubs/circuit_knitting.cutting.qpd.WeightType.html#circuit_knitting.cutting.qpd.WeightType">[docs]</a>
+<span class="k">class</span> <span class="nc">WeightType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Type of weight associated with a QPD sample.&quot;&quot;&quot;</span>
+
+    <span class="c1">#: A weight given in proportion to its exact weight</span>
+    <span class="n">EXACT</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="c1">#: A weight that was determined through some sampling procedure</span>
+    <span class="n">SAMPLED</span> <span class="o">=</span> <span class="mi">2</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">_min_filter_nonzero</span><span class="p">(</span><span class="n">vals</span><span class="p">:</span> <span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">],</span> <span class="o">*</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">_NONZERO_ATOL</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">vals</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">logical_not</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">vals</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">))])</span>
+
+
+<span class="k">def</span> <span class="nf">__update_running_product_after_increment</span><span class="p">(</span>
+    <span class="n">running_product</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span>
+    <span class="n">state</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span>
+    <span class="n">coeff_probabilities</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">]],</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Update the ``running_product`` list after the ``state`` has been incremented.</span>
+
+<span class="sd">    This snippet is used twice in</span>
+<span class="sd">    :func:`_generate_exact_weights_and_conditional_probabilities_assume_sorted`;</span>
+<span class="sd">    hence, we write it only once here.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">prev</span> <span class="o">=</span> <span class="n">running_product</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>
+    <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+        <span class="n">prev</span> <span class="o">=</span> <span class="mf">1.0</span>
+    <span class="n">running_product</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">prev</span> <span class="o">*</span> <span class="n">coeff_probabilities</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">state</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">][</span><span class="n">state</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
+
+
+<span class="k">def</span> <span class="nf">_generate_exact_weights_and_conditional_probabilities_assume_sorted</span><span class="p">(</span>
+    <span class="n">coeff_probabilities</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">]],</span> <span class="n">threshold</span><span class="p">:</span> <span class="nb">float</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Determine all exact weights above ``threshold`` and the conditional probabilities necessary to sample efficiently from all other weights.</span>
+
+<span class="sd">    Each yielded element will be a 2-tuple, the first element of which will be</span>
+<span class="sd">    a ``state``, represented by a tuple of ``int``\ s.</span>
+
+<span class="sd">    If ``state`` contains the same number of elements as</span>
+<span class="sd">    ``coeff_probabilities``, then the second element will be a ``float``,</span>
+<span class="sd">    greater than or equal to ``threshold``, that contains the exact probability</span>
+<span class="sd">    of this ``state``.</span>
+
+<span class="sd">    If, on the other hand, ``state`` contains fewer elements than</span>
+<span class="sd">    ``coeff_probabilities``, then the second element is a 1D ``np.ndarray`` of</span>
+<span class="sd">    the conditional probabilities that can be used to sample the remaining</span>
+<span class="sd">    weights that have not been evaluated exactly (i.e., are below</span>
+<span class="sd">    ``threshold``).  These will all be normalized *except* the</span>
+<span class="sd">    top-level one, which is given by ``state == ()``.</span>
+
+<span class="sd">    If there are no exact weights below a given level, then the conditional</span>
+<span class="sd">    probabilities at that level are equivalent to the corresponding element of</span>
+<span class="sd">    `coeff_probabilities``, and this generator does not yield them.  Skipping</span>
+<span class="sd">    redundant conditional probabilities, like this, allows for efficient</span>
+<span class="sd">    sampling without traversing the entire tree, which is desirable since the</span>
+<span class="sd">    tree grows exponentially with the number of cut gates.  By contrast, the</span>
+<span class="sd">    performance with the provided strategy is no worse than linear in</span>
+<span class="sd">    ``threshold``, in both time and memory.</span>
+
+<span class="sd">    This function assumes each element of ``coeff_probabilities`` contains</span>
+<span class="sd">    non-negative numbers, ordered largest to smallest.  For a generalization</span>
+<span class="sd">    that allows ``coeff_probabilities`` to be provided in any order, see</span>
+<span class="sd">    :func:`_generate_exact_weights_and_conditional_probabilities`.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">coeff_probabilities</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
+
+    <span class="n">next_pop</span> <span class="o">=</span> <span class="kc">False</span>  <span class="c1"># Stores whether the next move is a pop or not</span>
+    <span class="n">state</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">running_product</span> <span class="o">=</span> <span class="p">[</span><span class="n">coeff_probabilities</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+    <span class="n">running_conditional_probabilities</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">next_pop</span><span class="p">:</span>
+            <span class="c1"># Pop</span>
+            <span class="n">state</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">state</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">running_conditional_probabilities</span><span class="p">):</span>
+                <span class="c1"># There were some exact weights found below us, so we need to</span>
+                <span class="c1"># yield the conditional probabilities, unless all probabilities</span>
+                <span class="c1"># at this level are zero *and* it&#39;s not the top level.</span>
+                <span class="n">current_condprobs</span> <span class="o">=</span> <span class="n">running_conditional_probabilities</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
+                <span class="n">current_condprobs</span><span class="p">[</span>
+                    <span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">current_condprobs</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">_NONZERO_ATOL</span><span class="p">)</span>
+                <span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">state</span><span class="p">:</span>
+                    <span class="c1"># Don&#39;t normalize the top-level conditional probabilities.</span>
+                    <span class="k">yield</span> <span class="p">(),</span> <span class="n">current_condprobs</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">current_condprobs</span><span class="p">)</span>
+                    <span class="k">if</span> <span class="n">norm</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="c1"># Some, but not all, weights below us are exact.  If,</span>
+                        <span class="c1"># instead, _all_ weights had been exact, we could have</span>
+                        <span class="c1"># skipped this yield, as there is zero probability of</span>
+                        <span class="c1"># reaching this partial state when sampling.</span>
+                        <span class="k">yield</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">state</span><span class="p">),</span> <span class="n">current_condprobs</span> <span class="o">/</span> <span class="n">norm</span>
+                    <span class="c1"># Update the factor one level up from the popped one.</span>
+                    <span class="n">running_conditional_probabilities</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">state</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span> <span class="o">*=</span> <span class="n">norm</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">state</span><span class="p">:</span>
+                <span class="c1"># We&#39;re all done</span>
+                <span class="k">return</span>
+            <span class="n">running_product</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
+            <span class="c1"># Increment the state counter.</span>
+            <span class="n">state</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="k">if</span> <span class="n">state</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">coeff_probabilities</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">state</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]):</span>
+                <span class="c1"># Increment successful (no overflow).  We don&#39;t need to pop again.</span>
+                <span class="n">next_pop</span> <span class="o">=</span> <span class="kc">False</span>
+                <span class="n">__update_running_product_after_increment</span><span class="p">(</span>
+                    <span class="n">running_product</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">coeff_probabilities</span>
+                <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">running_product</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">threshold</span><span class="p">:</span>
+                <span class="n">next_pop</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">state</span><span class="p">)</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">coeff_probabilities</span><span class="p">):</span>
+                <span class="c1"># Append 0 to work toward a &quot;full&quot; `state`</span>
+                <span class="n">running_product</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                    <span class="n">running_product</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">coeff_probabilities</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">state</span><span class="p">)][</span><span class="mi">0</span><span class="p">]</span>
+                <span class="p">)</span>
+                <span class="n">state</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="c1"># `state` is full.  Yield first.</span>
+                <span class="k">yield</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">state</span><span class="p">),</span> <span class="n">running_product</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+                <span class="c1"># Since we found something exact, we need to update running_conditional_probabilities.</span>
+                <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">running_conditional_probabilities</span><span class="p">)</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">coeff_probabilities</span><span class="p">):</span>
+                    <span class="n">running_conditional_probabilities</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                        <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
+                            <span class="n">coeff_probabilities</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">running_conditional_probabilities</span><span class="p">)],</span>
+                            <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span>
+                        <span class="p">)</span>
+                    <span class="p">)</span>
+                <span class="c1"># It&#39;s exact, so we want no probability of sampling it going forward.</span>
+                <span class="n">running_conditional_probabilities</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">state</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span> <span class="o">=</span> <span class="mi">0</span>
+                <span class="c1"># Increment the state counter.</span>
+                <span class="n">state</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="k">if</span> <span class="n">state</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">coeff_probabilities</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+                    <span class="c1"># The state counter has overflowed, so our next move should be a</span>
+                    <span class="c1"># pop.</span>
+                    <span class="n">next_pop</span> <span class="o">=</span> <span class="kc">True</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">__update_running_product_after_increment</span><span class="p">(</span>
+                        <span class="n">running_product</span><span class="p">,</span> <span class="n">state</span><span class="p">,</span> <span class="n">coeff_probabilities</span>
+                    <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_invert_permutation</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
+    <span class="c1"># https://stackoverflow.com/questions/11649577/how-to-invert-a-permutation-array-in-numpy</span>
+    <span class="n">s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">p</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="n">s</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">s</span>
+
+
+<span class="k">def</span> <span class="nf">_generate_exact_weights_and_conditional_probabilities</span><span class="p">(</span>
+    <span class="n">coeff_probabilities</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">]],</span> <span class="n">threshold</span><span class="p">:</span> <span class="nb">float</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Generate exact weights and conditional probabilities.</span>
+
+<span class="sd">    This is identical in behavior to</span>
+<span class="sd">    :func:`_generate_exact_weights_and_conditional_probabilities_assume_sorted`,</span>
+<span class="sd">    except it makes no assumption on the order of the coefficients.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">permutations</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">cp</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">cp</span> <span class="ow">in</span> <span class="n">coeff_probabilities</span><span class="p">]</span>
+    <span class="n">sorted_coeff_probabilities</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">cp</span><span class="p">[</span><span class="n">permutation</span><span class="p">]</span> <span class="k">for</span> <span class="n">cp</span><span class="p">,</span> <span class="n">permutation</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">coeff_probabilities</span><span class="p">,</span> <span class="n">permutations</span><span class="p">)</span>
+    <span class="p">]</span>
+    <span class="n">ipermutations</span> <span class="o">=</span> <span class="p">[</span><span class="n">_invert_permutation</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">permutations</span><span class="p">]</span>
+    <span class="k">for</span> <span class="p">(</span>
+        <span class="n">coeff_indices</span><span class="p">,</span>
+        <span class="n">probability</span><span class="p">,</span>
+    <span class="p">)</span> <span class="ow">in</span> <span class="n">_generate_exact_weights_and_conditional_probabilities_assume_sorted</span><span class="p">(</span>
+        <span class="n">sorted_coeff_probabilities</span><span class="p">,</span> <span class="n">threshold</span>
+    <span class="p">):</span>
+        <span class="n">orig_coeff_indices</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span>
+            <span class="n">perm</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">perm</span><span class="p">,</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">permutations</span><span class="p">,</span> <span class="n">coeff_indices</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">coeff_indices</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">sorted_coeff_probabilities</span><span class="p">):</span>
+            <span class="c1"># In this case, `probability` is actually a vector of conditional</span>
+            <span class="c1"># probabilities, so apply the inverse permutation.</span>
+            <span class="n">probability</span> <span class="o">=</span> <span class="n">probability</span><span class="p">[</span><span class="n">ipermutations</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">coeff_indices</span><span class="p">)]]</span>
+        <span class="k">yield</span> <span class="n">orig_coeff_indices</span><span class="p">,</span> <span class="n">probability</span>
+
+
+<div class="viewcode-block" id="generate_qpd_weights">
+<a class="viewcode-back" href="../../../../stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.html#circuit_knitting.cutting.qpd.generate_qpd_weights">[docs]</a>
+<span class="k">def</span> <span class="nf">generate_qpd_weights</span><span class="p">(</span>
+    <span class="n">qpd_bases</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">QPDBasis</span><span class="p">],</span> <span class="n">num_samples</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">1000</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">WeightType</span><span class="p">]]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Generate weights from the joint quasiprobability distribution.</span>
+
+<span class="sd">    Each weight whose absolute value is above a threshold of ``1 /</span>
+<span class="sd">    num_samples`` will be evaluated exactly. The remaining weights -- those in</span>
+<span class="sd">    the tail of the distribution -- will then be sampled from, resulting in at</span>
+<span class="sd">    most ``num_samples`` unique weights.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        qpd_bases: The :class:`QPDBasis` objects from which to generate weights</span>
+<span class="sd">        num_samples: Controls the number of weights to generate</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        A mapping from a given decomposition to its weight.</span>
+<span class="sd">        Keys are tuples of indices -- one index per input :class:`QPDBasis`. The indices</span>
+<span class="sd">        correspond to a specific decomposition mapping in the basis.</span>
+
+<span class="sd">        Values are tuples.  The first element is a number corresponding to the</span>
+<span class="sd">        weight of the contribution.  The second element is the :class:`WeightType`,</span>
+<span class="sd">        either ``EXACT`` or ``SAMPLED``.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">independent_probabilities</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">basis</span><span class="o">.</span><span class="n">probabilities</span><span class="p">)</span> <span class="k">for</span> <span class="n">basis</span> <span class="ow">in</span> <span class="n">qpd_bases</span><span class="p">]</span>
+    <span class="c1"># In Python 3.7 and higher, dicts are guaranteed to remember their</span>
+    <span class="c1"># insertion order.  For user convenience, we sort by exact weights first,</span>
+    <span class="c1"># then sampled weights.  Within each, values are sorted largest to</span>
+    <span class="c1"># smallest.</span>
+    <span class="n">lst</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span>
+        <span class="n">_generate_qpd_weights</span><span class="p">(</span><span class="n">independent_probabilities</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span>
+        <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">((</span><span class="n">v</span> <span class="o">:=</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="o">-</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="n">lst</span><span class="p">)</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">_generate_qpd_weights</span><span class="p">(</span>
+    <span class="n">independent_probabilities</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">]],</span>
+    <span class="n">num_samples</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
+    <span class="o">*</span><span class="p">,</span>
+    <span class="n">_samples_multiplier</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">WeightType</span><span class="p">]]:</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">num_samples</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;num_samples must be at least 1.&quot;</span><span class="p">)</span>
+
+    <span class="n">retval</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">WeightType</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="n">threshold</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">num_samples</span>
+
+    <span class="c1"># Determine if the smallest *nonzero* probability is above the threshold implied by</span>
+    <span class="c1"># num_samples.  If so, then we can evaluate all weights exactly.</span>
+    <span class="n">smallest_probability</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span>
+        <span class="p">[</span><span class="n">_min_filter_nonzero</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span> <span class="k">for</span> <span class="n">probs</span> <span class="ow">in</span> <span class="n">independent_probabilities</span><span class="p">]</span>
+    <span class="p">)</span>
+    <span class="k">if</span> <span class="n">smallest_probability</span> <span class="o">&gt;=</span> <span class="n">threshold</span><span class="p">:</span>
+        <span class="c1"># All weights exactly</span>
+        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;All exact weights&quot;</span><span class="p">)</span>
+        <span class="n">multiplier</span> <span class="o">=</span> <span class="n">num_samples</span> <span class="k">if</span> <span class="n">math</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">num_samples</span><span class="p">)</span> <span class="k">else</span> <span class="mf">1.0</span>
+        <span class="k">for</span> <span class="n">map_ids</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span>
+            <span class="o">*</span><span class="p">[</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">probs</span><span class="p">))</span> <span class="k">for</span> <span class="n">probs</span> <span class="ow">in</span> <span class="n">independent_probabilities</span><span class="p">]</span>
+        <span class="p">):</span>
+            <span class="n">probability</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span>
+                <span class="p">[</span>
+                    <span class="n">probs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">probs</span> <span class="ow">in</span> <span class="n">strict_zip</span><span class="p">(</span><span class="n">map_ids</span><span class="p">,</span> <span class="n">independent_probabilities</span><span class="p">)</span>
+                <span class="p">]</span>
+            <span class="p">)</span>
+            <span class="k">if</span> <span class="n">probability</span> <span class="o">&lt;</span> <span class="n">_NONZERO_ATOL</span><span class="p">:</span>
+                <span class="k">continue</span>
+            <span class="n">retval</span><span class="p">[</span><span class="n">map_ids</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">multiplier</span> <span class="o">*</span> <span class="n">probability</span><span class="p">,</span> <span class="n">WeightType</span><span class="o">.</span><span class="n">EXACT</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">retval</span>
+
+    <span class="n">conditional_probabilities</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">weight_to_sample</span> <span class="o">=</span> <span class="mf">1.0</span>
+
+    <span class="n">largest_probability</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span>
+        <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span> <span class="k">for</span> <span class="n">probs</span> <span class="ow">in</span> <span class="n">independent_probabilities</span><span class="p">]</span>
+    <span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">largest_probability</span> <span class="o">&gt;=</span> <span class="n">threshold</span><span class="p">:</span>
+        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;No exact weights&quot;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Some exact weights&quot;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="p">(</span>
+            <span class="n">map_ids</span><span class="p">,</span>
+            <span class="n">probability</span><span class="p">,</span>
+        <span class="p">)</span> <span class="ow">in</span> <span class="n">_generate_exact_weights_and_conditional_probabilities</span><span class="p">(</span>
+            <span class="n">independent_probabilities</span><span class="p">,</span> <span class="n">threshold</span>
+        <span class="p">):</span>
+            <span class="c1"># As described in the docstring to</span>
+            <span class="c1"># _generate_exact_weights_and_conditional_probabilities_assume_sorted,</span>
+            <span class="c1"># there are two possible pieces of information that might be</span>
+            <span class="c1"># yielded by the generator.  The following branch distinguishes</span>
+            <span class="c1"># between them.</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">map_ids</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">independent_probabilities</span><span class="p">):</span>
+                <span class="c1"># The generator produced a state together with its exact probability.</span>
+                <span class="n">weight</span> <span class="o">=</span> <span class="n">probability</span> <span class="o">*</span> <span class="n">num_samples</span>
+                <span class="n">retval</span><span class="p">[</span><span class="n">map_ids</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="n">WeightType</span><span class="o">.</span><span class="n">EXACT</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="c1"># The generator produced a partial state along with a vector of</span>
+                <span class="c1"># conditional probabilities.</span>
+                <span class="n">conditional_probabilities</span><span class="p">[</span><span class="n">map_ids</span><span class="p">]</span> <span class="o">=</span> <span class="n">probability</span>
+                <span class="k">if</span> <span class="n">map_ids</span> <span class="o">==</span> <span class="p">():</span>
+                    <span class="n">weight_to_sample</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">probability</span><span class="p">)</span>
+                    <span class="n">conditional_probabilities</span><span class="p">[</span><span class="n">map_ids</span><span class="p">]</span> <span class="o">/=</span> <span class="n">weight_to_sample</span>
+
+    <span class="c1"># Loop through each gate and sample from the remainder of the distribution.</span>
+
+    <span class="c1"># Start by rescaling.</span>
+    <span class="n">weight_to_sample</span> <span class="o">*=</span> <span class="n">num_samples</span>
+    <span class="c1"># The following variable, `samples_needed`, must be integer and at least 1.</span>
+    <span class="c1"># `_samples_multiplier` will typically be 1, but we set it higher in</span>
+    <span class="c1"># testing to collect additional statistics, faster.</span>
+    <span class="n">samples_needed</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">weight_to_sample</span><span class="p">)</span> <span class="o">*</span> <span class="n">_samples_multiplier</span>
+    <span class="c1"># At the time of writing, the below assert should never fail.  But if</span>
+    <span class="c1"># future code changes result in inputs where it _may_ fail, then the only</span>
+    <span class="c1"># thing that should be needed if this is reached is to return `retval` in</span>
+    <span class="c1"># this case, since presumably it must contain all weights as exact weights.</span>
+    <span class="k">assert</span> <span class="n">samples_needed</span> <span class="o">&gt;=</span> <span class="mi">1</span>
+    <span class="n">single_sample_weight</span> <span class="o">=</span> <span class="n">weight_to_sample</span> <span class="o">/</span> <span class="n">samples_needed</span>
+
+    <span class="c1"># Figure out if we&#39;ve reached the special case where everything except</span>
+    <span class="c1"># *one* weight has been calculated exactly, so there&#39;s only one thing left</span>
+    <span class="c1"># to sample.  If that&#39;s the case, then we can calculate that one remaining</span>
+    <span class="c1"># weight exactly as well and skip sampling.</span>
+    <span class="k">if</span> <span class="n">conditional_probabilities</span><span class="p">:</span>
+        <span class="n">running_state</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="p">()</span>
+        <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">running_state</span><span class="p">)</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">independent_probabilities</span><span class="p">):</span>
+            <span class="c1"># If it&#39;s missing from `conditional_probabilities`, that just means</span>
+            <span class="c1"># to use the corresponding entry in `independent_probabilities`.</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">probs</span> <span class="o">=</span> <span class="n">conditional_probabilities</span><span class="p">[</span><span class="n">running_state</span><span class="p">]</span>
+            <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
+                <span class="n">probs</span> <span class="o">=</span> <span class="n">independent_probabilities</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">running_state</span><span class="p">)]</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">flatnonzero</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span>
+            <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">break</span>
+            <span class="n">running_state</span> <span class="o">+=</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="n">running_state</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">retval</span>
+            <span class="n">retval</span><span class="p">[</span><span class="n">running_state</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">weight_to_sample</span><span class="p">,</span> <span class="n">WeightType</span><span class="o">.</span><span class="n">EXACT</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">retval</span>
+
+    <span class="c1"># Form the joint samples, collecting them into a dict with counts for each.</span>
+    <span class="n">random_samples</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">_populate_samples</span><span class="p">(</span>
+        <span class="n">random_samples</span><span class="p">,</span>
+        <span class="n">samples_needed</span><span class="p">,</span>
+        <span class="n">independent_probabilities</span><span class="p">,</span>
+        <span class="n">conditional_probabilities</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="c1"># Insert the samples into the dict we are about to return.</span>
+    <span class="k">for</span> <span class="n">outcome</span><span class="p">,</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">random_samples</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="k">assert</span> <span class="n">outcome</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">retval</span>
+        <span class="n">retval</span><span class="p">[</span><span class="n">outcome</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">count</span> <span class="o">*</span> <span class="n">single_sample_weight</span><span class="p">,</span> <span class="n">WeightType</span><span class="o">.</span><span class="n">SAMPLED</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">retval</span>
+
+
+<span class="k">def</span> <span class="nf">_populate_samples</span><span class="p">(</span>
+    <span class="n">random_samples</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">int</span><span class="p">],</span>
+    <span class="n">num_desired</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+    <span class="n">independent_probabilities</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">,</span>
+    <span class="n">conditional_probabilities</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">]],</span>
+    <span class="n">running_state</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="p">(),</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Generate random samples from the conditional probabilitity distributions.</span>
+
+<span class="sd">    Items get populated into the ``random_samples`` dict, rather than returned.</span>
+
+<span class="sd">    This function is designed to call itself recursively.  The number of</span>
+<span class="sd">    elements in ``running_state`` will match the recursion depth.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">running_state</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">conditional_probabilities</span><span class="p">:</span>
+        <span class="c1"># Everything below us is sampled, so we can sample directly from the</span>
+        <span class="c1"># remaining independent probability distributions.</span>
+        <span class="n">samples_by_decomp</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">probs</span> <span class="ow">in</span> <span class="n">independent_probabilities</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">running_state</span><span class="p">)</span> <span class="p">:]:</span>
+            <span class="n">samples_by_decomp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">probs</span><span class="p">)),</span> <span class="n">num_desired</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">probs</span><span class="p">)</span>
+            <span class="p">)</span>
+        <span class="k">for</span> <span class="n">outcome</span><span class="p">,</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">Counter</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">samples_by_decomp</span><span class="p">))</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">assert</span> <span class="p">(</span><span class="n">running_state</span> <span class="o">+</span> <span class="n">outcome</span><span class="p">)</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">random_samples</span>
+            <span class="n">random_samples</span><span class="p">[</span><span class="n">running_state</span> <span class="o">+</span> <span class="n">outcome</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>
+        <span class="k">return</span>
+
+    <span class="c1"># There are some exact weight(s) below us, so we must consider the</span>
+    <span class="c1"># conditional probabilities at the current level.</span>
+    <span class="n">probs</span> <span class="o">=</span> <span class="n">conditional_probabilities</span><span class="p">[</span><span class="n">running_state</span><span class="p">]</span>
+    <span class="n">current_outcomes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">probs</span><span class="p">)),</span> <span class="n">num_desired</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">probs</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">current_outcome</span><span class="p">,</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">Counter</span><span class="p">(</span><span class="n">current_outcomes</span><span class="p">)</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">outcome</span> <span class="o">=</span> <span class="n">running_state</span> <span class="o">+</span> <span class="p">(</span><span class="n">current_outcome</span><span class="p">,)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">outcome</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">independent_probabilities</span><span class="p">):</span>
+            <span class="c1"># It&#39;s a full one</span>
+            <span class="k">assert</span> <span class="n">outcome</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">random_samples</span>
+            <span class="n">random_samples</span><span class="p">[</span><span class="n">outcome</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># Recurse</span>
+            <span class="n">_populate_samples</span><span class="p">(</span>
+                <span class="n">random_samples</span><span class="p">,</span>
+                <span class="n">count</span><span class="p">,</span>
+                <span class="n">independent_probabilities</span><span class="p">,</span>
+                <span class="n">conditional_probabilities</span><span class="p">,</span>
+                <span class="n">outcome</span><span class="p">,</span>
+            <span class="p">)</span>
+</pre></div>
+        </article>
+      </div>
+      <footer>
+        <div class="related-pages">
+          
+          
+        </div>
+        <div class="bottom-of-page">
+          <div class="left-details">
+            <div class="copyright">
+                Copyright &#169; 2023
+            </div>
+            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
+            
+            <a href="https://github.com/pradyunsg/furo">Furo</a>
+            
+          </div>
+          <div class="right-details">
+            <div class="icons">
+              <a class="muted-link " href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox" aria-label="GitHub">
+                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
+                </svg>
+            </a>
+              
+            </div>
+          </div>
+        </div>
+        
+      </footer>
+    </div>
+    <aside class="toc-drawer no-toc">
+      
+      
+      
+    </aside>
+  </div>
+</div><script src="../../../../_static/jquery.js?v=5d32c60e"></script>
+    <script src="../../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+    <script src="../../../../_static/documentation_options.js?v=fe7df9b0"></script>
+    <script src="../../../../_static/doctools.js?v=9a2dae69"></script>
+    <script src="../../../../_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="../../../../_static/thebelab-helper.js"></script>
+    <script src="../../../../_static/clipboard.min.js?v=a7894cd8"></script>
+    <script src="../../../../_static/copybutton.js?v=f281be69"></script>
+    <script src="../../../../_static/scripts/qiskit-sphinx-theme.js?v=4d77b8ca"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/@jupyter-widgets/html-manager@^1.0.1/dist/embed-amd.js"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    </body>
+</html>
\ No newline at end of file
diff --git a/_modules/circuit_knitting/cutting/wire_cutting_transforms.html b/_modules/circuit_knitting/cutting/wire_cutting_transforms.html
index 4fd23ab7b..457a7bcb1 100644
--- a/_modules/circuit_knitting/cutting/wire_cutting_transforms.html
+++ b/_modules/circuit_knitting/cutting/wire_cutting_transforms.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.wire_cutting_transforms - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.wire_cutting_transforms - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -519,7 +523,7 @@ <h1>Source code for circuit_knitting.cutting.wire_cutting_transforms</h1><div cl
   </div>
 </div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/utils/bitwise.html b/_modules/circuit_knitting/utils/bitwise.html
index d97072cf5..5a3b4857f 100644
--- a/_modules/circuit_knitting/utils/bitwise.html
+++ b/_modules/circuit_knitting/utils/bitwise.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.bitwise - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.bitwise - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -373,7 +377,7 @@ <h1>Source code for circuit_knitting.utils.bitwise</h1><div class="highlight"><p
   </div>
 </div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/utils/conversion.html b/_modules/circuit_knitting/utils/conversion.html
index 918711540..c8f4f0a66 100644
--- a/_modules/circuit_knitting/utils/conversion.html
+++ b/_modules/circuit_knitting/utils/conversion.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.conversion - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.conversion - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -321,10 +325,16 @@ <h1>Source code for circuit_knitting.utils.conversion</h1><div class="highlight"
 <span class="sd">&quot;&quot;&quot;</span>
 
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">qiskit.utils.deprecation</span> <span class="kn">import</span> <span class="n">deprecate_func</span>
 
 
 <div class="viewcode-block" id="quasi_to_real">
 <a class="viewcode-back" href="../../../stubs/circuit_knitting.utils.conversion.quasi_to_real.html#circuit_knitting.utils.conversion.quasi_to_real">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">quasi_to_real</span><span class="p">(</span><span class="n">quasiprobability</span><span class="p">,</span> <span class="n">mode</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Convert a quasi probability to a valid probability distribution.</span>
@@ -347,6 +357,11 @@ <h1>Source code for circuit_knitting.utils.conversion</h1><div class="highlight"
 
 <div class="viewcode-block" id="nearest_probability_distribution">
 <a class="viewcode-back" href="../../../stubs/circuit_knitting.utils.conversion.nearest_probability_distribution.html#circuit_knitting.utils.conversion.nearest_probability_distribution">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">nearest_probability_distribution</span><span class="p">(</span><span class="n">quasiprobability</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Convert quasiprobability distribution to the nearest probability distribution.</span>
@@ -385,6 +400,11 @@ <h1>Source code for circuit_knitting.utils.conversion</h1><div class="highlight"
 
 <div class="viewcode-block" id="naive_probability_distribution">
 <a class="viewcode-back" href="../../../stubs/circuit_knitting.utils.conversion.naive_probability_distribution.html#circuit_knitting.utils.conversion.naive_probability_distribution">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">naive_probability_distribution</span><span class="p">(</span><span class="n">quasiprobability</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Convert quasiprobability dist to probability dist by zero-ing out negative values.</span>
@@ -407,6 +427,11 @@ <h1>Source code for circuit_knitting.utils.conversion</h1><div class="highlight"
 
 <div class="viewcode-block" id="dict_to_array">
 <a class="viewcode-back" href="../../../stubs/circuit_knitting.utils.conversion.dict_to_array.html#circuit_knitting.utils.conversion.dict_to_array">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">dict_to_array</span><span class="p">(</span><span class="n">distribution_dict</span><span class="p">,</span> <span class="n">force_prob</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Convert dictionary of shot results to array of distribution.</span>
@@ -478,7 +503,7 @@ <h1>Source code for circuit_knitting.utils.conversion</h1><div class="highlight"
   </div>
 </div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/utils/iteration.html b/_modules/circuit_knitting/utils/iteration.html
index c826d6caa..fc4366161 100644
--- a/_modules/circuit_knitting/utils/iteration.html
+++ b/_modules/circuit_knitting/utils/iteration.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.iteration - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.iteration - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -416,7 +420,7 @@ <h1>Source code for circuit_knitting.utils.iteration</h1><div class="highlight">
   </div>
 </div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/utils/metrics.html b/_modules/circuit_knitting/utils/metrics.html
index 56f3c34f6..d99dfff44 100644
--- a/_modules/circuit_knitting/utils/metrics.html
+++ b/_modules/circuit_knitting/utils/metrics.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.metrics - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.metrics - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -325,9 +329,16 @@ <h1>Source code for circuit_knitting.utils.metrics</h1><div class="highlight"><p
 
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
+<span class="kn">from</span> <span class="nn">qiskit.utils.deprecation</span> <span class="kn">import</span> <span class="n">deprecate_func</span>
+
 
 <div class="viewcode-block" id="chi2_distance">
 <a class="viewcode-back" href="../../../stubs/circuit_knitting.utils.metrics.chi2_distance.html#circuit_knitting.utils.metrics.chi2_distance">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">chi2_distance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">obs</span><span class="p">):</span>  <span class="c1"># noqa: D301</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Measure the Chi-square distance.</span>
@@ -378,6 +389,11 @@ <h1>Source code for circuit_knitting.utils.metrics</h1><div class="highlight"><p
 
 <div class="viewcode-block" id="MSE">
 <a class="viewcode-back" href="../../../stubs/circuit_knitting.utils.metrics.MSE.html#circuit_knitting.utils.metrics.MSE">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">MSE</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">obs</span><span class="p">):</span>  <span class="c1"># noqa: D301</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Compute the Mean Squared Error (MSE).</span>
@@ -432,6 +448,11 @@ <h1>Source code for circuit_knitting.utils.metrics</h1><div class="highlight"><p
 
 <div class="viewcode-block" id="MAPE">
 <a class="viewcode-back" href="../../../stubs/circuit_knitting.utils.metrics.MAPE.html#circuit_knitting.utils.metrics.MAPE">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">MAPE</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">obs</span><span class="p">):</span>  <span class="c1"># noqa: D301</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Compute the Mean Absolute Percentage Error (MAPE).</span>
@@ -494,6 +515,11 @@ <h1>Source code for circuit_knitting.utils.metrics</h1><div class="highlight"><p
 
 <div class="viewcode-block" id="cross_entropy">
 <a class="viewcode-back" href="../../../stubs/circuit_knitting.utils.metrics.cross_entropy.html#circuit_knitting.utils.metrics.cross_entropy">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">cross_entropy</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">obs</span><span class="p">):</span>  <span class="c1"># noqa: D301</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Compute the cross entropy between two distributions.</span>
@@ -547,6 +573,11 @@ <h1>Source code for circuit_knitting.utils.metrics</h1><div class="highlight"><p
 
 <div class="viewcode-block" id="HOP">
 <a class="viewcode-back" href="../../../stubs/circuit_knitting.utils.metrics.HOP.html#circuit_knitting.utils.metrics.HOP">[docs]</a>
+<span class="nd">@deprecate_func</span><span class="p">(</span>
+    <span class="n">removal_timeline</span><span class="o">=</span><span class="s2">&quot;no sooner than CKT v0.8.0&quot;</span><span class="p">,</span>
+    <span class="n">since</span><span class="o">=</span><span class="s2">&quot;0.7.0&quot;</span><span class="p">,</span>
+    <span class="n">package_name</span><span class="o">=</span><span class="s2">&quot;circuit-knitting-toolbox&quot;</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">HOP</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">obs</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Compute the Heavy Output Probability (HOP).</span>
@@ -614,7 +645,7 @@ <h1>Source code for circuit_knitting.utils.metrics</h1><div class="highlight"><p
   </div>
 </div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/utils/observable_grouping.html b/_modules/circuit_knitting/utils/observable_grouping.html
index 49077e90b..3399cda7c 100644
--- a/_modules/circuit_knitting/utils/observable_grouping.html
+++ b/_modules/circuit_knitting/utils/observable_grouping.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.observable_grouping - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.observable_grouping - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -618,7 +622,7 @@ <h1>Source code for circuit_knitting.utils.observable_grouping</h1><div class="h
   </div>
 </div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/utils/simulation.html b/_modules/circuit_knitting/utils/simulation.html
index dff010afd..9dfb6c619 100644
--- a/_modules/circuit_knitting/utils/simulation.html
+++ b/_modules/circuit_knitting/utils/simulation.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.simulation - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.simulation - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -516,7 +520,7 @@ <h1>Source code for circuit_knitting.utils.simulation</h1><div class="highlight"
   </div>
 </div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/utils/transforms.html b/_modules/circuit_knitting/utils/transforms.html
index a31d4fe2c..1c26a4b6a 100644
--- a/_modules/circuit_knitting/utils/transforms.html
+++ b/_modules/circuit_knitting/utils/transforms.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.transforms - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.transforms - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -623,7 +627,7 @@ <h1>Source code for circuit_knitting.utils.transforms</h1><div class="highlight"
   </div>
 </div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/circuit_knitting/utils/transpiler_passes.html b/_modules/circuit_knitting/utils/transpiler_passes.html
index def9f5d56..ca61ab8d4 100644
--- a/_modules/circuit_knitting/utils/transpiler_passes.html
+++ b/_modules/circuit_knitting/utils/transpiler_passes.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.transpiler_passes - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.transpiler_passes - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -407,7 +411,7 @@ <h1>Source code for circuit_knitting.utils.transpiler_passes</h1><div class="hig
   </div>
 </div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/index.html b/_modules/index.html
index edf2a8203..86492126a 100644
--- a/_modules/index.html
+++ b/_modules/index.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>Overview: module code - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>Overview: module code - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -295,7 +299,8 @@
         </div>
         <article role="main">
           <h1>All modules for which code is available</h1>
-<ul><li><a href="circuit_knitting/cutting/cutqc/wire_cutting.html">circuit_knitting.cutting.cutqc.wire_cutting</a></li>
+<ul><li><a href="circuit_knitting/cutting/automated_cut_finding.html">circuit_knitting.cutting.automated_cut_finding</a></li>
+<li><a href="circuit_knitting/cutting/cutqc/wire_cutting.html">circuit_knitting.cutting.cutqc.wire_cutting</a></li>
 <li><a href="circuit_knitting/cutting/cutqc/wire_cutting_evaluation.html">circuit_knitting.cutting.cutqc.wire_cutting_evaluation</a></li>
 <li><a href="circuit_knitting/cutting/cutqc/wire_cutting_post_processing.html">circuit_knitting.cutting.cutqc.wire_cutting_post_processing</a></li>
 <li><a href="circuit_knitting/cutting/cutqc/wire_cutting_verification.html">circuit_knitting.cutting.cutqc.wire_cutting_verification</a></li>
@@ -304,9 +309,11 @@ <h1>All modules for which code is available</h1>
 <li><a href="circuit_knitting/cutting/cutting_reconstruction.html">circuit_knitting.cutting.cutting_reconstruction</a></li>
 <li><a href="circuit_knitting/cutting/instructions/cut_wire.html">circuit_knitting.cutting.instructions.cut_wire</a></li>
 <li><a href="circuit_knitting/cutting/instructions/move.html">circuit_knitting.cutting.instructions.move</a></li>
+<li><a href="circuit_knitting/cutting/qpd/decompose.html">circuit_knitting.cutting.qpd.decompose</a></li>
+<li><a href="circuit_knitting/cutting/qpd/decompositions.html">circuit_knitting.cutting.qpd.decompositions</a></li>
 <li><a href="circuit_knitting/cutting/qpd/instructions/qpd_gate.html">circuit_knitting.cutting.qpd.instructions.qpd_gate</a></li>
-<li><a href="circuit_knitting/cutting/qpd/qpd.html">circuit_knitting.cutting.qpd.qpd</a></li>
 <li><a href="circuit_knitting/cutting/qpd/qpd_basis.html">circuit_knitting.cutting.qpd.qpd_basis</a></li>
+<li><a href="circuit_knitting/cutting/qpd/weights.html">circuit_knitting.cutting.qpd.weights</a></li>
 <li><a href="circuit_knitting/cutting/wire_cutting_transforms.html">circuit_knitting.cutting.wire_cutting_transforms</a></li>
 <li><a href="circuit_knitting/utils/bitwise.html">circuit_knitting.utils.bitwise</a></li>
 <li><a href="circuit_knitting/utils/conversion.html">circuit_knitting.utils.conversion</a></li>
@@ -358,7 +365,7 @@ <h1>All modules for which code is available</h1>
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/thebelab-helper.js"></script>
diff --git a/_modules/qiskit/primitives/base/base_sampler.html b/_modules/qiskit/primitives/base/base_sampler.html
index 45478e8b3..df51357e1 100644
--- a/_modules/qiskit/primitives/base/base_sampler.html
+++ b/_modules/qiskit/primitives/base/base_sampler.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../../genindex.html" /><link rel="search" title="Search" href="../../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>qiskit.primitives.base.base_sampler - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>qiskit.primitives.base.base_sampler - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -515,7 +519,7 @@ <h1>Source code for qiskit.primitives.base.base_sampler</h1><div class="highligh
   </div>
 </div><script src="../../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../../_static/thebelab-helper.js"></script>
diff --git a/_modules/qiskit/transpiler/basepasses.html b/_modules/qiskit/transpiler/basepasses.html
index 76ee09736..70ab3ce8c 100644
--- a/_modules/qiskit/transpiler/basepasses.html
+++ b/_modules/qiskit/transpiler/basepasses.html
@@ -4,8 +4,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>qiskit.transpiler.basepasses - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>qiskit.transpiler.basepasses - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -556,7 +560,7 @@ <h1>Source code for qiskit.transpiler.basepasses</h1><div class="highlight"><pre
   </div>
 </div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/thebelab-helper.js"></script>
diff --git a/_sources/circuit_cutting/cutqc/index.rst.txt b/_sources/circuit_cutting/cutqc/index.rst.txt
index 0e286f7d4..c751c98d2 100644
--- a/_sources/circuit_cutting/cutqc/index.rst.txt
+++ b/_sources/circuit_cutting/cutqc/index.rst.txt
@@ -6,14 +6,8 @@ The ``cutqc`` module implements the wire cutting technique and
 automatic cut finding method described in the research paper
 `arXiv:2012.02333 <https://arxiv.org/abs/2012.02333>`_.  Historically,
 this was the original circuit cutting implementation in the Circuit
-Knitting Toolbox.  Going forward, this module is considered legacy
-code that is supported, but new features are not expected.  New users
-of the toolbox should use the new circuit cutting interface, instead,
-unless they specifically need a feature that is not (yet) supported by
-the new code.  These features currently specific to ``cutqc`` include:
-
-- Reconstruction of probability distributions (rather than expectation values)
-- Automatic cut finding
+Knitting Toolbox.  Going forward, this module is deprecated.  Users
+of the toolbox should use the new circuit cutting interface, instead.
 
 .. _cutqc tutorials:
 
diff --git a/_sources/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb.txt b/_sources/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb.txt
index 3a05d1bf2..9177e30fc 100644
--- a/_sources/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb.txt
+++ b/_sources/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb.txt
@@ -7,6 +7,8 @@
    "source": [
     "# CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding\n",
     "\n",
+    "**NOTE: CutQC is deprecated and will be removed no sooner than Circuit Knitting Toolbox v0.8.0.  The circuit cutting workflow in `circuit_knitting.cutting` now implements similar and improved functionalities, which will be maintained going forward.**\n",
+    "\n",
     "Circuit cutting is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output. \n",
     "\n",
     "The circuit knitting toolbox implements a wire cutting method presented in [CutQC](https://doi.org/10.1145/3445814.3446758) (Tang et al.). This method allows a circuit wire to be cut such that the generated subcircuits are amended by measurements in the Pauli bases and by state preparation of four Pauli eigenstates (see Fig. 4 of [CutQC](https://doi.org/10.1145/3445814.3446758)).\n",
@@ -269,7 +271,7 @@
     "\n",
     "# Uncomment the following lines to instead use Qiskit Runtime Service as configured above.\n",
     "# subcircuit_instance_probabilities = evaluate_subcircuits(cuts,\n",
-    "#                                                          service_args=service.active_account(),\n",
+    "#                                                          service=service,\n",
     "#                                                          backend_names=backend_names,\n",
     "#                                                          options=options,\n",
     "#                                                         )"
diff --git a/_sources/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb.txt b/_sources/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb.txt
index 7518e53ff..4b3037e35 100644
--- a/_sources/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb.txt
+++ b/_sources/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb.txt
@@ -7,6 +7,8 @@
    "source": [
     "# CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting\n",
     "\n",
+    "**NOTE: CutQC is deprecated and will be removed no sooner than Circuit Knitting Toolbox v0.8.0.  The circuit cutting workflow in `circuit_knitting.cutting` now implements similar and improved functionalities, which will be maintained going forward.**\n",
+    "\n",
     "Circuit cutting is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output. \n",
     "\n",
     "The circuit knitting toolbox implements a wire cutting method presented in [CutQC](https://doi.org/10.1145/3445814.3446758) (Tang et al.). This method allows a circuit wire to be cut such that the generated subcircuits are amended by measurements in the Pauli bases and by state preparation of four Pauli eigenstates (see Fig. 4 of [CutQC](https://doi.org/10.1145/3445814.3446758)).\n",
@@ -296,7 +298,7 @@
     "\n",
     "# Uncomment the following lines to instead use Qiskit Runtime Service as configured above.\n",
     "# subcircuit_instance_probabilities = evaluate_subcircuits(cuts,\n",
-    "#                                                          service_args=service.active_account(),\n",
+    "#                                                          service=service,\n",
     "#                                                          backend_names=backend_names,\n",
     "#                                                          options=options,\n",
     "#                                                         )"
diff --git a/_sources/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb.txt b/_sources/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb.txt
index 425b8b458..4e4ea5c5d 100644
--- a/_sources/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb.txt
+++ b/_sources/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb.txt
@@ -18,7 +18,7 @@
    "outputs": [],
    "source": [
     "from qiskit import QuantumCircuit\n",
-    "from qiskit.quantum_info import PauliList\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
     "from circuit_knitting.cutting import (\n",
     "    partition_problem,\n",
@@ -44,9 +44,9 @@
     "circuit = QuantumCircuit(2)\n",
     "circuit.h(0)\n",
     "circuit.cx(0, 1)\n",
-    "observables = PauliList([\"ZZ\"])\n",
+    "observable = SparsePauliOp([\"ZZ\"])\n",
     "partitioned_problem = partition_problem(\n",
-    "    circuit=circuit, partition_labels=\"AB\", observables=observables\n",
+    "    circuit=circuit, partition_labels=\"AB\", observables=observable.paulis\n",
     ")\n",
     "subcircuits = partitioned_problem.subcircuits\n",
     "subobservables = partitioned_problem.subobservables"
@@ -132,7 +132,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,
diff --git a/_sources/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb.txt b/_sources/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb.txt
index 72d59cd6a..b1976bfa2 100644
--- a/_sources/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb.txt
+++ b/_sources/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb.txt
@@ -21,7 +21,7 @@
    "source": [
     "import numpy as np\n",
     "from qiskit.circuit.library import EfficientSU2\n",
-    "from qiskit.quantum_info import PauliList\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
     "from circuit_knitting.cutting import (\n",
     "    partition_problem,\n",
@@ -37,7 +37,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 831.997x294.311 with 1 Axes>"
       ]
@@ -50,7 +50,7 @@
    "source": [
     "circuit = EfficientSU2(4, entanglement=\"linear\", reps=2).decompose()\n",
     "circuit.assign_parameters([0.8] * len(circuit.parameters), inplace=True)\n",
-    "observables = PauliList([\"ZZZZ\"])\n",
+    "observable = SparsePauliOp([\"ZZZZ\"])\n",
     "circuit.draw(\"mpl\", scale=0.8)"
    ]
   },
@@ -70,7 +70,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 684.861x120.4 with 1 Axes>"
       ]
@@ -82,7 +82,7 @@
    ],
    "source": [
     "partitioned_problem = partition_problem(\n",
-    "    circuit=circuit, partition_labels=\"AABB\", observables=observables\n",
+    "    circuit=circuit, partition_labels=\"AABB\", observables=observable.paulis\n",
     ")\n",
     "subcircuits = partitioned_problem.subcircuits\n",
     "bases = partitioned_problem.bases\n",
@@ -98,7 +98,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAABwCAYAAADMr3DBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAbpElEQVR4nO3dfVxUdd7/8dfAMNyrgGlomkauut6URWhea1qL1uYvTcu8aVW0LVHLlm7sfvutpnV1lbmti6gba3tphRVtWluZlfmzLNNi8wasEFG8qUUBZbgZYOb3BysuK+CAM3POwPv5ePjoMTNnzvlA5334zPec8x2Ly+VyISIiImKQAKMLEBERkbZNzYiIiIgYSs2IiIiIGErNiIiIiBhKzYiIiIgYSs2IiIiIGErNiIiIiBhKzYiIiIgYSs2IiIiIGErNiIiIiBhKzYiIiIgYSs2IiIiIGErNiIiIiBhKzYiIiIgYSs2IiIiIGErNiIiIiBhKzYiIiIgYSs2IiIiIGErNiIiIiBhKzYiIiIgYSs2IiIiIGErNiIiIiBhKzYiIiIgYSs2IiIiIGErNiPiV4uJinnvuOZ9u86mnnuLSSy8lLi6OhQsX+nTbIg3xdQ527drFL37xC8LDw5k0aZLPtitth5oR8Su+Pghv2bKFN998k127drF7925ef/11tmzZ4rPtizTE1zno2LEjzz//PEuWLPHZNqVtsRpdgL+oLDqFo7Tc69uxRYQSHBXZrPeUOMBe7aWC/iXcCu1t3ln31q1bmT9/Pna7HYvFQmpqKuPHj+fYsWMA5OTkcMMNN3DgwAHmzp3L8ePHufzyy4mKiuKTTz5pcJ0ul4unnnqKjIwMAgMD6dSpEx9++CELFixg3759rF27lpKSEuLj40lPT2fYsGENricjI4Pp06cTGhoKQFJSEhkZGVxzzTXe+WWYnC9y0BYzAObOQWxsLLGxsWRnZ3vt5/cXyoB31q1mxA2VRad4PWE21aUVXt+WNSKECduXu70jljjgpk1Q5uWdMMwKGxI9vyMeP36cCRMmsGHDBuLj46murmbPnj2NLv+nP/2JIUOGkJWV1eR6V69ezaZNm/jyyy8JDw+nsLAQgMcff5zExETS09N57733mD59eqMHYICDBw+SmJhY97hHjx5s3ry5WT9ja+GrHLS1DID5cyC1lAHvZUDNiBscpeU+aUQAqksrcJSWu70T2qu9vwNC7Tbs1Z7fCbdt28aAAQOIj48HwGq10r59+/Ne77vvvsusWbMIDw8HaoeZAQICAli7di2XXXYZ/fv359FHHz3vbbUVvspBW8sAKAf+QhnwXgZ0zYiYjtVqxel01j2uqPBs+HNzcwkKCuL48eNUVlY2uWz37t3Jz8+ve3zgwAG6devm0XpEGmKmHIh4m5oRMdTQoUPZtWsXO3bsAKC6uprIyEjKy8vZv38/AK+88krd8u3atcNut1NTU9PkekePHs2KFSuw2+0AdcPTJ06cYNq0aWRmZjJy5Eh++9vfNrmeCRMm8PLLL1NeXk55eTmrV6/mtttua+mPK9Igs+dAxNt0msZDbt2eSnWFA6ejmgCblT1pG/j+lY+MLotdd/YgwBaKxWrDVe2g89j76TjqN0aXVSc6Opp169Zx9913U1ZWRmBgIKmpqSxZsoTExEQ6duzI9ddfX2/5iRMnMmDAADp37tzohXtJSUkcOnSIhIQEgoKC6NSpExs3bmTGjBkkJyczePBgrrjiCoYOHcrrr7/OhAkTGlzPiBEjuPnmm+nfvz8ul4tp06YxfPhwr/wu/J1ZMwDKwfnmoKCggCFDhlBWVkZFRQUXXXQRzzzzDL/+9a+98vvwV8pAy1lcLpfL6CLM7tShn3gzYU6Ty9y6PZUPpzxFyQ9H6NC7GzdtfJY3EuZQ/mNRs7d3y/ZUIrt1cmvZI2UwZlPjr++6swe9nnyfkIv6UJ6/m+z7rqD/ygPYYro0u671idAlrNlvk1biXDkwawbAczlQBto2ZcB7GfC7kZHMzEyWL1/Ozp07KSoqIi8vjx49ehhdVj3F+w7hKLETHhvD4EV3sOP3f6X00E/0mnwdgcE2cla/b0hdoRf3JzA8iqrjBZTseIcTW2qHfZ3lp7BYbfR5dpshdZ2Pr776qsnXm/q0d9VVV3mjJMG8GYDWlwNlwJyUgebxu2bEbrczbNgwxo8fz5w5TY9WGKVTQh8qi0s5sfcAWc+t4/IHbuPzB9O4ZPwwNk5+yrC6SvduxRoZTWjPywj/WQIXXH8XAPmpyXRIGGNYXedj2rRpZ503Hzx4MPPmzQPgjTfeaPRAfNpPP/3EqFGjznp++vTppKSkeK5YLygsquDwT3Y6x4RyYUfzfGQ3awag7eXAnQyA/+bgZKmDvMOniGoXTPfYCKPLqaMMNI8pm5Gvv/6a5ORkvv32WwYMGMA999xDcnIypaWlTJ06FaidAMhsrk2fj8ViIbLnhWyZvRSno5rinIO4nE4GL7qD79ZswlXd9AVn3pD79DhcTieVx36g5/2vEhAUXPdaafZn1NiLaR9/o8/r8oS//vWv572OTp06nXO+BrM5VljG3EWf8/YnB6lxurAAo4Z2JfXxoVxyUTvD6jJrBkA5OBd/y0FpWRX3/8+XrF7/PY6q2ruOBg+4gGWPXk18vwsMq0sZaBnT3U2TlZXF8OHDmTx5MtnZ2SQlJTF37lz69etHQIDpyq3nk5nP8tawe/k0eSlDn59NSMfaeQKynl9Hp6v6kPf2Z4bUFffIW/Rfvo9LHniN/GW/oar4RwBc1VUUrJ5PtzteMKQuX2jok56/Kz5ZydW/3sCGTw9R46y95MsFfPTlEYbcvoEjP9kNq82sGYC2m4PWmIHqaiej7nqfv7x9phEB+GpPIcOmv8s/9h03rDZloGVM99f99ChISkoKPXv2ZO7cuXTp0oWBAwcaXZrb8t/ZxpHN/2DgvHEA2AsKKTtSaHBVEPVfE2g36HqOvfE0AMcynyVmxFSComMNrsx7br31VqNL8LhVb+7jyD/LqKp21nu+usZFSamDF9Y0PnOnr5g1A9D2ctAaM7Dh04N8ufufZ2XA6XRRVe3k8T/uNKiyM5SB5jHVaZrc3Fy2bt3K2rVr6z1vs9ma1YysW7eOF154AZfLxbx585gyZUqTy48ZM4bc3NxGX29XE8QsLnV7+wA7F6/lpg/+m13L/kb5T8XNeu+okaM4GVjl1rKWDl0IeehDt9fdddrTZN93JR2uvoVTuz6m14JzXIJdr66RuIqPuL28L4wePbrJ19PT05k5c2aDryUlJXmhIu/LDZiKw9LwMLSjysnSv2zj7y/N8Ph2m5sDs2YAWp4DZcAcCiyjcVp6geXsz9M1ThfvfHqQvv0GEoBnT4coA+5lIC4ujvXr1zerHlM1I1lZWXTo0IHu3bvXPXd60h93m5GSkhKefPJJvvrqKywWCwkJCdxwww1ER0d7q2wA3viP271OHTjGK72ne3Wb7hiw6kC9xyGxlzLo1RLyl8/GUVjAd49fC4A1Ipq4RzINqFCay0nQOV43JtZmzQAoB62N0xLUYCNSx2LBRSB4uBk5F2Wg5Uw1z0hmZiZJSUkUFxfXXR+ydOlSUlJSKCwsJCYmpm7ZnJwc+vbte9atvRs3biQjI4OXXnoJgNmzZ3PjjTdy0003tbgud+YZ8SRP31/uKWacY+FctzVOnDiRjIyMBl/z19sakx7/lFf+vv+sIWqAwAALvxzShQ/SbvD4dn2ZA2XAfW0xA4tXZfH7tG/qXS/y73p2jSD377dhsVg8ul1lwHsZMNU1I/Hx8ZSXl7Nw4ULy8vJYtWoVixcvJjY2tq4ROXHiBFlZWXV30+zdu5esrCxKS0uB2m+/jIqKqltndHR03RTI0vY0dhD2Z/fe3o+amoYPwk6Xi/lJ/nN9lXhfa8zAHeN7ExhooaFeIzDAwsN3XObxRkS8y1TNSPfu3Vm2bBlpaWkMGjSIHTt2MGnSpHqnaNavX8+gQYMYN672oqDRo0fXLQu1zUdR0ZnZ7oqKiuq+qVLaniVLlhhdgscN6tuRNU+PIMgagC2oNsIWICAAXnz4an45pPmz60rr1Roz0DkmlPdSryciNIgg65kMAMy7/efceUtv44qTFjFVMwIwa9Ysjh49SnFxMStWrCAvL69eM5KUlITL5Trr34gRIwAYMmQIX3zxBWVlZZSXl7NlyxaGDh1q0E8jRvvyyy+NLsErJt8Yx+FNk3jmt1cR3d7GBdEhHPxgEndP/rnRpYnJtNYMDI+P5cjHk1n26NVEt7cRExXMvvW3suTBIRoV8UOmuoC1Ibt372bixIluL9++fXueeOIJrrvuOiwWCw8//HC9a028JSw2misfvR2A3anrKcqu/dr5hIUzsYbaCLAGsjUlFQy4RMdRWMDh/30EgM7jHiSsR21zd3DlPFyOclw1VVx8TzoWk8/jIvVdEB1KytT+/DlzHwBdO4cbWo8yIL4WERbEXbf24Q9ra29n/1mP9obWowy0nKmTZ7fbyc/Pb/YcI5MmTeKLL75g27ZtPvtWyV6TruObZzP4/MEV9J42su75AGsAnz+QRkXRKaxhwU2swXsKN6XTZcoCLp6zksL308684Kzm4rtXERgZg7PCuImyvGnGDM/f4ioNUwbMSRnwHWWg5Uw9MhIeHo7T2fCFemYTFhuN/UghrhongcG2uuer7BUkrn2MsqPHqbZXGFJb1YnD2Dp2wxJoxek4U0NASATf//5XBEV3JTAs0pDavK1bt25Gl9BmKAPmpAz4jjLQcqYeGfEnZUdPEB4bQ2BwEDWVDgCCoyMJDA5i0+2LqLJXEBQRakhtQdFdcRQW4HRUEGALAaD6ZCGuqkp6PfkegaGR1JSdNKQ2b1uwYIHRJbQZyoA5KQO+owy0nKlHRvzJ9699zBUPTwGXiz0r3yFh4Uy2P5GONSyEIc/ciTU0mCqDOuKOiTM5vOYxLBYLncakcHDlPLrd+QdqKkrJXz4bZ2UZASHm+bZL8U/KgLR1ykDLqRnxkLKjJ9h677K6x9ufSAfg8/uXG1VSHVvHi+j525frHne/60UAetzzklEl+UzXrl2NLqHNUAbMSRnwHWWg5XSaRlq11jjHgkhzKAPiD9SMSKt23333GV2CiKGUAfEHakbcYIsIxRoR4pNtWSNCsDXjAqdwK4T54GRbmLV2W/7m8OHDRpfQavgqB8qAZykDnqMMeC8Dfhgt3wuOimTC9uU4Ssu9vi1bRCjBUe7fXtXeBhsSwV7txaKo3QHb2869nLRevsqBMiBmpQx4LwNqRtwUHBXZrJ3Dl9rbdJBszO9+9zujS2hVzJoDZaBxyoBnKQPeodM00qodOnTI6BJEDKUMiD9QMyKt2l/+8hejSxAxlDIg/kDNiIiIiBhKzYi0aoMHDza6BBFDKQPiDywulwHfZSwiHtNv3JsA7HnrFoMrETGGMuD/NDIiIiIihlIzIiIiIoZSMyIiIiKGUjMiIiIihtIMrG6qLDplyungAUoc/j0NsPgPX+RAGRAzUwa8s241I26oLDrF6wmzqS6t8Pq2rBEhTNi+3O0dscQBN22CMi/vhGHW2u8+0MG47fJVDpQBMStlwHsZ0GkaNzhKy33SiABUl1Y0q+u2V3t/B4TabXi76xZz81UOlAExK2XAexlQMyIiIiKG0mkaET/jcrnYvuufvPTWd+wvOMX+glMEWCwkPb6FpLG9GB5/IRaLxegyRbwqJ6+YFa/nsCe36F8ZgEnzP2bKr+IYfU03AgP1WdufqBkR8RNOp4u/bvieP76yl6+zjwNgscDpOZRfXv89L6//nr6XdGDupL7MurUPVqsOyNK6bNh8kKVrdvPx9qNA/QxkvJ9Hxvt5dLswnOQJfZh3ez8iwoIMrFbcpWbEQ27dnkp1hQOno5oAm5U9aRv4/pWPjC6LXXf2IMAWisVqw1XtoPPY++k46jdGlyXNVOmoYcYTW3j1vf38+6BHQ1/mkJNXzN2Lt/HulkOse+46nx2MzZoBUA5aA6fTxWN/3MEzL31b7/mGMlDwo53H/riTNz48wLt/GkXsBWE+qVEZaDk1Ix70ycxnKfnhCB16d+Omjc9S8NHXlP9YZHRZxD3yFiEX9aE8fzfZ911BuytvxBbTxeiyxE01NU6mPLSZzI8OAA0ffP/d6dff21rATXdv5P20Gwi2BXq3yH8xawZAOfB3D73wFc+9vMutZU9n4Juc44yY+S7b1owhun2wF6s7QxloGb8bw83MzGTkyJFER0djsVg4cOCA0SWdpXjfIRwldsJjYxjx5weI6NYJgF6Tr6NP0g2G1RV6cX8Cw6OoOl7APz9Yyb7HRrDvsRFk33clOfOvNqwuadpTK7PqGpGGvP1iIm+/mNjga5t3HOO+//nSS5U1zqwZAOXAH61994cmG5GmMvBd/kkmz//EW6U1ShloHr9rRux2O8OGDWPRokVGl9KoTgl9qCwu5cTeA2Q9t47LH7iNAJuVS8YPY9+aDw2rq3TvVqyR0YT2vIwLrr+L3os203vRZsIuvYrY254wrC5pXFl5NUvX7GlymbiL2hF3UbtGX/9z5j4Ki3xza/ppZs0AKAf+xuVy8cxL39LUNdnnysDGbYfJyjnuheoapww0jylP03z99dckJyfz7bffMmDAAO655x6Sk5MpLS1l6tSpAOTk5Bhc5dmuTZ+PxWIhsueFbJm9FKejmuKcg7icTgYvuoPv1mzCVV3j87pynx6Hy+mk8tgP9Lz/VQKCzgxXlmZ/Ro29mPbxN/q8Ljm3jA/2U3zKcV7rcFQ5SX/rO+bPHOihqhpn1gyAcuCvPvvmR3b/cP6nOVIzsln55C88UFHTlIGWMd3ISFZWFsOHD2fy5MlkZ2eTlJTE3Llz6devHwEBpiu3nk9mPstbw+7l0+SlDH1+NiEd2wOQ9fw6Ol3Vh7y3PzOkrrhH3qL/8n1c8sBr5C/7DVXFPwLgqq6iYPV8ut3xgiF1ybmlvpbd5CdCd1gstQdip/McF5t4gFkzAMqBv0rNyPbIeta88wMl59nYu0MZaBnT/XU/PQqSkpJCz549mTt3Ll26dGHgQO9/qvOU/He2cWTzPxg4bxwA9oJCyo4UGlwVRP3XBNoNup5jbzwNwLHMZ4kZMZWg6FiDK5OGOJ0udmYXnvOC1XNxuSD/aCknSio9U5gbzJoBUA78zY49ntlvyitryN5f7JF1uUMZaB5TnabJzc1l69atrF27tt7zNputWc3I2LFj2b59O8OHD+e111475/JjxowhNze30dfb1QQxi0vd3j7AzsVruemD/2bXsr9R/lNxs947auQoTgZWubWspUMXQh5y//xj12lPk33flXS4+hZO7fqYXgs2NaOukbiKj7i9vJyfGmy4Au+u99zbLyaedW48rlvt492Z4+s9n1twkrHzzvz/vfoXv8RGcYvraW4OzJoBaHkOlAHf2x+QDJYzt+aeTwZumzKDSA60uBZlwL0MxMXFsX79+mbVY6pmJCsriw4dOtC9e/e658rLy9m/f3+zmpFly5aRm5tLWlqaN8ps0BsJc+o9PnXgGK/0nu6z7TdmwKoD9R6HxF7KoFdLyF8+G0dhAd89fi0A1oho4h7JNKBCaYwFp4fX6N3z1GbNACgH/syTObAoA3XMlgGLy3W+g8Cek5mZSVJSEsXFxXXXhyxdupSUlBQKCwuJiYmpWzYnJ4e+ffuSl5dHjx49zlrX5s2bSUtLc2tk5FxOHfqJN/9jJ/OmW7anEvmv28DO5UgZjHF/cOO8rE+ELr6ZO0iovYsgLOFlKiqbPoCe/jTYf3zTB5CT26YSGd7yr9v0ZQ6UATntslsz2fVdEU39oXI3A9+su5nL+8Q0uUxTlAHvZcBU14zEx8dTXl7OwoULycvLY9WqVSxevJjY2Ni6RuTEiRNkZWXV3U2zd+9esrKyKC0tNbJ0EY+zWCyM/2UPj6xr5NVdzqsRETHKuOt6NNmIuMNigZ5dIxj4s2iP1CSeZ6pmpHv37ixbtoy0tDQGDRrEjh07mDRpUr1TNOvXr2fQoEGMG1d7UdDo0aPrlhVpbeZM7Guq9Yj42p239CYw4PxuKXO5YM7EnxNwnusR7zFVMwIwa9Ysjh49SnFxMStWrCAvL69eM5KUlITL5Trr34gRI4wrWsRLhl7eiQG9olp8e6/FAl07hfF/rul+7oVFTKhr53DGXnvxea3DFhTAjJt7eagi8QZTXcDakN27dzNx4sRmvWfevHl89tlnHD58mMTERFasWEFcXJyXKqwVFhvNlY/eDsDu1PUUZecDkLBwJtZQGwHWQLampJ77i0W8wFFYwOH/fQSAzuMeJKxHbXN3cOU8XI5yXDVVXHxPOhaTz+PSFlksFl54cAg3zH6fGqerwd0nt+Bkw++ldndb8uBgn3x7rzIg3rLw7iv4cNthSsurmpWB034/5wpiOoR4qbozlIGWM3Xy7HY7+fn5zZ5j5MUXX2Tnzp0cO3aMTZs2eb0RAeg16Tq+eTaDzx9cQe9pI+ueD7AG8PkDaVQUncIa5psvavpPhZvS6TJlARfPWUnh+/92h5GzmovvXkVgZAzOCrshtcm5/XJIF/78f4cBNDhCMnbepnq3L55ezgU8/0ACt11/iQ+qVAbEe34eF8Xf/pBIsC3Q7QycNmdiXx7ywezDoAycD1M3I+Hh4TidTr+Y8CwsNhr7kUJqKhwEBp+5ULDKXkHi2sewRYZRbfft94PU1XDiMLaO3QgIDsXpOFNDQEgE3//+V9TYSwgMizSkNnHP9LG9eGtpIqHBtYOZjZ22Of28NTCA1Quv4b5pA3xUoTIg3nXd4C58mj6aC6JqRzgazcDp/1pqR0SWPXo1lvOdxthNykDLmboZ8SdlR08QHhtDYHAQNZW1Uw4HR0cSGBzEptsXUWWvICgi1JDagqK74igswOmoIMBWG+Tqk4W4qirp9eR7BIZGUlPW9DCnGG/stRdTsGkSSx4cXDfJ03+6qHM4i+fFc+jDSUwf69tz5MqAeFvCgAvIe28i6QuGcUUjt+hGtQ/moZkDyX33Nn6XPMhnjQgoA+fD9NeM+IvvX/uYKx6eAi4Xe1a+Q8LCmWx/Ih1rWAhDnrkTa2gwVQZ1xB0TZ3J4zWNYLBY6jUnh4Mp5dLvzD9RUlJK/fDbOyjICQiIMqU2aJ6pdMClT+3Pv7f34f18fI+/wKU6WVhERFkT32HCuvSqWwEBjPmMoA+ILYaFWZtz8M2bc/DN27i1kzw9FlJQ6CA22cmHHUBKHdCEk2Jg/bcpAy5lq0jOz0qRntTThU9umCZ+UgbZOGWgjk56JiIhI26NmxA22iFCsEd6/LQzAGhGCrRnnFMOtEOaDEckwa+22pO3yVQ6UATErZcB7GdBpGjdVFp3CUVru9e3YIkIJjmreFc0lDrBXe6mgfwm3QnvNJt7m+SIHyoCYmTLgnXWrGRERERFD6TSNiIiIGErNiIiIiBhKzYiIiIgYSs2IiIiIGErNiIiIiBhKzYiIiIgYSs2IiIiIGErNiIiIiBhKzYiIiIgYSs2IiIiIGErNiIiIiBhKzYiIiIgYSs2IiIiIGErNiIiIiBhKzYiIiIgYSs2IiIiIGErNiIiIiBhKzYiIiIgY6v8DGYHTsZL4cNsAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 684.982x120.4 with 1 Axes>"
       ]
@@ -342,7 +342,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,
diff --git a/_sources/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb.txt b/_sources/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb.txt
index f91e143ab..380971d51 100644
--- a/_sources/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb.txt
+++ b/_sources/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb.txt
@@ -19,8 +19,8 @@
    "source": [
     "import numpy as np\n",
     "from qiskit import QuantumCircuit\n",
-    "from qiskit.quantum_info import PauliList\n",
-    "from qiskit_aer.primitives import Estimator, Sampler\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "from qiskit_aer.primitives import EstimatorV2, SamplerV2\n",
     "\n",
     "from circuit_knitting.cutting import (\n",
     "    partition_problem,\n",
@@ -50,7 +50,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1290.83x618.722 with 1 Axes>"
       ]
@@ -96,7 +96,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 956.385x618.722 with 1 Axes>"
       ]
@@ -115,9 +115,9 @@
    "id": "dcd8dea0",
    "metadata": {},
    "source": [
-    "### Specify some observables\n",
+    "### Specify an observable\n",
     "\n",
-    "These observables have 7 qubits, just like the original circuit."
+    "This observable has 7 qubits, just like the original circuit."
    ]
   },
   {
@@ -127,7 +127,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "observables_0 = PauliList([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])"
+    "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])"
    ]
   },
   {
@@ -150,7 +150,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1290.83x785.944 with 1 Axes>"
       ]
@@ -170,9 +170,9 @@
    "id": "c22b9572",
    "metadata": {},
    "source": [
-    "### Update the observables\n",
+    "### Update the observable terms to account for the extra qubit\n",
     "\n",
-    "The transformed circuit contains additional qubits (one for each `CutWire` instruction), so the observables must be updated for the new circuit.  This can be done using the `expand_observables` function.\n",
+    "The transformed circuit contains additional qubits (one for each `CutWire` instruction), so the observables must be updated for the new circuit.  This can be done using the `expand_observables` function. Since the observable coefficients are ignored until reconstruction of the final expectation value, the input and output types of the observables for ``expand_observables`` are ``PauliList``s.\n",
     "\n",
     "The resulting observables have 9 qubits, just like the transformed circuit."
    ]
@@ -195,8 +195,8 @@
     }
    ],
    "source": [
-    "observables_1 = expand_observables(observables_0, qc_0, qc_1)\n",
-    "observables_1"
+    "observable_expanded_paulis = expand_observables(observable.paulis, qc_0, qc_1)\n",
+    "observable_expanded_paulis"
    ]
   },
   {
@@ -216,7 +216,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "partitioned_problem = partition_problem(circuit=qc_1, observables=observables_1)\n",
+    "partitioned_problem = partition_problem(\n",
+    "    circuit=qc_1, observables=observable_expanded_paulis\n",
+    ")\n",
     "subcircuits = partitioned_problem.subcircuits\n",
     "subobservables = partitioned_problem.subobservables"
    ]
@@ -269,9 +271,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 974.414x451.5 with 1 Axes>"
+       "<Figure size 974.616x451.5 with 1 Axes>"
       ]
      },
      "execution_count": 9,
@@ -291,9 +293,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 723.783x367.889 with 1 Axes>"
+       "<Figure size 723.984x367.889 with 1 Axes>"
       ]
      },
      "execution_count": 10,
@@ -334,9 +336,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sampler = Sampler(run_options={\"shots\": 2**12})\n",
+    "sampler = SamplerV2()\n",
     "results = {\n",
-    "    label: sampler.run(subexperiment).result()\n",
+    "    label: sampler.run(subexperiment, shots=2**12).result()\n",
     "    for label, subexperiment in subexperiments.items()\n",
     "}"
    ]
@@ -352,7 +354,8 @@
     "    results,\n",
     "    coefficients,\n",
     "    subobservables,\n",
-    ")"
+    ")\n",
+    "final_expval = np.dot(reconstructed_expvals, observable.coeffs)"
    ]
   },
   {
@@ -365,29 +368,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reconstructed expectation values: [0.17901284, 0.70971423, 0.68885177]\n",
-      "Exact expectation values: [0.1767767, 0.70710678, 0.70710678]\n",
-      "Errors in estimation: [0.00223614, 0.00260745, -0.01825501]\n",
-      "Relative errors in estimation: [0.01264952, 0.00368749, -0.02581648]\n"
+      "Reconstructed expectation value: 1.56514591\n",
+      "Exact expectation value: 1.59099026\n",
+      "Error in estimation: -0.02584435\n",
+      "Relative error in estimation: -0.01624419\n"
      ]
     }
    ],
    "source": [
-    "estimator = Estimator(run_options={\"shots\": None}, approximation=True)\n",
-    "exact_expvals = (\n",
-    "    estimator.run([qc_0] * len(observables_0), list(observables_0)).result().values\n",
-    ")\n",
-    "print(\n",
-    "    f\"Reconstructed expectation values: {[np.round(reconstructed_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Exact expectation values: {[np.round(exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Errors in estimation: {[np.round(reconstructed_expvals[i]-exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
+    "estimator = EstimatorV2()\n",
+    "exact_expval = (\n",
+    "    estimator.run([(qc_0.decompose(\"cut_wire\"), observable)]).result()[0].data.evs\n",
     ")\n",
+    "print(f\"Reconstructed expectation value: {np.real(np.round(final_expval, 8))}\")\n",
+    "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n",
+    "print(f\"Error in estimation: {np.real(np.round(final_expval-exact_expval, 8))}\")\n",
     "print(\n",
-    "    f\"Relative errors in estimation: {[np.round((reconstructed_expvals[i]-exact_expvals[i]) / exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
+    "    f\"Relative error in estimation: {np.real(np.round((final_expval-exact_expval) / exact_expval, 8))}\"\n",
     ")"
    ]
   }
@@ -408,7 +405,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,
diff --git a/_sources/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb.txt b/_sources/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb.txt
index 7b0d38c4f..b06524fc0 100644
--- a/_sources/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb.txt
+++ b/_sources/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb.txt
@@ -7,7 +7,7 @@
    "source": [
     "## Gate Cutting to Reduce Circuit Width\n",
     "\n",
-    "In this tutorial we will simulate expectation values of a four-qubit circuit using only two-qubit experiments by cutting gates in the circuit.\n",
+    "In this tutorial we will simulate the expectation value of a four-qubit circuit using only two-qubit experiments by cutting gates in the circuit.\n",
     "\n",
     "Like any circuit knitting technique, gate cutting can be described as three consecutive steps:\n",
     "\n",
@@ -32,7 +32,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 831.997x294.311 with 1 Axes>"
       ]
@@ -56,9 +56,7 @@
    "id": "8638fdf1",
    "metadata": {},
    "source": [
-    "### Specify some observables\n",
-    "\n",
-    "Currently, only `Pauli` observables with phase equal to 1 are supported. Full support for `SparsePauliOp` is expected in CKT v0.7.0."
+    "### Specify an observable"
    ]
   },
   {
@@ -68,9 +66,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit.quantum_info import PauliList\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
-    "observables = PauliList([\"ZZII\", \"IZZI\", \"IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])"
+    "observable = SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])"
    ]
   },
   {
@@ -78,9 +76,11 @@
    "id": "162a5629",
    "metadata": {},
    "source": [
-    "### Separate the circuit and observables according to a specified qubit partitioning\n",
+    "### Separate the circuit and observable according to a specified qubit partitioning\n",
+    "\n",
+    "Each label in `partition_labels` corresponds to the `circuit` qubit in the same index. Qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut.\n",
     "\n",
-    "Each label in `partition_labels` corresponds to the `circuit` qubit in the same index. Qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut."
+    "**Note:** The ``observables`` kwarg to `partition_problem` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value."
    ]
   },
   {
@@ -93,7 +93,7 @@
     "from circuit_knitting.cutting import partition_problem\n",
     "\n",
     "partitioned_problem = partition_problem(\n",
-    "    circuit=qc, partition_labels=\"AABB\", observables=observables\n",
+    "    circuit=qc, partition_labels=\"AABB\", observables=observable.paulis\n",
     ")\n",
     "subcircuits = partitioned_problem.subcircuits\n",
     "subobservables = partitioned_problem.subobservables\n",
@@ -138,7 +138,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 913.147x160.533 with 1 Axes>"
       ]
@@ -160,7 +160,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 913.309x160.533 with 1 Axes>"
       ]
@@ -241,68 +241,91 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d8870454-2173-4454-90b4-a034779510e0",
+   "id": "444b0038-b3d5-469c-85b2-4c1d9d8fce05",
    "metadata": {},
    "source": [
-    "### Run the subexperiments using the Qiskit Sampler primitive"
+    "### Choose a backend\n",
+    "\n",
+    "Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "7430eef1",
+   "id": "36c89aa0-70aa-4615-8198-77ec85e8aa93",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit_aer.primitives import Sampler\n",
-    "\n",
-    "# Set up a Qiskit Aer Sampler primitive for each circuit partition\n",
-    "samplers = {\n",
-    "    label: Sampler(run_options={\"shots\": 2**12}) for label in subexperiments.keys()\n",
-    "}\n",
+    "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n",
     "\n",
-    "# Retrieve results from each partition's subexperiments\n",
-    "results = {\n",
-    "    label: sampler.run(subexperiments[label]).result()\n",
-    "    for label, sampler in samplers.items()\n",
-    "}"
+    "backend = FakeManilaV2()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "68eb0522-8c01-4f56-aacc-0bfc60159896",
+   "id": "85a1d76a-5c47-4210-a742-9b22f02f7200",
    "metadata": {},
    "source": [
-    "To use the Qiskit Runtime Sampler, replace the code above with this commented block."
+    "### Prepare the subexperiments for the backend\n",
+    "\n",
+    "We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "3c9a7e78-4bf9-4dbd-a8f9-3db185591d9c",
+   "id": "184e0f36-1279-48a3-aab7-b7603bb71f66",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# from qiskit_ibm_runtime import Session, Options, Sampler, QiskitRuntimeService\n",
-    "# from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
-    "#\n",
-    "# service = QiskitRuntimeService()\n",
-    "# backend = service.least_busy(operational=True, simulator=False)\n",
-    "#\n",
-    "# # Prepare transpiler for target backend\n",
-    "# pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
-    "#\n",
-    "# with Session(backend=backend) as session:\n",
-    "#     # Set up Qiskit Runtime Sampler primitives.\n",
-    "#     samplers = {\n",
-    "#         label: Sampler(Options(execution={\"shots\": 2**12})) for label in subexperiments.keys()\n",
-    "#     }\n",
-    "#\n",
-    "#     # Retrieve results from each subexperiment\n",
-    "#     results = {\n",
-    "#         label: sampler.run(pass_manager.run(subexperiments[label])).result()\n",
-    "#         for label, sampler in samplers.items()\n",
-    "#     }"
+    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
+    "\n",
+    "# Transpile the subexperiments to ISA circuits\n",
+    "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
+    "isa_subexperiments = {\n",
+    "    label: pass_manager.run(partition_subexpts)\n",
+    "    for label, partition_subexpts in subexperiments.items()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8870454-2173-4454-90b4-a034779510e0",
+   "metadata": {},
+   "source": [
+    "### Run the subexperiments using the Qiskit Runtime Sampler primitive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3bc1c07b-b286-4a5f-8b0a-67efd4a6309e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/garrison/Qiskit/qiskit-ibm-runtime/qiskit_ibm_runtime/session.py:157: UserWarning: Session is not supported in local testing mode or when using a simulator.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit_ibm_runtime import SamplerV2, Batch\n",
+    "\n",
+    "# Set up a Qiskit Runtime Sampler primitive for each circuit partition\n",
+    "samplers = {label: SamplerV2(backend=backend) for label in subexperiments.keys()}\n",
+    "\n",
+    "# Submit each partition's subexperiments as a single batch\n",
+    "with Batch(backend=backend):\n",
+    "    jobs = {\n",
+    "        label: sampler.run(isa_subexperiments[label], shots=2**12)\n",
+    "        for label, sampler in samplers.items()\n",
+    "    }\n",
+    "\n",
+    "# Retrive results\n",
+    "results = {label: job.result() for label, job in jobs.items()}"
    ]
   },
   {
@@ -312,23 +335,27 @@
    "source": [
     "### Reconstruct the expectation values\n",
     "\n",
-    "Use the subexperiment results, subobservables, and sampling coefficients to reconstruct the expectation value of the original circuit."
+    "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "7d57339c",
    "metadata": {},
    "outputs": [],
    "source": [
     "from circuit_knitting.cutting import reconstruct_expectation_values\n",
     "\n",
+    "# Get expectation values for each observable term\n",
     "reconstructed_expvals = reconstruct_expectation_values(\n",
     "    results,\n",
     "    coefficients,\n",
     "    subobservables,\n",
-    ")"
+    ")\n",
+    "\n",
+    "# Reconstruct final expectation value\n",
+    "final_expval = np.dot(reconstructed_expvals, observable.coeffs)"
    ]
   },
   {
@@ -336,12 +363,12 @@
    "id": "53beaca3",
    "metadata": {},
    "source": [
-    "### Compare the reconstructed expectation values with the exact expectation values from the original circuit"
+    "### Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "e3385ba5",
    "metadata": {},
    "outputs": [
@@ -349,31 +376,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reconstructed expectation values: [0.37551016, 0.52859873, 0.58741736, 0.12230408, 0.29614246, -0.1243059]\n",
-      "Exact expectation values: [0.36916216, 0.52511814, 0.59161991, 0.10927476, 0.28001606, -0.12940509]\n",
-      "Errors in estimation: [0.00634799, 0.00348059, -0.00420255, 0.01302933, 0.01612639, 0.00509918]\n",
-      "Relative errors in estimation: [0.01719568, 0.0066282, -0.00710346, 0.11923456, 0.05759096, -0.0394048]\n"
+      "Reconstructed expectation value: 0.6991539\n",
+      "Exact expectation value: 0.56254612\n",
+      "Error in estimation: 0.13660778\n",
+      "Relative error in estimation: 0.24283836\n"
      ]
     }
    ],
    "source": [
-    "from qiskit_aer.primitives import Estimator\n",
+    "from qiskit_aer.primitives import EstimatorV2\n",
     "\n",
-    "estimator = Estimator(run_options={\"shots\": None}, approximation=True)\n",
-    "exact_expvals = (\n",
-    "    estimator.run([qc] * len(observables), list(observables)).result().values\n",
-    ")\n",
-    "print(\n",
-    "    f\"Reconstructed expectation values: {[np.round(reconstructed_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Exact expectation values: {[np.round(exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Errors in estimation: {[np.round(reconstructed_expvals[i]-exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
+    "estimator = EstimatorV2()\n",
+    "exact_expval = estimator.run([(qc, observable)]).result()[0].data.evs\n",
+    "print(f\"Reconstructed expectation value: {np.real(np.round(final_expval, 8))}\")\n",
+    "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n",
+    "print(f\"Error in estimation: {np.real(np.round(final_expval-exact_expval, 8))}\")\n",
     "print(\n",
-    "    f\"Relative errors in estimation: {[np.round((reconstructed_expvals[i]-exact_expvals[i]) / exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
+    "    f\"Relative error in estimation: {np.real(np.round((final_expval-exact_expval) / exact_expval, 8))}\"\n",
     ")"
    ]
   }
@@ -394,7 +413,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,
diff --git a/_sources/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb.txt b/_sources/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb.txt
index 3f69d56ca..2a657a926 100644
--- a/_sources/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb.txt
+++ b/_sources/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb.txt
@@ -55,9 +55,7 @@
    "id": "452cd19e",
    "metadata": {},
    "source": [
-    "### Specify some observables\n",
-    "\n",
-    "Currently, only `Pauli` observables with phase equal to 1 are supported. Full support for `SparsePauliOp` is expected in CKT v0.7.0."
+    "### Specify an observable"
    ]
   },
   {
@@ -67,9 +65,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit.quantum_info import PauliList\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
-    "observables = PauliList([\"ZZII\", \"IZZI\", \"IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])"
+    "observable = SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])"
    ]
   },
   {
@@ -79,7 +77,7 @@
    "source": [
     "### Specify a backend\n",
     "\n",
-    "You can provide either a fake backend from Qiskit or a hardware backend from Qiskit Runtime."
+    "You can provide either a fake backend or a hardware backend from Qiskit Runtime."
    ]
   },
   {
@@ -89,31 +87,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit.providers.fake_provider import GenericBackendV2\n",
+    "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n",
     "\n",
-    "backend = GenericBackendV2(num_qubits=4, coupling_map=[[0, 1], [1, 2], [2, 3]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b5debc22-7e92-465e-b37d-05763946b6e6",
-   "metadata": {},
-   "source": [
-    "To use a hardware backend, replace the code above with the following commented block."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "322fb191-6b85-4887-9883-71f94bb0be87",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from qiskit_ibm_runtime import QiskitRuntimeService\n",
-    "#\n",
-    "# service = QiskitRuntimeService()\n",
-    "# # backend = service.backend(\"ibm_cairo\")\n",
-    "# backend = service.least_busy(operational=True, simulator=False)"
+    "backend = FakeManilaV2()"
    ]
   },
   {
@@ -128,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "b394da7a",
    "metadata": {},
    "outputs": [
@@ -136,7 +112,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Transpiled circuit depth: 101\n"
+      "Transpiled circuit depth: 30\n"
      ]
     }
    ],
@@ -148,23 +124,23 @@
     ")\n",
     "\n",
     "transpiled_qc = pass_manager.run(circuit)\n",
-    "print(f\"Transpiled circuit depth: {transpiled_qc.depth()}\")"
+    "print(f\"Transpiled circuit depth: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "4fe4af43",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 3447.07x147.156 with 1 Axes>"
+       "<Figure size 2008.96x147.156 with 1 Axes>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -185,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "12e73c69",
    "metadata": {},
    "outputs": [
@@ -196,7 +172,7 @@
        "<Figure size 1634.66x294.311 with 1 Axes>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -226,12 +202,14 @@
     "\n",
     "`generate_cutting_experiments` accepts a circuit containing `TwoQubitQPDGate` instances and observables as a `PauliList`.\n",
     "\n",
-    "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst)."
+    "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst).\n",
+    "\n",
+    "**Note:** The ``observables`` kwarg to `generate_cutting_experiments` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "83b1efed-bafa-48c4-bbf0-cf7eb9027ac5",
    "metadata": {},
    "outputs": [],
@@ -241,7 +219,7 @@
     "\n",
     "# Generate the subexperiments and sampling coefficients\n",
     "subexperiments, coefficients = generate_cutting_experiments(\n",
-    "    circuits=qpd_circuit, observables=observables, num_samples=np.inf\n",
+    "    circuits=qpd_circuit, observables=observable.paulis, num_samples=np.inf\n",
     ")"
    ]
   },
@@ -259,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "c7b28e2c",
    "metadata": {},
    "outputs": [
@@ -287,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "70e2f1b6",
    "metadata": {},
    "outputs": [
@@ -295,8 +273,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Original circuit depth after transpile: 101\n",
-      "QPD subexperiment depth after transpile: 26\n"
+      "Original circuit depth after transpile: 30\n",
+      "QPD subexperiment depth after transpile: 7\n"
      ]
     },
     {
@@ -306,7 +284,7 @@
        "<Figure size 2320.28x428.089 with 1 Axes>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -315,8 +293,12 @@
     "# Transpile the decomposed circuit to the same layout\n",
     "transpiled_qpd_circuit = pass_manager.run(subexperiments[100])\n",
     "\n",
-    "print(f\"Original circuit depth after transpile: {transpiled_qc.depth()}\")\n",
-    "print(f\"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth()}\")\n",
+    "print(\n",
+    "    f\"Original circuit depth after transpile: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth(lambda x: len(x[1]) >= 2)}\"\n",
+    ")\n",
     "transpiled_qpd_circuit.draw(\"mpl\", scale=0.8, idle_wires=False, fold=-1)"
    ]
   },
@@ -325,46 +307,29 @@
    "id": "fd9a126c",
    "metadata": {},
    "source": [
-    "### Run the subexperiments using the Qiskit Sampler primitive"
+    "### Prepare subexperiments for the backend and run them using the Qiskit Runtime Sampler primitive"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "80dd2a66",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit_aer.primitives import Sampler\n",
+    "from qiskit_ibm_runtime import SamplerV2\n",
     "\n",
-    "# Set up Qiskit Aer Sampler primitive.\n",
-    "sampler = Sampler(run_options={\"shots\": 2**12})\n",
+    "# Transpile the subeperiments to the backend's instruction set architecture (ISA)\n",
+    "isa_subexperiments = pass_manager.run(subexperiments)\n",
     "\n",
-    "# Retrieve results from each subexperiment\n",
-    "results = sampler.run(subexperiments).result()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "878c9d46-53b9-4ac5-8170-f4abd1072a1c",
-   "metadata": {},
-   "source": [
-    "To use the Qiskit Runtime Sampler, replace the code above with this commented block, and be sure to specify a hardware backend (above) when specifying a backend."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "a437de20-2042-4e62-87a7-804058cff5db",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from qiskit_ibm_runtime import Session, Options, Sampler\n",
-    "#\n",
-    "# with Session(backend=backend) as session:\n",
-    "#     sampler = Sampler(options=Options(execution={\"shots\": 2**12}))\n",
-    "#\n",
-    "#     results = sampler.run(pass_manager.run(subexperiments)).result()"
+    "# Set up the Qiskit Runtime Sampler primitive.  For a fake backend, this will use a local simulator.\n",
+    "sampler = SamplerV2(backend=backend)\n",
+    "\n",
+    "# Submit the subexperiments\n",
+    "job = sampler.run(isa_subexperiments)\n",
+    "\n",
+    "# Retrieve the results\n",
+    "results = job.result()"
    ]
   },
   {
@@ -374,12 +339,12 @@
    "source": [
     "### Reconstruct the expectation values\n",
     "\n",
-    "Use the subexperiment results, subobservables, and sampling coefficients to reconstruct the expectation value of the original circuit."
+    "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "ace12f7f",
    "metadata": {},
    "outputs": [],
@@ -389,8 +354,10 @@
     "reconstructed_expvals = reconstruct_expectation_values(\n",
     "    results,\n",
     "    coefficients,\n",
-    "    observables,\n",
-    ")"
+    "    observable.paulis,\n",
+    ")\n",
+    "# Reconstruct final expectation value\n",
+    "final_expval = np.dot(reconstructed_expvals, observable.coeffs)"
    ]
   },
   {
@@ -398,12 +365,12 @@
    "id": "3fc2327f",
    "metadata": {},
    "source": [
-    "### Compare reconstructed expectation values to exact expectation values from the original circuit"
+    "### Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "id": "4928e703",
    "metadata": {},
    "outputs": [
@@ -411,31 +378,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reconstructed expectation values: [0.52972412, 0.5692749, 0.37542725, -0.20349121, 0.25061035, -0.24511719]\n",
-      "Exact expectation values: [0.50983039, 0.56127511, 0.36167086, -0.23006544, 0.23416169, -0.20855487]\n",
-      "Errors in estimation: [0.01989373, 0.00799979, 0.01375639, 0.02657423, 0.01644866, -0.03656231]\n",
-      "Relative errors in estimation: [0.03902028, 0.01425288, 0.03803565, -0.11550725, 0.07024487, 0.17531269]\n"
+      "Reconstructed expectation value: 0.80566406\n",
+      "Exact expectation value: 0.50497603\n",
+      "Error in estimation: 0.30068803\n",
+      "Relative error in estimation: 0.5954501\n"
      ]
     }
    ],
    "source": [
-    "from qiskit_aer.primitives import Estimator\n",
+    "from qiskit_aer.primitives import EstimatorV2\n",
     "\n",
-    "estimator = Estimator(run_options={\"shots\": None}, approximation=True)\n",
-    "exact_expvals = (\n",
-    "    estimator.run([circuit] * len(observables), list(observables)).result().values\n",
-    ")\n",
-    "print(\n",
-    "    f\"Reconstructed expectation values: {[np.round(reconstructed_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Exact expectation values: {[np.round(exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Errors in estimation: {[np.round(reconstructed_expvals[i]-exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
+    "estimator = EstimatorV2()\n",
+    "exact_expval = estimator.run([(circuit, observable)]).result()[0].data.evs\n",
+    "print(f\"Reconstructed expectation value: {np.real(np.round(final_expval, 8))}\")\n",
+    "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n",
+    "print(f\"Error in estimation: {np.real(np.round(final_expval-exact_expval, 8))}\")\n",
     "print(\n",
-    "    f\"Relative errors in estimation: {[np.round((reconstructed_expvals[i]-exact_expvals[i]) / exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
+    "    f\"Relative error in estimation: {np.real(np.round((final_expval-exact_expval) / exact_expval, 8))}\"\n",
     ")"
    ]
   }
diff --git a/_sources/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb.txt b/_sources/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb.txt
index dc25adb29..871590f14 100644
--- a/_sources/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb.txt
+++ b/_sources/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb.txt
@@ -35,7 +35,7 @@
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x7fb6f8b361f0>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x7f16ab191a80>"
       ]
      },
      "execution_count": 1,
@@ -69,7 +69,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAHwCAYAAADJpfudAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABt/klEQVR4nO3deVxU5f4H8M8Zhn0RWRQUUFBQREATXLLcLc1E00yLSr2VLXr1lsmv5ZbZqmm5Xs1rlpVmlEua5BqWuIO4ICIai8oywgAKss/y+4PkSoIy48ycOcPn/Xr1Ss45zznfGfHMZ57znOcIWq1WCyIiIiIikiSZ2AUQEREREZH+GOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjC5GIXQI3TarVQVVaLXUazye1tIQiC2GUQERERtTgM9GZKVVmNDZ2eFruMZovOWA9rBzuxyyAiIiJqcTjkhoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwjgPvQXx6heCEVvmNVhWW16J0sx8ZGw6gLS1v0Kr1ohUHREREREZAwO9BcrckoCc+GRAEGDv6YrOEwai97wpaBXYHkfmrBa7PCIiIiIyIAZ6C1SUkoXMzQn1P6ev243HEpYi6KmhSJ6/EdVFpSJWR0RERESGxDH0LYCqshqFyRchyGRw6dBW7HKIiIiIyIAY6FsI5451Qb762g2RKyEiIiIiQ+KQGwskt7eBrZtz/Rj6Ls8+BPfQABQmX0RpZr7Y5RERERGRAbWIHnqlUomYmBh07twZdnZ28PX1xaxZs1BeXo7nnnsOgiBgxYoVYpdpMD1jJuHJ1K/x5NmvMHb/5wieOgLZcUcRP2WB2KWJ6nL+DRxPKUTyOSWUJVVil0NERCZWfL0aJ9OUOJ5SiOzcMrHLITIYi++hP3XqFEaOHAmFQgFHR0d069YNeXl5WLZsGTIyMlBcXAwA6NGjh7iFGlD6d3uQ/csRyKzlaN3VD92nj4WjtzvU1TX128hs5Bi9ZyGytibgzNIt9csfWDIddp6u2Bf9kRilG1xNrRqb9mRj1Y9pOHjyav1yuZWAx4Z2xCsTgzEwwguCIIhYJRERGdOhk1exMjYNP+3JQq3qf9M39wn1xCsTg/HEw/6ws7X4SEQWzKJ76JVKJUaPHg2FQoHZs2cjPz8fycnJUCgUWLBgAeLi4pCYmAhBEBAWFiZ2uQZTmqlAfkIKcuNP4uzKbfht8nx49OiEfgterN9GU6PCwZnLETpzHFp36wAA8BsRCZ/hETj02kqxSjeo4uvVGPr8TkS/+XuDMA8AKrUWP+3JwuDnfsWMj49Azfn5iYgsjkajRcznx/HA5B34/teMBmEeAI6lFGLyvw9gwNQ4FBRVilQl0b2z6EA/c+ZM5OTkYMaMGVi0aBGcnZ3r18XExCA8PBwqlQodO3aEi4uLiJUaV2FSOjI2HYD/2P7wjOhSv7zoTCZSV23Hg8v+CQdvN/Rb+BKOvfUlKq+WiFitYVRUqvDIK7tvC/KNWRmbhn99egxardYElRERkam8tSwJC9el3HW7xLNKPPzyLpSV19x1WyJzZLGBPi0tDbGxsfDw8MAnn3zS6Da9evUCAISHhzdYnpWVhaioKDg7O6N169Z49tlnUVRUZPSajen04k3QqNToOWdiw+VLNkOjViNq70IoDp1F1rZDIlVoWEs3pOJYSmGzt1+x8RwOnyowYkVERGRKp84XYcFXZ3TYvhiffn338E9kjiw20G/cuBEajQbR0dFwcnJqdBt7e3sADQN9WVkZBg8ejJycHGzcuBH//e9/kZCQgEcffRQajXSHZZRlK5C17RDaDQhDmz7B9cu1KjUKE9Nh594Kf8buF7FCw1GrNVi96bzO7Vb9mGaEaoiISAwrY3U/p6/ZnI6aWrURqiEyLosN9PHx8QCAwYMHN7lNTk4OgIaB/r///S9yc3Px888/49FHH8WECRPw/fff4+jRo9i+fbtxizayM0vreuNv7aVv0ycYnScORtraX9H7/amwsrMRsULDiD+ej0t5us+3/9OeLFwv4+VWIiKpq6xSYUNchs7trhZVIu7AFSNURGRcgtZCBw77+voiJycHJ0+ebHQGG5VKBW9vbyiVSmRkZCAgIADA/74A7N/fsLe6U6dOGDRoENauXatzLREREVAoFDq1sdbKMFfTW+dj6ULuYIeo3xbh3OodOP/Nbozc+j6UpzOQOHedzvuaJzuOWsE8rmCU20bgmuNovdq2ubYc1hqlgSsiIiJTUsla4arra3q1danYBeeqIwauiOjuvLy8kJSUpFdbi52jqby8HABQWdn4XeuxsbFQKpVwdnaGv79//fJz585hwoQJt20fEhKCc+fO6VWLQqFAbm6uTm1sBCugrV6Ha7bI957FjcsFOL9uFwDg4KwViNq3CJd3HsPVo7pdqszLz0ON1kwuU7oFAY76NS0oUALVuv1dERGRmbGuBlz1a1p6vQylSn4OkLRYbKD38vJCSUkJkpOT0a9fvwbr8vPzMWfOHABAWFhYgznIS0pK4Orqetv+3NzckJ6erncturLWygAjdni3H9IT/lH9sW3o7PplZZeu4sRHG9B/8XRsHzIbqsrqZu+vnXc7s+mhr7SWoVjPtl6eDrDStjdoPUREZFoa2CBfqwEE3UcWt3aWwcGWnwNkevrkxZssNtAPGzYMaWlpWLBgAYYPH46goCAAQGJiIp555hkolXXDKkzxQCl9Lp/UVlRhQ6enjVBNndz4k/i+6+Tblp9ft6u+x14XFy5egLWDnSFKu2eVVSr4DP8Bxdeb/4UEAEb098HOVReMVBUREZnS+Fd/w5bfsnVq4+QgR/bhn+HiJP37yahlsdibYmNiYuDu7o4rV64gJCQEoaGhCAwMRO/evREQEIAhQ4YAuH3KytatW+PatWu37a+4uBhubm6mKJ3ukb2dHP8YG6Rzu1cmBt99IyIikgR9zunPPNqZYZ4kyWIDvY+PDxISEjBq1CjY2dkhOzsbbm5uWL16NeLi4nDhQl1P7N8DfXBwcKNj5c+dO4fgYAY+qXh9Sig6tGt8utLGjOjvg0ce9DFiRUREZEpD+nhj3NCOzd6+XRsHvPV8D6PVQ2RMFhvogbpwvmPHDpSVlaGsrAzHjh3DtGnTUF5ejuzsbMhkMnTv3r1Bm0cffRQHDx6sn9ISAI4dO4aMjAyMHq3fzClkem3d7bHnixHo2IxQP7RPO/y4aDCsrCz6nwMRUYsiCALWfzIQjw7wveu27ds4YPeqh+HjpeeMCkQis9hpK+/k2LFj6Nu3L7p06YLz5xs+gKi0tBShoaHw8PDAvHnzUFVVhZiYGHh6euLIkSOQyUwT+ow9ht7QojPWm80Y+lsVFFViyfpUfLklHYUlVQ3WBQe44uUnuuLFCV1hY20lUoVERGRMKpUGX25Jx8rYNKRcLGmwzq2VLZ57LAj/ejoE7dowzJN0WexNsXeSklL3aOe/D7cBABcXF8THx2PWrFmYNGkS5HI5Hn30USxevNhkYZ4Mp427PT6eFYG5L/fEvqO5ePrNP3CtrAYerrZI3TquwQxHRERkeeRyGV56IhgvTuiKY2cK8cgru1FSVgO3VjbI2TsJ9nYtMgqRhWmRv8V3CvRA3UOkduzYYcqSyMhsbawwaoAfHO3luFZWA1sbK4Z5IqIWRBAE9A1vAwd7OUrKamBvK2eYJ4vRIruc7xboLVmHUX3Rd/4LDZZ1njgYU/I3wW9EpEhVEREREZG+WuRX0/j4eLFLEI3fI32Q8dPv9T87+XgiKHoYCpL0e2gWEREREYmrRQZ6S2bj4oAx+xfDys4GFXlKyGyt4ezXFhmb/sCRN9agbWQXHJy1om5jQcD9n72MY/9ei8i5tz9kioiIiIjMHwO9hakprUDm1gTUllfhzOJNaDcoHGEzx+Hw61+g3cBwFCSmQ6tSAwBCXhyNgsTzKDqTKXLVRERERKSvFjmG3tK5dfdHcUoWAMA9rBOKz9b92W9EJC7tPA4AcO3iiw6j+uD0ks2i1UlERERE94499BbILaRjfYh3DwvAld2JAIB2g3og6YP1AIC2fYLh5NsG4w8vBwDYe7qi38KXYN+mNdK/3SNO4URERESkMwZ6C+Pg5QZotahQFAMA3II74MzSzfDoGYjrF3Ohqqh7uFL6t3saBPcRm+fh3JoduLwrUZS6iYiIiEg/DPQWxq27f33vPADUlJaj6+SHUV1chsu7jotYGREREREZAwO9hcnZdwI5+07U/7xj5BsAgDG/L8bu8XObbLfrDuuIiIiIyHwx0LcQ2wa9KnYJRERERGQEnOWGiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMI4ht5Mye1tEZ2xXuwymk1ubyt2CUREREQtEgO9mRIEAdYOdmKXQURERERmjkNuiIiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwuRiF0CN02q1UFVWi11Gs8ntbSEIgthlEBEREbU4DPRmSlVZjQ2dnha7jGaLzlgPawc7scsgIiIianE45IaIiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMI4D70F8eoXghFb5jVYVlteidLMfGRsOoC0tb9Cq9aIVB0RERERGQMDvQXK3JKAnPhkQBBg7+mKzhMGove8KWgV2B5H5qwWuzwiIiIiMiAGegtUlJKFzM0J9T+nr9uNxxKWIuipoUievxHVRaUiVkdEREREhsQx9C2AqrIahckXIchkcOnQVuxyiIiIiMiAGOhbCOeOdUG++toNkSshIiIiIkNqEYFeqVQiJiYGnTt3hp2dHXx9fTFr1iyUl5fjueeegyAIWLFihdhlGozc3ga2bs6wdXeBa1c/9Pn4ebiHBqAw+SJKM/PFLo+IiIiIDMjix9CfOnUKI0eOhEKhgKOjI7p164a8vDwsW7YMGRkZKC4uBgD06NFD3EINqGfMJPSMmdRgWXbcURx780uRKiIxabVaHD1TgFWx53H2zxJU1ajh3soW44Z1xJQxgWjtYit2iUREZGSX8srw303p2Hc0D2UVtXC0l+OBnm3x8hPBCOrYSuzy6B5ZdKBXKpUYPXo0FAoFZs+ejblz58LZ2RkA8Omnn+L//u//IJfLIQgCwsLCRK7WcNK/24PsX45AZi1H665+6D59LBy93aGurqnfRmYjx+g9C5G1NQFnlm6pX/7Akumw83TFvuiPxCidDOxC9nU8+X/7kZxWdNu6gyev4u3lSZgzOQxzX+4JmUwQoUIiIjKm8opavPjBIWzcmQmNRttgXVKqEkvWp2L0QD9889EAdvBImEUPuZk5cyZycnIwY8YMLFq0qD7MA0BMTAzCw8OhUqnQsWNHuLi4iFipYZVmKpCfkILc+JM4u3Ibfps8Hx49OqHfghfrt9HUqHBw5nKEzhyH1t06AAD8RkTCZ3gEDr22UqzSyYDOZZSg3zO/NBrmb6qsUuP91Scxbd5BaLXaJrcjIiLpqahU4aGXdmFDXMZtYf5Wv/xxGQ9O3oGS0moTVkeGZLGBPi0tDbGxsfDw8MAnn3zS6Da9evUCAISHh9cvu/kFoHfv3rC1tYUgSL/XsjApHRmbDsB/bH94RnSpX150JhOpq7bjwWX/hIO3G/otfAnH3voSlVdLRKyWDKGmVo1R0/eg+HrzTs5rt17Aqtg0I1dFRESmNHP+ERw+VdCsbVMzruHZt/4wckVkLBYb6Ddu3AiNRoPo6Gg4OTk1uo29vT2AhoH+zz//xObNm+Hl5YXIyEiT1GoKpxdvgkalRs85ExsuX7IZGrUaUXsXQnHoLLK2HRKpQjKkLfuykZ2n24xGn3939o49OEREJB1Xiyrx7S9/6tRmx4ErOJ91zTgFkVFZbKCPj48HAAwePLjJbXJycgA0DPQDBgxAfn4+tm/fjmHDhhm3SBMqy1Yga9shtBsQhjZ9guuXa1VqFCamw869Ff6M3S9ihWRI//lB9972jCtl2HM41wjVEBGRqa3dko5alUbndrxaK00We1PspUuXAAAdOnRodL1KpcKhQ3W90bcGepnM8N9xIiIioFAodGpjrZVhLnobtI4zSzfDf2x/9JwzEbsffw8A0KZPMDpPHIy0tb+i9/tTsX34HKirau68o0YEBQahVtD9xGFq+a6vAbJWyFfkw8fHR+xyjEILAXmt3wUE3X+XJ0x9G60qfzNCVURE5qElfA4AgNLpacAmUOd2q77dh80rJhihIrobLy8vJCUl6dXWYgN9eXk5AKCysrLR9bGxsVAqlXB2doa/v79Ra1EoFMjN1a3n00awAnR8qKviSCrWeT/e5PrrF3Pxrc//htzIHezwwJLpOPHRBpz/ZjdGbn0f9735FBLnrtPtwADy8vNQo1Xr3M7knNWADNCo1Tr/nUiGzBZw0++L6Y3yGtzIt9D3hYgIaBmfAwAQoAVsdG9Wq5ZZ9vtioSw20Ht5eaGkpATJycno169fg3X5+fmYM2cOACAsLMzoN756eXnp3MZaKwOM3OEd+d6zuHG5AOfX7QIAHJy1AlH7FuHyzmO4elS3S27tvNtJo4feygoaADIrK3i3by92OUahhYA8rUavHnpnR2u4WOj7QkQEtIzPAQBQWgP6zFljbaVGGwt+X8yZPnnxJosN9MOGDUNaWhoWLFiA4cOHIygoCACQmJiIZ555BkqlEoBpHiilz+WT2ooqbOj0tBGqqdN+SE/4R/XHtqGz65eVXbqKEx9tQP/F07F9yGyoKpt/Krhw8QKsHeyMUapB+QzbiNyCCnh7eSPnbI7Y5RjNsBd24rdjeTq327ZhPgb3bmeEioiIzENL+Rz47JsUvP7ZcZ3bvfrCCCx49V0jVETGZLE3xcbExMDd3R1XrlxBSEgIQkNDERgYiN69eyMgIABDhgwB0HD8fEuSG38S33edjPJcZYPl59ftwpZ+M3QK82R+XpkYfPeN/qarfysMivQ2QjVERGRqU8YEws7WSqc2ggC8OKGrkSoiY7LYQO/j44OEhASMGjUKdnZ2yM7OhpubG1avXo24uDhcuHABQMsN9GTZogb5oau/bo/y/r9/GH/4GRERmYa7qx1eGN/l7hveYsJD/gjwsZwHbbYkFjvkBgCCg4OxY8eO25bfuHED2dnZkMlk6N69uwiVERmXXC5D3H8ewoNT4pBXUHHX7Wc/2x1TxgSZoDIiIjKVRbN7Iz37erOmJO7d3RNr5z1ogqrIGCy2h/5OUlNTodVqERgYCAcHh9vWb9q0CZs2bcK5c+ca/KzvVEJEYgjwccHR9aMxtE/TY+LdWtni8zl9sHC2YadIJSIi8dlYW+GX5cMx48lusLVpfPiN3ErAs6M7I/7LkXBysDZxhWQoFt1D35SUlBQATQ+3mTBhQqM/T548GevWrTNqbUSG5OvlhH1rRuJcRglW/3Qeq386j+paDexsrLDqnfsx8eEA2Nu1yNMAEVGLYGNtheVv9sN7L/fE1z9fxL6judh/PB81Kg2cHa1xftt4tGvjKHaZdI9aZA/93QK9Vqtt9D+GeZKqbp1aY+kb/eDRum4mIndXW0wZE8QwT0TUQri72uH1KaHY9cUIeLrVfRa4OFozzFsIBvoWpsOovug7/4UGyzpPHIwp+ZvgNyJSpKqIiIiISF8tsnsuPj5e7BJE4/dIH2T89Hv9z04+ngiKHoaCpHTxiiIiIiIivbXIQG/JbFwcMGb/YljZ2aAiTwmZrTWc/doiY9MfOPLGGrSN7IKDs1bUbSwIuP+zl3Hs32sROXeyuIUTERERkV4Y6C1MTWkFMrcmoLa8CmcWb0K7QeEImzkOh1//Au0GhqMgMR1alRoAEPLiaBQknkfRmUyRqyYiIiIifbXIMfSWzq27P4pTsgAA7mGdUHy27s9+IyJxaWfdY6Bdu/iiw6g+OL1ks2h1EhEREdG9Yw+9BXIL6Vgf4t3DAnBldyIAoN2gHkj6YD0AoG2fYDj5tsH4w8sBAPaerui38CXYt2mN9G/3iFM4EREREemMgd7COHi5AVotKhTFAAC34A44s3QzPHoG4vrFXKgqqgAA6d/uaRDcR2yeh3NrduDyrkRR6iYiIiIi/TDQWxi37v71vfMAUFNajq6TH0Z1cRku7zouYmVEREREZAwM9BYmZ98J5Ow7Uf/zjpFvAADG/L4Yu8fPbbLdrjusIyIiIiLzxUDfQmwb9KrYJRARERGREXCWGyIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJ4xh6MyW3t0V0xnqxy2g2ub2t2CUQERERtUgM9GZKEARYO9iJXQYRERERmTkOuSEiIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgmTi10ANU6r1UJVWS12Gc0mt7eFIAhil0FERETU4jDQmylVZTU2dHpa7DKaLTpjPawd7MQug4iIiKjF4ZAbIiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJ4zz0FsSrXwhGbJnXYFlteSVKM/ORsekA0tb+Cq1aI1J1RERERGQMDPQWKHNLAnLikwFBgL2nKzpPGIje86agVWB7HJmzWuzyiIiIiMiAGOgtUFFKFjI3J9T/nL5uNx5LWIqgp4Yief5GVBeVilgdERERERkSx9C3AKrKahQmX4Qgk8GlQ1uxyyEiIiIiA2KgbyGcO9YF+eprN0SuhIiIiIgMiUNuLJDc3ga2bs71Y+i7PPsQ3EMDUJh8EaWZ+WKXR0REREQG1CICvVKpxKeffootW7YgJycHnp6eGDduHD7++GPMnDkTX331FZYvX44ZM2aIXapB9IyZhJ4xkxosy447imNvfilSRUTiu5x/A7sP5eBaWQ0c7OWI6OaB3qGeEARB7NKIiMgEyitq8csfl5FXWAGZICDAxxkjH/CFtbX0B6xYfKA/deoURo4cCYVCAUdHR3Tr1g15eXlYtmwZMjIyUFxcDADo0aOHuIUaUPp3e5D9yxHIrOVo3dUP3aePhaO3O9TVNfXbyGzkGL1nIbK2JuDM0i31yx9YMh12nq7YF/2RGKUTGdzR0wWY/9Vp/PLHFWg02gbrenZ1x6zoEDwb1ZnBnojIQuUVlGP+V2fwzfaLKL1R22Cdl4c9XhjfBXOmhMLZ0UakCu+d9L+S3IFSqcTo0aOhUCgwe/Zs5OfnIzk5GQqFAgsWLEBcXBwSExMhCALCwsLELtdgSjMVyE9IQW78SZxduQ2/TZ4Pjx6d0G/Bi/XbaGpUODhzOUJnjkPrbh0AAH4jIuEzPAKHXlspVulEBvV9XAYenLID2/Zfvi3MA8DJ80WY8s4BTJt3sNH1REQkbWmZ19An+hcs//7cbWEeABTKSnyw+hQGTI1DQVGlCBUahkUH+pkzZyInJwczZszAokWL4OzsXL8uJiYG4eHhUKlU6NixI1xcXESs1LgKk9KRsekA/Mf2h2dEl/rlRWcykbpqOx5c9k84eLuh38KXcOytL1F5tUTEaokMY++RXDz77z+gUt89qH+55QLeXJpogqqIiMhUrhZVYsTLu5Bztfyu2546X4xH/7kHVdUqE1RmeBYb6NPS0hAbGwsPDw988sknjW7Tq1cvAEB4eHj9sk2bNmH8+PHo0KEDHBwc0LVrV7z99tu4cUPas8OcXrwJGpUaPedMbLh8yWZo1GpE7V0IxaGzyNp2SKQKiQxHq9UiZvFxqJsR5m/67NuzyFHc/aRPRETSsHR9Ki7nN/+8nnhWidhdWUasyHgsNtBv3LgRGo0G0dHRcHJyanQbe3t7AA0D/aJFi2BlZYWPP/4YO3fuxMsvv4xVq1ZhxIgR0Gg0JqndGMqyFcjadgjtBoShTZ/g+uValRqFiemwc2+FP2P3i1ghkeEcPVOAU+eLdWqjVmuxZnO6kSoiIiJTqq5R48stup/TV8amGaEa47PYQB8fHw8AGDx4cJPb5OTkAGgY6H/55Rf8+OOPiI6OxsCBAzFr1iysWLEChw4dwsGDB41btJGdWVrXG39rL32bPsHoPHEw0tb+it7vT4WVnXRvCCG6aePOTP3a7cowcCVERCSG+GN5KCyp0rnd8bOFyMwpNUJFxmWxs9xcunQJANChQ4dG16tUKhw6VDe85NZA7+npedu2ERERAIDc3Fy9aomIiIBCodCpjbVWhrnorVMbxZFUrPN+vMn11y/m4luf/4V5uYMdHlgyHSc+2oDz3+zGyK3v4743n0Li3HU6HRcAggKDUCuY/xWMfNfXAFkr5Cvy4ePjI3Y5JtdSXn+x4+OAbajO7f7MKrDo94WIWs558E5awntQbhMOOI3Tq22fBx6CrSrHwBXdnZeXF5KSkvRqa7GBvry8bsxUZWXjdyzHxsZCqVTC2dkZ/v7+d9zX/v11Q1GCg4PvuF1TFAqFzl8GbAQroK1eh2u2yPeexY3LBTi/bhcA4OCsFYjatwiXdx7D1aO6XXLKy89DjVZtjDINy1kNyACNWq33FzRJaymv36ccsNW9mVZTa9nvCxG1nPPgnbSE98DVB2h8xPVdKQsUQKW03heLDfReXl4oKSlBcnIy+vXr12Bdfn4+5syZAwAICwu74/zTubm5eOeddzBixAi956r38vLSuY21VgYYscO7/ZCe8I/qj21DZ9cvK7t0FSc+2oD+i6dj+5DZUFVWN3t/7bzbSaOH3soKGgAyKyt4t28vdjkm11Jef6ltJcr0aGejLYGnBb8vRNRyzoN30hLegxorLQoBQKsFdHnOiFYNL3cbWGlN/77okxdvErRarUVOvjxz5kwsX74cvr6+2LdvH4KCggAAiYmJeOaZZ5CZmYna2lpMnz4dK1asaHQfN27cwKBBg6BQKJCYmAhvb2+T1V9bUYUNnZ422fHuVXTGelg72Ildxl35DNuI3IIKtG/jgJx9T4pdjsm1lNd/RXEDHUf8qPPc8l+9/yCmjg0yUlVEZA5aynnwTlrCe6DVatHziZ9xOl23CRKeeNgfsQuHGKkq47HYm2JjYmLg7u6OK1euICQkBKGhoQgMDETv3r0REBCAIUPq/rJuHT9/q8rKSowePRpZWVnYs2ePScM8Ed0bXy8nRA3y06lNaxcbTHw4wEgVERGRKQmCgFcm6j5UWp825sBiA72Pjw8SEhIwatQo2NnZITs7G25ubli9ejXi4uJw4cIFAI0H+traWjz++ONISkrCzp070a1bN1OXT0T3aPkb/dC+jUOztpXJBHz70UA42FvsKEQiohbnH2ODMGqAb7O3nxUdgoER0uzAtehPr+DgYOzYseO25Tdu3EB2djZkMhm6d+/eYN3Nuet/++03/Prrr+jdW7eZZojIPPh4OeL3r0Zh5Cu78eflpqcgs7O1wvfzB+HRgbr16BMRkXmTy2X4adEQRL/5O7b+dumO2772bHcsfE26mc+iA31TUlNTodVqERQUBAeHhj1406dPx08//YQ33ngDDg4OOHr0aP26Tp06NTqtJRGZp85+Ljj902P4YVcm/vPDOSSnFdWvk8kEvDOtB14Y3wXt2zqKWCURERmLvZ0cmz4bivjjeVgZm4Zt+y83uL9q6thAvPxEMCK7SzvftchAn5KSAqDx4TY7d+4EAMyfPx/z589vsO7rr7/GlClTjF4fERmOg70c/3gsCFPHBqKguAph47egoLgKXu52eO+V+8Quj4iIjEwmEzCsb3sM69se18tq0GX0T7haXAVvT3t89f4AscszCIsdQ38ndwr02dnZ0Gq1jf5nCWG+w6i+6Dv/hQbLOk8cjCn5m+A3IlKkqoiMTxAEtHW3h7VcVv8zERG1LK2cbSD/63NAZkGfAwz0LYzfI31wedfx+p+dfDwRFD0MBUnpIlZFRERERPpqkUNu4uPjxS7BaGxcHDBm/2JY2dmgIk8Jma01nP3aImPTHzjyxhq0jeyCg7P+mndfEHD/Zy/j2L/XInLuZHELJyIiIiK9tMhAb8lqSiuQuTUBteVVOLN4E9oNCkfYzHE4/PoXaDcwHAWJ6dCq1ACAkBdHoyDxPIrOZIpcNRERERHpq0UOubF0bt39UZySBQBwD+uE4rN1f/YbEYlLO+uG27h28UWHUX1weslm0eokIiIionvHHnoL5BbSsT7Eu4cF4MruRABAu0E9kPTBegBA2z7BcPJtg/GHlwMA7D1d0W/hS7Bv0xrp3+4Rp3AiIiIi0hkDvYVx8HIDtFpUKIoBAG7BHXBm6WZ49AzE9Yu5UFVUAQDSv93TILiP2DwP59bswOVdiaLUTURERET6YaC3MG7d/et75wGgprQcXSc/jOrisgaz2xARERGRZWCgtzA5+04gZ9+J+p93jHwDADDm98XYPX5uk+123WEdEREREZkvBvoWYtugV8UugYiIiIiMgLPcEBERERFJGAM9EREREZGEMdATEREREUkYAz0RERERkYTxplgzJbe3RXTGerHLaDa5va3YJRARERG1SAz0ZkoQBFg72IldBhERERGZOQ65ISIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjC52AVQ47RaLVSV1WKX0Wxye1sIgiB2GUREREQtDgO9mVJVVmNDp6fFLqPZojPWw9rBTuwyiIiIiFocDrkhIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMM5yQxZLq9UiOa0ISalKnDinxPmsaygorgIAKEuq8NrCo+jVzQN9w9qgk6+LyNUaR15BOQ6fKsCJNCVOnS+uf/2FJVV49q0/EBHigV7dPNAn1BNyueV9v6+qVuHomUKcOKdEUqoSlxU3UFBcCQBQXqvCu/85gV7dPHB/eBt4utmLXC0RGUN2bhmOnC7AiXNKnL7Q8Dz43NwERHTzqD8XymSWN/3yjYra+teflKpEXmFF/Xmw6Fo1Plh9sv486OpiK3K1hqfVanE6vRiJZwtxIq0IaZnXGvwOzJp/BBEhdVkgsEMrkavVn6DVarViF0G3q62o4rSVerpWWo1vtl/EytjzuHDperPaDIzwwisTg/HYkI6wtpZ2sNVotNh7JBcrY9Ow48AVaDR3/yfevo0Dpj3eFS+M7wJvTwcTVGlcWTll+OKnNKzdegFF1+7+PAdruQzjh3XE9EnB6N+zLZ+pQCRxKpUGv/xxGStj07DvaF6z2nTydcZLE4IxdWwg3F3N4/PsXpzLKMGqH8/jm+0XUVZee9ft7e2s8NTITnhlYjDu6+ZhggqNq/RGDb7b8SdWxqbhXMa1ZrXp37MtXnkiGOOHd4StjZVxCzQwBnozpU+g9+oXghFb5jXcT3klSjPzkbHpANLW/gqtWmPIMuuZQ6DXarX476Z0zPn8eLNOXo0J8HHGV+8/iIER3gauzjRSLhRj6rsJOHFOqVd7a7kMb78Qjree7yHJLzYVlSq8vTwJSzekQt8z2+BIb6yd9yD8fZwNWxwRmcSR01cx9Z0EpGc3r0Pn7xzs5PhkVgRmPNlNkj32JaXVePXTY/hm+0W99/HY0A5Y+fb98PKQXgePVqvFt9v/xL8+PYprZTV67cPP2xFr5j6Ah+73MXB1xsNAb6buJdBnbklATnwyIAiw93RF5wkD0Tq4A9LX78WROauNUq/Ygf6K4gb+8W5Cs3ti7mbGk93w6auRsLeTxqg0jUaL+WtP471VJ1GruvcvbT26uuG7jwaie6CbAaozjcOnrmLyvw/gz8ul97wvR3s5Fr7WGy890ZW99UQSUV2jxr+Xn8Dn351t1pXJu3nwvrb45sOBkvpyvzPhCp6fdxB5BRX3vC+3VrZY+fb9mDgiwACVmYZCWYHn3zuIuANXDLK/F8Z3wedz+sDJwdog+zMmBnozdS+BPnHet0j9Ynv9crm9LR5LWAoHbzf8EPY8qovuPfD8nZiB/nzWNQyftgs5V8sNut+BEV7Yvmw4XJxsDLpfQ6ut1WDKOwfw/a8ZBt2vs6M1diwfjgESuFrxc3w2Js7Zj5paw16B+tfTIfh8Th+GeiIzV15Ri3Gv/YY9h3MNut+27vbY/cXDCO/ibtD9GsOXm9Mx7f2Del+dbMonsyLwxnPhht2pEWTmlGLYC7uQlVtm0P32CfXErysfhlsr876/QHrX1ElnqspqFCZfhCCTwaVDW7HLMaiMK6UY8vxOg4d5APgjSYFHZ+xBRaXK4Ps2FLVag8n//sPgYR4Ayspr8cj0PThy+qrB921IcQcuY8Lr8QYP8wCwZH0qZi86BvZ7EJmv6ho1Hnt1n8HDPABcLarEsGm7kJZ5zeD7NqR12y7ghXmGD/MA8ObSJCz8+ozhd2xAVxQ3MPi5Xw0e5gHgWEohRry8C2Xl+g3fMRUG+hbCuWNdkK++dkPkSgynukaNsbP2Ib/w3i8tNiUh+Sr+Of+I0fZ/rz79OgUbd2Yabf/llSqMmbkPhX/NiGBuMq6U4onX90OlMl7gXvxdKr7d/qfR9k9E92bO58ex94hhhls2RllShaiZe822cycptRDPv3fQqMeIWZyI3YdyjHoMfalUGox79Tdczjd8x95NiWeVeGGecd/je8VAb4Hk9jawdXOGrbsLXLv6oc/Hz8M9NACFyRdRmpkvdnkG8/4XJ3H2zxKd2iRujMKVvZOQuDGq2W2+2noBuw6a34ks9c8SvLcqWac2+rz+wpIqs/xSo9Fo8Y93E1BRpduHrD7vwaxPjyLXCFeBiOje/JGUj+Xfn9OpjT7ngD8vl+Lt5Um6lmd01TVqTPn3AajVunVq6PMePP/eQVzX8yZTY1q4LgVJqbpNBKHP64/dlYXNe7N0Lc9kWkSgVyqViImJQefOnWFnZwdfX1/MmjUL5eXleO655yAIAlasWCF2mQbTM2YSnkz9Gk+e/Qpj93+O4KkjkB13FPFTFohdmsGcOl+EBXpcAvTycIBPW0ed79x/Yd5B3KjQb+YcY9Bq68KsrsNM9H39sbuy8HN8tk5tjG31T+dx4IRC53b6vAfXy2rw8oeHdT6WudFotLheVoNrpdUGuWlQiiqrVCi6VoVaIwzRkgK1WoPi69W4UVEr+aFk1TVq/OPdBJ3b6XseXLoh1eyGIH605hRSmzkl4630eQ9yrpYjZvFxnY9lTOlZ13Tu2AL0/x14+cPDKCm9+1TIYpDGFB734NSpUxg5ciQUCgUcHR3RrVs35OXlYdmyZcjIyEBxcTEAoEePHuIWakDp3+1B9i9HILOWo3VXP3SfPhaO3u5QV//vm7XMRo7RexYia2sCzizdUr/8gSXTYefpin3RH4lRerMt+iZF5x6Je5FztRzf/5qBaY93Ndkx72T/8XwcP1to0mMu+OoMxg7paNJjNkWt1uBTE4/p/OWPyziXUYJunVqb9LiGkJ51Dat+PI912y/W97A5O1rjmUc745WJwQjpLL3XpIsbFbVY/9d81CkX/3dVb0hvb7wyMRhRgzpIcprW5tJqtThwQoGVsWnY8lt2/RA1Xy9HvPh4Vzw/vgvaukvvwWqb9mYhM8fwY6abotXW9QZvWWwe96KVV9RimY5XJ+7V1z9fxPvTe5nN78vi9alGuX+qKYUlVVi37SJefaa7yY7ZXJZ7BkNdz/zo0aOhUCgwe/Zs5OfnIzk5GQqFAgsWLEBcXBwSExMhCALCwsLELtdgSjMVyE9IQW78SZxduQ2/TZ4Pjx6d0G/Bi/XbaGpUODhzOUJnjkPrbh0AAH4jIuEzPAKHXlspVunNUlhciZ/2mP6y18rYNLPp0VoZm2byYx49U4hkPee3N7Rdh3KQnWf6+0FW/Xje5Me8F1qtFm8vS0LXMZuxdENqg8vlZeW1WBmbhu7jtuC1hUcttsf+yOmr8B/5I17+8HCDMA8A8cfz8fjsePR8Yisu51vO/UW3KiuvwajpezDoH7/ix91ZDe43uaIox79XnIDfQz/g+zjD31hvbGKcB7ftv4wchXkMv9u4M9PkQ2BqVRqs3ZJu0mM25XpZDdbvMP39Tat+TDPL86VFB/qZM2ciJycHM2bMwKJFi+Ds/L+5ZGNiYhAeHg6VSoWOHTvCxcVFxEqNqzApHRmbDsB/bH94RnSpX150JhOpq7bjwWX/hIO3G/otfAnH3voSlVd1G5duat/t+NOk38hvOp1erPM4PWMoKKrEz/sviXLsNZvN40QuVh3f/nIR1TVqUY6tjzmfHcfHX56+63aLv0vF9I8Om80XVkM5nlKIoc/vhLKk6o7bpWZcw4CpcUa9wV4MVdUqjJq+Bzvvcg9QTa0G0W/+Lko40te5jBIcPlVg8uNqNFp89fMFkx+3MV+KFKzN5XPgh12ZKBfhRuWLl0rxR5L53Y9osYE+LS0NsbGx8PDwwCeffNLoNr169QIAhIf/b37VhIQEDBs2DN7e3rC1tYWPjw8mTpyItDTT9wQY0unFm6BRqdFzzsSGy5dshkatRtTehVAcOousbYdEqrD5Dp4UbwzjIRGPfdOxlEKTDje61aFT4r9+rVYr2u9A6Y1anW/EFsu+o7n47Nuzzd7+i5/OY/vvl41YkWmp1Ro8MSceldXN+wJ2Ke8GXvrA/M9/uvjkyzNISG7+v5Xn5iZI5kuNqJ8DZnAerKxSIUmkK6bZeTeQVyD+VYqDJ3W/h8pQzOF34O8sNtBv3LgRGo0G0dHRcHJyanQbe/u6MWC3BvqSkhKEhoZi2bJl2LNnDxYsWIDU1FT069cPOTnmN9NJc5VlK5C17RDaDQhDmz7B9cu1KjUKE9Nh594Kf8buF7HC5jsh4rCPE2ni99CL+frPZV4Tfeq2y/k3UHRNvJuSxHz/dfGfH3TvhPjPD6Ydj2tMcQeu4JKOw7J2HLiCS3mmG5NtTDW1avx3s25DxGpqNaL1+upK1M+Bc0rRr2aduVAsWscOAJw4VyTasc2hBnN4/X9nsYE+Pj4eADB48OAmt7kZ0G8N9FFRUVi8eDEmTJiAgQMHIjo6Glu2bMH169exefNm4xZtZGeW1vXG39pL36ZPMDpPHIy0tb+i9/tTYWVn3k9FLb5ebdS5Zu/mZJr4/4hPnhevBrVaK3oP9an0YlGPbw6/A3eTV1CuV2/73iN5yLhi+CdJi2H1Jt3vd9BotPhyi3kMp7hXv/x+GQql7s+PWP3TebMcH/x3p86Ldx4oulZtlIcZ6kLMzwFzOH5llQrns66JdvxTIr/+xghasb9mGomvry9ycnJw8uTJRmewUalU8Pb2hlKpREZGBgICAprcV1FRETw8PLBixQpMnz5d51oiIiKgUOh2achaK8NcTW+dj6ULuYMdon5bhHOrd+D8N7sxcuv7UJ7OQOLcdTrva57sOGoF449rV8la46rrv5pcn7gx6o7TUHl52ENuJYNKrbnjh51CWYHIJ7fftlymKYX3tc90qtnQCp2nosa6Y6PrDPX6gabfA/fSb2GnEu8GunKbcFxzGtfouru9fuDefwfsq1PgVr5Jt6JNrFreEUqXqXq1dSvbAPta6YdaRat/QW2l++w9djXn4H4j1ggVmVap3UCUOQzRq613yceQac1zar6brrb6J1RWHo2uM8V5sM31/8Babfox/DeV2T2AUofhja4zxXnQseoIXCt26Va0AakFRyhaxzS53thZQNBUot21+boV3QxeXl5IStLveQcWO21leXndt+fKysb/omJjY6FUKuHs7Ax/f//b1qvVamg0Gly6dAlvvvkmvLy88MQTT+hVi0KhQG6ubo+kthGsACPPjBX53rO4cbkA59fV/aM8OGsFovYtwuWdx3D1qG6X6/Py81CjNcHNgjYqwLXp1Tfnlr0buZWsWdv9nUYNnf8uDS6gFrBufJWxXz8AFBWXAGUivget/YHGR9E1+/UD+r8HlVXV4v8O3I2TK6Dnff7FxdeBUjN/fc3hLABWujerqqo1/7/f5mhbCeg2xXa9/PxCQG3mQ48cNU3+/ZriPFhQoASqRPw98bzR5N+vKc6D5TcqUJ4v4uuXtwLu8H3d2L8DWq1gducJiw30Xl5eKCkpQXJyMvr169dgXX5+PubMmQMACAsLgyAIt7UfOHAgDh2qu0Gqc+fOiI+Ph6enp9616MpaKwOM2OHdfkhP+Ef1x7ahs+uXlV26ihMfbUD/xdOxfchsqCqb30PTzrudiXroXXCnW1EUyjvf0KXLt/LGWMnU8GrfvjmlGo3SRoam/mYM9frvtC8PNxfYuoj3HlTYOKGpQT93e/3Avf8O2NtZw03k34G7qbFygL5PKfBobQdbZ/N+fc1xVaiGPnd7ONhq0NrM/36b44atFa7r01CrQTuv1hD0/UZoIlettE3+/ZriPNjG0w3WGvEeTlZm54CmBseZ4jzo5GiLViL+O9EI9rjTPDPGzgIyQQ1vI7x+ffLiTRY75GbmzJlYvnw5fH19sW/fPgQFBQEAEhMT8cwzzyAzMxO1tbWYPn16o0+JTU9Px7Vr15CVlYWFCxeioKAAhw4dgp+fn0nqr62owoZOT5vkWIYQnbEe1g52Rj+OSqWBy/3forJKv6sBV/ZOgk9bR+RcLYfv8B90bj+sbzvs/e9IvY5tKC99cAirf9JvPvR7ff0AcHnPRPh6NdFFbgLHzhSg79O/6N3+Xt+Dea/ch3df6qn38U1BrdYg8NFNyMrVrZfVy8Mel3dPsoiHLM1blYz3Vp3Uud0vy4fj0YGmOc8bU3ZuGQIe+RG6fsI/NrQDtiweZpyiDGjMzL16z8p0r+cAKysBZUeehb2deH2i2/ZfwthZ+/Ruf6/vwX/f7Y8XRHzQolarhfuD61FSqt88/Pf6+vuGeeLI+ii9jm0s0j9rNyEmJgbu7u64cuUKQkJCEBoaisDAQPTu3RsBAQEYMqRubOGtN8TeqkuXLujTpw8mTZqE3377DWVlZfj0009N+RKoEXK5DD26uIt2/F7dGh+zadIagsV7/Z6t7fS+RG0oYUFusLK6/aqaqfTqJt7731xWVjK8NEH3D9tp47taRJgHgBfGd4Fcx9+TDu2cMPIBHyNVZFod2ztj1IO+Ord7ZWLw3TcyA2Kei0M6tRY1zANAr2BxP4vE/iwUBEHUGsR+/Y2xjDN3I3x8fJCQkIBRo0bBzs4O2dnZcHNzw+rVqxEXF4cLF+pu+moq0N/K1dUVnTt3xp9/SuehG5YsIqRl/yMW+/U3NkTNlOzt5OjeWfebHQ3FHH4HmuOFx7sgwMf57hv+xaetI6ZPkkaYa452bRwxKzpEpzYf/bMXrKws52Nx7ss9YWfb/BsJhvVth6F92hmxIsMR9zwo/pf69m0d0NbdXpRj21jL0D1QvHPwTWJ/FpobyzlzNSI4OBg7duxAWVkZysrKcOzYMUybNg3l5eXIzs6GTCZD9+7d77qfgoICpKeno1OnTiaomu7m8WEdRTmuk4M1Hr5f/LG14V3c0cm3+UHNkCY8dPsN5GJ4fHhHUY77QM+2d509wly0drHFzpUPw9fr7ldUvDzssXPlQ2gjUkAwlgWvRuLpR5t33l74Wm9Ej+ps5IpMKyLEEz8uHNKsUN83zBObPhsq+hf25hoc6Y3WLuJMszxhuPjnQUEQRDsPjhncATbWetxxbmCPi/T3YGtjhdFmOCzPogN9U1JTU6HVahEYGAgHh4Yfzk8//TTee+89/Pzzz/j999+xZs0aDBo0CHK5HK+++qpIFdOtHuzlhZBOriY/7jOPdoKLk/jz9MtkAl5+wvQ9qa7ONpg0ounpXU3p+XFdIJebPnhIZTjCTUEdW+Ho+tF4flwQ7O1u/wC2tbHClDGBOLYhCt0D3USo0LisrGT45sOBWP5mvya/BPcJ9cTPS4fh9SmhJq7ONEYP8sOBr0dh1ABfNJbVPVvb4a3nwxH/5SNo5Sz++a257O3k+MfYIJMf17+9Mx7ubx7DssT4HADM5zzYq5sHenfXb7KSe/HEQ/7waG38ewZ11SIDfUpKCoDGh9v07dsXv/76K6ZOnYqRI0di4cKFePDBB3Hq1Cl07mxZvTdSJQgCpk/qZvLjinXybMzUsY0HNGP6x2NBcLA3j4mxvDwc8Pgw0/bOtHW3xziRrg7di3ZtHLHmvQeRt+9JrH63P5wd6v4OWzlZI3ffJHz9wQD4eYt3k7OxyWQCZjzZDRd+mYBdqx6Gs2PdnK8ujtZI+mEMjm6IwpjBHUSu0rgiu3tix4qHkBH3BBbN7g2nv34HWrvY4MreSfhoZoToY8L18dITwZDJTPvF/pWJpj9mU0I6t8agSG+THrNbJ1cMjNB/JhZDE2OYoLkOTWSg/5sZM2bg+PHjKCkpQWVlJS5cuIDVq1ejQwfLPuFLzfPjuqBnV9ONY3xlYjBCg8ynB9OtlS0++meEyY7n7emAf0/rYbLjNceCVyPrw5kpLInpA1sb8S8z68vVxRbTHu9af5XJycEa7q7m18tkLDKZgIf7+8Dlr98ZZ0drsxwHa0z+Ps6YPTkUrf76HXCwk0v6d7qznwtmP3v3YbOGEhzgihlPmleYWxLTx6RXK//z1v1mNSwrelQn3N+jjcmONzkqEH3CTHc8XTDQtzAdRvVF3/kvNFjWeeJgTMnfBL8RkSJVpTtraxnWfTgA1nLj/wp3bOeEBa+a33sz86lu6N/TyE8f+8t/3+2P1i62JjlWc/l5O2HRbOM+TfmmcUM7YqKZDDciov+Z98p96NKxldGPI5MJWPfBANjZmteVjPAu7nhnmmmm0Z0+KdjkVwTuxspKhq/fH6DTzd/6atfGAYtj+hj9OPpqkYE+Pj4eWq0Wo0aNErsUk/N7pA8u7zpe/7OTjyeCooehICldxKr0Exbkhk91DNoKZQVyrpY368EbQN0Y4+8+HggnB9P1BDeXlZUM6z4YAHfX5gdtXV8/UHd1wlzn5X5hfBeMG9pRpza6vgcd2jlh1b/Nq1eKiOrY28mx/pOBcNBhyJA+58F5r/RE71DTj9dujjefC8eD9+nWuaPrexAa2Brz/2V+HVtA3b1CS/+vr05tdH39crmAbz4cYHYdW7cyr6+adM9sXBwwZv9iWNnZoCJPCZmtNZz92iJj0x848sYatI3sgoOz/nqQliDg/s9exrF/r0Xk3MniFq6nfz3THUXXq/Hhf081a/vIJ7c3e9821jL8tGgIHrjPfMYL/l1nPxfsWvUwhk/bhWtld3/Ahi6vHwCeeqQTlr2h24nSlARBwIb5AzFmVi32HG7eY7h1eQ/atXHAvv+OtLjZX4gsSURI3Y3NUTP3oqr67g8d1PU8+K+nQ/D2Cz30rM74rK1l+GX5Qxg2bSeSUpXNaqPLe9ClYyvsWT3CLDu2bpr2eFcUXavGW8uSmrW9Lq/fykrAhk8GYVhf8We5u5MW2UNvyWpKK5C5NQHnvozD9uFzcPzdr1GYfAGHX/8C3v27oyAxHVpV3Qkv5MXRKEg8j6IzmSJXfW8+mNELn74aadAblVydbfDL8uEYPcg8e6ZvFRHiiT++HtWs6Ql1MX1SML79aIDZz8ttZyvH9mXDMXGEYW+S7erfCgfXPYrOfi4G3S8RGd7wfu2xe9XDBp19RBCA917uic/n9DH7K3StnG3w25qRGN7PsM8R6N3dEwe+HiWJ6XrffD4cy9/sZ9B7CpwdrbHl86F44mHzH3Jp3p/UpBe37v4oTskCALiHdULx2bo/+42IxKWddcNtXLv4osOoPji9ZLNodRrSnKlhOLp+NLoZYDrLRwf4InXrODx0v3lMTdYcYUFuSNk8Ds+Pu/dp3Nq1cUDcfx7CirfuN/swf5OtjRU2LhiMDZ8Mglure7skKpMJmDMlFMmxY+Gvw4OZiEhcAyK8kbplHMYbYDaqwA4uOPD1KMx9+T6zD/M3uTjZYNeqEVjxVj843uOMZDbWMnw8MwKHvn1UUlcoZzzZDYnfj0F4l3ufxGJ4v3Y4u2UcoiQyC5Y0Pq1JJ24hHetDvHtYAIr+CvftBvVAbvxJAEDbPsFw8m2D8YeX4/HjK+F5XyD6LXwJXZ59SLS671Vkd0+c+GEMPpkVoVdvda9uHti4YBC2Lx+Odm0M29ttCq2cbbDmvQex978j9Hrao7urLWKmhiJ1yzg8oscj48UmCAKeGtUJqVvHYfqkYJ1nwBEEYPRAPxz+9lF8+lpvSU7jR9TStXG3x6bPh2LL4qHoF677bCTt2jjg/en34dSPj5n1cMumyGR10zqf2fwYpo4N1PlmUblcwKQRAUiOHYs3nw+H3AQTTxhaj67uOP59FD57vTf82+veKRPexQ3ffjQAu78YIakpfQWtVqsVuwi6XW1FFTZ0elrndg5ebhi951PEhj0PABh3aDl+f+lzyORyhL/6OH579pNG243YPA/n1uzA5V2JetUbnbEe1g7mMwWeSqVBXMIVbNyZgaRUJTKulN22jVwuIKRTa/QNa4PnHgtCpAgPqDCm81nX8OXmdBw8eRWn0otRXXP72FKfto7o1c0d44d1xISH/M1uBod7UVZeg/U7MrDjwGWcOFeEq0WVt23jaC9Hj67uGBThhefHdUFHPU7+UuMzbCNyCyrQvo0DcvY9KXY5JtfSXz/Qst6Dk2lKrN16AUdOFyDlYglqVZrbtvFv74xe3dwxaUQAogZ1gLW19EJsU4qvV2PdtgvYdSgHJ84Vofh69W3buDhZ475gDwzr0w7PjQuSxPCa5lKrNdh9OBcb4jKQmFqIi5dKb9vGykpAtwBX9An1xNSxQegX3kYyV2VuZTmf3gTgr+E2f/XOA0BNaTm6Tn4Y1cVlDWa3sXRyuQxjBneof2DMtdJqpGdfR0WVClYyAc6O1ggOcLWoAPt3Xf1dsej1uim2ams1SM++hpLSGtSqNLC3s0InHxdJXUrVlbOjDV6eGIyXJwZDq9Uir6ACl/JvoKpaDRtrGTxa2yHQz0Uyw4qISHc9gz2wIrjueQPVNWqcz7qG62U1UGu0cLCTI7BDq3sepmfO3FrZ4rVnQ/Has6HQarW4lHcDuQUVqK6pOw96eTggwMfZbB6WZWhWVjI88qBv/VXn62U19VlAEABnh7osYAlXZKX/CqiBnH0nkLPvRP3PO0a+AQAY8/ti7B4/t8l2u+6wzhK4utia7cMgTMHaWobugebzYCxTEwQB7ds6on1b6Q2lIiLDsLWxQngX0z2Q0NwIgoCO7Z1bxJXIprRytjHb6UfvFQN9C7Ft0Ktil0BERERERsBrzUREREREEsZAT0REREQkYQz0REREREQSxjH0Zkpub4vojPVil9FscnvLnSWAiIiIyJwx0JspQRDMal53IiIiIjJPHHJDRERERCRhDPRERERERBLGQE9EREREJGEM9EREREREEsZAT0REREQkYQz0REREREQSxkBPRERERCRhDPRERERERBLGQE9EREREJGEM9EREREREEsZAT0REREQkYQz0REREREQSxkBPRERERCRhDPRERERERBLGQE9EREREJGEM9EREREREEsZAT0REREQkYQz0REREREQSJhe7AGqcVquFqrJa7DKaTW5vC0EQxC6DiIiIqMVhoDdTqspqbOj0tNhlNFt0xnpYO9iJXQYRERFRi8MhN0REREREEsZAT0REREQkYQz0REREREQSxkBPRERERCRhvCmWiMjC1dSqcfZiCVIzSnCjohYAUFGpQlJqIUID3WBrYyVyhUREdC8Y6ImILFBZeQ3W78jAN9sv4uT5ItTUahqsLymrQeST22EtlyG8ixueebQznh3dGa4utiJVTERE+mKgJyKyIKU3ajB3ZTK+3HKhvjf+TmpVGiSlKpGUqsSbS5MwdWwgPpzRi8GeiEhCGOgtiFe/EIzYMq/BstrySpRm5iNj0wGkrf0VWrWmidZEJHX7jubiubkJuJxfrlf7iioV/vNDGrbGX8KauQ/gkQd9DVwhEREZAwO9BcrckoCc+GRAEGDv6YrOEwai97wpaBXYHkfmrBa7PCIyMK1Wi/e/OIn3Vp00yP7yCiowavoexEwNxfx/RfIp0EREZo6B3gIVpWQhc3NC/c/p63bjsYSlCHpqKJLnb0R1UamI1RGRob25NAkLvjpj8P1++nUKKqrUWPZGX4Z6IiIzxmkrWwBVZTUKky9CkMng0qGt2OUQkQEt/z7VKGH+phUbz2H+WuPtn4iI7h0DfQvh3LEuyFdfuyFyJURkKOezrmHO54k6tUncGIUreychcWNUs9u8u/IETp0v0rU8IiIyEQZ6CyS3t4GtmzNs3V3g2tUPfT5+Hu6hAShMvojSzHyxyyMiA1CrNZj6zgFU16h1aufl4QCfto7w8nBodhuVSosp7xxATa1uxyIiItOw+ECvVCoRExODzp07w87ODr6+vpg1axbKy8vx3HPPQRAErFixQuwyDapnzCQ8mfo1njz7Fcbu/xzBU0cgO+4o4qcsELs0IjKQTXuzcfRMocmOdzq9GOt3ZJjseERE1HwWfVPsqVOnMHLkSCgUCjg6OqJbt27Iy8vDsmXLkJGRgeLiYgBAjx49xC3UwNK/24PsX45AZi1H665+6D59LBy93aGurqnfRmYjx+g9C5G1NQFnlm6pX/7Akumw83TFvuiPxCidiJppZWyayY/5nx/OYerYQN4gS0RkZiy2h16pVGL06NFQKBSYPXs28vPzkZycDIVCgQULFiAuLg6JiYkQBAFhYWFil2tQpZkK5CekIDf+JM6u3IbfJs+HR49O6LfgxfptNDUqHJy5HKEzx6F1tw4AAL8RkfAZHoFDr60Uq3QiaoazF4tx4ITC5MdNTivC8RTTXRUgIqLmsdhAP3PmTOTk5GDGjBlYtGgRnJ2d69fFxMQgPDwcKpUKHTt2hIuLi4iVGl9hUjoyNh2A/9j+8IzoUr+86EwmUldtx4PL/gkHbzf0W/gSjr31JSqvlohYLRHdTVzClRZ5bCIiapxFBvq0tDTExsbCw8MDn3zySaPb9OrVCwAQHh7e5H5GjhwJQRDw3nvvGaNMkzq9eBM0KjV6zpnYcPmSzdCo1YjauxCKQ2eRte2QSBUSUXOdOCfejDMnzilFOzYRETXOIgP9xo0bodFoEB0dDScnp0a3sbe3B9B0oP/xxx9x6tQpY5VocmXZCmRtO4R2A8LQpk9w/XKtSo3CxHTYubfCn7H7RayQiJpLzFB94lwRtFqtaMcnIqLbWWSgj4+PBwAMHjy4yW1ycnIANB7oS0tL8a9//QuLFi0yToEiObO0rjf+1l76Nn2C0XniYKSt/RW9358KKzsbESskoua4rBDveRJXiyqhUjHQExGZE0FrgV0tvr6+yMnJwcmTJxudwUalUsHb2xtKpRIZGRkICAhosP6f//wnUlJS8Pvvv0MQBMydO/eeht1ERERAodDtBjZrrQxzNb31PmZzyB3sEPXbIpxbvQPnv9mNkVvfh/J0BhLnrtN5X/Nkx1EraAxfJBE1oIWAPLf3mlyfuDHqjnPMe3nYQ24lg0qtgUJZ2eR2CmUFIp/c3ug67+KPIENNo+vMXb7ra9DIWkGmuQ7va5+LXY4o+B4QmScvLy8kJSXp1dYip60sLy8HAFRWNv5hFRsbC6VSCWdnZ/j7+zdYl5SUhDVr1uDEiRMGq0ehUCA3N1enNjaCFdDWYCU0KvK9Z3HjcgHOr9sFADg4awWi9i3C5Z3HcPWoblPi5eXnoUbLh84QmURrNSBYNbrq5oOj7kZuJWvWdo3Jz7sCaFV6tRWdsxqQARq1WufzssXge0BkcSwy0Ht5eaGkpATJycno169fg3X5+fmYM2cOACAsLKzBfMpqtRovvvgiZsyYgZCQEIPWoytrrQwwYod3+yE94R/VH9uGzq5fVnbpKk58tAH9F0/H9iGzoaqsbvb+2nm3Yw89kYnkayugEZwbXadQVtyxrS499I0RtFXwbtcWUp2JPt/KChoAMisreLdvL3Y5ouB7QGSe9MmLN1lkoB82bBjS0tKwYMECDB8+HEFBQQCAxMREPPPMM1Aq624o+/twnBUrVuDq1asGn9VGn8sntRVV2NDpaYPWcavc+JP4vuvk25afX7ervsdeFxcuXoC1g50hSiOiuxg1fTd+TchpdF1Tw2RuurJ3EnzaOkKhrITv8B90PvaAyI74/avGjy0FPsM2IregAt5e3sg5K93XcS/4HhBZHou8KTYmJgbu7u64cuUKQkJCEBoaisDAQPTu3RsBAQEYMmQIgIY3xCqVSrzzzjt49913oVKpcO3aNVy7dg0AUFVVhWvXrkGjYQ80EYmvVzePFnlsIiJqnEUGeh8fHyQkJGDUqFGws7NDdnY23NzcsHr1asTFxeHChQsAGgb6nJwclJWV4cUXX0Tr1q3r/wOABQsWoHXr1rh8+bIor4eI6FaDI71b5LGJiKhxFjnkBgCCg4OxY8eO25bfuHED2dnZkMlk6N69e/3yzp07Y//+2+dhHzx4MCZPnowpU6bc09gmIiJDGRTpjS4dWyE9+7pJj+vn7YiRD/iY9JhERHR3Fhvom5KamgqtVougoCA4OPxvajcnJycMGjSo0TYdO3Zsch0RkakJgoBXJgZj1oKjJj3uSxOCYWVlkRd2iYgkrcWdmVNSUgA0/YRYIiIpmDo2EL5e+k07qY+27vZ4cUJXkx2PiIiaj4H+LrRarcFnvRFTh1F90Xf+Cw2WdZ44GFPyN8FvRKRIVRGRrpwdbfDlew+Y7HhfvHM/3FrZmux4RETUfAz0LYzfI31wedfx+p+dfDwRFD0MBUnpIlZFRPp46H4fnXvNFcoK5Fwtv+t89beKHtUJY4d01LE6IiIylRY3hj4+Pl7sEozKxsUBY/YvhpWdDSrylJDZWsPZry0yNv2BI2+sQdvILjg4a0XdxoKA+z97Gcf+vRaRc2+fk56IzN+yN/riUt4N7DrUvPnE7zZP/d8NjPDCmrmmuxJARES6a3E99JauprQCmVsTcO7LOGwfPgfH3/0ahckXcPj1L+DdvzsKEtOhVakBACEvjkZB4nkUnckUuWoi0peNtRW2LB6KRwf4Gnzfw/u1w44VD8HersX1/RARSQoDvQVy6+6P4pQsAIB7WCcUn637s9+ISFzaWTfcxrWLLzqM6oPTSzaLVicRGYa9nRxblwzDhzN6wVp+76d1KysB77zYAztWPAQnB2sDVEhERMbEQG+B3EI61od497AAFP0V7tsN6oHc+JMAgLZ9guHk2wbjDy/H48dXwvO+QPRb+BK6PPuQaHUTkf7kchnentYDJ34Yg75hnnrv575gdxzfEIX3p/eCjbWVASskIiJj4XVUC+Pg5QZotahQFAMA3II74MzSzfDoGYjrF3OhqqgCAKR/uwfp3+6pbzdi8zycW7MDl3clilI3ERlGaJAbDn83GsdTCrEyNg2xu7NQXaO+YxtruQwTHvLHKxODcX+PNhAEwUTVEhGRITDQWxi37v71vfMAUFNajq6TH0Z1cVmD2W2IyHIJgoA+YW3QJ6wNVr/bH2culODEOSXO/lmCGxW10GoBR3s5uge2Rq9gD4R3ceM4eSIiCeMZ3MLk7DuBnH0n6n/eMfINAMCY3xdj9/i5TbbbdYd1RCRddrZy9A71RO9Q/YfhEBGReWOgbyG2DXpV7BKIiIiIyAh4UywRERERkYQx0BMRERERSRgDPRERERGRhDHQExERERFJGG+KNVNye1tEZ6wXu4xmk9vbil0CERERUYvEQG+mBEGAtYOd2GUQERERkZnjkBsiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJk4tdADVOq9VCVVktdhnNJre3hSAIYpdBRERE1OIw0JspVWU1NnR6Wuwymi06Yz2sHezELoOIiIioxeGQGyIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiImoxtFptg/8TEVkCznJDREQWSaPRYu+RXMQfz0NSqhLJaUW4VlYDAMgrrES7oRvRK9gdESEeGDXAFxEhniJXTESkHwZ6IiKyKNdKq7FmczpW/XgeWbllTW6XX1iBHYUV2HHgCt5bdRIRIR54ZWIwokd1go21lQkrJiK6Nwz0FsSrXwhGbJnXYFlteSVKM/ORsekA0tb+Cq1aI1J1RETGF3fgMqa9fwh5BRU6t01KVeIf7yZg6YZUfPPhAIR3cTdChUREhsdAb4EytyQgJz4ZEATYe7qi84SB6D1vCloFtseROavFLo+IyOCqqlV45aPD+Prni/e8r9PpxYh4chs+mN4L//ePMD4Fm4jMHgO9BSpKyULm5oT6n9PX7cZjCUsR9NRQJM/fiOqiUhGrIyIyrIpKFaJm7sVvx/IMtk+VSos3lyYhr7ACS/+vL0M9EZk1znLTAqgqq1GYfBGCTAaXDm3FLoeIyGBqazUY/9pvBg3zt1r+/Tm8sSTRKPsmIjIUBvoWwrljXZCvvnZD5EqIiAxn/lensetQjlGP8enXKYg7cNmoxyAiuhcccmOB5PY2sHVzrh9D3+XZh+AeGoDC5IsozcwXuzwiIoM4c6EYH6w+pVObxI1R8PJwgEJZgcgntze73QvzDiF1a1u0drHVsUoiIuNrET30SqUSMTEx6Ny5M+zs7ODr64tZs2ahvLwczz33HARBwIoVK8Qu02B6xkzCk6lf48mzX2Hs/s8RPHUEsuOOIn7KArFLIyIyCK1Wi2nzDqJWpdvMXV4eDvBp6wgvDwed2uUXVuDtZUk6tSEiMhWL76E/deoURo4cCYVCAUdHR3Tr1g15eXlYtmwZMjIyUFxcDADo0aOHuIUaUPp3e5D9yxHIrOVo3dUP3aePhaO3O9TVNfXbyGzkGL1nIbK2JuDM0i31yx9YMh12nq7YF/2RGKUTETXL8ZRCHEspNOkx122/iI9nRsCVvfREZGYsuodeqVRi9OjRUCgUmD17NvLz85GcnAyFQoEFCxYgLi4OiYmJEAQBYWFhYpdrMKWZCuQnpCA3/iTOrtyG3ybPh0ePTui34MX6bTQ1KhycuRyhM8ehdbcOAAC/EZHwGR6BQ6+tFKt0IqJm+c8PaSY/ZmWVGt9sv/dpMYmIDM2iA/3MmTORk5ODGTNmYNGiRXB2dq5fFxMTg/DwcKhUKnTs2BEuLi4iVmpchUnpyNh0AP5j+8Mzokv98qIzmUhdtR0PLvsnHLzd0G/hSzj21peovFoiYrVERHdWVa3Cj3uyRDn2t7/8KcpxiYjuxGIDfVpaGmJjY+Hh4YFPPvmk0W169eoFAAgPD69f9vvvv0MQhNv+k/qQnNOLN0GjUqPnnIkNly/ZDI1ajai9C6E4dBZZ2w6JVCERUfOkXCxBdY1alGOfuViMqmqVKMcmImqKxY6h37hxIzQaDaKjo+Hk5NToNvb29gAaBvqb/vOf/+C+++6r/9nR0dE4hZpIWbYCWdsOodP4AWjTJxgFx+ouV2tVahQmpsMjrBP+jN0vcpVERHd34pxStGOrVFqcuVCC3qGeotVARPR3FttDHx8fDwAYPHhwk9vk5NTNXdxYoO/WrRv69u1b/19oaKhxCjWhM0vreuNv7aVv0ycYnScORtraX9H7/amwsrMRsUIiortLzbgm8vE5LJGIzIug1Wq1YhdhDL6+vsjJycHJkycbHS6jUqng7e0NpVKJjIwMBAQEAKgbcjN48GDs378fgwYNMkgtERERUCgUOrWx1sowV9PbIMdvitzBDlG/LcK51Ttw/pvdGLn1fShPZyBx7jqd9zVPdhy1gm7TxxER6aPEcSwqbHs2uu7mPPNN8fKwh9xKBpVaA4Wy8o7HaWqu+lblv8Kp+phuRZuRfNfXoJG1gkxzHd7XPhe7HCL6i5eXF5KS9Jse12KH3JSXlwMAKisbP2HHxsZCqVTC2dkZ/v7+t62fOHEilEol3N3dERUVhfnz58PDw0OvWhQKBXJzc3VqYyNYAW31OlyzRb73LG5cLsD5dbsAAAdnrUDUvkW4vPMYrh7VbQaJvPw81GjFGdNKRC1M+3KgiZkjb84zfzdyK1mztmvM9esluF6k2zndrDirARmgUat1/mwiIvNksYHey8sLJSUlSE5ORr9+/Rqsy8/Px5w5cwAAYWFhEAShfl2rVq0wZ84cDBgwAE5OTjhy5Ag++eQTHD16FElJSbCzs9OrFl1Za2WAETu82w/pCf+o/tg2dHb9srJLV3Hiow3ov3g6tg+ZDVVldbP31867HXvoicgkrjnIUd7EOoWy4o5tde2hb4yriwMc7do3p1SzlG9lBQ0AmZUVvNtL93UQWRp98uJNFjvkZubMmVi+fDl8fX2xb98+BAUFAQASExPxzDPPIDMzE7W1tZg+ffpdnxL7yy+/ICoqCl999RWmTp1qivJRW1GFDZ2eNsmxDCE6Yz2sHXT/skNEpKvVP53HSx/oNyPXlb2T4NPWETlXy+E7/Ae99nH8+yhEdpfuTbE+wzYit6AC7ds4IGffk2KXQ0QGYLE3xcbExMDd3R1XrlxBSEgIQkNDERgYiN69eyMgIABDhgwB0PgNsX/36KOPwtHRUe9xTUREZDi9urmLdmy5XEBoYGvRjk9E1BiLDfQ+Pj5ISEjAqFGjYGdnh+zsbLi5uWH16tWIi4vDhQsXADQv0N9069AcIiISR2igG+xsrUQ5dniQO+xsLXa0KhFJlEWflYKDg7Fjx47blt+4cQPZ2dmQyWTo3r37Xfezfft2lJeXo3dv4846Q0REd2drY4WJDwfgm+0XTX7syVGdTX5MIqK7sehA35TU1FRotVoEBQXBwaHh9GZPP/00AgICcN9999XfFPvpp5+iR48emDRpkkgVExHRrV6ZGGzyQO9gJ8ezowNNekwiouZokYE+JSUFQOPDbUJCQvD9999jyZIlqKyshI+PD1544QXMnTsXNjZ86BIRkTnoHeqJ+3u0weFTBSY75j8eC0IrZ34OEJH5sdgx9Hdyp0D/5ptvIiUlBaWlpaitrUVWVhY+//xztGrVytRlGkWHUX3Rd/4LDZZ1njgYU/I3wW9EpEhVERHpbvU7/WFjbZqPMZ+2jvhwRi+THIuISFcM9C2M3yN9cHnX8fqfnXw8ERQ9DAVJ6SJWRUSku+6Bbpj7UuNPjG2KQlmBnKvld52v/u/WzH2AvfNEZLZa5JCb+Ph4sUswGhsXB4zZvxhWdjaoyFNCZmsNZ7+2yNj0B468sQZtI7vg4Ky/5t0XBNz/2cs49u+1iJw7WdzCiYj0EDM1DEdOF2DHgSvN2j7yye06H+PtF8Ix4gEfndsREZlKi+yht2Q1pRXI3JqAc1/GYfvwOTj+7tcoTL6Aw69/Ae/+3VGQmA6tSg0ACHlxNAoSz6PoTKbIVRMR6Ucul+HHRUPw8P3GeeLpq8+E4AMOtSEiM8dAb4HcuvujOCULAOAe1gnFZ+v+7DciEpd21g23ce3iiw6j+uD0ks2i1UlEZAj2dnJsXz4c0x7vYrB9WstlWPhab3z2eh8+g4SIzB4DvQVyC+lYH+LdwwJQ9Fe4bzeoB3LjTwIA2vYJhpNvG4w/vByPH18Jz/sC0W/hS+jy7EOi1U1EpC8bayusfvcB7Fr1MHzaOt7Tvnp180By7Bi8PiWUYZ6IJKFFjqG3ZA5eboBWiwpFMQDALbgDzizdDI+egbh+MReqiioAQPq3e5D+7Z76diM2z8O5NTtweVeiKHUTERnCw/19kLp1HL7aegErf0zDxUulzW57f482ePmJYEwaEQC5nP1dRCQdDPQWxq27f33vPADUlJaj6+SHUV1c1mB2GyIiS+XiZIN/PdMdM6NDsP94PvYn5uHEuSIkpylRUFzXqSEIgJ+3E3oFe6BXN3c88qAvenR1F7lyIiL9CFqtVit2EXS72ooqbOj0tMH2N+b3xdg9fi6qiprfW6WL6Iz1sHawM8q+iYgMRavVQqXSQi4XWuxwGp9hG5FbUIH2bRyQs+9JscshIgNgD30LsW3Qq2KXQEQkOkEQYG3dMoM8EVkuDhIkIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCeNNsWZKbm+L6Iz1YpfRbHJ7W7FLICIiImqRGOjNlCAInAaSiIiIiO6KQ26IiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMgZ6IiIiISMIY6ImIiIiIJIyBnoiIiIhIwhjoiYiIiIgkTC52AdQ4rVYLVWW12GU0m9zeFoIgiF0GERERUYvDQG+mVJXV2NDpabHLaLbojPWwdrATuwwiIiKiFodDboiIiIiIJIyBnoiIiIhIwhjoiYiIiIgkjIGeiIiIiEjCGOiJiIiIiCSMs9wQERFZsMLiSpw4V4QT55T480opiq/XTYl8rawGazadR69uHuge2Bo21lYiV0pE+mKgJyIisjA1tWr8HH8JK2PT8EeSotFtyitVmPb+IQCAi5M1JkcF4uUnghEc4GrCSonIEAStVqsVuwi6XW1FFeehJyIinW3em4WZC44ir6BCr/ZjBvth5dv3o10bRwNXRkTGwh56C+LVLwQjtsxrsKy2vBKlmfnI2HQAaWt/hVatEak6IiIyJmVJFaZ/fBg/7s66p/1s238ZfyQpsPT/+uKZ0Z35FHAiCWCgt0CZWxKQE58MCALsPV3RecJA9J43Ba0C2+PInNVil0dERAaWlVOGYdN2IjOnzCD7u1ZWg8n/PoBT6UX47PU+DPVEZo6B3gIVpWQhc3NC/c/p63bjsYSlCHpqKJLnb0R1UamI1RERkSFdzr+Bgf+IwxVFucH3vfi7VGg0wOIYhnoic8ZpK1sAVWU1CpMvQpDJ4NKhrdjlEBGRgVTXqPHojD1GCfM3Ld2QilWxaUbbPxHdOwb6FsK5Y12Qr752Q+RKiIjIUN7/4iRSLpbo1CZxYxSu7J2ExI1RzW4z5/NEZObw6i6RubL4QK9UKhETE4POnTvDzs4Ovr6+mDVrFsrLy/Hcc89BEASsWLFC7DINSm5vA1s3Z9i6u8C1qx/6fPw83EMDUJh8EaWZ+WKXR0REBnDinBILvj6jczsvDwf4tHWEl4dDs9tUVKnw3NyD4MR4RObJosfQnzp1CiNHjoRCoYCjoyO6deuGvLw8LFu2DBkZGSguLgYA9OjRQ9xCDaxnzCT0jJnUYFl23FEce/NLkSoiIiJD+2jNKajVpgvYvyfmI+GEAgMivE12TCJqHovtoVcqlRg9ejQUCgVmz56N/Px8JCcnQ6FQYMGCBYiLi0NiYiIEQUBYWJjY5RpU+nd7sPuJedgb/RGSPvgOVcVlcPR2h7q6pn4bmY0cY35fjLBZ4xq0fWDJdAzb8LapSyYiIh3kKMqxbf9lkx935Y8cS09kjiw20M+cORM5OTmYMWMGFi1aBGdn5/p1MTExCA8Ph0qlQseOHeHi4iJipYZXmqlAfkIKcuNP4uzKbfht8nx49OiEfgterN9GU6PCwZnLETpzHFp36wAA8BsRCZ/hETj02kqxSiciomZYuzUdGo3ph79s3peNgqJKkx+XiO7MIgN9WloaYmNj4eHhgU8++aTRbXr16gUACA8Pv23d1q1bcf/998PR0RGtWrVC//79kZqaatSajakwKR0Zmw7Af2x/eEZ0qV9edCYTqau248Fl/4SDtxv6LXwJx976EpVXdbvBioiITGt/ojj3Q6lUWhw6dVWUYxNR0ywy0G/cuBEajQbR0dFwcnJqdBt7e3sAtwf6ZcuW4YknnsADDzyA7du3Y+PGjRg2bBgqK6XdI3F68SZoVGr0nDOx4fIlm6FRqxG1dyEUh84ia9shkSokIqLm0Gi0SE4rEu34J84pRTs2ETXOIm+KjY+PBwAMHjy4yW1ycnIANAz0GRkZmDNnDhYvXowZM2bUL3/kkUeMVKnplGUrkLXtEDqNH4A2fYJRcKxuHKRWpUZhYjo8wjrhz9j9IldJRER3k3GlFGXltaIdX8wvE0TUOIsM9JcuXQIAdOjQodH1KpUKhw7V9UTfGui/+uorWFtb44UXXjBoPREREVAoFDq1sdbKMBe9DVrHmaWb4T+2P3rOmYjdj78HAGjTJxidJw5G2tpf0fv9qdg+fA7UVTV33lEjggKDUCtoDFovERHdrlruC7g83+i6xI1Rd52O0svDvv7/V/ZOanI7hbICkU9uv235vt+PwsfnOR0qJqLm8PLyQlJSkl5tLTLQl5fXPTGvqWEysbGxUCqVcHZ2hr+/f/3yw4cPo0uXLli/fj0+/PBDXLlyBYGBgXj33Xfx5JNP6l2PQqFAbm6uTm1sBCtAx4e6Ko6kYp33402uv34xF9/6/G/IjdzBDg8smY4TH23A+W92Y+TW93Hfm08hce463Q4MIC8/DzVatc7tiIhIR46OQBNzOdycY7455FayZm97q9pajc6faURkXBYZ6L28vFBSUoLk5GT069evwbr8/HzMmTMHABAWFgZBEBqsy83NxZtvvokFCxbA19cXa9euxVNPPQVPT08MGzZM73p0Za2VAUbu8I5871ncuFyA8+t2AQAOzlqBqH2LcHnnMVw9qtvUZO2827GHnojIBGqsWqOwiXUKZcVd23t52ENuJYNKrYFC2fT9YU3ty9paQJv27ZtTKhHpQJ+8eJOgtcDHvs2cORPLly+Hr68v9u3bh6CgIABAYmIinnnmGWRmZqK2thbTp09v8JTYoKAgXLx4EVu3bsXYsWMBAFqtFj169ICrqyv++OMPk72G2ooqbOj0tNH2335ITwxc+S9sGzob5bn/u8Gp65QR6Pbio9g+ZDZUldXN3l90xnpYO9gZo1QiIrpFXkE52g/7Qe/2V/ZOgk9bR+RcLYfvcN33M+Ehf/y4aIjexyciw7PIWW5iYmLg7u6OK1euICQkBKGhoQgMDETv3r0REBCAIUPqTkR/n+HGzc0NABr0xAuCgGHDhuHs2bOmewEmkBt/Et93ndwgzAPA+XW7sKXfDJ3CPBERmU67No7w9rzzOHlj6tXNXbRjE1HjLDLQ+/j4ICEhAaNGjYKdnR2ys7Ph5uaG1atXIy4uDhcuXABwe6APCQlpcp9VVVVGrZmIiKi5egWLF6p7dfMQ7dhE1DiLDPQAEBwcjB07dqCsrAxlZWU4duwYpk2bhvLycmRnZ0Mmk6F79+4N2owZMwYAsGfPnvplGo0Ge/fuRWRkpEnrJyIiasroQX6iHLe1iw3uD9dxxgYiMjqLvCn2TlJTU6HVahEUFAQHh4aXLEePHo0HH3wQ06ZNQ1FREfz8/PDll18iNTUVe/fuFaliIiKihp56pBNe/+y4yeejnzo2CA72LS46EJk9i+2hb0pKSgqA24fbAHXj5bdv347x48fjrbfeQlRUFC5duoRff/21ftw9ERGR2JwcrDE5KtDkx31pQleTH5OI7o6B/m9cXV2xevVqFBYWorq6GsePH8fDDz9syhKJiIju6u0XwuHWytZkx3tlYjACO7Qy2fGIqPkY6FuYDqP6ou/8hk/C7TxxMKbkb4LfCN4nQEQkFV4eDlj+Rr+7b2gAHds5YcGr/IwgMlctbiBcfHy82CWIyu+RPsj46ff6n518PBEUPQwFSeniFUVERHp58pEA/PLHZfywK7PZbW4+MKo5D6ECAGu5DOs+HAAnB2u9aiQi42txgd7S2bg4YMz+xbCys0FFnhIyW2s4+7VFxqY/cOSNNWgb2QUHZ/31MC1BwP2fvYxj/16LyLmTxS2ciIh0JggC1n04AMWl1dhzOLdZbSKf3N7s/VtZCdgwfxAGRnjrWyIRmUCLG3Jj6WpKK5C5NQHnvozD9uFzcPzdr1GYfAGHX/8C3v27oyAxHVqVGgAQ8uJoFCSeR9GZ5vfsEBGRebG1scK2pcPw2NAOBt2vna0VNn8+FBMe8jfofonI8BjoLZBbd38Up2QBANzDOqH4bN2f/UZE4tLO4wAA1y6+6DCqD04v2SxanUREZBh2tnJs/nwoVr/b3yBDY/r3bIszmx7DmMGG/ZJARMbBQG+B3EI61od497AAFP0V7tsN6oHc+JMAgLZ9guHk2wbjDy/H48dXwvO+QPRb+BK6PPuQaHUTEZH+BEHAtMe74uyWxzBpRADkckHnffh6OWLZG33xx1ePcEYbIgnhGHoL4+DlBmi1qFAUAwDcgjvgzNLN8OgZiOsXc6GqqAIApH+7B+nf/u+JuCM2z8O5NTtweVeiKHUTEZFhdGjnjI2fDsbnhX3w5ZZ0/LQnC+cyr0Gt1ja6vauzDfr3bIsXxnfBqAd9IZezr49IahjoLYxbd//63nkAqCktR9fJD6O6uAyXdx0XsTIiIjIlb08HvPNiT7zzYk9UVKpw+kIR/rxcispqNeRWMrg626BHVzf4t3eGIOjem09E5kPQarWNf2UnUdVWVGFDp6cNtr8xvy/G7vFzUVVUarB93io6Yz2sHeyMsm8iIiIiahp76FuIbYNeFbsEIiIiIjICDpQjIiIiIpIwBnoiIiIiIgljoCciIiIikjDeFGumtFotVJXVYpfRbHJ7W86SQERERCQCBnoiIiIiIgnjkBsiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgljoCciIiIikjAGeiIiIiIiCWOgJyIiIiKSMAZ6IiIiIiIJY6AnIiIiIpIwBnoiIiIiIgn7f2Ec/HOg66AzAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 956.385x618.722 with 1 Axes>"
       ]
@@ -88,9 +88,7 @@
    "id": "bd1617a1-e793-43a9-a24a-c81c18fd2a1e",
    "metadata": {},
    "source": [
-    "### Specify some observables\n",
-    "\n",
-    "Next, we specify a list of observables whose expectation values we would like to determine."
+    "### Specify an observable"
    ]
   },
   {
@@ -100,9 +98,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit.quantum_info import PauliList\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
-    "observables_0 = PauliList([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])"
+    "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])"
    ]
   },
   {
@@ -127,7 +125,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1290.83x702.333 with 1 Axes>"
       ]
@@ -163,9 +161,9 @@
    "id": "088431d9-de2f-4b5d-90d2-7e7a5947dcb5",
    "metadata": {},
    "source": [
-    "### Create observables to go with the new circuit\n",
+    "### Create observable to go with the new circuit\n",
     "\n",
-    "These observables correspond with `observables_0`, but we must account correctly for the extra qubit wire that has been added (i.e., we insert an \"I\" at index 4).  Note that in Qiskit, the string representation qubit-0 corresponds to the right-most Pauli character."
+    "This observable corresponds with `observable`, but we must account correctly for the extra qubit wire that has been added (i.e., we insert an \"I\" at index 4).  Note that in Qiskit, the string representation qubit-0 corresponds to the right-most Pauli character."
    ]
   },
   {
@@ -175,7 +173,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "observables_1 = PauliList([\"ZIIIIIII\", \"IIIIZIII\", \"IIIIIIIZ\"])"
+    "observable_expanded = SparsePauliOp([\"ZIIIIIII\", \"IIIIZIII\", \"IIIIIIIZ\"])"
    ]
   },
   {
@@ -198,7 +196,7 @@
     "from circuit_knitting.cutting import partition_problem\n",
     "\n",
     "partitioned_problem = partition_problem(\n",
-    "    circuit=qc_1, partition_labels=\"AAAABBBB\", observables=observables_1\n",
+    "    circuit=qc_1, partition_labels=\"AAAABBBB\", observables=observable_expanded.paulis\n",
     ")\n",
     "subcircuits = partitioned_problem.subcircuits\n",
     "subobservables = partitioned_problem.subobservables\n",
@@ -243,7 +241,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1058.03x367.889 with 1 Axes>"
       ]
@@ -265,7 +263,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkEAAAEvCAYAAABCEhUaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA5hklEQVR4nO3deVxV9b7/8ddmBsUBQVExJyAVFVMkxTIp66Tm1GhZacf0aJlWJo9T2cnubUI9HrOy7DYdz+0q9+Sxm5qlJ/WXURam5mxOqCg73YoDisiwfn/sxBBQNm5Y7L3ez8djP5A1fT8bYfHmu77ru2yGYRiIiIiIWIyP2QWIiIiImEEhSERERCxJIUhEREQsSSFIRERELEkhSERERCxJIUhEREQsSSFIRERELEkhSERERCxJIUhEREQsSSFIRERELEkhSERERCxJIUhEREQsSSFIRERELEkhSERERCxJIUhEREQsSSFIRERELEkhSERERCxJIUhEREQsSSFIRERELEkhSERERCxJIUhEREQsSSFIRERELEkhSERERCxJIUhEREQsSSFIRERELEkhSERERCxJIUhEREQsSSFIRERELEkhSERERCxJIUhEREQsSSFIRERELEkhSERERCxJIUhEREQsSSFIRERELEkhSCrtxIkTTJ06ldWrV5tdisc4efIkTzzxBM2bNycoKIi4uDjeeecdDMMwuzQRqQKdB12TnZ3N888/z+23305ERAQ2m42RI0eaXVYJhSCptBMnTvDSSy/ph7+Szp8/z6233sq7777Lfffdx5tvvsm1117LY489xksvvWR2eSJSBToPumbnzp28+uqrbNu2je7du5tdThl+Zhcg4q3ef/99MjIymD17Nk888QQAo0eP5q677uLVV1/lkUceoWXLliZXKSJSfbp168aRI0eIiIjA4XAQERFhdkmlqCfIi50/f55p06bRpUsXQkJCqF+/PgkJCbz11lsl24wcORKbzVbu/r/vtly9ejWtW7cG4KWXXsJms2Gz2WjVqpVLNV045sqVK+nZsychISFERUWRmpoKQE5ODqNGjaJx48aEhIRwxx13cPjw4TLHyczM5KGHHqJJkyYEBgbStm1bnnvuOc6ePVuyzTvvvIPNZuPzzz8vs39xcTFRUVF06dKl1PJ169YxdOhQwsPDCQwM5Nprr+WVV16hsLDQpfcJ8D//8z+EhIQwevToUsuffPJJCgoKSEtLc/mYIuIanQfNPQ+GhobWuuBTiiFeKT8/3+jTp48BGLfddpsxffp048033zTGjBljJCcnl2w3YsQIo6JvA8AYMWKEYRiGYbfbjb/97W8GYAwdOtT4xz/+YfzjH/8wFi1a5FJdgNG5c2cjLCzM+POf/2zMmTOnpM5Zs2YZ1113nTF06FDj7bffNp566inD19fXuOWWW0odIzMz04iIiDACAgKMJ5980nj77beNe+65xwCM3r17GwUFBYZhGMbx48eNwMBA46677ipTx/Llyw3AmDlzZsmyJUuWGAEBAUaHDh2MV1991Xj33XeNESNGGD4+Psbdd9/t0vssKioygoODjV69epVZd+7cOcNms7l8TBFxjc6D5p4HL3X06NFSX8/aQCHIS6WmphqA8eyzz5ZZV1RUVPLvyv7wG4Zh7Nu3zwCMF198scp1AYbNZjPWrl1bsiw/P9+IjIw0bDab8cQTT5Ta/qmnnjIAY8eOHSXLHnjgAQMwli5dWmrbZ555xgCM999/v2TZ3XffbQQGBhrHjx8vte2DDz5o+Pn5Gb/++qthGIaRl5dnNGnSxLjxxhtLTh4XzJw50wCMVatWVfp9OhwOAzDuvffectdHREQYPXv2rPTxRMR1Og86mXUevFRtDEG6HOalPvnkExo2bMhf/vKXMut8fMz9b+/ZsyfXX399yecBAQEkJiZiGAYTJkwote2NN94IwK5duwBn9+3nn3/OddddR//+/Utt++yzz+Lj48OiRYtKlo0YMYL8/PxSl55yc3NZtGgRt99+O40bNwZgxYoV/PrrrzzyyCOcOHECh8NR8rrQzvLlyyv9Hi90RwcGBpa7PigoqFSXtYi4n86DTmadBz2BBkZ7qV27dtGlSxeCgoLMLqWMNm3alFnWsGFDgJLr7ZcuP3bsGABHjx4lNzeXuLi4MscICwujadOm7N27t2TZhR/wefPmMXbsWAAWLlzImTNnePjhh0u22759OwB//OMfK6z7119/rdT7AwgJCQEgPz+/3PXnzp0r2UZEqofOg05mnQc9gUKQxVU0GLAqA+Aqy9fX1+V1RhXn1fHz8+OBBx5g1qxZ7N69m+joaObNm0fDhg0ZNGhQmeNPnz69zCDBC5o1a1bpdhs2bEhwcDCHDh0qsy4/Px+Hw8FNN93k2psRkWqh82Dp47vrPOgJFIK8VGxsLDt27CA/P7/CSzLg/KsB4Pjx4yX/Bkr9FXFBRSeKmhQREUFoaChbt24tsy4nJ4fs7OwyP7wjRoxg1qxZzJs3j9GjR7N69WrGjBlT6usSExMDQJ06dejbt+9V1+nj40PXrl3ZsGFDmf+DH3/8EcMwSEhIuOp2RKRiOg9eZMZ50BNoTJCXGj58ODk5Obz88stl1v3+r4nY2FgA/v3vf5fa5q9//WuZ/erWrQs4TxRm8fHxYeDAgWzYsIEvv/yy1LrXX3+d4uJihg4dWmp5ly5d6Ny5M//93//NP/7xD4qLixkxYkSpbf7whz/QuHFjXn/99XLfX15eHqdPn3ap1vvvv5+zZ8/y3nvvlVo+a9Ys/Pz8uO+++1w6noi4RufBi8w6D9Z26gnyUhMnTmTx4sW8/PLLZGRkcNtttxEUFMTWrVvZuXNnyQ/7/fffz3PPPceYMWPYsWMHYWFhfPnllzgcjjLHbNSoEdHR0SxYsIC2bdvSpEkT6tSpw8CBA2v0vb366qusWLGCIUOG8NhjjxEdHc0333xDWloavXv3LvODDc6/giZNmkRqaiqxsbH06NGj1Po6deowb948hgwZwrXXXssf//hHoqOjOXHiBDt27OBf//oXixYtok+fPpWuc/To0Xz00Uc8/fTTZGZm0r59e7744gsWLVrElClTXJ5bRERco/NgaWacB4GSEHrhZpBNmzaVLOvduze9e/euwlfATUy7L02qXV5envHyyy8bHTp0MAIDA4369esbCQkJxttvv11qu7Vr1xpJSUlGYGCg0ahRI2P06NFGTk5Oubcy/vDDD0ZSUpIREhJiAEbLli1dqqm8YxpGxbeorlq1ygCMjz76qNTyvXv3Gg8++KARERFh+Pv7G61btzaeffZZ48yZM+W2a7fbDT8/PwMwXn755Qrr27x5szF8+HCjWbNmhr+/v9G4cWOjZ8+exn/8x38Yx44dc+m9GoZh5OTkGI8//rjRtGlTIyAgwGjfvr3x5ptvGsXFxS4fS0Rcp/PgRWadB4EKX1cz1YA72H4rUERERMRSNCZIRERELEljguSqHT16lKKiostuU7du3ZIBhZ4sNzeX3Nzcy27j6+tbu5+VIyJup/NgaZ5yHtTlsEtkZmby9ddfk5GRQUZGBps3b6agoIARI0bw8ccfm11erdSqVSv2799/2W1efPFFpk6dWjMFVaOpU6fy0ksvXXabli1bkpmZWTMFiUitoPNgaZ5yHlRP0CVmzZrFG2+8YXYZHuWTTz4hLy/vsttcOjvqjh07XGrjyJEj/O///i/33ntvyRTvV9KuXTuX2qiMhx9+mBtuuOGy2wQHB7u9XRGp3XQeLM1TzoMKQZcIDw+nf//+dO/enYSEBJYtW8acOXPMLqtW69WrV7W3cfToUd5++22Sk5Mr/cNfHdq0aVPudPfezDCgoMj5CvIHXwuOJCwqhnMF4O/rfNWC+fJqlGE437+B83vAx2LvvzJ0HvRMCkGXmDJlSqnP165da1IlIuY6kw8/7oX0XeD4bX40mw06NIMbYuHapt79y7C4GHZkw7e/wPbDzgAAEBEKvWIhsTWEVDwJsVc4ehq+2wU/7IGz553LAvygWyvn90DzhqaWJ3LVvP5vOofDQUpKCtHR0QQFBdGiRQsmTpzImTNnGDVqFDabjbfeesvsMkVqla2H4KXP4P/WXwxA4OwR2HoI5q6CN1dA7jnTSqxWp8/BGyvgvdWw7XcBCJzB4LOfnF+f7YdNKrCaGQZ8tRle+RxWbb8YgADOF8L3u2H6F/C/Pzp7yUQ8lVf3BG3cuJF+/fpht9upU6cOHTp04PDhw8yePZs9e/aUTAte0YPiRKxo2yH44P9B8RVumdh3FOZ8DRNuc14i8RZnz8Pb/wb7yctvl18I/7Ua/pTs7BXzJss2wfItV97uu11QUAgP9LTeJULxDl7bE+RwOBg4cCB2u51JkyaRnZ3N+vXrsdvtpKamsnTpUjIyMrDZbHTu3NnscuUK6tevz8CBA6lfv77ZpXi1cwUwL/3KAeiCwydg8YZqLanGfb7+ygHogmID/v6ts3fEW+w9UrkAdEHGPlh/+ZuixE10HnQ/rw1BEyZMICsri/HjxzNjxgxCQ0NL1qWkpBAfH09hYSGtWrWiXr16JlYqlREVFcW0adOIiooyuxSv9tM+ZxByRUYV9qmtzubDukwX9znvXSHg219c3ye9CvuI63QedD+vDEHbt28nLS2N8PBwXnvttXK36datGwDx8fFuaXPfvn0MGjSI0NBQGjZsyMMPP8yxY8fccmyB/Px89u/fT35+vtmleLXvdru+z/lCWLfP/bWYIWMfFF5+vrtyfbfL/bWYIfcc/HzQ9f32HoXsE24vRy6h86D7eeWYoPnz51NcXMzw4cMrnJ3zwhwG7ghBp0+fJjk5mbCwMObPn09eXh4pKSnccccdpKen4+NTtayZkJCA3W6/6vpqo5EjR7q0fXZ2Nh9++CF//OMfadq0cgMwNLml6+58ZS8+fgEu7/f6rI/Y+PkL1VBRzep65+u0uf5Bl/fbe/gMUVHXVkNFNSvsmq7c/PjnVdp3yP1/4tDmpW6uyLvpPOgekZGRrFu3rkr7emUIWrlyJQDJyckVbpOVlQW4JwS99957HDp0iG+++YZrrrkGcHZbJiUl8fnnnzNkyJAqHddut3Po0KGrrq82Onv2rEvbnzt3ruRjZff11q9dtbHZqhSAAPLyC7zi690hv2qDe2y+AV7x/gltW+VdT50+6x1fgxqk86D5vDIEXZi6vGXLluWuLywsJD09HXBPCFqyZAk33HBDSQAC6NmzJ23atGHx4sVVDkGRkZFXXVttFRIS4tL2QUFBJR8ru2/z5s1drsvq8s/kEFjH9clf/Mn3iq+3H1W75//82eNe8f7rhfgCYBgGNhdv9woJMLzia1CTdB50j6v5XemVIejMmTMAFU5hnpaWhsPhIDQ0lNatW191e9u2beOee+4pszwuLo5t27ZV+bhV7d7zBK5OF79161Y+/PBD+vfvT1xcXKX2efnll6tSmqWl/eCcA8ZV/zX9CVp88IT7C6phmQ6Y9ZXr+92W0IS5v/UuezLDgNeXwK+nXA1AsHbFf+PvW02FeSmdB83nlQOjL6TC9evXl1mXnZ3N5MmTAejcubPLf+2UJycnhwYNGpRZHhYWVjIXkYgn6BXj+j4tG0GLMPfXYoaWjSCqCu+lV6z7azGDzeacCdpVPdqiACQeyStDUN++fQFITU3ll18u3ruZkZFBcnIyDocD0CSJniQuLo7t27dX+q8fqZqoMLiu/KvI5fKxQX/33GBZK9hs0L8zuPKnUffWEOlF07Z0bwONXZg1JDQIerv/GZ1SDp0H3c8rQ1BKSgqNGjXi4MGDxMXF0alTJ2JiYkhMTKRNmzbcfPPNQPnjgdLT0wkPDy95zZw5E4AFCxaUWn5hTBFAw4YNOXHiRJljHT9+nLAwL/kTWSzjgZ6VmwHZxwbDenjfbMkdmsN9PSoXhDo0g/uur/aSalSQv3MW7Ebl31hbSp1AGJMMDVwb2iJSa3hlCIqKimLNmjUMGDCAoKAgMjMzCQsLY+7cuSxdurSkd6i8EFRQUMCxY8dKXhfGFeXn55daXlBwcXa49u3blzv2Z9u2bbRv376a3qW17Nu3j2HDhrFvn5dMSFOL+fvCmD7OHp76weVvE9MExt0Cid7xIOkyerR1vr/oJuWvbxACd3SBUTeBnxdeBmpUF578AyTFOB+YeilfH0hoBU/9wXsuhXoCnQfdzysHRoMzmCxZsqTM8tzcXDIzM/Hx8aFjx45l1vfp0wfDqOQzA35zxx138Nxzz5GVlVUyk+cPP/zAnj17mD59etXegJRy9uxZfv75Z5dvKZWq8fWB2zrCLR2cD0z95Dvns7KC/ODJ273r8k9FYiOdr+wTzq/Bii3Or0FIALww2Pk18mahQXBvIgy6Djbsdz40Nr/Q2VM0ZRDUDTK7QuvRedD9vPzHuKytW7diGAYxMTEu355YkTFjxtC0aVMGDx7MkiVL+PTTT7n//vtJTExk8ODBbmlDxAy+PtC5xcUHpAb6WyMA/V7TBtA37uLXwN/X+wPQ7wX5Q8/o330P+CkAifew0I+y0+bNmwH3PS4DoF69eqxcuZKmTZsybNgwHn30UZKSkliyZEmVZ4sWERGR6uW1l8MqUh0hCKBt27blXn4TERGR2sly3RTVFYKkejVv3pzU1FRLzH4qIlIenQfdz3I9QReeKyaepUGDBgwaNMjsMkRETKPzoPtZridIPNPx48f55JNPNAO3iFiWzoPupxAkHiE7O5uXX36Z7Oxss0sRETGFzoPupxAkIiIilqQQJCIiIpakECQiIiKWpBAkHqFOnTr06tWLOnXqmF2KiIgpdB50P8vdIi+eqVWrVrz//vtmlyEiYhqdB91PPUHiEYqKisjNzaWoqMjsUkRETKHzoPspBIlH2LFjB927d2fHjh1mlyIiYgqdB91PIUhEREQsSSFIRERELEkhSERERCxJIUhEREQsSbfIi0eIjY0lPT2d0NBQs0sRETGFzoPupxAkHsHf35+wsDCzyxARMY3Og+6ny2HiEQ4cOMBjjz3GgQMHzC5FRMQUOg+6n0KQeITTp0+zatUqTp8+bXYpIiKm0HnQ/RSCRERExJIUgkRERMSSNDBaTNGuXTuXtq9fvz4vvvgiSUlJNG3atJqqEhGpOToPmk8hSDxC06ZNmTp1qtlliIiYRudB99PlMBEREbEkhSARERGxJIUgERERsSSFIBEREbEkhSARERGxJIUgERERsSSFIBEREbEkhSARERGxJE2WKG5lGFBcYHYVrvHxB5vN7CpERKSmKQSJWxUXwKrZZlfhmuQJ4BtgdhUiIlLTdDlMRERELEkhSERERCxJIUhEREQsSSFIRERELEkhSETKZRhw9DRsPAD5hc5l5wvhFzucPW9ubSIi7qC7w0SkRHExbD8M3+2GPUfg3CXTHeQVwJyvnf8OrwudWkCvGAgPrflaRUSulkKQiFBswPe74d9bIedM5fZx5MKq7c5Xu6YwuCs0bVCtZYqIuJVCkJju5z2reebd5FLLggLqEBURS9+uDzGk1xP4+upbtbocy4UFa2HXr1U/xo5s2LUMbu8EN3cAX11oFxEPoN8sUmskd7mfxHb9MTDIOW1nxU/zeHfx0xw4sp2n7n7P7PK80s5s+PCbi2N+rkZRMSz92RmIHr0JgjUBpYjUcvp7TWqNmOZd6dvtQW7t9hD39pnM7CfWElE/imU/vs+J3KNml+d1th+G91a7JwD93p4jznFDeRo8LSK1nELQJTIzM/nggw8YO3Ys3bp1IyAgAJvNxsiRI80uzXKCA+rQrmUPDMPg8LE9ZpfjVbKOO3uAioqr5/gHfzt+cTUdX0TEHXQ57BKzZs3ijTfeMLsM+U32b+GnXkiYyZV4j8Ii+OQ7KCiq/D5P3w71guFUHsz8snL77PoVvtkJfdpXrU4RkeqmEHSJ8PBw+vfvT/fu3UlISGDZsmXMmTPH7LIs4VzBWU6ecWAYzjFBi79/l92HNtCuRSJREbFml+c1lm+B7JOu7VMvGBqEuN7W0p+hQ3NoXM/1fUVEqptC0CWmTJlS6vO1a9eaVIn1zFv+IvOWv1hq2Q0d7+SJoW+bVJH3OZ0HX2+rufYKiuCLn2HkjTXXpohIZXn9mCCHw0FKSgrR0dEEBQXRokULJk6cyJkzZxg1ahQ2m4233nrL7DIFGHD9GFJHr+CVUV/waP9UQkPCcJzMIsA/qGSb84X5PDojjk++fqXUvtMWjOS59/vVdMkeZ+2e6hsHVJFNB+FkXs22KSJSGV7dE7Rx40b69euH3W6nTp06dOjQgcOHDzN79mz27NnD8ePHAejSpYu5hQoAzcNj6BrbF4DEdv3o2PoGnppzA28sHMvzDy4AIMAvkJRh85g0pzc92t9B22bxpG/5jLXbF/Pe05vNLL/WKy6G73aZ0O5vEzHe3qnm2xYRuRyv7QlyOBwMHDgQu93OpEmTyM7OZv369djtdlJTU1m6dCkZGRnYbDY6d+5sdrlSjrhWSfTt+hCrf05ja+Z3Jctjo7px903PMG3Bwxw9kcWsT8fwxNC3Ca/fzMRqa7/DJyDnrDltbztkTrsiIpfjtSFowoQJZGVlMX78eGbMmEFo6MWHG6WkpBAfH09hYSGtWrWiXj2N2qythvd9AR8fX/7+1V8uWT4FXx8/xs26jvjoZJK7DDOpQs+Rddy8tg/n1PxlOBGRK/HKELR9+3bS0tIIDw/ntddeK3ebbt26ARAfH3/V7V0IW4mJiQQGBmKz2a76mOLUPDya5PhhbNj9NZv3rilZ7ufrT4dWSZw84+APCY+YWKHnOGhiCCosBruLd6SJiFQ3rwxB8+fPp7i4mOHDh1O3bt1ytwkODgbcE4J2797NwoULiYyMpHv37ld9PCnt/luex8fmw9+XX+wN2rx3DcvXfczgXuOZ8/lE8gs08vZKKvtg1OpywuT2RUQu5ZUDo1euXAlAcnJyhdtkZWUB7glBvXv3Jjs7G4CpU6eSnp5+1ccESEhIwG63u+VYNSXAL5j3xrs2+ja+bR9WTDcqXN+ySXu+mnZxZr+8/Fymp41kVL/XGdhzHJPevYkPlz3HuEF/q1LNMbExnC/0/hB146P/Q5OY3uWuuzAZYkXqBV38OHXo5dupaELFP47+E4c2L61ktbVP/+cyCKnflGx7NlFR1vtjx+rvX2qvyMhI1q1bV6V9vTIE7d+/H4CWLVuWu76wsLAkqLgjBPn4VE+Hmt1u59AhzxpRGuRfhRn1XDR38SQiw1ozKOkxbDYbk+/9mLGzutCr41A6tyn/l/zlZB8+zLkCk0YM16C8M7kVrqvsZIg+PlWbNBHg6JFsj/t+/r2ioqKSj578PqrK6u9fvJNXhqAzZ5z97nl55f91n5aWhsPhIDQ0lNatW9dkaS6JjIw0uwSXBfhdpjvBDX7csYzVP6fx3tObSsZeNQtvy6h+rzMj7RHmTtpEcEAdl47ZtFkzS/QEUVTx9ahTV3j79YKcAai4GE6du/y2FR2rbiA0b978CkXWXr6+viUfPfl9VJXV37/UXlfzu9IrQ1BkZCQ5OTmsX7+enj17llqXnZ3N5MmTAejcuXOtHsRc1e49MxWdh1Wzq+/4ie368dl/niizfHCvxxnc6/EqHXPXL7vwDbjKwjzA/9sBi34qf92Vngc2daizB+jUOZi6yPW2bTZY983nBHjwGefFfzknfWwa2bTkcrqVWP39i3fyyoHRffs6J9xLTU3ll19+KVmekZFBcnIyDocD0CSJYi0tTHwGbZN6eHQAEhHv5JUhKCUlhUaNGnHw4EHi4uLo1KkTMTExJCYm0qZNG26++Wag/PFA6enphIeHl7xmzpwJwIIFC0otd9fgZ5GaEhUGQf7mtB3TxJx2RUQuxytDUFRUFGvWrGHAgAEEBQWRmZlJWFgYc+fOZenSpSW9Q+WFoIKCAo4dO1byujCuKD8/v9TygoKCGn1PIlcrwA+6tzGn7aQYc9oVEbkcr+2gbt++PUuWLCmzPDc3l8zMTHx8fOjYsWOZ9X369MEwKr5dW8ST9YqBNTtrts22jaFpg5ptU0SkMrw2BFVk69atGIZBbGwsISHuu537008/BWDbtm2lPm/VqhUJCQlua0fkakTWh+tawob9NdfmH/TgVBGppbzyctjlbN7sfNK4O+YH+r177rmHe+65h3/+85+lPn/rrbfc2o5VfbPpU95YOK7Usi8zPuLWyTbSt3xmTlEe6q4E5+3qNSEpBmI9b6YHEbEIy/UEVVcI0iW06pW+ZRF9uz1c8rn9eCbLfvgv2l/Tw8SqPFPdILgnET5ac+VtL7gw98+V5hP6vUZ1YdB1rtUmIlKTFIKkVsjNO8Hov3YkvyCPiPotKCjKx35sL7d0e4iJd77D1sx0Jt/3MQDFxcXM/OejPD7kTeYunmRu4R4q/hoY2AUWb6zc9leaR+hSdYPgT8nm3Y0mIlIZlgtBF54rJrVL3eAG3NzlAYIDQ3nw1hfI2PkV81e+yqR73mfdzuV0aJmEn6/zN+rCb2YS16oXsVHdTK7as90S55zE8PMN7j1u/WAYdws0rufe44qIuJvlxgRJ7bX78Eaimzuvn+zK+onoZs5/f7f1M3p1dD61c599C2s2L2R43ymm1elNbu4Ao2+6/MNTXdExCp7u5xyALSJS21muJ0hqr72XhKCecYMwDIN1O79i9IBpAGzZu4ZfczIZmeqceOb4aTuzPh3D8VPZDEwaV+GxpWJxUfDnCPhsPWTsg6oMb6sbBEO6QrdWzt4lERFPoBAktYLj5CGw2Qiv73ww4177Jh645Xl2HPyRa5q0JziwLgADk8aVCjuT3unDnTc+Sa+OQ8wo22uEBMIDPaFfZ/h+t/N1+goPSgVoEwG9YiG+Bfj5Vn+dIiLupBAktcLuQxtKLn8B1A1qwOffz6F+nXCS4oaYV5jFNKwD/ePh9s5w9BQcPA6HcuBMPhQVg7+v866vFo0gqqGzB0hExFMpBEmt0KPDHfTocEfJ529PzADg0RlxTB+7qsL9/jpudXWXZkk+NmhS3/lKaG12NSIi1UMhSGq195/ZanYJIiLipXR3mIiIiFiSQpCIiIhYkkKQiIiIWJJCkIiIiFiSBkaLW/n4Q/IEs6twjY+ebyUiYkkKQeJWNhv4BphdhYiIyJXpcpiIiIhYkkKQiIiIWJJCkIiIiFiSQpCIiIhYkkKQiIiIWJJCkIiIiFiSQpCIiIhYkkKQiIiIWJJCkIiIiFiSQpCIiIhYkkKQiIiIWJJCkIiIiFiSQpCIiIhYkkKQiIiIWJJCkIiIiFiSQpCIiIhYkkKQiIiIWJKf2QWIdzEMKC4wuwrX+PiDzWZ2FSIiUtMUgsStigtg1Wyzq3BN8gTwDTC7ChERqWm6HCYiIiKWpBAkIiIilqQQJCIiIpakECQiIiKWpBAkInIFxcXOOx/h4kcR8Xy6O0xE5BKHc2DLIcg6DgeOwYmzF9edOgdvroCoMIhuDB2ag6/+nBTxSApBIiI4e3s2HoBvf4G9Ry+/7Z4jztf/2wH1g6FnDPSKgdCgmqlVRNxDIUhM9/Oe1TzzbnKpZUEBdYiKiKVv14cY0usJfH31rSrV58gpmP897HO4vu/JPPhyE3yzA+7qDl1bavJNEU+h3yxSayR3uZ/Edv0xMMg5bWfFT/N4d/HTHDiynafufs/s8sRLpf8Cn62HgqKrO87Z8/CPdPj5ADzQE4L83VOfiFQfhSCpNWKad6VvtwdLPh+Y9BijprVj2Y/v88jtr9CgboSJ1Yk3+nKzsxfHnTYddI4hGpsMIYHuPbaIuJeG85UjMzOTDz74gLFjx9KtWzcCAgKw2WyMHDnS7NIsJTigDu1a9sAwDA4f22N2OeJlVm5zfwC64MAxeG81nC+snuOLiHuoJ6gcs2bN4o033jC7DAGyfws/9ULCTK5EvMm+o7B4Q/W2kemAJRvhzoTqbUdEqk4hqBzh4eH079+f7t27k5CQwLJly5gzZ47ZZXm9cwVnOXnGgWE4xwQt/v5ddh/aQLsWiURFxJpdnniJ84XwP9+Dq9P9PH071AuGU3kw88vK7fPNTohvAW2buFymiNQAhaByTJkypdTna9euNakSa5m3/EXmLX+x1LIbOt7JE0PfNqki8UZfb4Ojp13fr14wNAhxfb8FP8Czd4CPBh+I1DqW+LF0OBykpKQQHR1NUFAQLVq0YOLEiZw5c4ZRo0Zhs9l46623zC7T8gZcP4bU0St4ZdQXPNo/ldCQMBwnswjwvzj5yvnCfB6dEccnX79Sat9pC0by3Pv9arpk8TCFRc67wWrS0dOwPbtm2xSRyvH6nqCNGzfSr18/7HY7derUoUOHDhw+fJjZs2ezZ88ejh8/DkCXLl3MLVRoHh5D19i+ACS260fH1jfw1JwbeGPhWJ5/cAEAAX6BpAybx6Q5venR/g7aNosnfctnrN2+mPee3mxm+eIBNh6A3Pyab/fbXyCuec23KyKX59U9QQ6Hg4EDB2K325k0aRLZ2dmsX78eu91OamoqS5cuJSMjA5vNRufOnc0uVy4R1yqJvl0fYvXPaWzN/K5keWxUN+6+6RmmLXiYoyeymPXpGJ4Y+jbh9ZuZWK14gnX7zGl3x2E4fc6ctkWkYl4dgiZMmEBWVhbjx49nxowZhIaGlqxLSUkhPj6ewsJCWrVqRb169UysVCoyvO8L+Pj48vev/nLJ8in4+vgxbtZ1xEcnk9xlmEkViqcwDOet66a0DRw0qW0RqZjXhqDt27eTlpZGeHg4r732WrnbdOvWDYD4+Pirbu/TTz/lrrvuomXLloSEhNCuXTuef/55cnNzr/rYVtY8PJrk+GFs2P01m/euKVnu5+tPh1ZJnDzj4A8Jj5hYoXiKY7nOWZ3NcvC4eW2LSPm8NgTNnz+f4uJihg8fTt26dcvdJjg4GHBPCJoxYwa+vr68+uqrLFu2jHHjxvHOO+9w++23U1xcfNXHt7L7b3keH5sPf19+sTdo8941LF/3MYN7jWfO5xPJL8gzsULxBPaT1m5fRMry2oHRK1euBCA5ObnCbbKysgD3hKDFixcTEXHxsQ433XQTERERDB8+nG+//ZbevXu7fMyEhATsdvtV11aTAvyCeW/8Lpf2iW/bhxXTK561pWWT9nw17eKDnfLyc5meNpJR/V5nYM9xTHr3Jj5c9hzjBv2tSjXHxMZwvlAhyttFxQ+ixwPlz/d1YQ6gy6kXdPHj1KEVb1fRPEJffPlvnh82snLF1kL9n8sgpH5Tsu3ZREV1N7sckRKRkZGsW7euSvt6bQjav38/AC1btix3fWFhIenp6YB7QtDvA9AFCQnOqWIPHTpUpWPa7fYq72uWIP8qTKTiormLJxEZ1ppBSY9hs9mYfO/HjJ3VhV4dh9K5jethM/vwYc4VnK2GSqU2CYmqeFCOK3MA+fhUbb6gvLyzHvfz/HtFRUUlHz35fYj8nteGoDNnzgCQl1f+X/hpaWk4HA5CQ0Np3bp1tdSwatUqANq3b1+l/SMjI91ZTo0I8LvCn9NX6ccdy1j9cxrvPb0Jm80GQLPwtozq9zoz0h5h7qRNBAfUcemYTZs1U0+QBdSvG1ThulOV+O+vF+QMQMXFcOoyd3pVdCx/X4PmzT33PnlfX9+Sj578PsT7XM3vSq8NQZGRkeTk5LB+/Xp69uxZal12djaTJ08GoHPnziW/TN3p0KFDvPDCC9x+++1VnoOoqt17Zio6D6tmV9/xE9v147P/PFFm+eBejzO41+NVOuauX3bhG3CVhUmtdywX/vP/yl9XmcdgTB3q7AE6dQ6mLnK9/T89NJiF0wa7vmMt8eK/4GQeNI1sWjKUQMTTee3A6L59nZPupaam8ssvF6eIzcjIIDk5GYfDAVTPJIm5ubkMHjyYgIAAPvzwQ7cfX0RcF1YHQkwMu1F6BrBIreO1ISglJYVGjRpx8OBB4uLi6NSpEzExMSQmJtKmTRtuvvlmoPzxQOnp6YSHh5e8Zs6cCcCCBQtKLb8wpuj38vLyGDhwIPv27WP58uU0bdq0et+oiFSKzQbXNDKpbcxrW0Qq5rUhKCoqijVr1jBgwACCgoLIzMwkLCyMuXPnsnTp0pLeofJCUEFBAceOHSt5XRhXlJ+fX2p5QUFBmf3uvvtu1q1bx7Jly+jQoUP1v1ERqbTubcxpt30zuMyQJBExideOCQLngOQlS5aUWZ6bm0tmZiY+Pj507NixzPo+ffpgGBXfsl2eC3MSff3113zxxRckJiZWuW4RqR7xLWBREOTW8CMsboit2fZEpHK8OgRVZOvWrRiGQWxsLCEh7rml+/HHH+ef//wnf/7znwkJCWHt2rUl69q2bVvuLfQiUrP8fJ2B5MtNNddm43rQTo+1E6mVvPZy2OVs3ux82rg75ge6YNmyZQC8/vrr9OzZs9Rr6dKlbmvHqr7Z9ClvLBxXatmXGR9x62Qb6Vs+M6co8Ui3dIAmNfSoQBsw7Hrwcf8NqCLiBpbsCaqOEJSZmem2Y0lZ6VsW0bfbwyWf249nsuyH/6L9NT1MrEo8kb8vPNATZi13PlS1si7M/1OZOYUu6N0O2jR2rT4RqTkKQVIr5OadYPRfO5JfkEdE/RYUFOVjP7aXW7o9xMQ732FrZjqT7/sYcI6/mvnPR3l8yJvMXTzJ3MLFI7UMh8Fd4bOfKr9PZeYS+r02ETBApxiRWs2SIejCc8Wk9qgb3ICbuzxAcGAoD976Ahk7v2L+yleZdM/7rNu5nA4tk/Dz9Qdg4TcziWvVi9iobiZXLZ6sTzsoKISlP7v/2K3CYXQfCLDkGVbEc1hyTJDUTrsPbyS6+XUA7Mr6iehmzn9/t/UzenV0PrFyn30LazYvZHjfKabVKd7j1o7OMTsBvu475nUtYdwtEKxZyEVqPf2dIrXG3ktCUM+4QRiGwbqdXzF6wDQAtuxdw685mYxMjQHg+Gk7sz4dw/FT2QxMGlfhsUUq0iMaopvA/LWw50jVj1M3EO5OhC7XuK82EaleCkFSKzhOHgKbjfD6zgcz7rVv4oFbnmfHwR+5pkl7ggPrAjAwaVypsDPpnT7ceeOT9Oo4xIyyxUuEh8LjfWHzQfj2F9j1a+X3bRgCSTHOV53A6qtRRNxPIUhqhd2HNpRc/gKoG9SAz7+fQ/064STFDTGvMLEMHxvEX+N82U/C1iw4eByyjsOxMxfvJAsJcD4HrEUYtG0M7Zo6ny4vIp7HZrg6NbLIZbj7KfKPzohj+thVNKxbffcZJ09AT5GXyzIMKDacQclm0Tl/LjxFvn4wvHSn2dWIuId6gqRWe/+ZrWaXIILNBr4WDT8i3kyduCIiImJJCkEiIiJiSQpBIiIiYkkKQSIiImJJGhgtbuXj77zbypP4+JtdgYiImEEhSNzKZtPt5iIi4hl0OUxEREQsSSFIRERELEkhSERERCxJIUhEREQsSSFIRERELEkhSERERCxJIUhEREQsSSFIRERELEkhSERERCxJIUhEREQsSSFIRERELEkhSERERCxJIUhEREQsSSFIRERELEkhSERERCxJIUhEREQsSSFIRERELMnP7ALEuxgGFBeYXYVrfPzBZjO7ChERqWkKQeJWxQWwarbZVbgmeQL4BphdhYiI1DRdDhMRERFLUggSERERS1IIEhEREUtSCBIRERFLUggSERERS9LdYSIiUsb5QjiUAwePQ/YJOHveufzseVi1HaLCoEUYBPmbWqbIVVEIEhERwDnP1/5j8O0vsGE/FBWX3aagCP5vvfPfNht0bA43xEJMJPhovi3xMApBYrqf96zmmXeTSy0LCqhDVEQsfbs+xJBeT+Drq29VkeqUfQLSfoBMR+X3MQzYnOV8RdaHexOhTeNqK1HE7fSbRWqN5C73k9iuPwYGOaftrPhpHu8ufpoDR7bz1N3vmV2eiFcqKoaV2+DLzeX3/FSW/SS8uQJ6t4MB8RCg3y7iAfRtKrVGTPOu9O32YMnnA5MeY9S0diz78X0euf0VGtSNMLE6Ee9TUAR//xa2ZLnneAbw/3bAAQeMToYQzcQutZzuDpNaKzigDu1a9sAwDA4f22N2OSJepagYPvrGfQHo9/Y54N2VcM7DniMo1qMQdInMzEw++OADxo4dS7du3QgICMBmszFy5EizS7Ok7N/CT72QMJMrEfEun62HbYer7/gHjsF/f+ccNyRSW+ly2CVmzZrFG2+8YXYZlnSu4CwnzzgwDOeYoMXfv8vuQxto1yKRqIhYs8sT8Rq77LBmp2v7PH071AuGU3kw88vK7bMlC37KhITWLpcoUiMUgi4RHh5O//796d69OwkJCSxbtow5c+aYXZYlzFv+IvOWv1hq2Q0d7+SJoW+bVJGI98kvhAU/uL5fvWBoEOL6fv9aB7GRzv1FahuFoEtMmTKl1Odr1641qRLrGXD9GHp3vofC4gL2ZW8mbXUqjpNZBPgHlWxzvjCfx2Z1Jfm6Bxh+y/Mly6ctGMmJ3F959dFlZpQu4jHW7YNjuTXX3tnzzl6nAV1qrk2RyvL6MUEOh4OUlBSio6MJCgqiRYsWTJw4kTNnzjBq1ChsNhtvvfWW2WUK0Dw8hq6xfUls14/7klP4z0cWszMrgzcWji3ZJsAvkJRh81jw9avsOfwzAOlbPmPt9sU8fc8HZpUu4hEMA9J/qfl2v98DhUU1367IlXh1CNq4cSOdOnVi+vTp2O12OnToQEFBAbNnz+a+++5j+/btAHTp0sXcQqVcca2S6Nv1IVb/nMbWzO9KlsdGdePum55h2oKHOXoii1mfjuGJoW8TXr+ZidWK1H6ZDjh8oubbzT0Hmw7WfLsiV+K1IcjhcDBw4EDsdjuTJk0iOzub9evXY7fbSU1NZenSpWRkZGCz2ejcubPZ5UoFhvd9AR8fX/7+1V8uWT4FXx8/xs26jvjoZJK7DDOpQhHPsftXE9s+Yl7bIhXx2hA0YcIEsrKyGD9+PDNmzCA0NLRkXUpKCvHx8RQWFtKqVSvq1atnYqVyOc3Do0mOH8aG3V+zee+akuV+vv50aJXEyTMO/pDwiIkViniOg8fNazvLxLZFKuKVIWj79u2kpaURHh7Oa6+9Vu423bp1AyA+Pv6q21uzZg19+/aladOmBAYGEhUVVepym1yd+295Hh+bD39ffrE3aPPeNSxf9zGDe41nzucTyS/IM7FCEc9wKMfctq/msRwi1cEr7w6bP38+xcXFDB8+nLp165a7TXCw835Nd4SgnJwcOnXqxJ/+9CcaN25MVlYWr732Gj179mTLli1ERUVV6bgJCQnY7farrq8mBfgF8974XS7tE9+2DyumVzyjWssm7flq2sVRlXn5uUxPG8mofq8zsOc4Jr17Ex8ue45xg/5WpZpjYmM4X6gQJd5v8H/swD+w/HPihXmAKlIv6OLHqUMr3q6ieYSKiiH62jgK8k66ULHIlUVGRrJu3boq7euVIWjlypUAJCcnV7hNVpZzrnh3hKBBgwYxaNCgUsu6d+/Otddey8KFC5k4cWKVjmu32zl06NBV11eTgvyrMJGIi+YunkRkWGsGJT2GzWZj8r0fM3ZWF3p1HErnNr1dPl724cOcKzhbDZWK1C42W8Wn/MrOA+TjU7X5ggCOHHFw9pQGB0nt4ZUhaP/+/QC0bNmy3PWFhYWkp6cD7glB5WnUqBEAfn5V/xJHRka6q5waE+BXvTOi/bhjGat/TuO9pzdhs9kAaBbellH9XmdG2iPMnbSJ4IA6Lh2zabNm6gkSSzCKzwNB5a47dYUfgXpBzgBUXAynzlW83eWOExERRmGo/5ULFXHB1fyu9MoQdObMGQDy8sr/aUxLS8PhcBAaGkrr1u6bz72oqIji4mL279/Ps88+S2RkJPfee2+Vj1fV7j0zFZ2HVbOr7/iJ7frx2X+eKLN8cK/HGdzr8Sodc9cvu/DV067FAlKXQHYFV6Ou9CiMqUOdPUCnzsHURa63HeQP+3Zt57e/XURqBa8cGH0hFa5fv77MuuzsbCZPngxA586dS3oT3OGmm24iICCAmJgYNm7cyMqVK4mIiHDb8UVErkZUI/PabhGGApDUOl4Zgvr27QtAamoqv/xycXrUjIwMkpOTcTgcgPsnSfzggw9Yu3Yt8+fPp169etx2220cOHDArW2IiFRVizBrti1SEa8MQSkpKTRq1IiDBw8SFxdHp06diImJITExkTZt2nDzzTcD5Y8HSk9PJzw8vOQ1c+ZMABYsWFBq+YUxRb937bXXcv311zNs2DC+/vprTp8+zbRp06r3zYqIVFKHZmBWZ0xcc5MaFrkMrwxBUVFRrFmzhgEDBhAUFERmZiZhYWHMnTuXpUuXlvQOlReCCgoKOHbsWMnrwrii/Pz8UssLCgouW0ODBg2Ijo5m9+7d7n+DIiJVEB4K7Ux4ukxkfWjTuObbFbkSrxwYDdC+fXuWLFlSZnlubi6ZmZn4+PjQsWPHMuv79OmDYVQ8Z01lHTlyhJ07d3L99ddf9bFERNzlhhjYfrhm2+wVo/FAUjt5bQiqyNatWzEMg9jYWEJC3DOnzYMPPkh0dDRdunShQYMG7Nq1i7/97W/4+fnx1FNPuaUNERF3aN8cYprArhp6jlhkfegZXTNtibjKKy+HXc7mzZsB984P1KNHD7744gseeeQR+vXrx/Tp07nxxhvZuHEj0dH66XeHbzZ9yhsLx5Va9mXGR9w62Ub6ls/MKUrEA/nYYFgPCKiBP4F9bPBAT/Dzrf62RKrCcj1B1RGCxo8fz/jx4912PCkrfcsi+nZ7uORz+/FMlv3wX7S/poeJVYl4pkZ14c4EWLC28vtcmATxSpMq/t5tHeEaE2/LF7kShSCpFXLzTjD6rx3JL8gjon4LCorysR/byy3dHmLine+wNTOdyfd9DEBxcTEz//kojw95k7mLJ5lbuIiH6tEWTufB0p8rt/2VJlO8VFIM/KGT63WJ1CTLhaALzxWT2qVucANu7vIAwYGhPHjrC2Ts/Ir5K19l0j3vs27ncjq0TMLP1znd/sJvZhLXqhexUd1MrlrEs93aEfx94f/Ww9XfDnJRcnsYdJ0GQ0vtZ7kxQVJ77T68kejm1wGwK+snops5//3d1s/o1dH52Op99i2s2byQ4X2nmFaniDfp0x4m3AYRoVd/rPrBMLoPDO6qACSewXI9QVJ77b0kBPWMG4RhGKzb+RWjBzgnndyydw2/5mQyMjUGgOOn7cz6dAzHT2UzMGlchccWkYq1joDJ/WHFFkjfBWfPu7Z/gB8ktoH+nSEksHpqFKkOCkFSKzhOHgKbjfD6zmll99o38cAtz7Pj4I9c06Q9wYF1ARiYNK5U2Jn0Th/uvPFJenUcYkbZIl4jwA8GdHFeItt4AH7YAwePwfmi8rf384HmDSGhtfMVrIcQiwdSCJJaYfehDSWXvwDqBjXg8+/nUL9OOElxQ8wrTMRiLvTqJLaB4mI4choO58D5Que4oQBfaFIfmjYAXw2oEA9nM9wxPbLIb4rOw6rZ7jveozPimD52FQ3rVt+c+8kTwFd/xYqIWI56gqRWe/+ZrWaXICIiXkqdmSIiImJJCkEiIiJiSQpBIiIiYkkaGC1uZRhQXGB2Fa7x8dfEbiIiVqQQJCIiIpaky2EiIiJiSQpBIiIiYkkKQSIiImJJCkEiIiJiSQpBIiIiYkkKQSIiImJJCkEiIiJiSQpBIiIiYkkKQSIiImJJCkEiIiJiSQpBIiIiYkkKQSIiImJJCkEiIiJiSQpBIiIiYkkKQSIiImJJCkEiIiJiSQpBIiIiYkkKQSIiImJJCkEiIiJiSQpBIiIiYkkKQSIiImJJCkEiIiJiSQpBIiIiYkkKQSIiImJJCkEiIiJiSf8fZF0qimzLKAkAAAAASUVORK5CYII=",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 723.783x367.889 with 1 Axes>"
       ]
@@ -337,68 +335,99 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5327bee0-39eb-4da9-8316-a617e5a6ff01",
+   "id": "9f66e3eb-7cbc-45a9-8f40-9f92b61b6e65",
    "metadata": {},
    "source": [
-    "### Run the subexperiments using the Qiskit Sampler primitive"
+    "### Choose a backend\n",
+    "\n",
+    "Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "1bce51e7-0ae6-4626-b204-573a7c014955",
+   "id": "6944546f-e3e0-4863-9049-9feed1086e68",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit_aer.primitives import Sampler\n",
+    "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n",
     "\n",
-    "# Set up a Qiskit Aer Sampler primitive for each circuit partition\n",
-    "samplers = {\n",
-    "    label: Sampler(run_options={\"shots\": 2**12}) for label in subexperiments.keys()\n",
-    "}\n",
-    "\n",
-    "# Retrieve results from each partition's subexperiments\n",
-    "results = {\n",
-    "    label: sampler.run(subexperiments[label]).result()\n",
-    "    for label, sampler in samplers.items()\n",
-    "}"
+    "backend = FakeManilaV2()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "03bf7ba4-14c7-46de-901a-855456372d7b",
+   "id": "fdb47142-690c-4b1a-ad0b-85e1260a2cea",
    "metadata": {},
    "source": [
-    "To use the Qiskit Runtime Sampler, replace the code above with this commented block."
+    "### Prepare the subexperiments for the backend\n",
+    "\n",
+    "We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "d30a278d-0c8a-4c96-b7ca-718fa6f59991",
+   "id": "0229e6f2-a637-481d-ac76-84dfaadc264f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# from qiskit_ibm_runtime import Session, Options, Sampler, QiskitRuntimeService\n",
-    "# from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
-    "#\n",
-    "# service = QiskitRuntimeService()\n",
-    "# backend = service.least_busy(operational=True, simulator=False)\n",
-    "#\n",
-    "# # Prepare transpiler for target backend\n",
-    "# pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
-    "#\n",
-    "# with Session(backend=backend) as session:\n",
-    "#     # Set up Qiskit Runtime Sampler primitives.\n",
-    "#     samplers = {\n",
-    "#         label: Sampler(Options(execution={\"shots\": 2**12})) for label in subexperiments.keys()\n",
-    "#     }\n",
-    "#\n",
-    "#     # Retrieve results from each subexperiment\n",
-    "#     results = {\n",
-    "#         label: sampler.run(pass_manager.run(subexperiments[label])).result()\n",
-    "#         for label, sampler in samplers.items()\n",
-    "#     }"
+    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
+    "\n",
+    "# Transpile the subexperiments to ISA circuits\n",
+    "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
+    "isa_subexperiments = {\n",
+    "    label: pass_manager.run(partition_subexpts)\n",
+    "    for label, partition_subexpts in subexperiments.items()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9578c64a-8087-4696-bb2b-ca53d45442ba",
+   "metadata": {},
+   "source": [
+    "### Run the subexperiments using the Qiskit Runtime Sampler primitive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "49cfcf6f-7ac3-4c1e-9dc7-ea79f011d37c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/garrison/Qiskit/qiskit-ibm-runtime/qiskit_ibm_runtime/session.py:157: UserWarning: Session is not supported in local testing mode or when using a simulator.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit_ibm_runtime import SamplerV2, Batch\n",
+    "\n",
+    "# Set up a Qiskit Runtime Sampler primitive for each circuit partition\n",
+    "samplers = {label: SamplerV2(backend=backend) for label in subexperiments.keys()}\n",
+    "\n",
+    "# Submit each partition's subexperiments as a single batch\n",
+    "with Batch(backend=backend):\n",
+    "    jobs = {\n",
+    "        label: sampler.run(isa_subexperiments[label], shots=2**12)\n",
+    "        for label, sampler in samplers.items()\n",
+    "    }\n",
+    "\n",
+    "# Retrive results\n",
+    "results = {label: job.result() for label, job in jobs.items()}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03bf7ba4-14c7-46de-901a-855456372d7b",
+   "metadata": {},
+   "source": [
+    "To use the Qiskit Runtime Sampler, replace the code above with this commented block."
    ]
   },
   {
@@ -408,12 +437,12 @@
    "source": [
     "### Reconstruct the expectation values\n",
     "\n",
-    "Use the subexperiment results, subobservables, and sampling coefficients to reconstruct the expectation value of the original circuit."
+    "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "a1904c54-27fa-45e0-a9dd-727b4542db71",
    "metadata": {},
    "outputs": [],
@@ -424,7 +453,8 @@
     "    results,\n",
     "    coefficients,\n",
     "    subobservables,\n",
-    ")"
+    ")\n",
+    "final_expval = np.dot(reconstructed_expvals, observable.coeffs)"
    ]
   },
   {
@@ -432,12 +462,12 @@
    "id": "cdc793f2-3e2b-417b-b863-7b9d57b86a8f",
    "metadata": {},
    "source": [
-    "### Compare the reconstructed expectation values with the exact expectation values from the original circuit"
+    "### Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "6e3d63e4-a510-4712-bc43-48df6e2f7ded",
    "metadata": {},
    "outputs": [
@@ -445,31 +475,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reconstructed expectation values: [0.15630245, 0.68548346, 0.69142044]\n",
-      "Exact expectation values: [0.1767767, 0.70710678, 0.70710678]\n",
-      "Errors in estimation: [-0.02047424, -0.02162333, -0.01568635]\n",
-      "Relative errors in estimation: [-0.11581981, -0.03058, -0.02218384]\n"
+      "Reconstructed expectation value: 1.51319069\n",
+      "Exact expectation value: 1.59099026\n",
+      "Error in estimation: -0.07779957\n",
+      "Relative error in estimation: -0.04890009\n"
      ]
     }
    ],
    "source": [
-    "from qiskit_aer.primitives import Estimator\n",
+    "from qiskit_aer.primitives import EstimatorV2\n",
     "\n",
-    "estimator = Estimator(run_options={\"shots\": None}, approximation=True)\n",
-    "exact_expvals = (\n",
-    "    estimator.run([qc_0] * len(observables_0), list(observables_0)).result().values\n",
-    ")\n",
-    "print(\n",
-    "    f\"Reconstructed expectation values: {[np.round(reconstructed_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Exact expectation values: {[np.round(exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Errors in estimation: {[np.round(reconstructed_expvals[i]-exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
+    "estimator = EstimatorV2()\n",
+    "exact_expval = estimator.run([(qc_0, observable)]).result()[0].data.evs\n",
+    "print(f\"Reconstructed expectation value: {np.real(np.round(final_expval, 8))}\")\n",
+    "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n",
+    "print(f\"Error in estimation: {np.real(np.round(final_expval-exact_expval, 8))}\")\n",
     "print(\n",
-    "    f\"Relative errors in estimation: {[np.round((reconstructed_expvals[i]-exact_expvals[i]) / exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
+    "    f\"Relative error in estimation: {np.real(np.round((final_expval-exact_expval) / exact_expval, 8))}\"\n",
     ")"
    ]
   }
@@ -490,7 +512,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,
diff --git a/_sources/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb.txt b/_sources/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb.txt
new file mode 100644
index 000000000..7843ebf17
--- /dev/null
+++ b/_sources/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb.txt
@@ -0,0 +1,283 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Automatically find cuts using CKT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create a circuit and observables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA/0AAAGRCAYAAAA3ltNoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB+I0lEQVR4nO3deVwU5R8H8M9eXHIJCIuAggfmgSKeeB9pmpXmbaX5Ky0zz8qy8qw0zcozzay01DzySFMjBe8TU0HwABEPQBaQQ+5j2f39QaEr5wK7s7t83q9Xr9xnZnY/A8zx3XnmGZFarVaDiIiIiIiIiEyOWOgARERERERERKQbLPqJiIiIiIiITBSLfiIiIiIiIiITxaKfiIiIiIiIyESx6CciIiIiIiIyUSz6iYiIiIiIiEwUi34iIiIiIiIiE8Win4iIiIiIiMhEsegnIiIiIiIiMlEs+omIiIiIiIhMFIt+IiIiIiIiIhPFop+IiIiIiIjIRLHoJyIiIiIiIjJRLPqJiIiIiIiITBSLfiIiIiIiIiITxaKfiIiIiIiIyESx6CciIiIiIiIyUSz6iYiIiIiIiEwUi34iIiIiIiIiE8Win4iIiIiIiMhEsegnIiIiIiIiMlEs+omIiIiIiIhMFIt+IiIiIiIiIhPFop+IiIiIiIjIRLHoJyIiIiIiIjJRLPqJiIiIiIiITBSLfiIiIiIiIiITxaKfiIiIiIiIyESx6CciIiIiIiIyUSz6iYiIiIiIiEwUi34iIiIiIiIiE8Win4iIiIiIiMhESYUOQKQvQa8vQcZdhdAxYOMpR99fZlfrPQxlXYCaWR8iIiJ9MJTjJ4+dZExMabsxlHUB9LsfYNFPtUbGXQXSImOFjlEjTGldiIiI9IXHTyLtmdJ2Y0rrog127yciIiIiIiIyUSz6iYiIiIiIiEwUi34iIiIiIiIiE8Win4iIiIiIiMhEcSA/oqd0W/EumozqDQBQFRYiJyEN8WfCcXnxVmQrUgROR0RERLrGcwEi7XG7MVy80k9UCsX569jRegJ2tX8HJ99dAcdWnuj1w/tCxyIiIiI94bkAkfa43RgmFv1EpVDlK5GTlIZsRQoSzt9AxJZAOHdoBpm1pdDRiIiISA94LkCkPW43holFP1EFLF3qwvOFzlApC6EuVAkdh4iIiPSM5wJE2uN2Yzh4Tz9RKeRdWuLVqM0QicWQWpoDAMLX7YcyJw8A0GvD+3hwIhSRWwIBAA6tvNBj7XT82W8WCvMKBMtNRERENaOicwEruQOeP7AIB577CLnJ6ZBYmmFw4Dc4+uYypN28L2R0IsFUtN00GNgRvu+N0FjGztsdwXM3IuLXw3rPW1sYddEfGhqKefPm4fjx41Cr1ejTpw/WrVsHb29vDBo0CNu3bxc6IhmppMu3cHr6GkjMZfB8qQvqd2+NK0u3FU8PnrsRA/d9jnuHLiAvNRP+Sybiwic/seAnIiIyERWdC2QrUnB9/QF0WDgep6asgu/7I3Hvrwss+KlWq2i7uf9XMO7/FVz8usGADvD7+BVE/X5cgLS1h9EW/UFBQXjhhRfQsGFDzJkzB5aWlti0aRMGDhyIzMxM+Pr6Ch2RjFhhbj4y7ioAACHLdsDGU45Oi97E2Q++B1B0oL+2/gDazx2Lh1ei8Cg6HvGnw4SMXGkSCzO0njYUXoO7wsrVoWhd7yXg9q6TuPHTIaHjERmkzJgkRGw+jKRLkVApC2HbyBXer/VDPb+mEIlEQscjIh2o6FwAAG789BdeCFiK5hOeR8PnO2F/3w+EiktkECqz3fzHytUBnRZPQOCri1GYk6/vqFrr99unkFlb4a8hc6FWPb5dwcHHC4MOLMbJd1fh3oFzAiYsm1EW/UlJSRg1ahT8/PwQGBgIS8uigSHGjh0LLy8vAGDRTzUq5OsdePnkSkRsPoLk0NsAgJsbAzDo4GK4dm2FPwfOFjhh5fkvmQh511YInvszUq7dg8zGEo6tvFDHzUnoaEQGR61W48rS7bi6ag+gVgMiABAhMfgmorYfg1tvX/Rc/x7MbKyEjkpEOlbauYBapcLF+ZswYM9CHH3jq+IuzERUpLTtBgAgEqHHmukIW/MHUm/cEy6gFk7P+A6Dj34Dn2kv4+qK3QCKLqb1WDMN0XtOGWzBDxjpQH5Lly5FamoqNm7cWFzwA4CdnR38/PwAsOinmpVxR4GYI//Ab/aYx41qNSJ+PYLYoMvIS04XLpyWGgzoiPC1+3A/4CIyYxKRev0eonYeR+jyXUJHIzI4IV/vxNWVu4sKfgBQ4/G/AcQdC8HR8UuhKlAKE5CI9KbUcwEAbn3bIluRgrrPNBAoGZHhKmu7aTNjGPIzsnHz578ESqa9nMQ0nP3ge7SZORyObRoDANp9+hrEZjJcmPOzwOnKZ5RF//bt29G9e3d4e3uXOt3FxQVyuRwAoFQqMX36dDg4OMDe3h5vvvkmcnNz9RmXTET42v1w6+ULuX/Lx40qFdQqddkLGaDsxFS49W4LM3troaMQGbTshNSigr8CirPXcD8guML5iMj4PX0uYP9MAzQY0BEHBs5G01f6wrqBs8AJiQzP09uNc4dmaPpKX5yZ+Z3AybR3P+AionYeR4810+DRvz2ajeuHU1NWQZll2PWl0XXvVygUiIuLw6hRo0pMU6lUCAsLQ9u2bYvbFi9ejGPHjiEsLAxmZmZ46aWX8OGHH2LVqlWV+jylUgmFQlFj+Uk4BZW8End6Ruk7oKR/IrDJdXiN5IiNja32e1TV2ffXocfaGRgd/hPSImKRdDkScUGXcT/gYpWzVHd9iAxR9E+HK/2IodD1+yFty6t8RIaups8F/Je+hYvzNyFbkYIrX21Hp0VvImjsl5XKwWMnGYua3G7MbK3QffU0nJ6+BnmpmVrnEPIc+j8X523Ci0eWoffPs3B1+W4kXYqschZt10cul0Mq1b6EN7qiPysrCwBKHThp3759SExM1Oja/+OPP+Krr76Cm5sbAGDBggUYMWIEli9fDolEUuHnKRQKeHh41Ex4EtQXjv3gJrMVOgYiIyMxspp/U9VZl8SLEdjd+V04tW0K53becOncAr02fIC4o1cQ9PoSrd+vJtaHyBBNs/eHr7lrpQbqi794k8cKIiNQk+cCTV99FrkPHyE26DIA4PbvJ9B0TB80eL4T7h+6UO6yPHaSManJ7abZ68/B0tkeHReO12iP+v0Erv9woNxlhT6H/o8yJw/h6/bDf8lEhK6o+u2xVVmfmJgYuLu7a/1ZRlf0e3h4QCKR4MSJExrt9+7dw9SpUwE8vp8/LS0NMTExGl8C+Pn5ISMjA3fv3kXjxo31FZtMVNTO44jaeVzoGFpTF6qQ9E8Ekv6JwLX1f6LRsO7osWY6XPxbIOHcdaHjERkEESo/Kr+YI/gT1Tq3tgbi1tZAjbaAofMFSkNkHMJW70XY6r1Cx6g29b89BirbI1BoRlf0m5mZYdy4cdi4cSMGDx6MQYMGISYmBhs2bICLiwvi4uKKi/yMjAwAgL29ffHy//37v2kVkcvliImJqclVIIGcG7kEWXeEv1XD29sbMTurN9hHTa/Lo1txAAALRzutl62J9SEyRLdW78e9X49WPKMIcHymIWL+4bGCyNCZ0rkAkb6Y0nZjKOsCVG19/hu3TltGV/QDwKpVqyCTybBv3z4cPXoU/v7+2Lt3Lz777DNERUUVD/BnY2MDAHj06FHxDygtLU1jWkWkUmmVulCQ4ZHJDOPPXSar/t9UddZlwJ6FuPPHGTwMvY3c5Eew9XSF38evIC8tE4qz4VXKwm2ETJHtpJcrV/SrAZ8JL3A7IDICpnQuQKQvprTdGMq6APrdDxjOWmvB2toa69evx/r16zXaw8PD4ePjA7G46KEE9vb28PDwQEhICJo1awYAuHLlCmxsbODp6anv2EQGIe7oFTQa2h2+s0bBzNoSOcmPkHD+Bk7P/A55KZXrAUNUG9h6uaLpmD64ta38wt+uqRu8Xu6mp1RERERE2jHKor80aWlpiI2NxaBBgzTaJ0yYgC+//BLdu3eHTCbDggULMH78+EoN4kdkisLW/IGwNX8IHYPIKHReMhHKnDzc+eNMqdPtvN3R/7c5kFlZ6DkZERERCcXYxvUymaI/LCwMADQG7QOATz75BA8fPkTLli2hUqkwfPhwLF26VICERERkbCRmMvRYOwPNxvZH+Lr9iA28BABw8muK5m8MhOcL/pCYywROSURERFQ2ky/6pVIpVq1ahVWrVgmQioxF01f6ounoPlCrVTj30Qak3bxfPM2jf3u0njYUhQVKRG4+gug9pwAAXb6eBNvG9VGYm48z769D9oNkNBnZC23eG4GsuIcAgCOvLkJhbr4g60RENUMkEkHepSVsPOX4vd3bAIDeGz5AnfqOAicjIl2ydq+HHmtnQKVUQiSR4PzsDUi9ca94evc102DTwAUiiRg3NwXg9u8nynk3ItNV0bYisTRDp8/fgHUDF4glYgS+thjWHvXgv+xtqFVqqJWFOPP+OmTeTxRwLUybyRT9kydPxuTJk4WOQUbIzN4azV7vj4ODPoFNQxf4L5mIv0csLJooEqHdp6/iwMCPUZiXjwF7FiLmyCW4dm2JwrwCBLw8D46tG6Hdp6/h1LsrAQCRW46w+zwREZGRy4pPxqHBcwC1GvKurdB62lCceGd58fSQb3Yi444CYjMpBh/9Fnf+OAPVv4/xIqpNKtpWfN8biei9p6E483jA6NzkdAS+9iUKMrLh1tsXbWYOx5mZa4WIXyuYTNFPVFX12jaB4uw1qJWFSL/9AOYOtoBIBKjVsHCwQe7DdCizcwEAj6IeoJ5fU9g2qo/k0NsAgOSr0XDp9Ezx+zUZ1Rvu/drhfsBFXFu3X5B1IiIioup58vnbZjaWSLl+V2N6xr+P/VLlKwG1Gmq1Wp/xiAxGRduKvGtLSMyl8H1vBB6cuoqrK3YjNzm9eLqqoNBonndvrMRCByASmpm9NfIfZRW/LsjMgZmtFYCibyEtnGxh6WwPaR0LuHRqDnN7a6TevI/6vXwBAG69fWH57/Pt7wcE44+eM/H38IWQ+7eEazcfva8PERER1QyHlp54/s9F6LRoAuJPhZU6T6t3h+DuwfNQKwv1nI7IcJS3rTi08ETcsRAEDF8AR59GkPu3LJ4msTCD76yRuP7jIX1HrlVY9FOtl/8oC2a2dYpfy6wtkZ+eXfz63Ec/oMd309Fz3UykRcQgOyEFcUevID36AQbsXgi3Pm2R8u99S/np2VCrVFAVKHHv0AU4+HjpfX2IiIioZqRcu4tDL36KoPFL0GnxmyWmew3uCkcfL1xZul2AdESGo7xtJTclHXHHQwG1Gg9OhKJui4YAAJFEjB5rp+Pauv0a42lRzWPRT7Ve0uVbcOncHCKJGDaecuSlpANPdNFLOH8Df49YiBOTlkNqZY6kS7cAACFf70TAsPmI+fsfKM5eAwDIbKyKl5P7t0DGnXj9rgwRERHVCLHZ47tgC9KzUZijOTBv/V5t0HRMH5yatlrjvIGotqloW0k4fwOOrRsBABxbN0L6v+fHXb95Bw+Oh+J+wEX9ha2leE8/1Xr5aZm49VsQBu79HGq1Cuc//hFuvX1hZm+NO3tPo/38cXD0aQSVshCXv/wNqgIlzB1s0HvDB1ApC5EV9xAXPv0JANBy0otw6+ULtUqFhyG3uRMjIiIyUs4dnoHvByOhLlRBJBIheMEmjfOD7iunIDshFf23zQUAnJi0HDlJacKGJhJARdvKpcVb0PXrdyCxMENaRAzijl6BW29feL7UBdYezvAa3BUp1+4geN4moVfFZLHoJwIQuSUQkVsCi1+nXn/8mJF/Fv5aYv68lAwEDJtfoj1k2Q6ELNuhm5BERESkN4oz4Qh4YrTxp+1oM1GPaYgMV0XbSlbsQxwe/blGW9yxEGxp9Kquo9G/2L2fiIiIiIiIyESx6CciIiIiIiIyUSz6iYiIiIiIiEwU7+mnWsPGU16t5VXKQqRHF402atvIFWKpRJAcNfEeNbUuNZGFiIhIXwzl+MljJxkTU9puDGnb02cWkVrNZ4wQVUbWg2T83u5tAMCIS+tRp76jwImqzpTWhUhfuN0QEfcDRNrjdiM8du8nIiIiIiIiMlEs+omIiIiIiIhMFIt+IiIiIiIiIhPFop+IiIiIiIjIRLHoJyIiIiIiIjJRLPqJiIiIiIiITBSLfiIiIiIiIiITxaKfiIiIiIiIyESx6CciIiIiIiIyUSz6iYiIiIiIiEwUi34iIiIiIiIiE8Win4iIiIiIiMhEsegnIiIiIiIiMlEs+omIiIiIiIhMFIt+IiIiIiIiIhMlFToAGa6g15cg465C6BgAABtPOfr+MlvoGERERFozpOOpseBxv2KG9HdlTL+vmReAuGyhUwBuVsDyTkKnoNqCRT+VKeOuAmmRsULHICIiMmo8npIu8O+qauKygegMoVMQ6Re79xMRERERERGZKBb9RERERERERCaK3fsNyKOMfETcfYScPCVkUjEaudtA7mQldCwiIiIiIiIyUiz6BXb9dirW7byJv8/G4ta99BLT6ztboVd7V7w9vBm6t5NDJBIJkJKIiIiIiIiMEYt+gdx7kIHJi87i0KnyB2B5kJiN3w7dxm+HbqNNMwesn9sVnVo76yll5XVb8S6ajOoNAFAVFiInIQ3xZ8JxefFWZCtSBE5HREREVHvxPI2oduM9/QL4Zd8ttBq6t8KC/2mhESnoMu4APl55EYWFKh2lqzrF+evY0XoCdrV/ByffXQHHVp7o9cP7QsciIiIiqvV4nkZUe7Ho17NlG69i/NyTyMwuqNLyKpUaS366inGfnjS4wl+Vr0ROUhqyFSlIOH8DEVsC4dyhGWTWlkJHIyIiIqrVeJ5GVHux6NejX/ffwofLL5Y7j0QigpuLFdxcrCCRlH3//m+HbuO9ZRdqOmKNsXSpC88XOkOlLITawL6cICIiIqrNeJ5GVLuw6NeT+/GZmPLluQrnkztZIvbIGMQeGQO5U/nfvK767TqCzj+oqYjVJu/SEq9GbcZr0VsxKmQD5P4tcX3DQShz8gAAVnIHDP9nHSwcbQEAEkszDD2zGvbPNBAyNhEREZHJq+g8rdeG9+H92rPF8zu08sKQkysgMZcJFdmohE30FDoCUZmMuugPDQ3F4MGDYWdnB1tbWwwZMgTx8fGwsbHB6NGjhY6nYcric8jIqlqX/vJMWHAK+QWFNf6+VZF0+Rb2PzsLBwbORsi3vyPxYgSuLN1WPD1bkYLr6w+gw8LxAADf90fi3l8XkHbzvkCJiYhIl5QqICwFuJAE3HoEqNVCJyKqvSo6TwueuxE+U1+GuYMNIBLBf8lEXPjkJxTm1fz5KxHpl9EW/UFBQejcuTMiIiIwZ84cLF68GLGxsRg4cCAyMzPh6+srdMRiEXfS8OcJ3RS2dx9kYk/gXZ28t7YKc/ORcVeBtIgYhCzbgYyYRHRa9KbGPDd++gv23h5oPuF5NHy+E0K/+V2gtEREpCt5hcAPEcCgI8D/TgPvngPGnABGHwf+vM/i/0kunZujz8aPMPziOoyP34XWM4YJHYlMVEXnadmKFFxbfwDt545Fs7H98Cg6HvGnwwRMbBxifpyJ6zN8UZDyANdn+CL6q1FCRyIqwSgf2ZeUlIRRo0bBz88PgYGBsLQs6gY/duxYeHl5AYBBFf3rd93U6fuv3XEDowc21ulnVEXI1zvw8smViNh8BMmhtwEAapUKF+dvwoA9C3H0ja+Ku5QREZFpyFUCU88DV1KAp0emic4AFoYAkenAey0BUdlD19QaUisLpN2KQfTeU+j42f+EjkO1SGnnaTc3BmDQwcVw7doKfw6cLXBCYanychC/azFST21HfnIsxGaWMJc3hmOvsXB+cVrxfB4TlgMo6t7fYkWIQGmJymeUV/qXLl2K1NRUbNy4sbjgBwA7Ozv4+fkBMKyi/++zcTp9/zMhiciq4tMAdCnjjgIxR/6B3+wxGu1ufdsiW5GCuryXn4jI5Ky4XlTwA8DTF/T/e70tGvhLu6fWmqy4o1dwefFvuLv/LFT5hncsJ9NV6nmaWo2IX48gNugy8pLThQtnAO5//w5Sjv0K9/HL0HLNdXh/cQz1nn8Xyqw0oaMRac0oi/7t27eje/fu8Pb2LnW6i4sL5HI5AGDnzp3o1q0brK2t4enpqceURTKzC3DzziOdfoZKpUZIRIpOP6Oqwtfuh1svX8j9WwIA7J9pgAYDOuLAwNlo+kpfWDdwFjghERHVlPR8YH8l7mYTAdgazW7+REJ7+jwNAKBSQa3ixpl24Q+4vDwL9p2HwNzFC1ZebeDUdzzqj54ndDQirRld936FQoG4uDiMGlXyfhmVSoWwsDC0bdu2uK1u3bqYMmUKEhISsHz5cq0/T6lUQqFQVDlv+O10qJ7acUokojJH5nd9ot21jHkUD3NQWKj5nmcv3UHDejV7haCgQFnpeU/P+K7U9qR/IrDJdXjxa/+lb+Hi/E3IVqTgylfb0WnRmwga+2WlssTGCntZKDchrfjf8fHxsFDlCBemmkxpXYj0hdtNxYKSrZCvcqhwPjWAiEdA8G0F3Cwqf6wxVtocT6mIIRz3S2NI+wFdnKdVJ4sh/r5KU1DgAqDiJxLI6roi/XIAHHq8AqlNxfs17XMUIDY2ocbf1xAZ0nZj7ORyOaRS7Ut4oyv6s7KyAACiUm4E3LdvHxITEzW69vfr1w8A8Mcff1Tp8xQKBTw8PKq0LADAqjHQ+GONpv8ey1eRi9uGlNru3m8b4hKyNdo+nP0JPpx4rMoxS/OFYz+4yWxr7P2avvosch8+QmzQZQDA7d9PoOmYPmjwfCfcP3Sh3GUjIyMxsjq/hxpQV2yJb52fBwB07NgRqUa8wzKldSHSF243FXMZ8j7c//d1pefv++IwZN08q8NEhqGmj6e1gSEc90tjSPsBQ/q7MtTfV2larA6HZYOWFc7XcMqPuPPNKwgdVw+WHi1Rp1ln2LV7HnadBpdah2grMjISHs+1qvb7GAND2m6MXUxMDNzd3bVezuiKfg8PD0gkEpw4cUKj/d69e5g6dSoAw7qfHyo9fbuvMozH9pXn1tZA3NoaqNEWMHS+QGmIiKimFWZrdw9wYU7tvmeYyBBF7TyOqJ3HhY4hOOvmXdFq/W1kRQYjK+IcMq6dxO2lw2HXbiAaf7q/ROFv4dFCoKREFTO6ot/MzAzjxo3Dxo0bMXjwYAwaNAgxMTHYsGEDXFxcEBcXV6NFv1wuR0xMTJWXT8sogM+ooxptioc5cO+3rdT5XZ0si6/wdxjzB+IflvwmTFFK2/ZfV6Grr2OVc5bm3MglyLpT9VsbapK3tzdidv4saIbchDScfmEBACA4OBgWLvaC5qkOU1oXIn3hdlOx5HwxJoaroSoxbr8mEdRwMVNiz6m/IK4FI/gb0vHUWBjCcb80hrQfMKS/K0P9fZVm6nUXxORWbl6RRArr5l1g3bwLXIa8j+TjW3B3+VhkXjsJm1Y9NeZtOu+QVjm8vb3xdzVqDGNiSNuNsftv3DptGV3RDwCrVq2CTCbDvn37cPToUfj7+2Pv3r347LPPEBUVVeYAf1UhlUqr1IXiP+4AGrnbIDo2o7itsFBdont+aeIf5lRqPgB4rsczsLc1r2rMUslkhvPnIZNV7/dQE7LET4y34OqKOvVr9ksWfTKldSHSF243FXMH0CcFCHxQ/nxqiDCmqQwNPITdr+tLecdTqZUFbL2KTuLEMiks69nDoaUnCrJykXHXMAo6IRjCcb80hrQf4Hla1chuAahk0f80C/fmAADlo8Tq55DJjOZnVl2GtN3UVoazt9CCtbU11q9fj/Xr12u0h4eHw8fHB2KxYT2UoHcHV42iv6b5PuNQ4wU/ERFRVXzoA9x8BMRmlT1PdxdglJf+MhkypzaNMWDPwuLXzd8YiOZvDITi7DUEDOMtcERCifikJxy6j4FVk/aQ2tVDXnwU4jZ/Akkde9j49BY6HpFWjLLoL01aWhpiY2MxaNAgjfbCwkIUFBSgoKAAarUaubm5EIlEMDfXX5H89ohn8NPeSJ29/6QRzXX23kRERNpwMAc2dgO+CQeOPACefNiMtRQY7glMegaQGtb384JRnLtWI6OnE1HNsvMbiJSTW/Fg2zwUZqdDaucMm5Y94DltI6S2TkLHI9KKyRxyw8LCAJQcxG/z5s2wtLTEyJEjcf/+fVhaWqJZs2Z6zdahVT34t9HN8+gd7Mzx6qDGOnlvbdg2csW4+9tRz6+pRrvv+yMx/OI69Pvt0+I2iaUZnv9zEV65+Qu8BnfVd1QiItKxuubAF+2AX7o/bvu4NRDQH5jSggU/kb6VdZ72nwG7F8J/6VtaLWPq5MNno9mXp9Dm10T47cpF65/uw+u9LbBswAH7yPiYzGG3rKJ//PjxUKvVGv/dvXtX7/m+n9sVUmnNj1a0anZnWFtV/KxRXWszczgU566XaI/YfLhE90RVnhLH3liG6xsO6iseEREJoO4Tneq6uQAWJtO/kMi4lHWeBgDuz7ZDQWbJQaLLW4aIjIvJFP2TJ0+GWq1G586dhY5SqtbeDlgwya/C+f4b2d+937ZSR+l/0st9G+KV54W/yu/UtilyEtOQHZ9cYlpOYhqgUmu0qVUq5CSl6SccERERUS1W3nkaRCI8878BuLkpoPLLEJHRMZmi3xh8MrENJg4r/9aC/0b2j0vIRmGhusz5urV1weZFPUs8I1QIracPRdiavULHICIiIqKnlHee1mRkL9w7dAGFuQWVXoaIjA+Lfj0SiUT4fm5XfDKhDapTqw971hMB655DHQPo1u/e1w/JobeRl5opdBQiIiIiekJ552kScxkaDe2OqO1HK70MERkn3l2nZ2KxCIumtcegHh54Y94pRNx9VOllHe3NsXq2P0YPbGQQV/gBwKGVJ+RdWsK5QzPYP9MAto3r49iby4q69RMRERGRYMo7T7Nu4Awzuzp4dvPHMLO3hqWzPRqP6Ik69R15bkdkYlj0C6SLrwvC9wzFwVMxWLfjBo4Gx6NAqSoxn0gEtGvhhEkjnsHoAY0M4ur+k66u3IOrK/cAALqteBcRvx6GQ0tPmHW1xp29p+H92rNoPKIn7Jq4of+OeTg1bTVyElLR68cP4NjKC8rsXDj5NcXF+ZuEXREiIiIiE1PRedqBAR8BAOT+LeE1pCtu/36ieLknl2HBT2TcWPQLSCoVY3DvhhjcuyHy8gsRHpWKM5cTMP2r8wCAXd/0Qf8ubrCpYyZw0so5PeO7Em2RWwIRuSWwRPvxCV/rIxIRERERofTztP8ozl2D4tw1rZYhIuPBe/oNhLmZBO1aOGHos57FbZ18nI2m4CciIiIiIiLDw6KfiIiIiIiIyESx6CciIiIiIiIyUbynn8pk4ykXOkIxQ8pCREREJDRDOjcypCwVcbMSOkERQ8lBtQOLfipT319mCx2BiIiIiErB87SqWd5J6ARE+sfu/UREREREREQmikU/ERERERERkYli0U9ERERERERkolj0ExEREREREZkoFv1EREREREREJopFPxEREREREZGJYtFPREREREREZKJY9BMRERERERGZKBb9RERERERERCaKRT8RERERERGRiWLRT0RERERERGSiWPQTERERERERmSgW/UREREREREQmikU/ERERERERkYli0U9ERERERERkoqRCByDDNfMCEJctdIoiblbA8k5CpyAybUGvL0HGXYXQMSrNxlOOvr/MFjoGEZEgDGmfrYv9sSGtn7HgcbFitbW+YdFPZYrLBqIzhE5BRPqScVeBtMhYoWMQEVElmPo+29TXj4RRW+sbdu8nIiIiIiIiMlEs+omIiIiIiIhMFIt+IiIiIiIiIhPFop+IiIiIiIjIRLHoJyIiIiIyYd1WvIvx8bswPn4XxsXuwIhL69Ft1VRYyR2EjmawBuxeiC5fTyrRbu1eD+Pjd8G54zMCpCJ9ivi0F+6unlCiPS/hLi4NFiHz+mkBUlUNi34iIiIiIhOnOH8dO1pPwK727+Dkuyvg2MoTvX54X+hYRKQHLPqJiIiIiEycKl+JnKQ0ZCtSkHD+BiK2BMK5QzPIrC2FjkZEOsain4iIiIioFrF0qQvPFzpDpSyEulAldBwi0jGp0AGIiIiIiEi35F1a4tWozRCJxZBamgMAwtfthzInDwDQa8P7eHAiFJFbAgEADq280GPtdPzZbxYK8woEy23oBuz9DGbWlhDJpEi8cAPnP/4RahW/SKlN7qx4HemX/4LUzhktV4cLHadURn2lPzQ0FIMHD4adnR1sbW0xZMgQxMfHw8bGBqNHjxY6HhERERGRQUi6fAv7n52FAwNnI+Tb35F4MQJXlm4rnh48dyN8pr4McwcbQCSC/5KJuPDJTyz4KxA09kvs7zcL+3rNhLmjLTxf9Bc6EumZ07NvoOn8AKFjlMtor/QHBQXhhRdeQMOGDTFnzhxYWlpi06ZNGDhwIDIzM+Hr6yt0xFpNlZeD+F2LkXpqO/KTYyE2s4S5vDEce42F84vThI5HpJX8jGzc3nUCsUcuoSArF5b17NF4eA+4P9sOYqlE6Hg659K5OVq+/RIcWnnC2r0eLi/dhqsrdgsdi4iItFCYm4+MuwoAQMiyHbDxlKPTojdx9oPvAQDZihRcW38A7eeOxcMrUXgUHY/402FCRhZUfno2zGzrlGg3sytq++/LkILMHACASCqBRCaFWq3WX0jSKYmVHQqzH5VoL8xKAwCIZBYAAJtWPZGXcFePybRnlEV/UlISRo0aBT8/PwQGBsLSsmgAkrFjx8LLywsAWPQL7P737yAj7Bg8JqyEpVcbFGanIzv6CvKT7gsdjUgr9/4KxqkpK6HMzgNEACAC1GrcO3geNl6uePbX2bBr4iZ0TJ2SWlkg7VYMoveeQsfP/id0HCIiqgEhX+/AyydXImLzESSH3gYA3NwYgEEHF8O1ayv8OXC2wAmF9SgqDp4v+kMkFmt013dq2wQqZSEy7sQXtz23awEcW3khNugy7h04L0Rc0gEL92eQeuZ3qAsLIZI8vsiTdSsYEEtg7tpEwHTaMcru/UuXLkVqaio2btxYXPADgJ2dHfz8/ACw6Bda2oU/4PLyLNh3HgJzFy9YebWBU9/xqD96ntDRiCot7ngIjk/4Gsqc/KIGNYAnvsHPuBOPgGHzkRWfLExAPYk7egWXF/+Gu/vPQpXPbp5ERKYg444CMUf+gd/sMY8b1WpE/HoEsUGXkZecLlw4A3DzlwBY1LND1xXvwrF1I9g0dIHXkK5o++FoRO04hvz07OJ5/x6+ADt8J0JiaQZ5t1YCpqaaVG/gZCjTEnB31f+QFXUJefG3kXJyGx5snQunvv+D1Npe6IiVZpRF//bt29G9e3d4e3uXOt3FxQVyuRx5eXmYOHEiGjVqBBsbG3h7e2P16tV6Tls7yeq6Iv1yAJQZKUJHIaoStVqN4HmbirrpldNVLycxDeHf/aG/YERERDUkfO1+uPXyhdy/5eNGlQpqFbuoZ8U+xKEXP4W5XR30/WU2Xjr6DVpPG4rwtftxbvaGEvMX5ubj/l/BaPBcBwHSki6YOzdEs6VnUZiVittfvIjr01sjftdiuLw8Cw0mrRU6nlaMrnu/QqFAXFwcRo0aVWKaSqVCWFgY2rZtCwBQKpWQy+U4fPgwGjVqhKtXr+K5556Di4sLRo4cWanPUyqVUCgUNboO5Yl/mPv434p4QGmht89+WkGBCwBZlZZtOOVH3PnmFYSOqwdLj5ao06wz7No9D7tOgyESiaqQpQCxsQlVylJTchPSiv8dHx8PC1WOcGGqyZTWRVdSL0Xh0a3YSs0bue0o5K/3guTf0ZCNVUGBUugIWikoUCI2tnK/o5rA7UZ7D/MlAFwBFP3MCswKhQ0kEGPbtgyBvrfvyjKk/YA2f1enZ3xXanvSPxHY5Dq8RrLU9O/LELab1Ov3EPT6kjKny2ysIDaTIi85HSKJGB792kNx9poeE2ridlMxbesbK682aDLnTx1l0b6+kcvlkEq1L+GNrujPysoCgFILx3379iExMbG4a3+dOnXw+eefF0/39fXFSy+9hNOnT1e66FcoFPDw8Kh+8MqS1gWaLwMAdOzQEVCm6u+zn9JidTgsG7SseMZSWDfvilbrbyMrMhhZEeeQce0kbi8dDrt2A9H40/1aF/6RkZHweE7Y7lJ1xZb41vl5AEDHjh2RasQn/Ka0LrrynFVTjLZtXal5C7Pz0L1FO8QoSw72Yky+cOwHN5mt0DEqLTIyEiP1uH/mdqM9maMbWv9cdALasWMHFCTHCZxIGMa2bRkCfW/flWVI+wFD+rvSxe/LkNavLGZ2Vuj94yyIZVKIJGLEnwxFxObDguXhdlOx6tQ3Zbm9dAQyb5yGMv0hrr7hDvnwT+D8/OQKl6tKfRMTEwN3d3etMxpd0e/h4QGJRIITJ05otN+7dw9Tp04FUPb9/AUFBTh16hQ++OADXcckACKJFNbNu8C6eRe4DHkfyce34O7ysci8dhI2rXoKHY+oXGItv5gSQ/seLERERIYmaudxRO08LnQMo5AV+xAHBnwkdAwSWOOPfhc6QoWMrug3MzPDuHHjsHHjRgwePBiDBg1CTEwMNmzYABcXF8TFxZVZ9E+ZMgU2NjYYN25cpT9PLpcjJiamhtJXLP5hLjqOK/pCI/hiMFydhOveP/W6C2JyK56vsizcmwMAlI8StV7W29sbf+vx91Ca3IQ0nH5hAQAgODgYFi72guapDlNaF11JOhmO0Pd/rNS8IqkEx0LOQ2ZX8tE+xuTcyCXIuqO/25mqy9vbGzE7f9bb53G70d7DfAkmhBf9Ozj4Ipxqafd+Y9u2DIG+t+/KMqT9gCH9Xeni92VI62csuN1UrKbrm+qoSn0jl8ur9FlGV/QDwKpVqyCTybBv3z4cPXoU/v7+2Lt3Lz777DNERUWVOsDfe++9h3PnzuHo0aMwMzOr9GdJpdIqdaGoMmlW8T9d5a5wlwtXRMhuAajiRhHxSU84dB8DqybtIbWrh7z4KMRt/gSSOvaw8emtfRaZTL+/h1JkiR8/KcLV1RV16jsKmKZ6TGlddKX+SFfc+novshXJRaP2l8NrcFd4tWymn2A6JJOVfkiQWlnA1qvoICOWSWFZzx4OLT1RkJVb/MxnIchk+t0/c7vRniwHwL9Fv6urK1wsy53dZJW1bVHZ9L19V5Yh7QcM6e9KF78vQ1o/Y8HtpmLVqW9qmj7rG6PcmqytrbF+/XqsX79eoz08PBw+Pj4QizUfSjBjxgwEBQXh6NGjcHJy0mfUWsvObyBSTm7Fg23zUJidDqmdM2xa9oDntI2Q2vJ3QIZPLJWg7YejcGbmWkCE0gt/kQgSCzP4TH1Z3/H0yqlNYwzYs7D4dfM3BqL5GwOhOHsNAcPmC5iMiIiIiCpilEV/adLS0hAbG4tBgwZptE+bNg1Hjx7FsWPHUK9ePYHS1T7y4bMhHz5b6BhE1dJ0dB/kpWbgn882a07490sAWR0L9PnlI9RtZniD5tQkxblrNTK6MxERERHpn8kU/WFhYQA0B/G7d+8eVq9eDXNzc3h5eRW3d+/eHX/99Ze+IxKREWr1zmC49/FD+Lp9iNpxHABg6+WKpmP6oOnoPrBwshM2IBERERFROUy66G/YsCHU6gpuxiUiqoB9Mw+0/XBMcdH/3O8LeD83EREZFWv3euixdgZUSiVEEgnOz96A1Bv3iqd3XzMNNg1cIJKIcXNTAG7/fqKcdxOObSNXDDm+HH8NmYuky7c0plk3cEbXbydDLJPi/l/BuPb9fkgszfDczvmwb+qOcx/9gDv7zpT7/uaOtui86E1YONpCmZOPoHFfakxvMXEQvF7uBlVBIVLConFhTvkD57WZORz1e7VBYW4BTs9Yg+z4lAo/TyyTosd302HpbA+RRIwLn/6E5KvRaDNzOFy7+QAAbLzkCP9uH278dKiyPzrSwuURVqjj3REA4PzCdNT1L3krZ8SnvWDh9gwaTv6+uC03LhLXprZEsy9PwbpZZ73lrYjJFP2TJ0/G5MkVPw+RiIiIiKi2yYpPxqHBcwC1GvKurdB62lCceGd58fSQb3Yi444CYjMpBh/9Fnf+OANVgVLAxKVrM3M4FOeulzqt/ZyxuPzlb0i6FIkBexbi3sHzyIp7iGNvLEOzcf0r9f4d5r+OkK934FHUg1Knxxy5hOsbDgIAeq6bCRf/FkgoI4+9tzucOz6DvwbPhWuP1vD7aAxOz/iuws9z7e6D/IxsHH/rGzi1bYrW04fh2JvLELp8F0KX7wIAvHj4K9w7eL5S60TaM6vXAM0WHS9zetrFA5BY2pRoj9/5OWxaGt6jycUVz0JERERERMZMXagC/u0Ba2ZjiZTrdzWmZ/z7eDxVvhJQqw2yt6xT26bISUxDdnxyqdPtmroh6VIkACA28DJcOjeHWqVCTlJapd5fJBbDvpk7fKa8jAF7FqLpK31LzPPkU2tUSmXRz7UMLp1bIObIJQBA/MmrcGzdqFKfl3FXAYm5DABgZmeF3ORHGsvZe7sj/1EWshWavQao5hSkPEDEJz0RvWw0CtI0HzeuVqmQdOg71Hv+XY32rIgLkNnLYeZkeE9QYNFPRERERFQLOLT0xPN/LkKnRRMQfyqs1HlavTsEdw+eh1pZqOd0FWs9fSjC1uwtc7pILCr+d96jLJjXLXkltjwWTrZwaOGJ8HX7cXj052g6ug9sGrqUOq9zx2dgJXdAYvDNMt/PzN4a+Y8yH+eTaJZeZX1eZmwSpJbmePnUSnT9djJu/KjZhb/RsB6I3ntaq3Uj7fj8EI1mi0/AvuNLiN34vsa05KO/wN5/KMQyC432+N8XQT7MMAcyZ9FPRERERFQLpFy7i0Mvfoqg8UvQafGbJaZ7De4KRx8vXFm6XYB05XPv64fk0NvIS80sc54nOyeY2VohLzVDq8/If5SFrAcPkRYRA1W+Egnnr8O+lCf02DV1Q/s5Y3H87W/Lf7+0TJjZ1nmc76leAWV9XpORvZAZk4i93afjr5fmoOu3mrcwN3y+E+4dOKfVupF2/nvEeN1uI5EdfaW4XZWfi5QTW+HU938a8z/65yCsmrSH1NYwx3xi0U9ERDWm24p3MT5+F8bH78K42B0YcWk9uq2aCiu5g9DRiIhqNbHZ46G8CtKzUZiTrzG9fq82aDqmD05NW61ZPRsIh1aekHdpiX6/fQrXHq3RYeF4WDrba8zzKDIWTr5NABR9SZBw4UaZ7yetYwEzWyuNtsK8AmTFPiw+Zjm0boT0J7rzA0AdNyd0WzkFJ99dibyUx18qWMkdIBJrllYJ56/DrU9bAIC8ayskX42u3OeJRMj9973zHmVB9kRO547PIO1WLPLTs8tcN6qewtwsqAuLerpkXDsJc9cmxdPyEu6gMCsNUZ+/gNhfPsSjS4eQfPRXZEeHIDP8OG4tGID0kCOI/WkmClLihVqFEkxmID8iIjIMivPXceKtbyGSiGHj6YLOiyeg1w/v49BLnwodjYio1nLu8Ax8PxgJdaEKIpEIwQs2wa23L8zsrXFn72l0XzkF2Qmp6L9tLgDgxKTllb4XXh+urtyDqyv3ACj6gjni18PISUzTWIdLi7ei6zfvQCSVIObvi8i8X3Qvdq8fP4BjKy8os3Ph5NcUF+dvgteQbpBamJUY/T54/ib0WDsdYqkUsceu4FFkLCzr2aPF2y/g0hdb0H7OWFg42KLbiqL7ucPW7EXcsRD0WDcDR19folGMp0XGIjnkNgbu+xyFeUqcmVk0iF+Tkb2QGfcQijPhpX5eVkwSeqydgQF7FkJqaY4rS7cVv2ejod0RvYdd+3UpN/Ym7n03ERILa4ikMjSYvB6PLgegMCMFDj1fQfNv/wEAZIQdR8qp7XDsMw4A4Dqy6Dzn7srxcBowCTIHV6FWoQQW/UREVKNU+criE8VsRQoitgSi86I3IbO2REFmjrDhiIhqKcWZcAScCS9z+o42E/WYpnqeHAE/7lhI8b8z7ioQMGx+ifmPT/i6RFvdZzwQumJ3ifaU8DsIGKr5HjlJabj0xRYA0HjiwX9EUgky7yeWevU95JudCPlmp0Zb1M7j5X6eMicPR/+3tMR7AcD52RtKbaeaU6dJO7RYflmjzeKJq/3/sfHpBRufXiXaPadv0lGyqmPRT0REOmPpUheeL3SGSllY7gjHRERE+hQ8d2ONvZdaWYjT09fU2PsR1TTe009ERDVK3qUlXo3ajNeit2JUyAbI/Vvi+oaDUObkASi673H4P+tg4WgLAJBYmmHomdWwf6ZBudOIiIiISHss+omIqEYlXb6F/c/OwoGBsxHy7e9IvBihcT9itiIF19cfQIeF4wEAvu+PxL2/LiDt5v1ypxERERGR9ti9n8rkZlXxPPpiSFmIqHyFufnI+He045BlO2DjKUenRW/i7AffF89z46e/8ELAUjSf8DwaPt8J+/t+UKlpRERUxMZTXq3lVcpCpEcXjS5u28gVYqlEsCz6ek9Tx59ZxQypptBnFhb9VKblnYROQESmIOTrHXj55EpEbD6C5NDbAAC1SoWL8zdhwJ6FOPrGV8Vd/yuaRkRERfr+Mrtay2c9SMbv7d4GADz3+wLUqW9Yzxev7voRlaa21jfs3k9ERDqVcUeBmCP/wG/2GI12t75tka1IQd1S7tcvbxoRERERVR6LfiIi0rnwtfvh1ssXcv+WAAD7ZxqgwYCOODBwNpq+0hfWDZyL5y1vGhERERFph0U/ERHVmNMzvsPhUZ+VaE/6JwKbXIdDce4aAMB/6Vu4OH8TshUpuPLVdnRa9GbxvOVNIyIiIiLtsOgnIiK9avrqs8h9+AixQZcBALd/PwFZHQs0eL5TudOIiIiISHscyI+IiPTq1tZA3NoaqNEWMHS+xvSyphERERGRdniln4iIiIiIiMhEsegnIiIiIiIiMlEs+omIiIiIiIhMFIt+IiIiIiIiIhPFop+IiIiIiIjIRLHoJyIiIiIiIjJRfGQfERHVOHtvd/gvextqlRpqZSHOvL8OmfcTi6f7vj8STUb3xqNbsTjyyqJKLUNERERE2uOVfiIiqnG5yekIfO1LBLw8D+Fr96HNzOEa0yM2H0bAsPlaLUNERERE2mPRT0RENS43OR0FGdkAAFVBIdSFKo3pOYlpgEqt1TJEREREpD0W/UREpDMSCzP4zhqJ6z8e0ukyRERERFQ63tNPRLVG0OtLkHFXUaVlVcrC4n//PWIBxFJJlXPYeMrR95fZVV7eWIgkYvRYOx3X1u1H2s37OluGiIiIqDKqcy5Y0/R5Psiin4hqjYy7CqRFxlb7fdKj42sgjenr+s07eHA8FPcDLup0GSIiIqLKqKlzQWPDop+IiGqcW29feL7UBdYezvAa3BUp1+4g7lgIzOytcWfvaXi/9iwaj+gJuyZu6L9jHk5NWw2HFg1LLBM8b5PQq0JERERk1Fj0ExFRjYs7FoItjV4tc3rklkBEbgnUXCYhtdxliIiIiEh7HMiPiIiIiIiIyESx6CciIiIiIiIyUezeT0T0lG4r3kWTUb0BAKrCQuQkpCH+TDguL96KbEWKwOmIiIiISNdM6XyQV/qJiEqhOH8dO1pPwK727+Dkuyvg2MoTvX54X+hYRERERKQnpnI+yKKfiKgUqnwlcpLSkK1IQcL5G4jYEgjnDs0gs7YUOhoRERER6YGpnA+y6CciqoClS114vtAZKmUh1IUqoeMQERERkZ4Z8/kg7+knIiqFvEtLvBq1GSKxGFJLcwBA+Lr9UObkAQAaDOwI3/dGaCxj5+2O4LkbEfHrYb3nJSIiIqKaZSrng0Zd9IeGhmLevHk4fvw41Go1+vTpg3Xr1sHb2xuDBg3C9u3bhY5IJkKtVuNRVFzxa5WyUMA0pA9Jl2/h9PQ1kJjL4PlSF9Tv3hpXlm4rnn7/r2Dc/yu4+HWDAR3g9/EriPr9uABpiYhI19RqNdLvxBe/VhUoBUxDZDzS7yiK/21s242pnA8abff+oKAgdO7cGREREZgzZw4WL16M2NhYDBw4EJmZmfD19RU6IpkAtVqNqJ3Hsf/ZD3B41GfF7Qdf+Bihy3ehMK9AwHSkS4W5+ci4q0BaRAxClu1ARkwiOi16s9R5rVwd0GnxBJyYtAKFOfl6Tqofbn3a4qUjyzD27jYMD16LFm+/IHQkIiK9if7jNA4M+Ah/D19Q3Hbg+Y9x5avtUGbnCReMyIDd2X8Wfw78CH8Pn1/cdmDgR7i8ZBsKsnMFTFZ5pnI+aJRFf1JSEkaNGgU/Pz9cuXIFs2bNwpQpUxAUFIT79+8DAIt+qja1Wo2L8zfh9PQ1SL1xT2Na7sNHuPLVdhx55Yvi7j1k2kK+3oEmo3rDsU1jzQkiEXqsmY6wNX+U+DsxFY5tGqPvpo8Qe+wK9vf7ACFf70S72a+g2bj+QkcjItK5y0u24eQ7K5AcFq3RnpeajtDluxAwYgEKsnIESkdkmEK+2YkTb3+L5NDbGu15aZm4unI3/h62APkZ2QKlqzpjPR80yqJ/6dKlSE1NxcaNG2Fp+XjkRDs7O/j5+QFg0U/VF73nFK5vOFj0Qv3UxH9fK85ewz9fbNFrLhJGxh0FYo78A7/ZYzTa28wYhvyMbNz8+S+Bkuley7dewMOQ27i8+Dc8uhWHqJ3HcePnv+AzZYjQ0YiIdOreoQu4unJ30YsyzgUeXr6FC5/+rNdcRIYs5sg/CPl6Z9GLsrabkCic//hHveaqCcZ6PmiURf/27dvRvXt3eHt7lzrdxcUFcrkcADB58mR4eHjA1tYWbm5umDFjBvLzDau7BRketVqNa+v/BEQVz3vrtyDkp2fpPhQJLnztfrj18oXcvyUAwLlDMzR9pS/OzPxO4GS65dzxGcQdu6LRFncsBNYezrBydRAoFRGR7l3fcKBS893efRI5Dx/pOA2RcSi+aFaBO3+cRnZCqo7T1DxjPB80uoH8FAoF4uLiMGrUqBLTVCoVwsLC0LZt2+K2KVOmYNmyZahTpw4ePnyIESNGYPHixViwYEGlPk+pVEKhUFQ8Yw2Jf/j4/pZ4RTygtNDbZ9NjWfcSkRJ2p1LzFubmI2Tb36g/qKOOU9Wc3IS04n/Hx8fDQlU7uiUWVHLwmNMzSt9pJ/0TgU2uwwEAZrZW6L56Gk5PX4O81Eytc8TGxmq1jD6U9fOxdLZHTlKaRltOYuq/0+oiOz5F19FKpe+fY23dbqrjYb4EgCuAop9ZgVntHAS1svseeswQ9pO5CWlIOH+jUvOqlYUI3fIX3Id303GqmsN9GulC3sN0xJ8Kq9S86kIVQjYfQoPRPXWc6jFt9seGeD4ol8shlWpfwhtd0Z+VVXRFVSQqeQl23759SExM1Oja36JFi+J/q9VqiMVi3Lp1q9Kfp1Ao4OHhUfXA2pLWBZovAwB07NARUBrft1+moKnMEZ849qr0/PPem42/J1X+70podcWW+Nb5eQBAx44dkVpLDvRfOPaDm8y2Rt6r2evPwdLZHh0Xjtdoj/r9BK7/UP6VocjISIzU536lkmry56MP+v451tbtpjpkjm5o/XPRCU3Hjh1QkBxXwRKmydi2LUNgCPvJBlI7LHR6ttLzL5nzGfbNrNyXBIaA+zTSBTepLb5w6lfp+b9euBh7Z72mw0Saanp/rO/zwZiYGLi7u2sb0/iKfg8PD0gkEpw4cUKj/d69e5g6dSqAkvfzL1myBF988QWysrLg6OiIJUuW6CsuGakctXaj8ueqeRWntglbvRdhq/cKHUMvchLTYFnPXqPN4t/X/13xJyLdklpZ4OXTK3H0f1+VGBjLGEnrWGDY2dU4POYLpF43vEGvACBHy2O7tucORKYoR1W7zqGN5XzQ6Ip+MzMzjBs3Dhs3bsTgwYMxaNAgxMTEYMOGDXBxcUFcXFyJon/27NmYPXs2bty4ga1bt8LV1bXSnyeXyxETE1PDa1G2+Ie56Diu6AuN4IvBcHVi934hqFUqnHn5C+TGp5QcgORpYhF+OvMnzOvZ6SVbTchNSMPpFxYAAIKDg2HhYi9oHn05N3IJsu7o73adsnh7eyNmp+EN+lTWzycx+Cbq9/JF6PJdxW1uvX2RGZMoWNd+QP8/x9q63VTHw3wJJoQX/Ts4+CKcamn3/prY9/hMGYLk0Ggkh96GXZP6ePHwMlyYuxG3tgYWz2PtXg8vBX2NkG9/x/X1ByD3b4n+O+ch8LXFeHAitHg+J98meH7/Fzg+aTnuH7qgVQ6vIV3RbcUUHHh+tkaxLpKI8fz+RchNSYdYIobM2gp/DZkLtUpVPI+DjxcGHViMk++uwr0D53Bt/QF0mP+6xiNx/2MI+0m1Wo3zo5cW/e4qOhcQAauDfoeVu5NestUE7tNIF9RqNYJf+xoZt+Iq3m4ALA/4DT94uug+2L8M5VwQqNp+7r9x67RldEU/AKxatQoymQz79u3D0aNH4e/vj7179+Kzzz5DVFRUmQP8NW/eHG3atMHYsWNx7NixSn2WVCqtUheKKpM+HhDOVe4Kd3kd/X02afCZ+CIuLvylwvk8B3VG47Yt9ZCo5mSJHz/1wtXVFXXqOwqYRn9kMsPY5clket6vVFJZP59rPxzAoD8Xoe3sMYjedQJObZui+RsDcXFBxduHLun751hbt5vqkOUA+Lfod3V1hYtlubObrOrueyTmMjR7vT9OTV0NAHgU9QD/fLYZHRe+DsWZcGTcVUAkFqP7d9PwMDQa19cXdSlVnLuG6z8cQNflk7G/7/vIS82E1NIcPb6bjtu7TpZZ8Mv9W6Lbynexq+PkEtPu/HEG7s+2Q4/vpuPAgI9QmFd0Va/NjOGw9qiHoHFfQiQRY/DRb+Az7WVcXVE08r3Ewgw91kxD9J5TuHfgHAAgascx+M0eA/tmHkiL0LzAYij7yey3X8K5j36ocD633m3h3dlX94FqEPdppCu5k17CmffWVThf/R6t0axbOz0kesxQzgUB/e7njHL0fmtra6xfvx4KhQIZGRk4fPgw/P39ER4eDh8fH4jFZa9WQUEBIiMj9ZiWjFXzNwfCva9fufPYNHRBp8UT9JSISBjJobdx9H9fwePZdngp8Bu0/XA0Li/dhohfDwsdjahWcOvtC4mFmcbV+pubApBw/gZ6rJkGkUQMn2kvw97bA6enr9ZY9vKSbchLyYD/V28DADp+/j+IJGJcmFv1q+jnP/4RsjoW8PvkVQBFPQd8pr2MMzPXIjc5HTmJaTj7wfdoM3N48bOs2336GsRmMlyY8/hzc5PTkfhPBBoP61HlLLrW9NW+aPhC56IXZTzRx6q+I7osm6S/UEQGrsmo3vAa0rXoRVnbjdwBXb8t+cUi6YZRFv2lSUtLQ2xsrEbX/kePHmHTpk1IS0uDWq3G1atX8cUXX+C5554TLigZDbFMit4/z0KrdwdDZqN5eUokEcNrSFc8f2AxLJ2Mp1s/UVXFBl3G/mc/wGbPMdjV4Z3iK4lEpHsu/i2REn4H6kKVRvuZmWth4+mC7qunwfe9ETg/e0OJW25UBUqcfHcl3Pv6ofvqqWgyqjdOTV0FZVYuqqogIxsnp65G8/8NgEf/9ui+eioitwYhNuhy8Tz3Ay4iaudx9FgzDR7926PZuH44NaXk5yZdvgV511ZVzqJrYokEPdfNROsZw2BmY6UxTSQWo+Ggznjh4Je8Sk70BJFYjO5rpqHN+yNgZvtUr2WxCA0GdsSgg1+ijpvx3A5j7Aynf0M1hYUVPRriyaJfJBJhy5YteO+995Cfnw9nZ2cMHToUCxcuFCglGRuJmQzt54xFm/dGIDbwMnKT0iC1toRbb19YOdcVOh7pwGvRW/HwShQA4PqPB3H/r+Diad3XTINNAxeIJGLc3BSA27+fgL23O/yXvQ21Sg21shBn3l+HzPuJQsUnIhNk08C51PEzcpLScOnLbej69STcPXAOd/adKXX5tIgYXPvhANpMH4bwdfuReDGi2pkSL9xA2Np96P3zLKRHx+Ofz34tMc/FeZvw4pFl6P3zLFxdvhtJl0r2tMyOT4FNQ+dq59ElsVQCv4/GoPW0oYgNuoychFRIrcxRv5cv6riy2CcqjVgiQdsPRsHn3SGIDbqCnMRUSC3NUL9HG4Mv9is6tyvtfBAAOi16E46tG0EkESNk2Q7EHQsRaA1KMumi39bWFoGBgWUsQVR5MisLeL3URegYpAdZcQ8RMGx+qdNCvtmJjDsKiM2kGHz0W9z54wxyk9MR+NqXKMjIhltvX7SZORxnZq7Vc2oiMmUSCzPkp2eXaBdJxGg6ujcKsnLg6NMI0joWpV7Bl9axQKMh3VCQlQPnDs0gEos1Btir4+aEISeWP35fsRgScxlejdpc3JYZ+xD7es3UeN+Qr3cWfZGw5g8U5uaX+FxlTh7C1+2H/5KJCF2xq8R0ACjMy4fEwqziH4IBkFqaw/MFf6FjEBmVou2ms9AxtFLRuV1p54M2ni6wa+qOQy9+Cst69ui75WMW/bowefJkTJ7M+0KIqHosXepiwJ6FyElIw4U5PyE3Ob14Wsa/o72q8pWAWg21Wq0xXVVQWKL7LRFRdeUmp8Pc3rpEe5sZw2HbyBV/PvcR+m+bg44Lx+PsB9+XmK/zojehUhbiwMDZGPTnYo0B9gAgW5GC/c/OKn5dz68p2n36msYXoCplycdqqZVFT2NQFZb9VAZ1QdFyZe0bze2tNfajRERCq+jcrrTzwZyEVBTm5UMkEcPMzgp5KRl6zVwRk7mnn4ioJuzu/C4Chs7H/cMX0WHB66XO0+rdIbh78HzxCS9QdCXOd9ZIXP/xkL6iElEtkRwWDftmHhptTm2bovX0oTg7az3Sbz/Aqelr0GR0b7j30xwJu+GgTmg0tDtOTVmFR7ficH7OT2gzczgcfLyK51EXqpBxV1H8X3Z8CtSFhRptWbEPdbJu9s0bIjk0WifvTURUHRWd2z15Ppifno3M+4kYemY1BuxeiLDVe/Wctnws+omInvDfN7N395+FQyuvEtO9BneFo48XrizdXtwmkojRY+10XFu3H2k37+stKxHVDnFHr8CmoQus/h0sTmppjh5rpuH27seP3Us4dx3X1x9A168nwdzRFgBg6WwP/6/eRuiK3XgYUjRWSfSuk4j5+x90Xz0NEnOZMCv0BHmn5ogNvCR0DCIiDRWd2z19Pli/ZxtYOttjt/8U7O05Ex0/fwMiieGU2oaThIhIYFJLc4j+feSnS+cWyLir0Jhev1cbNB3TB6emrQbU6uL2rt+8gwfHQ3E/4KJe8xJR7fDoVhziz4Sj8fCeAIAOn42HSCrWePwdAFxeug05D9PRZVnR4/m6rZyCjLsJuLpyt8Z8Zz9cD3O7OsWP3BOKvEtLSOtY4M6fZwXNQUT0tPLO7Uo9HxQBeWmZgFqNgswcSMykEEslek5dNpO5p5+IqLrsmrqhy9eTUJCVC1VBIc59uB5uvX1hZm+NO3tPo/vKKchOSEX/bXMBACcmLYdDK094vtQF1h7O8BrcFSnX7iB43iZhV4SITM6VZTvQc90MXP/hAM7NWl/qPKp8Jfb3fb/49ZExX5Q6X35aJna2favMz1Kcu4ZdHSs3TtIm1+HlTo/aeRxRO4+XOq3V5MEIW/MHCnNKDgJIRCQUt96+Jc7t4o6FlHs+GH8yDI2GdMPAPz6HxFyGGz/9hcK8AoHX5DEW/URE/0q+Go0/+3+o0fbk1f4dbSaWWCbuWAi2NBL2ahkRmb7ECzcQ+u3vsGngjLTIWKHjVJu0jgUSL0Xi+g8HhI5CRKShonO70s4HAeD0jO90FanaWPQTERERGYHILabzGGJlVi6uLi/9MX5ERFSzeE8/ERERERERkYli0U9ERERERERkoti9n4hqDRtPudARABhOjqcZaq6yGFteIiIiElZ1zx1UykKkR8cDAGwbuVZrhH59nsew6CeiWqPvL7OFjmDQ+PMhIiIiU1bdc52sB8n4vV3RY1Gf+30B6tR3rIlYOsfu/UREREREREQmikU/ERERERERkYli0U9ERERERERkolj0ExEREREREZkoFv1EREREREREJopFPxEREREREZGJYtFPREREREREZKJY9BMRERERERGZKBb9RERERERERCaKRT8RERERERGRiWLRT0RERERERGSiWPQTERERERERmSgW/UREREREREQmikU/ERERERERkYli0U9ERERERERkoqRCByDSl5kXgLhsoVMAblbA8k5CpzBMQa8vQcZdhdAxjJ6Npxx9f5ldI+/10tQjuB2bXiPvVR2N3W2xf3U/oWMQkZEzlONMTe6niYgqwqKfao24bCA6Q+gUVJ6MuwqkRcYKHYOecDs2Hddvpwkdg4ioRvA4Q0S1Ebv3ExEREREREZkoFv1EREREREREJopFPxEREREREZGJYtFPREREREREZKJY9BMRERERERGZKBb9RETlGLB7Ibp8PalEu7V7PYyP3wXnjs8IkIqIiIiIqHJY9BMRERERERGZKBb9RERERERERCaKRT8RERERERGRiTLqoj80NBSDBw+GnZ0dbG1tMWTIEMTHx8PGxgajR48WOh7VEmETPYWOQEREREREVCqjLfqDgoLQuXNnREREYM6cOVi8eDFiY2MxcOBAZGZmwtfXV+iIRES1VuCGgTixcRBEIs32P1Y+i4vbXoJUKip9QSIiA9Tvt0/x/P5FEIk1T50dfLww9t42NHzBX6BkpC8F2bmI3BqIwLGL8deQuTg24WvcO3geKmWh0NFIT7ITUnF9w8Hi1xc/+xWJ/0RArVYLmKpypEIHqIqkpCSMGjUKfn5+CAwMhKWlJQBg7Nix8PLyAgAW/aRzMT/OREb4MRSkPMD1Gb6wqN8MjT7cIXQsqmH56dkws61Tot3MrqitMK9A35GMwutzTuDqrqH46I3WWPLTVQDAW8OboV9nN/iN+gNKpeEfIImI/nN6xncYfPQb+Ex7GVdX7AYASCzM0GPNNETvOYV7B84JnJB0Ke54CE68/S3y07OLGkQiQK3GvYPnYd3AGX1//Rh1m3kIG5J0Rq1WI3ztPlz+8jeoC1XF7Xf3ncHdfWfg2t0HvX54H+b21gKmLJ9RFv1Lly5FamoqNm7cWFzwA4CdnR38/PwQFBTEop+q7NLg8q9Amjk3hM+Gu/CYsBxAUff+FitC9JCMhPAoKg6eL/pDJBZDrXq8o3dq2wQqZSEy7sQLmM5wxSVk450vzmDz4p4IOBOH7Fwlvp3VCbO+DUbE3UdCxyMi0kpOYhrOfvA9eq5/D3HHQpAcehvtPn0NYjMZLsz5Weh4pEMJ568jaNyXUCkfnwPgiSu7mfcTETBsPl74awlsPJwFSEi6dn39AVz6YgtQRokQfyoMgWO/xIBdCyAxl+k3XCUZZdG/fft2dO/eHd7e3qVOd3FxgVwu12jLycmBj48PFAoFMjMz9RGTjFTrTY+LuMybZxG9ZBiaL78MWV3XokaxRKBkJISbvwTgmTcGoOuKd3Hjx4PIf5QFp7ZN0PbD0Yjacezxt/5Uws6/7+DFng2w9cueyM5V4uQlBdbuuCF0LCKiKrkfcBFRO4+jx5pp+OfzzWg2rh8Chs6HMitX6GikQxc/+7WoC385HdTyktNxdcVudP3mHf0FI73IS8vE5SW/Fb0o528g6Z8I3Nl3Bk1G9tJLLm0ZXdGvUCgQFxeHUaNGlZimUqkQFhaGtm3blpg2b948NGzYEAqFQqvPUyqVWi9THfEPHx844hXxgNJCb59t6goKXABU/O2brO7jL4yk1g5F/7etp9FevRwFiI1NqJH3qqrchLTif8fHx8NClSNcmCcUFCiFjlBCVuxDHHrxU/h9NAZ9f5kNma0VMu8lIHztflz/8WDFbyCAggIlYmNja+S9lAXVu31hypfnEBc4GiqVGi9MOVKtHDW1TlVlqNuNIXuYLwFQ9IVpfHw8Csxq572vhrhvM3Q1uR97+n2r4+K8TXjxyDL0/nkWri7fjaRLkVXOwX2a4Uu/EYOHV6IqNe/tXSdQ/82+kNla6TgV6dP9345X7lZOEXD1h/2w6NJEp3nkcjmkUu1LeKMr+rOysgAAoqdHhwKwb98+JCYmlujaf+nSJQQEBOCbb77B0KFDtfo8hUIBDw893qMjrQs0XwYA6NihI6BM1d9nm7gWq8Nh2aCl0DEQGRkJj+daCZqhrtgS3zo/DwDo2LEjUg3kQP+FYz+4yWyFjlFC6vV7CHp9idAxKi0yMhIja2q/1XQhYOFW5cVfG9QYIohgZSFBuxZOOHQqpkrvExkZCQ+PMVXOURMMdbsxZDJHN7T+uaiw6dixAwqS4wROJAxd7tvsvd3hv+xtqFVqqJWFOPP+OmTeT9SYp/uaabBp4AKRRIybmwJw+/cTsHavhx5rZ0ClVEIkkeD87A1IvXGv3M8SSSV4+cQK3NoWhLA1f2hMazFxELxe7gZVQSFSwqKLu7ybO9qi86I3YeFoC2VOPoLGfVmp9arR/dgTqvu7UObkIXzdfvgvmYjQFbuq/D66Wj9tcJ9WsV6WXnjdzq9S86rylejv2wVRBck6TkX69JZdB/hbNqh4RjWQFn4PDTw8yusQUG0xMTFwd3fXejmjK/o9PDwgkUhw4sQJjfZ79+5h6tSpADQH8VMqlZg4cSK+++47qJ64H5eIiHTrGS87fDWzI6Z/dR4tGtnjxwXd4DNsD5LT8oSORmQycpPTEfjalyjIyIZbb1+0mTkcZ2au1Zgn5JudyLijgNhMisFHv8WdP84gKz4ZhwbPAdRqyLu2QutpQ3HineXlflazsf3wKKr0L25ijlwqHtW657qZcPFvgYRz19Fh/usI+XoHHkU9qJkVNgDqf3sLPDmgF5kmcSkXGcsjKeumbzJaYi1+p9r+veiT0RX9ZmZmGDduHDZu3IjBgwdj0KBBiImJwYYNG+Di4oK4uDiNon/ZsmVo27YtevTogePHj2v9eXK5HDExVbsyVRXxD3PRcVzRFxrBF4Ph6sTu/TVl6nUXxOjgtjsLjxZaze/t7Y2/9fg3VZrchDScfmEBACA4OBgWLvaC5vnPuZFLkHVHf7fTmCpvb2/E7KyZgaX6TjqNyPtZWi8nlYqw5cteCLwQhx93R8DcTIJ+/m5YP68rhr93VOv38/b2RtBRbjfG5mG+BBPCi/4dHHwRTrW0e78u9225yenF/1YVFJZaiGb8+9mqfCWgVkOtVmvMZ2ZjiZTrd8v9HKmVBdz6tMW9P8/B0tm+5Gfcfbx+KqUS6kIVRGIx7Ju5w2fKy7Bu4Izbu07i1m9BlVqvmtyPPclQjjO6Wj9tcJ9WsZSLkbg8eW3FMwKASISDF47DvJ6dbkORXkVvCED0DwEVzygCLN3r4f5F3Z6rPD1uXWUZXdEPAKtWrYJMJsO+fftw9OhR+Pv7Y+/evfjss88QFRVVPMBfVFQUvv/+e1y5cqXKnyWVSqvUhaLqH/j45NpV7gp3eclHhVHVyG4B0EHR33TeIe1yyGT6/ZsqRZb48VMvXF1dUae+o4BpHpPJjHKXZHBksprbb0llVRuF9rPJ7eDuUgcDJ/8NAMjLL8RrHx9H8G8vYeyLTbD5z8rdI/lkDm43xkeWA+Dfot/V1RUuluXObrL0sW+TWJjBd9ZInPtoQ5nztHp3CO4ePA/1v88Vd2jpic5LJqJOfScce3NZue/favJLuL7hIOrIHcqdz7njM7CSOyAx+CYsne3h0MITp6etQfqdeAzYtRCKM+HIuFfxuDY1uR97+n0Nga7WTxvcp1XMrX593Fq2Bxl3EzRG7C9NgwEd0Lit8LeRUs2q+/bLuPPjYY0nOJVKDbR683nBt+uyiIUOUBXW1tZYv349FAoFMjIycPjwYfj7+yM8PBw+Pj4Qi4tW6/Tp00hISIC3tzecnJwwePBgZGVlwcnJCSdPnhR4LYiITFPXti6YNd4HExacQlLK42/aQiNSMH/tZaz6qDM8+IUmUY0RScTosXY6rq3bj7Sb90udx2twVzj6eOHK0u3FbSnX7uLQi58iaPwSdFr8Zpnvb+FkB4dWXog/ebXcHHZN3dB+zlgcf/tbAED+oyxkPXiItIgYqPKVSDh/HfZ8ljkZEZFYjLYfji4q+MvquS0SQWwmRetp2o0bRsahTn1HNHu9f4XzWXvUQ5PRffSQqGoM4+vOGpCWlobY2FgMGjSouG3kyJF49tlni1+fO3cO48ePR0hICOrVqydETCIik3fmSgJkfhtLnbbkp6tY8lP5hQMRaafrN+/gwfFQ3A+4WOr0+r3aoOmYPggc92Xx1UqxmbSouz+AgvRsFObkAwCkdSwglog1Hkdat3kDWDjaot9vn8JK7gCxTIrk8Dt4cDy0eJ46bk7otnIKTry9HHkpGQCAwrwCZMU+hJXcAdmKFDi0boSoXZpjMhmjqJ3HEbXzuNAxSE8aDemGvJSMfwenfOJqv6jopcRcht4/fgAnX92O2k7C6bhwPPLTsxG9+6mLxv/+DVg3cEb/7XNhbme4FzRMpugPCwsDoDmIn5WVFaysHj82o169ehCJRAbb7YKIiIhIG269feH5UhdYezjDa3BXpFy7g+B5m+DW2xdm9ta4s/c0uq+cguyEVPTfNhcAcGLScth5u8P3g5FF996LRAhesAkA4DWkG6QWZrjx0+Nb1+JPhSH+VNF5VpORvWDpbI8Hx0NhWc8eLd5+AZe+2IL2c8bCwsEW3Va8CwAIW7MXccdCEDx/E3qsnQ6xVIrYY1fwKFLYx9QRVUXzNwbCtXtrXFu3D7e2FY1LY+3hjCajesP7lb6wquC2FzJuYpkU3VdPhfcrfXFzUwAU569DVVAIG08XNHv1WXgN7Q6ZlWGPw2bSRf/TevXqhczMTD0lIlNg49ML7fbp8sEbVBaJpRme2zkf9k3dce6jH3Bn35kS8/i+PxJNRvfGo1uxOPLKokov96QuX0+C+7PtEPP3RZz76IdS5/GZMgSu3VtDLJXg8tJtSAy+qdVjqMzrWqPbqqkws7HCw5AoXFzwi8Z0eZeW8Pv4FagKlFBm5+HklFXIT3u8r+q2cgos69kVr+Ow898hK+4hAODOvjOI+PVwuetIRKYr7lgItjR6tdT2/+xoM7HE9JykNAScCS/RXvcZD4Su2F3m5z15hTsnKQ2XvtgCAGWO/J8SfgcBQ+eX+X5ExsK+qRt8PxhVXPQP3Ps5x0GoRUQiEeRdWkLexTjHbTCZon/y5MmYPHmy0DGIqIao8pQ49sYyNBtX9n1UEZsPI+r34/BfMlGr5Z4U8vVORO8+Ba8hXUud7tanLSSW5jg86jONdm0eQ+Uz5WVE7z6JO3+cQffvpkPu3xKKc9eKp6ffVeDv4QtQmFeAZuP6o/kbAxH67e8AgLrNG8LMVrO7mKpAiYBhPIkmopoXPLf0W3OIiMh4GeVAfkRk+tQqFXKS0sqdJycxDVBp9sSozHJPylaklDvd80V/SK3M0X/nfHRb8S6kdSw0HkM1YM9CNH2lb7nv4dKpOWKOXAIAxAQEw8Vf8zGP2Q+SUZhXAKCooH9yhNg2M4fj6qo9GvOLxGI8t2sB+v4yGzaeVXt0CxERERHVDiz6iYjKYSV3gLqgEIdHLkTKtbtoNeklWDjZwqGFJ8LX7cfh0Z+j6eg+sGnoUuZ7yGwsocwqGsU+71EWzOtalzqfuaMtmo1/rvg51nL/lngU/QC5T32JcfDFT/D38AUIW7sPXb99p2ZWlIiIiIhMEot+IqJy5KVmFt8bG3fsCuq2aKj1Y6gKMnMh/XeAFzPbOshLLTm2iNTKAr3Wv4fzs38s6sEAwGfqEFxbu69kpn9Hxk68cAOW9eyrt4JEREREZNJY9BNRrSCtYwEzW6uKZ3yK4tw1OLZpDABwbNMY6XfiNR5DBQAOrRsh/a4CIokYls72Jd4j4fx1uPdtCwDw6N8eCeeua0wXy6ToteF9XPv+Tzy8cqs4r2U9e/T8fia6rZoCx9aN0PKdlyA2k0JiLgMA2DZyRUFmjtbrRERERES1h8kM5EdEpqfXjx/AsZUXlNm5cPJriovzNR9D5f3as2g8oifsmrih/455ODVtNXISUktdrrTHUAFF98x7DOgASyd79N8xD4dHfw5LJ7vix1BF7TiGrt+8g+d2FQ20d2raagAo9TFUNl5ytJ8zFsfeXKbxGWFr96H7yilo/ubzSL56u3gQv26rpuL0tNVoOqYP6rVtAqnFS2j1zkuIO3YFYWv+wP5+swAA1u714P/VW7i2bj8sXeri2c0fQ5mdB4iAc7M36OE3QURERETGikU/ERms4xO+LtH25GOoIrcEInJLYKWWK+sxVKHLdyF0+S6NticfQ6XKV+LU1NUllivtMVT12jYtfpTPk/KS0xH42uIS7af//QIh4tfD5T52LzM2qfhxfTkJqfiz/4dlzktERERE9CQW/URUK+jjMVTRe07p/DOIiIiIiLTBe/qJiIiIiIiITBSLfiIiIiIiIiITxe79VGu4aT9wu04YSg5DZOMpFzqCSajJn2Njd9sae6/qMJQcRGTcDOU4Yyg5iKh2YNFPtcbyTkInoIr0/WW20BHoKftX9xM6AhFRjeFxhohqI3bvJyIiIiIiIjJRLPqJiIhIUD/88AN69epV/J+rqys+/fTTMtufdObMGSxaVPRIy+zsbPj7+8Pe3h7bt28v8TlqtRoTJ05Ejx498NxzzyEmJgYAEBwcXPwZ7dq1g5+fHwAgJSUFr732mo7XnoiISLfYvZ+IiIgE9dZbb+Gtt94CANy+fRtDhgzBBx98gLp165ba/qSlS5di48aiR3Kam5tj7969+P7770v9nH379sHc3BwnT57EpUuXMHv2bGzduhUdO3bE8ePHAQArVqxATk4OAMDBwQF2dnYIDw9Hq1atdLHqREREOscr/URERGQQCgoK8Nprr2HdunWoW7duhe3p6el49OgRHB0dAQASiQRyedkDpEVGRqJ9+/YAAD8/P5w6darEPL/99hvGjBlT/HrgwIHYtWtXtdeNiIhIKCz6iYiIyCDMnj0bgwYNQrdu3SrVHhERAS8vr0q/v4+PD/7++2+o1Wr8/fffSExM1JgeGRkJMzMzeHp6Frc1btwYYWFh2q8MERGRgWD3fiIiIhLcoUOHEBoaisOHD1eqvSoGDhyI8+fPo3fv3mjTpg1at26tMX3r1q145ZVXqv05REREhoRFPxEREQkqPj4es2bNQmBgIMRicYXt//H29kZ0dLRWn7Vw4UIAQFBQEMzNzTWm7dy5s0SX/9u3b/N+fiIiMmos+omIiEhQX3zxBdLT0zXupe/Tpw8SEhJKbZ83bx4AwM7ODnZ2dkhOTi6+r3/YsGG4cuUK6tSpgwsXLmD58uUAgHHjxuHbb7/F8OHDIZVK0aBBA6xevbr4fS9cuIBGjRrByclJI9tff/2FSZMm6WzdiYiIdI1FPxEREQnqu+++w3fffVfmtPJ89NFH+P7774sf5bd79+5S5/v1118BoHiU/qd16tQJBw8e1GhLSUnBo0eP4OPjU24GIiIiQ8ain4iIiIxWt27dSgzwV1McHBywZcsWnbw3ERGRvnD0fiIiIiIiIiITxaKfiIiIiIiIyESx6CciIiIiIiIyUSz6iYiIiIiIiEwUi34iIiIiIiIiE8XR+6lMMy8AcdlCpyjiZgUs7yR0CiIiIjJmQa8vQcZdhdAxYOMpR99fZgsdg4hqCRb9VKa4bCA6Q+gURERERDUj464CaZGxQscgItIrdu8nIiIiIiIiMlEs+omIiIiIiIhMFLv3ExERERERUY3LyVUi4u4jZOcqIZWI4VnfGs6OlkLHqnVY9BMREREREVGNuBuXgfW7buLgyRhcj05DYaFaY7q7Sx10a+uCt4Y3Q68OrhCJRAIlrT1Y9BMREREREVG1JCbnYPrS89jxdzTU6rLni03IwvaAaGwPiEbLxvZYN6crureT6y9oLcR7+omIiIiIiKjK9h+7h5ZD92B7QPkF/9Ou3U5DzzcOYuZX51FQoNJdwFqORT8RERERERFVyaZ9kRgyIxAPU3OrtLxaDazYcg0jPghi4a8jLPqJiIiIiIhIawdP3seb80+Xe3VfIhHBzcUKbi5WkEjKvn9/37H7eOuz0zpISSz6Se/CJnoKHYGIiIiIiKohOS0Xb84/DZWq/P78cidLxB4Zg9gjYyB3Kn/k/k37bmFv0N0aTEmAkRf9oaGhGDx4MOzs7GBra4shQ4YgPj4eNjY2GD16tNDxtKZWqxF571Hx68JCdm8hIiLj9eRh7H4mtLrPk4iIDNusb4ORkJxT4+876fMzyMjKr/H3rc2MtugPCgpC586dERERgTlz5mDx4sWIjY3FwIEDkZmZCV9fX6EjVpparcav+2/Bb+Qf6Dvxr+L2LuMO4MsfQ5FfUChgupoT8+NMXJ/hi4KUB7g+wxfRX40SOhIREelAfiHwcyTwxhO9NN85B7x2AjgUw+L/SW592uKlI8sw9u42DA9eixZvvyB0JCpFv98+xfP7F0Ek1jx1dvDxwth729DwBX+BkhEJQ/EwG1sO3NbJeyem5GLrQd28d21llI/sS0pKwqhRo+Dn54fAwEBYWhZ1Exk7diy8vLwAwGiKfrVajfeWXcCKLdfw9CMq4x9m45NV/+Bo8AP8ubofLMwN89d1aXD5z9Y0c24Inw134TFhOYCi7v0tVoToIRkREelbbiEw4wLwz8OS0yLTgXlXiv4/vQVKHPdqG8c2jdF300cI/34/TkxegXptm8J/6VsozMlHxK+HhY5HTzg94zsMPvoNfKa9jKsrdgMAJBZm6LFmGqL3nMK9A+cETkikXz/tiUSBUne9ktfuuIFJI5vr7P1rG8OsIiuwdOlSpKamYuPGjcUFPwDY2dnBz88PQUFBRlP0bzkQhRVbrgEoeeXjv9eB5x9g9op/sOKjznpOVzmtN8UX/zvz5llELxmG5ssvQ1bXtahRLBEoGRER6duq66UX/ADw32Fuy23gGTtggLveYhmklm+9gIcht3F58W8AgEe34mDfzAM+U4aw6DcwOYlpOPvB9+i5/j3EHQtBcuhttPv0NYjNZLgw52eh4xHp3d9nY3X6/mG3UhGflA3XelY6/Zzawii792/fvh3du3eHt7d3qdNdXFwgl8sBAOPHj4eZmRmsra2L/wsICNBn3DKp1Wos3xxeqSsdP+6JQHqmYd7bIqsrL/5Pau0AAJDa1nvcbldP4IRERKQPGQXAvnsVzycC8Fu0zuMYPOeOzyDu2BWNtrhjIbD2cIaVq4NAqags9wMuImrncfRYMw0e/duj2bh+ODVlFZRZVXtMGZGxUqnUuHIzReefc+l6Gd8gk9aM7kq/QqFAXFwcRo0qeT+4SqVCWFgY2rZtq9H+1ltvYc2aNVX6PKVSCYVCUaVlKxIVk1npDSYrR4lf9obg5d71dZKlNAUFLgBkevu88hQUFCA2NkHoGCYjNyGt+N/x8fGwUNX8ICxEpobbTcWOJlshT1VxsaoGcD0NCI5SoL6FUue5hFZQUPo6WjrbIycpTaMtJzH132l1kR2v+5NqQ1VQoERsbM1fSSzrd1FZF+dtwotHlqH3z7NwdfluJF2KrHIOXayfNrhP0x5/ZkUeJOUgM7tAo00iEZU5Mr/rE+2uZcyjeJiDwkLNbs/nr9yDb2OjvEatM3K5HFKp9iW80RX9WVlZAABRKZfH9+3bh8TExBrt2q9QKODh4VFj76fBqinQ+KNKzz5t5hxMSz6imyylaLE6HJYNWurt88oTGRkJj+daCR3DZNQVW+Jb5+cBAB07dkRqLT1oEWmD203FXIa8D/f/fV3p+fu8OAxZN8/qMJFh+MKxH9xktkLHMCqRkZEYqYPzr+r+LpQ5eQhftx/+SyYidMWuKr+PrtZPG9ynaY8/s3+ZuQDNFmk0/fdYvopc3Dak1Hb3ftsQl5Ct0bboy6+waOaBKsc0RTExMXB31/7eOKP76sTDwwMSiQQnTpzQaL937x6mTp0KoOQgflu3boWDgwOaN2+ORYsWQak0kKsKKi27g2k7v4Gy8GghdAQiItKBwpwMreZXaTm/qclJTINlPXuNNot/X/93xZ8Mj/rf3gJqPlqZaiu1np4spq/PqQWM7kq/mZkZxo0bh40bN2Lw4MEYNGgQYmJisGHDBri4uCAuLk6j6J82bRq++uorODk54fLlyxgzZgxyc3Px+eefV+rz5HI5YmJidLIuhYVqdHvzJOKScit8fJFELML5Yz9D7mihkyylmXrdBTE6+J6h6bxDWi/j7e2Nv3X0e6iNchPScPqFBQCA4OBgWLjYC5qHyBhwu6nYw3wJ3gpXQ4XyB6sRQQ1ns0LsOXUI4lowgv+5kUuQdafkrYKJwTdRv5cvQpc/vmLs1tsXmTGJtbprP1B03I/ZWfMD5JX1u9A3Xa2fNrhP0x5/ZkXyC1RoPiwQ+crHBYziYQ7c+20rdX5XJ8viK/wdxvyB+Icle0goSmlbt/IzvND9h5oJbSL+G7dOW0ZX9APAqlWrIJPJsG/fPhw9ehT+/v7Yu3cvPvvsM0RFRWkM8Ofn51f87/bt22PhwoWYP39+pYt+qVRapS4UlTX1FR98tOJihfMN6+eJ9m2a6CxHaWS3ABhI5wKZTKbT30NtkyV+4t4qV1fUqe8oYBoi48DtpmLuAHolA0fjy59PDRHGNJGigUft2K/LZKWfbl374QAG/bkIbWePQfSuE3Bq2xTN3xiIiwt+0XNCwyOT6eb8q6zfhb7pav20wX2a9vgze6x1M0f8c+3xQHuFheoS3fNLE/8wp1LzAUD/7t5wd+etUTXB6Lr3A4C1tTXWr18PhUKBjIwMHD58GP7+/ggPD4ePjw/E4rJXSywWQ13RZXU9mjG2Jfp3cSt3nsbuNlg9219PiYiIiKruQx+gfgVPWOriDIxupJ88hiw59DaO/u8reDzbDi8FfoO2H47G5aXb+Lg+IjJ4PdtV7YpzZbm71IGXm41OP6M2McqivzRpaWmIjY0tcT//jh078OjRI6jValy9ehULFy7EiBEjhAlZCjOZBPtX9cN741rB2krz22epRIRRA7xwdvOLcHYsfaRLIiIiQ+JkAWzsBvSrD0ie6rpvJQXGNga+6QhITeYMpHpigy5j/7MfYLPnGOzq8A6ur+egVYYuaudx/OpR8ilSRLXJxGHNdPr+b49oVurA7VQ1htHHqQaEhYUBKDmI39q1azFp0iQUFBTA1dUVY8eOxccffyxAwrKZm0nwzQedsOCdtjhwIgaJKTmwqWOGgd3c4VqvgsslBsbGpxfa7TOcnhRERKR/jhbAl+2BpFzgbCKQrQQczIHuLkWFPxERGbdmXvYY0NUdAWdq/tGTFuYSTBiq2y8VahuTOfSWVfQ/Pcq/IbOpY4YxzzcWOgYREVGNqGcBDG4gdAoiItKF1R/7o/XwPcjJrdlR9hdPaw+5k3Fd+DR0JtO5bvLkyVCr1ejcubPQUYiIiIiIiExakwa2+Gpmxwrn+29kf/d+20odpf9JPdrJMe0VPt67pplM0U9ERERERET68+7o5nh/XKty5/lvZP+4hGwUFpZ9G7BP07rYu+JZSCQsUWsaf6JERERERESkNZFIhGXvd8QXU9pB8vTorVro09EVx38eBAc78xpMR/9h0U9ERERERERVIhKJ8Olbvji/5UW0alJXq2WtrWRY+2kXHPlhIAt+HTKZgfyIiIiIiIhIGO1b1kPI70MQcCYW322/gcDzD1CgVJU6b/NG9nhrWDOMH9wU9rYs9nWNRT8RERERERFVm0QixqAeDTCoRwPk5Rci7FYKTl1KwHtfXwAAbP2yF57r6gZHewuBk9Yu7N5PRERERERENcrcTIL2LethRH+v4rYe7eQs+AXAop+IiIiIiIjIRLHoJyIiIiIiIjJRvKefyuRmJXSCxwwpCxERkTZsPOVCRzA6uvqZGcrvwlByEFHtwKKfyrS8k9AJiIiIjF/fX2YLHYH+xd8FEdVG7N5PREREREREZKJY9BMRERERERGZKBb9RERERERERCaKRT8RERERERGRiWLRT0RERERERGSiWPQTERERERERmSgW/UREREREREQmikU/ERERERERkYli0U9ERERERERkolj0ExEREREREZkoFv1EREREREREJopFPxEREREREZGJYtFPREREREREZKJY9BMRERERERGZKBb9RERERERERCZKKnQAIn2ZeQGIyxY6BeBmBSzvJHQKoton6PUlyLirqPLyKmVh8b//HrEAYqmkyu9l4ylH319mV3l5IiKqmuocC3gcIGPFop9qjbhsIDpD6BREJJSMuwqkRcbWyHulR8fXyPsQEZF+1dSxgMcBMibs3k9ERERERERkolj0ExEREREREZkoFv1EREREREREJopFPxEREREREZGJ4kB+RERET+m24l00GdUbAKAqLEROQhriz4Tj8uKtyFakCJyOiIh0jccBMiW80k9ERFQKxfnr2NF6Ana1fwcn310Bx1ae6PXD+0LHIiIiPeFxgEwFi34iIqJSqPKVyElKQ7YiBQnnbyBiSyCcOzSDzNpS6GhERKQHPA6QqWDRT0REVAFLl7rwfKEzVMpCqAtVQschIiI943GAjBnv6SciIiqFvEtLvBq1GSKxGFJLcwBA+Lr9UObkAQAaDOwI3/dGaCxj5+2O4LkbEfHrYb3nJSKimlXRcaDXhvfx4EQoIrcEAgAcWnmhx9rp+LPfLBTmFQiWm+hpRl30h4aGYt68eTh+/DjUajX69OmDdevWwdvbG4MGDcL27duFjkhEREYq6fItnJ6+BhJzGTxf6oL63VvjytJtxdPv/xWM+38FF79uMKAD/D5+BVG/HxcgLRER1bSKjgPBczdi4L7Pce/QBeSlZsJ/yURc+OQnFvxkcIy2e39QUBA6d+6MiIgIzJkzB4sXL0ZsbCwGDhyIzMxM+Pr6Ch2RTIQqLwdxW+cifFJTXB5hiZBXHXDj/Q5I/HOV0NGqJOtBMq59v7/49eUlvyHl2l3hAhEZqMLcfGTcVSAtIgYhy3YgIyYRnRa9Weq8Vq4O6LR4Ak5MWoHCnHw9JyUi0k52Yiqu/3iw+PWlRVvwMCRKwESGqaLjQLYiBdfWH0D7uWPRbGw/PIqOR/zpMAETGxaVSo3DZ2Mxbcm54rZf9t9CeiaPk/pmlFf6k5KSMGrUKPj5+SEwMBCWlkWDaYwdOxZeXl4AwKKfasz9799BRtgxeExYCUuvNijMTkd29BXkJ90XOppW1Go1Qr7eiasrdkOtenwv2u3fT+D27yfQ8PlO6LZ6KmRWFgKmJDJcIV/vwMsnVyJi8xEkh95+PEEkQo810xG25g+k3rgnXEAiogqo1WqEf/cHLi/ZpnFfevSeU4jecwruff3QY90MmNlYCZjScJV2HLi5MQCDDi6Ga9dW+HPgbIETGo57DzLw0rQjuBqZqtE+Z80lLPk5FBs/64Hh/b0ESlf7GOWV/qVLlyI1NRUbN24sLvgBwM7ODn5+fgBY9FPNSbvwB1xengX7zkNg7uIFK682cOo7HvVHzxM6mlZCvtmJ0G9/1yj4n3Tv0AUcn/A1VIWFek5GZBwy7igQc+Qf+M0eo9HeZsYw5Gdk4+bPfwmUjIiocq6t249Li7aWORBdbNBlBL2+BIX57J5emlKPA2o1In49gtigy8hLThcunAFJSslB7zcPlSj4/5OVo8SoWUdx4IRxXUAzZkZZ9G/fvh3du3eHt7d3qdNdXFwgl8uLXx88eBB+fn6oU6cO5HI5li1bpq+oZAJkdV2RfjkAyowUoaNUWbYiBVeX765wvrhjIYgNvKyHRETGKXztfrj18oXcvyUAwLlDMzR9pS/OzPxO4GREROXLTcnA5SfuRy9LwrnruHfgvB4SGaenjwMAAJUKapVauFAGZvnma7gTl1nmdLUaUAOYvvQ8VPy56YXRde9XKBSIi4vDqFGjSkxTqVQICwtD27Zti9sOHz6Mt956C7/++it69uyJ7Oxs3L9f+W+VlEolFApFjWQnYRUUuACQab1cwyk/4s43ryB0XD1YerREnWadYdfuedh1GgyRSFSFHAWIjU3QernqiN4QUOYV/qeFfr8P4pauOk5EpH8FBcpKz3t6RulFfNI/EdjkOhwAYGZrhe6rp+H09DXISy375KasLLGxsVotQ0RUHfc2H4Uqv3L7wdAf9sOso2l2va7ssaAyx4Hq5jDF40B+gQrf/34dIhQV9mVRq4Ho2Az89mcoerVz0lc8oyeXyyGVal/CG13Rn5WVBQClFlv79u1DYmKiRtf+uXPnYu7cuejbty8AwNbWFq1atar05ykUCnh4eFQvNBmEFqvDYdmgZcUzPsW6eVe0Wn8bWZHByIo4h4xrJ3F76XDYtRuIxp/u17rwj4yMhMdzlf8brAkz7Lugtbm8Ulnvn72Kl/g3TyboC8d+cJPZ1tj7NXv9OVg626PjwvEa7VG/n8D1Hw6Uu2xkZCRGcjsjIj16x64TOlq6V2re5JDbJnv+W9PHgqoy2eOAmQvQbFGlZx/71lwgsfxjJj0WExMDd/fKbcdPMrqi38PDAxKJBCdOnNBov3fvHqZOnQrg8f38WVlZuHjxIgYOHIhnnnkGqamp6NSpE1auXFk84B9RZYgkUlg37wLr5l3gMuR9JB/fgrvLxyLz2knYtOopdLwKibX4YkIM7XsvENVGYav3Imz1XqFjEBFVilgkglqtrtQFAJ4JaCdq53FE7TwudAzDINL27nGjvNvc6Bhd0W9mZoZx48Zh48aNGDx4MAYNGoSYmBhs2LABLi4uiIuLKy76U1NToVarsXv3bgQEBMDZ2RkzZszA0KFDcfny5Urt9ORyOWJiYnS8VqQPU6+7ICa3Zt7Lwr05AED5KFHrZb29vfG3nv+mIr7Zg5jtJyueUQS4tGqMmF/4N0+m59zIJci6Yxi3a3l7eyNm589CxyCiWiRq7UHc3Xik4hlFgK2XK2L+Mc1zAUM5FpjqcSArRwnfMceQm1+520rXfDsHg3tyXJzKenLcOm0YXdEPAKtWrYJMJsO+fftw9OhR+Pv7Y+/evfjss88QFRVVPMCfjY0NAGD69Onw9PQEACxevBj16tVDTEwMGjRoUOFnSaXSKnWhIMMjuwWgCkV/xCc94dB9DKyatIfUrh7y4qMQt/kTSOrYw8ant/Y5ZDK9/01ZT3q5ckW/GvCZMIh/82SSZDLDOeTJZDy2EJF+2U0agrubAotupi6PGmj5xvMmu48ylGOBKR8Hxr7YFBt2R5Q7jwiAg505Jozwg7mZRD/BajGj7E9hbW2N9evXQ6FQICMjA4cPH4a/vz/Cw8Ph4+MDsbhotezs7NCwYcMqDbZG9B87v4FIObkVUZ8/j2uTm+Huqv/Bon5TNFtyBlJb4xh4xL6ZB7xe7lbhfHZN3OA1uOL5iIiIyLjYNHBB0zF9KpzPuoEzmozspftAZLI+eN0HNlYylFeCqQHMm9SWBb+eGMZXXTUgLS0NsbGxGDRokEb7pEmTsHLlSvTv3x/16tXD3Llz0a5du0pd5ScCAPnw2ZAPny10jGrr+s07UGbnIubvfzQn/Du8ql0TN/TfPhdSK3NB8hEREZFudf5yAgqycnB331nNCf+eC1g3cEH/HXNhZltHkHxkGrw97XBobX+8MOUwHmUWQCQq2cFk7tu+mPpKC2EC1kImU/SHhYUBgMbI/QDw4YcfIjU1FX5+flCpVOjWrRv27NkjQEIiYUktzdHn5w8RdzwUNzcFIOlSJNTKQtg1cYP3uP7weqkLpJYs+ImIiEyVxEyGnutmwvvVZ3FzYwASgm9CXaCEjZcrmr32LLyGdofMykLomGQCuvnJcevACPz8RyQ27buF+KRsWFlIMaiHB94Z2Rx+LYyjt6ypMPmiXywWY+nSpVi6dKkAqYgMi0gshnuftnDv01boKEQGyd7bHf7L3oZapYZaWYgz769D5v2SA3YO2L0Qj6LicO6jHyCxNMNzO+fDvqk7zn30A+7sOyNAciKiyhGJRKjfvTXqd28tdBSDVNFxQGJphk6fvwHrBi4QS8QIfG0x7Jt5oP3csQAAqbUFRCIR/uz/oVCrYDDqOVjiozfa4KM32ggdpdYzmaJ/8uTJmDx5stAxiIjIiOUmpyPwtS9RkJENt96+aDNzOM7MXKsxj/uz7VCQmVP8WpWnxLE3lqHZuP76jktERDWsouOA73sjEb33NBRnwovbHoZEIWDYfABAi4mDILEw03tuovIY5UB+REREupCbnI6CjGwAgKqgEOrCpx45JBLhmf8NwM1NAcVNapUKOUlpekxJRES6UtFxQN61JRo81x4Ddi9E6xnDSizv9XI33Nl7Wi9ZiSqLRT8REdFTJBZm8J01Etd/PKTR3mRkL9w7dAGFuQUCJSMiIn0o6zjg0MITccdCEDB8ARx9GkHu37J4mm0jV6gKlMiMTdJ3XKJysegnIiJ6gkgiRo+103Ft3X6k3bxf3C4xl6HR0O6I2n5UwHRERKRrZR0HACA3JR1xx0MBtRoPToSibouGxdMaDe2O6D28yk+Gh0U/ERHRE7p+8w4eHA/F/YCLGu3WDZxhZlcHz27+GO3mvga3vm3ReERPgVISEZGulHUcAICE8zfg2LoRAMCxdSOk34kvnub5Uhfc/fNsiWWIhGYyA/kRERFVl1tvX3i+1AXWHs7wGtwVKdfuIO5YCMzsrXFn72kcGPARAEDu3xJeQ7ri9u8nAAC9fvwAjq28oMzOhZNfU1ycv0nAtSAioqqq6DhwafEWdP36HUgszJAWEYO4o1cAAE5tmyLjXgLyUjIEXgOikkRqtVotdAgifRh5DIg2gP1wIxtgZ2+hUxDVPn/0nIG0yFihYwAoeiTUkBMrhI5BRFTrGMqxgMcB0id27yciIiIiIiIyUSz6iYiIiIiIiEwUi34iIiIiIiIiE8WB/KjWcLMSOkERQ8lBVNvYeMqFjlDMkLIQEdUmhrL/NZQcVDtwID8iIiIiIiIiE8Xu/UREREREREQmikU/ERERERERkYli0U9ERERERERkolj0ExEREREREZkoFv1EREREREREJopFPxEREREREZGJYtFPREREREREZKJY9BMRERERERGZKBb9RERERERERCaKRT8RERERERGRiWLRT0RERERERGSiWPQTERERERERmSgW/UREREREREQmikU/ERERERERkYli0U9ERERERERkolj0ExEREREREZkoFv1EREREREREJopFPxEREREREZGJYtFPREREREREZKJY9BMRERERERGZKBb9RERERERERCaKRT8RERERERGRifo/4J6/88UxBv8AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1300.22x494.978 with 1 Axes>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from qiskit.circuit.random import random_circuit\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "\n",
+    "circuit = random_circuit(7, 6, max_operands=2, seed=1242)\n",
+    "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])\n",
+    "\n",
+    "\n",
+    "circuit.draw(\"mpl\", scale=0.8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Find cut locations, given a maximum of 4 qubits per subcircuit. This circuit can be separated in two by making a single wire cut and cutting one `CRZGate`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found solution using 2 cuts with a sampling overhead of 127.06026169907257.\n",
+      "Wire Cut at circuit instruction index 19\n",
+      "Gate Cut at circuit instruction index 28\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1367.11x494.978 with 1 Axes>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from circuit_knitting.cutting.automated_cut_finding import (\n",
+    "    find_cuts,\n",
+    "    OptimizationParameters,\n",
+    "    DeviceConstraints,\n",
+    ")\n",
+    "\n",
+    "# Specify settings for the cut-finding optimizer\n",
+    "optimization_settings = OptimizationParameters(seed=111)\n",
+    "\n",
+    "# Specify the size of the QPUs available\n",
+    "device_constraints = DeviceConstraints(qubits_per_subcircuit=4)\n",
+    "\n",
+    "cut_circuit, metadata = find_cuts(circuit, optimization_settings, device_constraints)\n",
+    "print(\n",
+    "    f'Found solution using {len(metadata[\"cuts\"])} cuts with a sampling '\n",
+    "    f'overhead of {metadata[\"sampling_overhead\"]}.'\n",
+    ")\n",
+    "for cut in metadata[\"cuts\"]:\n",
+    "    print(f\"{cut[0]} at circuit instruction index {cut[1]}\")\n",
+    "cut_circuit.draw(\"mpl\", scale=0.8, fold=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Add ancillas for wire cuts and expand the observables to account for ancilla qubits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1367.11x561.867 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from circuit_knitting.cutting import cut_wires, expand_observables\n",
+    "\n",
+    "qc_w_ancilla = cut_wires(cut_circuit)\n",
+    "observables_expanded = expand_observables(observable.paulis, circuit, qc_w_ancilla)\n",
+    "qc_w_ancilla.draw(\"mpl\", scale=0.8, fold=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sampling overhead: 127.06026169907257\n"
+     ]
+    }
+   ],
+   "source": [
+    "from circuit_knitting.cutting import partition_problem\n",
+    "\n",
+    "partitioned_problem = partition_problem(\n",
+    "    circuit=qc_w_ancilla, observables=observables_expanded\n",
+    ")\n",
+    "subcircuits = partitioned_problem.subcircuits\n",
+    "subobservables = partitioned_problem.subobservables\n",
+    "print(\n",
+    "    f\"Sampling overhead: {np.prod([basis.overhead for basis in partitioned_problem.bases])}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: PauliList(['IIII', 'IZII', 'IIIZ']),\n",
+       " 1: PauliList(['ZIII', 'IIII', 'IIII'])}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subobservables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 980.198x294.311 with 1 Axes>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subcircuits[0].draw(\"mpl\", style=\"iqp\", scale=0.8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1114.14x294.311 with 1 Axes>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subcircuits[1].draw(\"mpl\", style=\"iqp\", scale=0.8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Generate the experiments to run on the backend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "96 total subexperiments to run on backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from circuit_knitting.cutting import generate_cutting_experiments\n",
+    "\n",
+    "subexperiments, coefficients = generate_cutting_experiments(\n",
+    "    circuits=subcircuits, observables=subobservables, num_samples=1_000\n",
+    ")\n",
+    "print(\n",
+    "    f\"{len(subexperiments[0]) + len(subexperiments[1])} total subexperiments to run on backend.\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/stubs/circuit_knitting.cutting.DeviceConstraints.rst.txt b/_sources/stubs/circuit_knitting.cutting.DeviceConstraints.rst.txt
new file mode 100644
index 000000000..b08839011
--- /dev/null
+++ b/_sources/stubs/circuit_knitting.cutting.DeviceConstraints.rst.txt
@@ -0,0 +1,28 @@
+circuit\_knitting.cutting.DeviceConstraints
+===========================================
+
+.. currentmodule:: circuit_knitting.cutting
+
+.. autoclass:: DeviceConstraints
+   :show-inheritance:
+
+   
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~DeviceConstraints.__init__
+      ~DeviceConstraints.get_qpu_width
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~DeviceConstraints.qubits_per_subcircuit
+   
+   
\ No newline at end of file
diff --git a/_sources/stubs/circuit_knitting.cutting.OptimizationParameters.rst.txt b/_sources/stubs/circuit_knitting.cutting.OptimizationParameters.rst.txt
new file mode 100644
index 000000000..588e96e85
--- /dev/null
+++ b/_sources/stubs/circuit_knitting.cutting.OptimizationParameters.rst.txt
@@ -0,0 +1,29 @@
+circuit\_knitting.cutting.OptimizationParameters
+================================================
+
+.. currentmodule:: circuit_knitting.cutting
+
+.. autoclass:: OptimizationParameters
+   :show-inheritance:
+
+   
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~OptimizationParameters.__init__
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~OptimizationParameters.max_backjumps
+      ~OptimizationParameters.max_gamma
+      ~OptimizationParameters.seed
+   
+   
\ No newline at end of file
diff --git a/_sources/stubs/circuit_knitting.cutting.find_cuts.rst.txt b/_sources/stubs/circuit_knitting.cutting.find_cuts.rst.txt
new file mode 100644
index 000000000..abe98d1d4
--- /dev/null
+++ b/_sources/stubs/circuit_knitting.cutting.find_cuts.rst.txt
@@ -0,0 +1,6 @@
+circuit\_knitting.cutting.find\_cuts
+====================================
+
+.. currentmodule:: circuit_knitting.cutting
+
+.. autofunction:: find_cuts
\ No newline at end of file
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
index 3fc46085e..3c89f4337 100644
--- a/_static/documentation_options.js
+++ b/_static/documentation_options.js
@@ -1,5 +1,5 @@
 const DOCUMENTATION_OPTIONS = {
-    VERSION: '0.6.0',
+    VERSION: '0.7.0',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/apidocs/circuit_cutting.html b/apidocs/circuit_cutting.html
index 17bff778f..43b7944df 100644
--- a/apidocs/circuit_cutting.html
+++ b/apidocs/circuit_cutting.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.cut_wires" href="../stubs/circuit_knitting.cutting.cut_wires.html" /><link rel="prev" title="Circuit Knitting Toolbox API References" href="index.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>Circuit Cutting - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>Circuit Cutting - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -350,6 +354,30 @@ <h2>Circuit Cutting<a class="headerlink" href="#id2" title="Link to this heading
 </tbody>
 </table>
 </div>
+<section id="automatic-cut-finding">
+<h3>Automatic Cut Finding<a class="headerlink" href="#automatic-cut-finding" title="Link to this heading">¶</a></h3>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="../stubs/circuit_knitting.cutting.find_cuts.html#circuit_knitting.cutting.find_cuts" title="circuit_knitting.cutting.find_cuts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">find_cuts</span></code></a></p></td>
+<td><p>Find cut locations in a circuit, given optimization parameters and cutting constraints.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="../stubs/circuit_knitting.cutting.OptimizationParameters.html#circuit_knitting.cutting.OptimizationParameters" title="circuit_knitting.cutting.OptimizationParameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OptimizationParameters</span></code></a></p></td>
+<td><p>Specify parameters that control the optimization.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="../stubs/circuit_knitting.cutting.DeviceConstraints.html#circuit_knitting.cutting.DeviceConstraints" title="circuit_knitting.cutting.DeviceConstraints"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DeviceConstraints</span></code></a></p></td>
+<td><p>Specify the constraints (qubits per subcircuit) that must be respected.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
 </section>
 <section id="quasi-probability-decomposition-qpd">
 <h2>Quasi-Probability Decomposition (QPD)<a class="headerlink" href="#quasi-probability-decomposition-qpd" title="Link to this heading">¶</a></h2>
@@ -485,6 +513,9 @@ <h2>CutQC<a class="headerlink" href="#cutqc" title="Link to this heading">¶</a>
             <ul>
 <li><a class="reference internal" href="#">Circuit Cutting</a><ul>
 <li><a class="reference internal" href="#id2">Circuit Cutting</a><ul>
+<li><a class="reference internal" href="#automatic-cut-finding">Automatic Cut Finding</a><ul>
+</ul>
+</li>
 </ul>
 </li>
 <li><a class="reference internal" href="#quasi-probability-decomposition-qpd">Quasi-Probability Decomposition (QPD)</a><ul>
@@ -504,7 +535,7 @@ <h2>CutQC<a class="headerlink" href="#cutqc" title="Link to this heading">¶</a>
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/apidocs/index.html b/apidocs/index.html
index dbd4f95dc..898c559a9 100644
--- a/apidocs/index.html
+++ b/apidocs/index.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="Circuit Cutting" href="circuit_cutting.html" /><link rel="prev" title="CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting" href="../circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>Circuit Knitting Toolbox API References - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>Circuit Knitting Toolbox API References - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -370,7 +374,7 @@
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/apidocs/utils.html b/apidocs/utils.html
index 6e4695f67..771e0515c 100644
--- a/apidocs/utils.html
+++ b/apidocs/utils.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.bitwise.bit_count" href="../stubs/circuit_knitting.utils.bitwise.bit_count.html" /><link rel="prev" title="circuit_knitting.cutting.cutqc.reconstruct_full_distribution" href="../stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>Utilities - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>Utilities - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -542,7 +546,7 @@
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/circuit_cutting/cutqc/index.html b/circuit_cutting/cutqc/index.html
index 9ff19462d..a1c870ea0 100644
--- a/circuit_cutting/cutqc/index.html
+++ b/circuit_cutting/cutqc/index.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding" href="tutorials/tutorial_1_automatic_cut_finding.html" /><link rel="prev" title="How to place wire cuts using a single-qubit CutWire instruction" href="../how-tos/how_to_specify_cut_wires.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>CutQC (legacy circuit cutting implementation) - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>CutQC (legacy circuit cutting implementation) - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -146,7 +146,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -173,7 +173,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -190,6 +190,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../explanation/index.html">Cutting Explanatory Material</a></li>
@@ -219,6 +220,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -310,15 +314,8 @@ <h1>CutQC (legacy circuit cutting implementation)<a class="headerlink" href="#cu
 automatic cut finding method described in the research paper
 <a class="reference external" href="https://arxiv.org/abs/2012.02333">arXiv:2012.02333</a>.  Historically,
 this was the original circuit cutting implementation in the Circuit
-Knitting Toolbox.  Going forward, this module is considered legacy
-code that is supported, but new features are not expected.  New users
-of the toolbox should use the new circuit cutting interface, instead,
-unless they specifically need a feature that is not (yet) supported by
-the new code.  These features currently specific to <code class="docutils literal notranslate"><span class="pre">cutqc</span></code> include:</p>
-<ul class="simple">
-<li><p>Reconstruction of probability distributions (rather than expectation values)</p></li>
-<li><p>Automatic cut finding</p></li>
-</ul>
+Knitting Toolbox.  Going forward, this module is deprecated.  Users
+of the toolbox should use the new circuit cutting interface, instead.</p>
 <section id="cutqc-tutorials">
 <span id="id1"></span><h2>CutQC Tutorials<a class="headerlink" href="#cutqc-tutorials" title="Link to this heading">¶</a></h2>
 <ul class="simple">
@@ -419,7 +416,7 @@ <h1>CutQC (legacy circuit cutting implementation)<a class="headerlink" href="#cu
   </div>
 </div><script src="../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.html b/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.html
index 1a3f39d7d..7a35889ef 100644
--- a/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.html
+++ b/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" /><link rel="next" title="CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting" href="tutorial_2_manual_cutting.html" /><link rel="prev" title="CutQC (legacy circuit cutting implementation)" href="../index.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -146,7 +146,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -173,7 +173,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -190,6 +190,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../explanation/index.html">Cutting Explanatory Material</a></li>
@@ -219,6 +220,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -306,6 +310,7 @@
         <article role="main">
           <section id="CutQC-Tutorial-1:-Circuit-Cutting-with-Automatic-Cut-Finding">
 <h1>CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding<a class="headerlink" href="#CutQC-Tutorial-1:-Circuit-Cutting-with-Automatic-Cut-Finding" title="Link to this heading">¶</a></h1>
+<p><strong>NOTE: CutQC is deprecated and will be removed no sooner than Circuit Knitting Toolbox v0.8.0. The circuit cutting workflow in ``circuit_knitting.cutting`` now implements similar and improved functionalities, which will be maintained going forward.</strong></p>
 <p>Circuit cutting is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output.</p>
 <p>The circuit knitting toolbox implements a wire cutting method presented in <a class="reference external" href="https://doi.org/10.1145/3445814.3446758">CutQC</a> (Tang et al.). This method allows a circuit wire to be cut such that the generated subcircuits are amended by measurements in the Pauli bases and by state preparation of four Pauli eigenstates (see Fig. 4 of <a class="reference external" href="https://doi.org/10.1145/3445814.3446758">CutQC</a>).</p>
 <p>This wire cutting technique is comprised of the following basic steps:</p>
@@ -318,7 +323,7 @@ <h1>CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding<a class="header
 <h2>Create a quantum circuit with Qiskit<a class="headerlink" href="#Create-a-quantum-circuit-with-Qiskit" title="Link to this heading">¶</a></h2>
 <p>In this case, we’ll create a hardware-efficient circuit (<code class="docutils literal notranslate"><span class="pre">EfficientSU2</span></code> from the Qiskit circuit library) with two (linear) entangling layers.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -341,7 +346,7 @@ <h2>Create a quantum circuit with Qiskit<a class="headerlink" href="#Create-a-qu
 </div>
 </div>
 <div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
@@ -359,7 +364,7 @@ <h2>Decompose the circuit with wire cutting<a class="headerlink" href="#Decompos
 <li><p><code class="docutils literal notranslate"><span class="pre">num_subcircuits=[2]</span></code>: A list of the number of subcircuits to try, in this case 2 subcircuits</p></li>
 </ul>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%capture</span>
@@ -384,7 +389,7 @@ <h2>Decompose the circuit with wire cutting<a class="headerlink" href="#Decompos
 <li><p><code class="docutils literal notranslate"><span class="pre">classical_cost</span></code>: the final value of the objective function used to find optimal cut(s). The objective function represents the postprocessing cost to reconstruct the original circuit output and is set to be the number of floating-point multiplications involved in the reconstruction. This quantity is also returned in the case of <a class="reference internal" href="tutorial_2_manual_cutting.html"><span class="doc">manual wire cutting</span></a>. See <a class="reference external" href="https://doi.org/10.1145/3445814.3446758">Section 4.1.4 of CutQC</a>.</p></li>
 </ul>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">cuts</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
@@ -401,7 +406,7 @@ <h2>Decompose the circuit with wire cutting<a class="headerlink" href="#Decompos
 </div>
 <p><strong>The two subcircuits produced:</strong></p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># visualize the first subcircuit</span>
@@ -410,7 +415,7 @@ <h2>Decompose the circuit with wire cutting<a class="headerlink" href="#Decompos
 </div>
 </div>
 <div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
@@ -418,7 +423,7 @@ <h2>Decompose the circuit with wire cutting<a class="headerlink" href="#Decompos
 </div>
 </div>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># visualize the second subcircuit</span>
@@ -427,7 +432,7 @@ <h2>Decompose the circuit with wire cutting<a class="headerlink" href="#Decompos
 </div>
 </div>
 <div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
@@ -440,7 +445,7 @@ <h2>Evaluate the subcircuits<a class="headerlink" href="#Evaluate-the-subcircuit
 <p><strong>Set up the Qiskit Runtime Service</strong></p>
 <p>The Qiskit Runtime Service provides access to Qiskit Runtime Primitives and quantum backends. See the <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit-ibm-runtime/">Qiskit Runtime documentation</a> for more information. Alternatively, if a Qiskit Runtime Service is not passed, then a local statevector simulator will be used with the <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/primitives">Qiskit Primitives</a>.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_ibm_runtime</span> <span class="kn">import</span> <span class="n">QiskitRuntimeService</span>  <span class="c1"># noqa: F401</span>
@@ -457,7 +462,7 @@ <h2>Evaluate the subcircuits<a class="headerlink" href="#Evaluate-the-subcircuit
 <p>The wire cutter tool uses a <code class="docutils literal notranslate"><span class="pre">Sampler</span></code> primitive to evaluate the probabilities of each subcircuit. Here, we configure the options for the Qiskit Runtime Sampler and specify the backend(s) to be used to evaluate the subcircuits. Backends could be <a class="reference external" href="https://quantum-computing.ibm.com/services/resources?tab=systems">simulator(s) and/or quantum device(s)</a>. In this tutorial, two local cores will be used to support each of the parallel backend threads we’ll specify below.</p>
 <p>If no service was set up, the <code class="docutils literal notranslate"><span class="pre">backend_names</span></code> argument will be ignored, and Qiskit Primitives will be used with statevector simulator.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_ibm_runtime</span> <span class="kn">import</span> <span class="n">Options</span>
@@ -471,8 +476,8 @@ <h2>Evaluate the subcircuits<a class="headerlink" href="#Evaluate-the-subcircuit
 </div>
 </div>
 <p><strong>Evaluate the subcircuits on the backend(s)</strong></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">circuit_knitting.cutting.cutqc</span> <span class="kn">import</span> <span class="n">evaluate_subcircuits</span>
@@ -481,17 +486,26 @@ <h2>Evaluate the subcircuits<a class="headerlink" href="#Evaluate-the-subcircuit
 
 <span class="c1"># Uncomment the following lines to instead use Qiskit Runtime Service as configured above.</span>
 <span class="c1"># subcircuit_instance_probabilities = evaluate_subcircuits(cuts,</span>
-<span class="c1">#                                                          service_args=service.active_account(),</span>
+<span class="c1">#                                                          service=service,</span>
 <span class="c1">#                                                          backend_names=backend_names,</span>
 <span class="c1">#                                                          options=options,</span>
 <span class="c1">#                                                         )</span>
 </pre></div>
 </div>
 </div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/tmp/ipykernel_2967/4196057829.py:3: DeprecationWarning: The function ``circuit_knitting.cutting.cutqc.wire_cutting.evaluate_subcircuits()`` is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package.
+  subcircuit_instance_probabilities = evaluate_subcircuits(cuts)
+</pre></div></div>
+</div>
 <p><strong>Inspecting the subcircuit results</strong></p>
 <p>In this case, the original circuit was cut 2 times (we can also get this info from the previous step: <code class="docutils literal notranslate"><span class="pre">cuts['num_cuts']</span></code>):</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of subcircuits: &quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">subcircuit_instance_probabilities</span><span class="p">))</span>
@@ -509,7 +523,7 @@ <h2>Evaluate the subcircuits<a class="headerlink" href="#Evaluate-the-subcircuit
 <p>From these two wire cuts, there are <span class="math notranslate nohighlight">\(4^2=16\)</span> variants of the first subcircuit corresponding to the combination of measurement bases: <span class="math notranslate nohighlight">\(P_i\otimes P_j\)</span>, for the Paulis <span class="math notranslate nohighlight">\(P_i \in \{I, X, Y, Z \}\)</span>. And there are <span class="math notranslate nohighlight">\(4^2=16\)</span> variants of the second subcircuit corresponding to the combination of initialization states: <span class="math notranslate nohighlight">\(|s_i\rangle\otimes|s_j\rangle\)</span>, where <span class="math notranslate nohighlight">\(|s_i\rangle \in \{ |0\rangle, |1\rangle, |+\rangle |+i\rangle\}\)</span>.</p>
 <p>Note that some subcircuit probabilities returned by the evaluate step can be negative (and not sum to unity). This is because the raw probabilities from subcircuits must be modified to account for the measurement bases of ancillary qubits. See Section 3 of <a class="reference external" href="https://doi.org/10.1145/3445814.3446758">CutQC</a> for more details.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span>
@@ -532,7 +546,7 @@ <h2>Evaluate the subcircuits<a class="headerlink" href="#Evaluate-the-subcircuit
 </div>
 <p>The first subcircuit has two ancillary qubits (induced by the two wire cuts) that do not appear in the original circuit. This means that the first subcircuit has <span class="math notranslate nohighlight">\(5-2=3\)</span> qubits from the original circuit and a probability distribution of size <span class="math notranslate nohighlight">\(2^3=8\)</span>. The second subcircuit has 5 qubits, all from the original circuit, and so its probability distribution is size <span class="math notranslate nohighlight">\(2^5=32\)</span>.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span>
@@ -560,7 +574,7 @@ <h2>Evaluate the subcircuits<a class="headerlink" href="#Evaluate-the-subcircuit
 <h2>Reconstruct the full circuit output<a class="headerlink" href="#Reconstruct-the-full-circuit-output" title="Link to this heading">¶</a></h2>
 <p>Next, the results of the subcircuit experiments are classically postprocessed to reconstruct the original circuit’s full probability distribution.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%capture</span>
@@ -577,7 +591,7 @@ <h2>Reconstruct the full circuit output<a class="headerlink" href="#Reconstruct-
 </div>
 <p><strong>Here are the reconstructed probabilities for the original 8-qubit circuit:</strong></p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span>
@@ -598,8 +612,8 @@ <h2>Reconstruct the full circuit output<a class="headerlink" href="#Reconstruct-
 <section id="Verify-the-results">
 <h2>Verify the results<a class="headerlink" href="#Verify-the-results" title="Link to this heading">¶</a></h2>
 <p>If the original circuit is small enough, we can use a statevector simulator to check the results of cutting against the original circuit’s exact probability distribution (ground truth).</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">circuit_knitting.cutting.cutqc</span> <span class="kn">import</span> <span class="n">verify</span>
@@ -608,10 +622,19 @@ <h2>Verify the results<a class="headerlink" href="#Verify-the-results" title="Li
 </pre></div>
 </div>
 </div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/tmp/ipykernel_2967/3419935587.py:3: DeprecationWarning: The function ``circuit_knitting.cutting.cutqc.wire_cutting_verification.verify()`` is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package.
+  metrics, exact_probabilities = verify(circuit, reconstructed_probabilities)
+</pre></div></div>
+</div>
 <p><strong>The verify step includes several metrics</strong></p>
 <p>For example, the chi square loss is computed. Since we’re using the Qiskit Sampler with statevector simulator, we expect the reconstructed distributed to exactly match the ground truth. More info about each metric can be found in the <a class="reference external" href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/main/circuit_knitting/utils/metrics.py">utils metrics file</a>.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">metrics</span>
@@ -619,27 +642,27 @@ <h2>Verify the results<a class="headerlink" href="#Verify-the-results" title="Li
 </div>
 </div>
 <div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 {&#39;nearest&#39;: {&#39;chi2&#39;: 0,
-  &#39;Mean Squared Error&#39;: 8.13178352181795e-35,
-  &#39;Mean Absolute Percentage Error&#39;: 4.4880309854901524e-10,
-  &#39;Cross Entropy&#39;: 3.564551116068219,
-  &#39;HOP&#39;: 0.9945381353717198},
+  &#39;Mean Squared Error&#39;: 1.8155290178685046e-34,
+  &#39;Mean Absolute Percentage Error&#39;: 7.550336449277775e-10,
+  &#39;Cross Entropy&#39;: 3.5645511160682197,
+  &#39;HOP&#39;: 0.9945381353717202},
  &#39;naive&#39;: {&#39;chi2&#39;: 0,
-  &#39;Mean Squared Error&#39;: 3.7794080473092745e-35,
-  &#39;Mean Absolute Percentage Error&#39;: 4.4880563544629694e-10,
-  &#39;Cross Entropy&#39;: 3.564551116068219,
-  &#39;HOP&#39;: 0.99453813537172}}
+  &#39;Mean Squared Error&#39;: 2.9965472816547853e-34,
+  &#39;Mean Absolute Percentage Error&#39;: 7.550351082551326e-10,
+  &#39;Cross Entropy&#39;: 3.5645511160682206,
+  &#39;HOP&#39;: 0.9945381353717199}}
 </pre></div></div>
 </div>
 <p><strong>Visualize both distributions</strong></p>
 <p>If we calculated the ground truth above, we can visualize a comparison to the reconstructed probabilities</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit.visualization</span> <span class="kn">import</span> <span class="n">plot_histogram</span>
@@ -676,7 +699,7 @@ <h2>Verify the results<a class="headerlink" href="#Verify-the-results" title="Li
 </div>
 </div>
 <div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
@@ -769,7 +792,7 @@ <h2>Verify the results<a class="headerlink" href="#Verify-the-results" title="Li
   </div>
 </div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb b/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb
index 3a05d1bf2..19915683c 100644
--- a/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb
+++ b/circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb
@@ -7,6 +7,8 @@
    "source": [
     "# CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding\n",
     "\n",
+    "**NOTE: CutQC is deprecated and will be removed no sooner than Circuit Knitting Toolbox v0.8.0.  The circuit cutting workflow in `circuit_knitting.cutting` now implements similar and improved functionalities, which will be maintained going forward.**\n",
+    "\n",
     "Circuit cutting is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output. \n",
     "\n",
     "The circuit knitting toolbox implements a wire cutting method presented in [CutQC](https://doi.org/10.1145/3445814.3446758) (Tang et al.). This method allows a circuit wire to be cut such that the generated subcircuits are amended by measurements in the Pauli bases and by state preparation of four Pauli eigenstates (see Fig. 4 of [CutQC](https://doi.org/10.1145/3445814.3446758)).\n",
@@ -30,18 +32,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "eb859bde",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:06.357187Z",
+     "iopub.status.busy": "2024-04-19T17:42:06.356901Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.721097Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.720238Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 1020.64x491.633 with 1 Axes>"
+       "<Figure size 786.525x491.633 with 1 Axes>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -82,9 +91,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "8c11457a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.724636Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.723928Z",
+     "iopub.status.idle": "2024-04-19T17:42:08.624148Z",
+     "shell.execute_reply": "2024-04-19T17:42:08.623218Z"
+    }
+   },
    "outputs": [],
    "source": [
     "%%capture\n",
@@ -115,9 +131,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "id": "465733e2",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:08.628048Z",
+     "iopub.status.busy": "2024-04-19T17:42:08.627669Z",
+     "iopub.status.idle": "2024-04-19T17:42:08.632223Z",
+     "shell.execute_reply": "2024-04-19T17:42:08.631531Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -141,18 +164,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "id": "938f7733",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:08.634712Z",
+     "iopub.status.busy": "2024-04-19T17:42:08.634506Z",
+     "iopub.status.idle": "2024-04-19T17:42:08.914796Z",
+     "shell.execute_reply": "2024-04-19T17:42:08.914094Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAacAAADkCAYAAAA8VOvuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/av/WaAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAxS0lEQVR4nO3de1zUVf748ddcGK4KildUFCFvCGQq4hXNvKRJa1a624btpmkSlr92tzZro9rv1u7WbrW2yVZbdtl02zKttMxMDc27qHgXEUTwAiJyU2Bmfn98FEO5MzOfA7yfj8c8kPkM57z9nPOZ93zO53zOGOx2ux0hhBBCIUa9AxBCCCGuJ8lJCCGEciQ5CSGEUI4kJyGEEMqR5CSEEEI5kpyEEEIoR5KTEEII5UhyEkIIoRxJTkIIIZQjyUkIIYRyJDkJIYRQjiQnIYQQypHkJIQQQjmSnIQQQihHkpMQQgjlSHISQgihHElOQgghlCPJSQghhHIkOQkhhFCOJCchhBDKkeQkhBBCOZKchBBCKEeSkxBCCOVIchJCCKEcSU5CCCGUI8lJCCGEcsx6B6CKshIoL3VuHWYLuHk6tw5XcsU+g+a331Qlx4BQiSQntIMy6S2wOvnANFlgxOzmcXC6ap9B89pvqpJjQKhGhvXQPi264k3WWuqaMw1XcNU+g+a131Qlx4BQjSQnIYQQypHkJIQQQjlyzamOfvmnHljcPHEzWSizlnL3qMeZNGSW3mEpTfZZ8yLtKVxJklM9JMxcTmCHPqSdTmHeq7cQ2WcS7XwD9A5LabLPmhdpT+EqMqzXAEGd+uPj2Yac/EyeWzKN7PNpAKze9g4rNr2hc3Rqkn3WvEh7CmdTMjnt2rWLyMhIPDw8GDx4MO+//z5eXl7YbDa9QwMgJS2JVl5t6RkQQez4BD5Yk0Bp+WXW7fqIyVEP6RpbuRWKL4PNrmsYN1B5n6mstByKS8Eu7SlaGOWG9ZKTk4mOjub5559n2bJlrFq1iri4OEJDQzEa9c2lCUumYrfbyMo5xlP3fYzF7E5Q5zAMBiNvfB7PpKiHMJvcdIkttxC+SobkDC0xebtDdG8YGwomHXebyvtMZVl58OUeOHgK7ICfF4ztByN6gcGgX1zSnsJVlDtzio+PZ+7cuSxYsICgoCDi4uIICAggPDxc79BImLmcd393mKfuW8rfPplFXsEZAGLHJ7D/xCZGR0zXJa7cQnhl9bXEBFB0Gb7ZB//eqO9ZlKr7TGUnc+Hv38DBLC0xAVwohuU74dMduoYm7SlcRqnklJqaSlJSEvHx8ZWet1gsFcnpv//9L0OHDiUqKor//Oc/eoRJdMQ9DOw9gY/XvQhAxzbdae/bDYNOH2lX74FLZTcmIasdDpyCw9m6hFWJavtMZZ/t1IZnrx/Ks9kh6QhkX9AlrEqkPYWzKTWsl5ycjJ+fH4GBgRXPlZSUcPz4ccLDw8nPz+fZZ59l+/btGAwGIiMjmThxIm3btq2x3JiYGFJTU6vd3tYngGfv+bZesT54+4vMe20g08c8gX/rznX+u/Hjx3G+MKteddXEYDBx6+92YqxmKMVmKyfhH1+TsvIJh9UJrt1n4Pj9pip3n/aMmr++2u3W8lLmPvUexza85tB6m/IxINQWHBzMypUr6/13SiUng8GA1WrFZrNVXF9KTEykuLiY8PBwtm7dyrBhw/Dx8QFg1KhRbNq0iSlTpjg9tg+fOlHp9y7tQljxQr7T662N0exebWICMBjNWLzauDCia1TdZyoze7SucbvRaKz1Nc4i7SlcyWC3qzMPKCMjg+DgYJ5++mliY2NZu3YtCxcuxGw2k5WVxccff8zOnTt5+eWXAVi4cCEhISH86le/alS9Jfmw6S1H/A9qN3w2ePo6rjy7HZ79DC5eqnq7yQjRfSBmgOPqBNfuM3D8flPV5TJY+Kk2rFcVkxF+dguM7O3YepvyMSCaJ6WuOQUGBrJo0SIWL17MgAED2LFjBzNmzKi43tS2bVvy8vIqXp+Xl0e7du30ClcJBgOM7gvGaob67XYYFuLamETDubtBVHD1MyxNRhgU5NqYhNCDUskJYM6cOWRnZ3PhwgUSExNJS0urSE5RUVFs2bKF4uJiSkpK2LhxI8OGDdM5Yv2N7gPh3So/ZzJqCeuXw6BdK33iEg0TMwB6tIOfft4wGcHNBLOjwdOiW2hCuIxS15yqkpKSwvTp2vRUX19fnnnmGW699VYMBgNPPvkk/v7+OkeoP6MRZo6A4WfgnStTx6N7Q1QI+PvoHZ2oL4sZ4sbCgSz4cLPWnuNCtTOqVvI9SKKFUDo5FRUVkZ6eXukepxkzZjBjxgyXx3LuQibvrP49APdG/5aeAVpMb331BJfLimnv243pY37n8riuMhjgpk7azZoAk2/WLZRqfbLhFfx8OrDv+EYKL10gdlwCPTqF6h2WkoxG6N/1WnuO669vPEK4mtLJydvbW5kli77e/m9mTnietq06kfjF48y/65+cyUvHYvZg9uQ/8+r/5lBWXoqbWcZcrkrat5wl3zxDZ/9gcvIziQgewz3RjzNu4P2kZu1hx+FvJDk1Ude37T/it2IyKf12IpoY6U11lJt/ig6+3TCZzJSWX7ryXBbt/bSLPa2923GxOLfe9+80Zx3aBBIz/BG6+IeQV3iGUzlHAbDZbHzx45v88rY/6ByhaKiftu2ForOSmITDKTchQlX+vl04l59JadklLGaPK88FkJOfCcDF4lxae8n1r59Ky95HUKcwUrP3cLE4l4jg0djtdhK/fJxJQ2bLVy00YT9t2x4dZcxROJ4kpzqaOPjXvL/mWV79dC6To+bwxufz6dimO6Xll3hz5QIC/INlSO86J06nENSpP4UleWxKWU5oj+EsT3qdAyc2s2rrW2xK+VzvEEUD/bRtC0ryav8DIepJqZtw9dKcbkB86Uvt55N3OK8OqP8+S/ziN8yZ8nKD62upN26q2p6N0VLbUtSPnDkJl2hMYhJCtDySnIQQQihHkhNgtoDJBZeLTBatrubAVfsMmtd+U5UcA0I1Mv8TcPOEEbOhvNS59ZgtWl3Ngav2GTSv/aYqOQaEaiQ5XeHmKQdNfck+a16kPYVKZFhPCCGEciQ5CSGEUI4kJyGEEMqR5CSEEEI5kpyEEEIoR5KTEEII5UhyEkIIoRxJTkIIIZQjyUkIIYRyZIWIK8pKZOmW5kTas2lzRfs1VS2l30lyQjsQkt4Cq5MPBpNFW7+sJXQsPUl7Nm2uar+mqqX0OxnWQ/uE5ooDwVoqnwZdQdqzaXNV+zVVLaXfSXISQgihHBnWayYyz8OudLhQDHY7LN0CHX1hcBD4eOgdnagPux3SzsGek1p7AizbCl3bwMAg8HDTNz4hXEGSUxNmtcHudEg6AidyKm/bkqr9/DIZbukOI3tDoL/LQxT1UFoO249r7ZmdX3nbj8e0nyt2Q2QQjOgNnXxdH6MQriLJqY5++aceWNw8cTNZKLOWcveox5k0ZJZu8Vwqg/d+gEPZNb/OaoPtadrjrkEwqrdr4lOZam0JUFAC/1oPJ8/X/LrSckg6Cj+mwv3D4ObuLgmvRVGxf7REkpzqIWHmcgI79CHtdArzXr2FyD6TaOcb4PI4Sssh8Xtt6Kc+PtsB5Va4tZ9z4mpKVGlLgKLL8Pq3cK6g7n9jtcF7SXC/HQb2cFpoLZZK/aOlkgkRDRDUqT8+nm3Iyc/kuSXTyD6fBsDqbe+wYtMbTq//v1urT0yzorVHdVbuhpRM58TVFOndlnY7/Htj9Ymptvb8z4+Qkeuc2IT+/aMlUzI57dq1i8jISDw8PBg8eDDvv/8+Xl5e2Gw2vUMDICUtiVZebekZEEHs+AQ+WJNAafll1u36iMlRDzm17pwC2HGi+u3+PtqjJmtSHBpSnZzOh42H4YfD2v9BFXq2JUDqWe1Rndra02qD7w44Pq7aZOTChkOw+Sjkl7i+flfRu3+0ZMoN6yUnJxMdHc3zzz/PsmXLWLVqFXFxcYSGhmI06ptLE5ZMxW63kZVzjKfu+xiL2Z2gzmEYDEbe+DyeSVEPYTY5dyrVpqONLyMjV3u4YoLEpTJ4PwkOZIH5SvN9ukMbivp5FJhNzo+hKiq0JWiTHxpr70nILwZfr8aXVZuCEnh7A6Tnau1pBz7ZDtG9IeYWMBqcH4MrqNI/WjLlzpzi4+OZO3cuCxYsICgoiLi4OAICAggPD9c7NBJmLufd3x3mqfuW8rdPZpFXcAaA2PEJ7D+xidER051af2n5tVl4jeWIJFcXH2yCw6e1f5fbtAdAcrqWpPSid1uCdsax92Tjy7Hbr83mcyabHd5cB5l52u/lNu3MzW7XzorX7nd+DK6iQv9o6ZRKTqmpqSQlJREfH1/peYvFUpGc7rzzTjp37syMGTP0CBGA6Ih7GNh7Ah+vexGAjm260963GwaDcz82nsmHEgfdGZ56xjHl1ORMPuw/pb2BXc9qh62pUHDJ+XHURK+2BEjP0d7wHaGmoUFHOXoasi5U3Z42O6w7AGVW58fhSnr2j5ZOqWG95ORk/Pz8CAwMrHiupKSE48ePVySnRYsWkZqayuLFi+tcbkxMDKmp1Z9ytPUJ4Nl7vq1XrA/e/iLzXhvI9DFP4N+6c53/bvz4cZwvzKpXXVe17RHFwF+8U/H7rOgbr0e0a6X9fGJy5edzC7XhmKuyz10gNHR4g+Koq64D7qXXuCcxmd2r3F5eXk7MLx7n7OG1Dq23vu3Z0LaExrVnQPhUQu/4Y8XvDW1Pu93Ozj0HCX30ngbFUVch0Y/SfchMjNW056UyGDVhOhezG3dRsyHHozM1pn84S2P6nasFBwezcuXKev+dUsnJYDBgtVqx2WwV15cSExMpLi6uSE7dunWrMdE4y4dPnaj0e5d2Iax4Ib/qFzuJ3e6gj9kOLqv6OmwYqOETpgHsNtd/1FahLcGRbWAHu/MnC9ntNmqL2G4rd3oczqZK/2jpDHZXvEvVUUZGBsHBwTz99NPExsaydu1aFi5ciNlsJivr2qeE9evXs3jxYpYuXeqQekvyYdNbDimqVsNng2cD7+zPPA8vr675NVc/Yf/5q5pf174VLIxpWBx1lVcEz31e/XazEV6YBp4Wx9bbVNozJbPy2WxV6tqevTrBvLENi6OuTuTAq99Uv72VByRMBVMjLxa4sv2aqsb0u6ZCqWtOgYGBLFq0iMWLFzNgwAB27NjBjBkzlJgMoYLOftDaQcvk9+vimHJq0sYbooLBVMXJk9EAY/o5PjE1JT07OG62Yj8X3B/a3V9LglUlHwMwKaLxiUmIq5TrSnPmzCE7O5sLFy6QmJhIWlqaJKcrTEYYdpNjynJUObW5JxKG96r8pmU2wW2hcHsLb1YvCwzq0fhyzCaI7Nn4cmpjMMCD0RDRjUqDtR5u2tJYQ0OcH4NoOZRLTtdLSUmplJzmz5/P448/zvr167ntttt0uf6kp6HBjb+XpFcn6NjaMfHUxmTU3rievwv8vKCNF/xxmvYpu7ncE9MYw3s1voyBPcCr6jkKDuduhtgR2vDd1fZ8YZq2sLAQjqTUhIjrFRUVkZ6eXik5vf766zpGBJ9seIVbB/yCi8W5fLT2BQI79CV2fAJWm5Ul3/yB4ssXGRAyluH9f+aU+n29ILoPfH+w6u25hTX/vckIE8McH1dtvN2vfdWDq7/yYdfR71iz/V3czO7Ejn+O9n5db3jNJxteoU+3SFZsXuTSNu3WFm4OhOSMqrfX1p7ubjBWh7USfb2utaObC26k3nf8B77b/RG5+ae4f3wCvboOJK/wLEu+foaCkjwG9Z7AhEG/4vXPHuZSaRHdOvblvrELef2zOGx2K7uOfMubC3bz9bZ3SD9zAHc3Tx6OeRWj0UjmuSMs+OdIPnn2DAdO/Mjq7e9w6XIhk6PmcHPIGN5cuYCD6Vt4Pf5H7HY7r306F4PBSGDHvkwdMR+Adbv/w3e7PuL/HtQuDv60zPQzB1ie9Dp5BaeZNOQhQroM4M2Vj+Hj4cfI8LsZ2GtcpToA9h7fyMa9n2A0mPj17X/Cw+KCO6wVo3Ry8vb21n3JoqR9y1nyzTN09g8mJz+TiOAx+LfujH/rzsya9GfW7HgPgM37V5BfdA53Ny86+AXWXGgjTbkZzhdq3/dzvZousBuAXwzVrnW0JJv3f84jUxeRezGbr7f/m6BOYZXa9B/xWzl/MZuwniNp79fN5W36i6HaDblVrZdYU3uajDBrFHRw0VmwnsJ6jiSs50iOndrNkcyd9Oo6kDY+HXjs7kRsNhv/XPkoRqORx+5OBOD1z+YBMP+uNyi5XMg/lsfh7dGao5k7efIXH/LF5jdJOZFEWNBIVm19i0G9JgDQr8dQ+vUYSlFJPu998ww3h4zh4Zi/85elDwBQUHwei5sn8+58lb8snUlZeSmFly5wNi8DX+/2gDYL86dldu/Yj8emLaagOI//bXyF0rISxtz8c4aF3smL/7mPgb3GVaoD4Isf36SDXyAeFm/cm/v3sVdD+WE9vXVoE0jM8Ef42fB4po36f3i6V73Q2alzR4gIHsOcKa/w6Q9/d2pMRqM2tFKfMX6zCX49qmWuYB0zLI53Vv2e75M/JvdiVqU2vTv6cUqtl6tsV1e1qcUMD98K/W88oauWlwXixsJNnZwWlnK+2vIv/v6/h+gfNKLiueRj3/P44mjCgkYCkJq1h6fevp3Oba9dhNu493+MDL8bgFER9/KP5Y9wMGMruRezWL3tbcbe8ksMhspvhUvX/5nbI2/8mozW3v60bd2ZN1cuICf/FAXF5/nfhlf42ZUzKKDKMn/Y+ym/f3sCg3pNYFCfiSSnfs/bXz1ByeWqF5rMOHOQWZNeop1vF3YfW9eAvdX0SXKqRVr2PoI6hZGavYeLxblEBI+u8nX+vl3w8fTDZDTVfG+Pg5iMcG8kzB+nJZzqrt+08oAJYfB0DIR1c3pYSgrs0IdHp71JVN876OIfUqlNe3Tsz55j31fZrq5sU4tZ+/AwZ4yWpKqrra03xAzQbgNoaWfAk6Me4v8eXMWKTYsqnrs5ZAx/n/cDP+z7FIDggAj+NGs1hzO3V7xm++HVRPa+HYBhoTHET11E9479CPAP4dipZL7Z/i6HT25j9TbtBveP171I/x4j6BlQ9YydGWOe4OGYv+Pr3Q5Pj1acyjnKv1c/xeGT29iTuqHKMkeGT+O1uM18tSURT4s3cXe+xswJz+Pt6VdlHd079sNgMODj2abaBNbcKT2sp4ITp1MY0X8q2w+vZv+JTUwZ+jAA5y5k8u7XCzl57jDdO4YyIuwu3lzxGD/uX0lUvztcEpvBoL1B9ewAP7tFG+a7WKItIePppn1Ne/+uMr33QPoW1ux4j9KyS8Td+Rofrn2hok0LSvLYn76ZmeOf071NjQboG6A9zhfCvkwovKStYedlga5toXfnljmR5Id9n7E3dT2FJReYGPkg/13/Vwb3nsiqrW9RVn6ZiOAx5ORnsfT7F7HZrPTo2B+A7PNptGvdBZNJe6tbs2MJhzK24mnxoXe3QfTuNgiAvyx9gNsjH2Tz/pWs3fkBYT3TOV+Qze2RD/LRd//HwYwfWbzy/zE35m/8e/VC8gpOE9l3Mp4WbxJmflZRRkRwNBHB0ZXK3JO6gaR9n3KptIgRYdMovlTAmysfo7T8EvdE/xbghjqi+t3Bos/juVRaxPyp/3T17laCUjfh6qWuN/0lfvEb5kx5uVF1tYSb56rz0pfazyed/D5f35s4G9Ou0p6Ob0+5Cbd2LaHftfDP1PXT2MQk1CTtKoR6JDkBZguYXLBSgcmi1SWcS9qzaXNV+zVVLaXfyTUnwM0TRsyGcgd9HUV1zBatLuFc0p5Nm6var6lqKf1OktMVbp4to8FbCmnPpk3aT8iwnhBCCOVIchJCCKEcSU5CCCGUI8lJCCGEciQ5CSGEUI4kJyGEEMqR5CSEEEI5kpyEEEIoR5KTEEII5UhyEkIIoRxZvuiKshJZi024hvQ14QpNvZ9JckJrxKS3wOrkhjRZtAUt5U2j5ZK+JlyhOfQzGdZD+3Th7EYErQ5Zabllk74mXKE59DM5cxIuI9+53HzY7dKewrkkOQmnsdngQBYkHYFjZ6Dcpj3/26XQNwBG9IKbOoLBoG+com7KrbDnpNaeGblgvdKeTyyD8G4wvBd095f2FI4hyamOfvmnHljcPHEzWSizlnL3qMeZNGSW3mEpa8sx+GYf5BXfuK3MCntPao/2rWDyzXBzoMtDVJKK/cxuh3UHYN1BKLp84/bL5bA9TXt0bQMxt0CvTq6PU9SPin3tpyQ51UPCzOUEduhD2ukU5r16C5F9JtHON0DvsJRit8PK3fD9wbq9/lwBvPcDTLkZxoY6NbQmQ6V+ZrXBf36EnSfq9vrMPFi8Dn4eBYN7OjU04QAq9bXryYSIBgjq1B8fzzbk5Gfy3JJpZJ9PA2D1tndYsekNnaPT1zf76p6YfuqLZG24SFyjdz+z2+GTbXVPTFfZ7FpCS8l0SljCCfTua1WR5NQAKWlJtPJqS8+ACGLHJ/DBmgRKyy+zbtdHTI56SO/wdHP8LHy9r/rts6K1R3U+3Q5n8h0fV1Oldz/bnQ5bUqvfXlN72oElSVUPAwr16N3XqqLksN6uXbuYO3cue/fuJSwsjPj4eObOnUthYSFGo375NGHJVOx2G1k5x3jqvo+xmN0J6hyGwWDkjc/jmRT1EGaTm27x6e2HWs58/H1q3m4HNh2FuwY5LKRaHc7W4j6bD75eMDREu/6lYzdTpp9tPFzz9tras8wK247DmL6Oi8mVTuVp+yDtHHi4waAgGNIT3JvRIa5KX6uKcmdOycnJREdH8/Of/5yDBw/ywAMPEBcXR2hoqK6JCbTx2Xd/d5in7lvK3z6ZRV7BGQBixyew/8QmRkdM1zU+PV0sgT0ZjS9na6p2gd0VVu7Sro8cOAVnC7QZhR9uhrc3XpuJpgcV+lnmeTiR0/hyko5ow3xNzc4T8PIq2HEczl7UZieu2AUvr4bCS3pH5zgq9LXqKJecrp4lLViwgKCgIOLi4ggICCA8PFzv0CpER9zDwN4T+HjdiwB0bNOd9r7dMLTgObTbjzvmTehyOSSnN76c2hw5rV0bs3Mt7qv/PpylxvUvPfvZ1hqG8+ojt1BL+k1JQQl8tFnrD9af9GmrDc4XwvKduoXmNCq+pymVnFJTU0lKSiI+Pr7S8xaLhfDwcI4cOcKoUaMYOXIkUVFRfPnllzpFCg/e/iLf7HiX3IvZusWgkpxCx5WV68CyqpN0pPr7caz22ocoXUWvfnauwHFluaI9HWnb8Zr7xu50KGmGq2+o9p5msNvVuc/7008/ZdasWeTl5VU8V1JSQrt27fjyyy8JCwvDYDDg7+/P2bNnGThwICdPnqy13JiYGFJTq/8o2NYngGfv+bZRsf/+rYm8OPvrWl/33CfjOF+Y1ai6VBR251/pFDqp4vdZ0Tdek2jXSvuZc90bX24hvL3h2u8Z2z/g8LcvOSlSzdBZn+PT4aZqt9vKS/nuLwMcXm9j+1pd+xk0rq8Nuv8D/LrejMGgfX5tTHseWfsX0rctaVAceug78Q8E3DwNo7H6S/KbE6dQlHvchVHVj0rvacHBwaxcubLe9St15mQwGLBardhs1wb8ExMTKS4uJjw8nHbt2uHv7w+Ap6enUsNodX3DaK6sZVXcbdtA5aWOK6s6lwpOY7NZq91eWnze6TE0hKv6mbWsxGFllTuwb7jC5cJz2G3VX3S0223K9g9H0vs9Takzp4yMDIKDg3n66aeJjY1l7dq1LFy4ELPZTFbWtcxst9u5//77GTZsGPPmzWt0vSX5sOmtRhdTJ8Nng6eva+pypa/31jyNHOCJydrPP39V8+vuHqwtbeRMe0/Cuz9UvT6cyQgT+sP4MMfX21T62tItNU8jh7q35+zRENqlYXHoIbcQXlhR9TajAfoEwEOjXRpSvTWVflYTpc6cAgMDWbRoEYsXL2bAgAHs2LGDGTNm3DAZ4pFHHqFHjx4OSUzCMQYFOaYcsxEGdHdMWTXp31VbD8543cm3yQidfCG6j/NjUJmjVndo5QF9OjumLFfx99FWLDGgPa4yGcHTAncN1CmwFka5+5zmzJnDnDlzKn6fMmVKpeT06KOP4uHhwR//+Ec9whPVaNdKW8z1YCMvp93SA7zdHRJSjYwGmDlcOzvYcFi7+ddogIlhMKoPuCt3ZLhWz/Zakj7dyJuih92kvak3NWNDtf//dwfg+DltgsSIm2BMP/Dz0ju6lkH5bpOSklKRnNauXcsbb7zBzp07GT16NKNHj6awsIlNBWrGRlQ/v6DuZTh5OO+njEbtzfP3d2hvRB1aw7j+kpjgyptxI9vCYNBuam6qQrvC/PFa3+jYGqYOksTkSkofhkVFRaSnp1ckp9tuu43ychfdoVmLTza8wqjwu3lzxWN4efgSHhzNxMG/0jssXfXrAkOCq79HprYpxbeFQqC/4+NqCvakrueDb5+ja/veTB0xn+4d+93wGlf3uaEh2vp4h6qZWVxbe94tb+ZK2nX0O9Zsfxc3szux45+jvV/XG16jwvub0snJ29u70sw9PSXtW86Sb56hs38wOfmZRASP4eTZw4wbNJOh/WJ4ffm8Fp+cDAa4NxIulVW9WsRPpxdfb1gITIpwXmzqM+Bh8cZms+Ln0+GG/vaP+K2cv5jt0j5nMsKvRsK/1kPq2Ru319Sek8K173cS6tm8/3MembqI3IvZfL393wR1CtO9r1VF6eSkkg5tAokZ/ghd/EPIKzzDqZyjhHQZwB8/vJf/rv8LMye8oHeISjAZYeYIWL0H1h/S1leriYcbjO+vrb+m0J0BLhfecxQRwdGknU5h+Q+vMaz/zyr624Wis5RaL+Pp7uPyPufuBg/fCp/t0K7P1bYKiLc73HkLRMrXZSgrZlgc76z6Pb4+7blQeJYhfScr0deuJ8mpjtKy9xHUKYyDGVswm9yICB7NtzuWMHfK3wjqHM7rnz3MLTeN1TtMJRgN2hcIjumnLWuUdOTGFQe6+MGI3toECLnGQ8U9e37e7SkpLazU3wbeNI49x77Xrc+ZTXDvELg9XEtQSUch/7pbl3q2165RhXfTXi/UFdihD49Oe5NDGdvYd3yjUn3tp+RtoY5OnE5hRP+pbD+8mv0nNjFl6MP4eLbh43V/wsPiTf8eI/QOUTleFm1K9qjeUHAJiku1qble7uDj3rLPlK63OWUF2w6tovDSBWLHP8fqrW9X9LeCkjz2p29m5vjndO1zrTy1CSNj+11rT5NRO1tyxQxL4RgH0rewZsd7lJZdIu7O1/hw7QvK9TVQ7CZcvdT3hrXEL37DnCkvN6iu5noTblP30pVlGp+8w7n1NPTmyIb0OelrjuGqvuFIjbkJt759rUXchNtUNDQxCdFQ0ueEq6jS1yQ5AWYLmCzOr8dk0eoSLZf0NeEKzaGfyTUnwM0TRsyGcicvg2+2aHWJlkv6mnCF5tDPJDld4eYpB7NwDelrwhWaej+TYT0hhBDKkeQkhBBCOZKchBBCKEeSkxBCCOVIchJCCKEcSU5CCCGUI8lJCCGEciQ5CSGEUI4kJyGEEMqR5CSEEEI5snzRFWUlTXsdKiEaS44BoRJJTmgHZdJbYHXygWmyaIsxysEpVCPHgFCNDOuhfVp09kEJWh3O/mQqREPIMSBUI2dOosWy2yE9F3IKoPiy9rXxR05DSAcwyse2Fu9MPmTmaX0DA6Rkwk2dwF3eNV1CdrNocS6VwY40SDoCp/Mrb/vnd+DnBcNvgqhgaCXDTy2K1Qb7MmHTETh6pvK2tzeAhxsMCYZhN0HH1vrE2FJIcqqjX/6pBxY3T9xMFsqspdw96nEmDZmld1iing5nw7s/aAmqOheK4as9sHov3BMJQ0NcF5/KmvsxcPYi/Ot7yCms/jWXymDDIe0R3QfuHCBn2c4iyakeEmYuJ7BDH9JOpzDv1VuI7DOJdr4Beocl6mhPBixJApu9bq+32WHZVii6DLeFOje2pqK5HgNZebBoLRTX43rYhkNwsQTuHyYJyhlklzZAUKf++Hi2ISc/k+eWTCP7fBoAq7e9w4pNb+gcnahK2jl4f1P1iWlWtPaoypfJsP2400JrkprTMZBfDIu/rz4x1dQ3dqfDit3Oi60lUzI57dq1i8jISDw8PBg8eDDvv/8+Xl5e2Gw2vUMDICUtiVZebekZEEHs+AQ+WJNAafll1u36iMlRD+kdnqjCyt3a9YTq+Ptoj+p8vgvKrY6PqyZ2uzZZIysPSstdW3dtmtMx8N0B7QyoOrX1jQ2H4FyB4+Nq6ZQb1ktOTiY6Oprnn3+eZcuWsWrVKuLi4ggNDcWo87lzwpKp2O02snKO8dR9H2MxuxPUOQyDwcgbn8czKeohzCY3XWMUNzqVp505NUbRZdhzEgb2cEhItTpyGj7bcW3ChsUEI3rDpHAwm1wTQ1Wa2zFwuRy2pja+nM1H4c5bGl+OuEa5M6f4+Hjmzp3LggULCAoKIi4ujoCAAMLDw/UOjYSZy3n3d4d56r6l/O2TWeQVaNN5YscnsP/EJkZHTNc5QlGVTUccU06Sg8qpzZHT8Oa6yjMJS62w4aB2zcxex2tmztDcjoFdJ7QE1Vhbjql3dtvUKZWcUlNTSUpKIj4+vtLzFoulIjkNHz6c0aNHM2jQIP7617/qESbREfcwsPcEPl73IgAd23SnvW83DAaDLvGImiVnOKactHNQeMkxZdXk851VJyCrXZvmnJHr/Bhq01yOgT0O6hslZZB61jFlCY1Sw3rJycn4+fkRGBhY8VxJSQnHjx+vSE7ff/89FouFsrIy+vTpw8yZM+nQoUON5cbExJCaWv25e1ufAJ6959t6xfrg7S8y77WBTB/zBP6tO9f578aPH8f5wqx61SUaw8Btv9+LwXDtc9is6BuvIbRrpf18YnLl53MLtftbrrp1/BSKcp03O8KjdWdGPrK22u228lIefe5Djq57xaH1ttRjYMivltGqU7+K/tGYvvHIY09wev+XToy2aQoODmblypX1/julkpPBYMBqtWKz2SquLyUmJlJcXFyRnCwWCwDFxcUEBATg6+vrktg+fOpEpd+7tAthxQv5Vb9YKMNgNFVKTI0uz8nXU0y1LTpnNNb+GidpjseA1p6OOdszNqFrbU2BwW7XcwS7soyMDIKDg3n66aeJjY1l7dq1LFy4ELPZTFaW9knLarUyduxY9u/fz69//WteeumlRg8llOTDprcc8T+o3fDZ4OmafCqu+O1SKKtlpt3VT8V//qrm1/3hTmhbw8ytxiqzwjOfVn+TsNEA04doqxQ4Uks9Bl5fA8drmSxT177xq5EQEVjza0TdKXXNKTAwkEWLFrF48WIGDBjAjh07mDFjRqXJECaTifXr13Py5Em2b9/OmjVrdIxYNAWB/o4pp5UH+Ho5pqzquJlgVG8tCV3PYABPCwzo7twYWpJuDuobAF3bOq4soVhyApgzZw7Z2dlcuHCBxMRE0tLSKpJTaWlpxb1O7u7ueHl54ekpi5+Jmg2/yTHlDA0BkwuOmAlhcPN1n8CNBvC2wLyxYFFqML5pc1Tf6BdQ871Qov6U7+YpKSlMn65NT01LS2P27NkYjUYuX77MxIkTGTVqlM4RCtWFdwMfdyi83PAyDAZtsU9XMBkhdgTc2k+bUm63w12DtCEjNx3vcWqOOrSG3p3g8OnGlTO8l2PiEdconZyKiopIT0+vOHPq3bs3GzdudHq9+47/wHe7PyI3/xT3j0+gV9eBN7zmkw2v0KdbJCs2LyKwQ19ixydgtVlZ8s0fKL58kQEhYxne/2dOj1XUzmyC0X21ZYiqk1vDYp+g3Xzr5+Qhvet1basNJQIMCnJNnWmnU/ho7QsVffr636vS1I+FMf1qTk619Y1OvtC37pMVRR0pnZy8vb11WbIorOdIwnqO5Nip3RzJ3MnZvAyWfPMMnf2DycnP5B/xWzl/MZuwniNp79eNNTveA2Dz/hXkF53D3c2LDn5yZVQlt/bTVorYnV719p9OCb5et7Zwb6Rz4lJNUKf+zJr054o+ff3vSfuWN7tjoU9niBmgLXFVlZr6ho87zB4tC786g9LJSU9fbfkXq7a+xRM//4BLpUXEDH+ELv4hXCg6S6n1Mp7uNw4wnzp3hIjgMURH3Mtflz3Akz//QIfIRVWMBrhvKJiNsD2t7n8X3AEeHCXXea7q0CawWR4LY/pqP6tLUFVp6w1zbpVrTc4ih1w1Jkc9xPD+U/ng2+fo1XUQQZ3COJixhYE3jWPPse+JCB59w9/4+3bBx9MPk9GEwUH3TgjHMZvgF0O1bzPdeBgyz1f/Wn8f7WL5qN76rmWnmrTsfc3yWDAYtLPrLm1g/SE4WMM9wl4WiArREtrVYVfheJKcqvDDvs/Ym7qewpIL3DF0Lkn7PmNE/6lsP7yagpI89qdvZub45zh3IZN3v17IyXOH6d4xlBFhd/Hmisf4cf9Kovrdofd/Q1TBYIDIntojIxd+PKat/F1Spn39tq8nDO4JvTtXPZ27ubu+T/frPrTS7ydOpzTrY6F3Z+2RU6At5pqZByWl2hm3t4c2i1ImpriGUjfh6qW+NyAmfvEb5kx5uUF1qXQDolDfS1dWw3nSye/vDb0JtyHHghwDoi7kMl4DNDQxCdHcyLEgnEWSkxBCCOVIcgLMFjBZnF+PyaLVJYRq5BgQqpEJEYCbJ4yYDeWlzq3HbNHqEkI1cgwI1UhyusLNUw4a0bLJMSBUIsN6QgghlCPJSQghhHIkOQkhhFCOJCchhBDKkeQkhBBCOZKchBBCKEeSkxBCCOVIchJCCKEcSU5CCCGUIytEXFFWIku3CCGEKiQ5oSWmpLfA6uTkZLJo65dJghJCiJrJsB7aGZOzExNodTj77EwIIZoDSU5CCCGUI8N6Qiim3Ap7TsKeDMgt1J5bvA66toVhIdDWR9/4hHAFSU5CKKLwEmw4BJuPQdHlytsOZWuPtfshtAuM6QshHfWJUwhXkORUR7/8Uw8sbp64mSyUWUu5e9TjTBoyS++wRDNx7iIs/v7amVJN9p/SHj8bCKP7OD82IfQgyakeEmYuJ7BDH9JOpzDv1VuI7DOJdr4Beoclmri8Inj9Wyi4VL+/+3wnYIfRfZ0SlhC6kgkRDRDUqT8+nm3Iyc/kuSXTyD6fBsDqbe+wYtMbOkcnmhKbDf61vvrENCtae1Tn811w5LRTQhNCV0omp127dhEZGYmHhweDBw/m/fffx8vLC5vNpndoAKSkJdHKqy09AyKIHZ/AB2sSKC2/zLpdHzE56iG9wxNNyMFsyL5Q/XZ/H+1Rk3UHHBqSEEpQblgvOTmZ6Ohonn/+eZYtW8aqVauIi4sjNDQUo1HfXJqwZCp2u42snGM8dd/HWMzuBHUOw2Aw8sbn8UyKegizyU3XGEXTsulI48s4lA05BdCuVePLEkIVyp05xcfHM3fuXBYsWEBQUBBxcXEEBAQQHh6ud2gkzFzOu787zFP3LeVvn8wir+AMALHjE9h/YhOjI6brHKFoSnIL4UCWY8radNQx5QihCqWSU2pqKklJScTHx1d63mKxVEpOJSUl9OjRgyeffNLVIQIQHXEPA3tP4ON1LwLQsU132vt2w2Aw6BKPaJoyzzuurJMOLEsIFSg1rJecnIyfnx+BgYEVz5WUlHD8+PFKyenll19mwIABdS43JiaG1NTUare39Qng2Xu+rVesD97+IvNeG8j0MU/g37pznf9u/PhxnC900Mdl0aQFRNxF6OQXKn6fFX3j9aWrQ3VPTK78fG4hvL1B+7fdbmf33sOEPjrNidEK0TDBwcGsXLmy3n+nVHIyGAxYrVZsNlvF9aXExESKi4srktOpU6fYtm0b06ZN49ChQy6L7cOnTlT6vUu7EFa8kO+y+kXzY7eWO6okbNYyB5UlhBoMdrvdrncQV2VkZBAcHMzTTz9NbGwsa9euZeHChZjNZrKytLONmTNn8thjj7Fnzx4OHTrESy+91Oh6S/Jh01uNLqZOhs8GT1/X1CXUduCUNo28JlfPmP78Vc2v69MZ5t7qkLCEUIJS15wCAwNZtGgRixcvZsCAAezYsYMZM2ZUnDVt3boVk8lUryE9IVQV0hE8HDS5MyKw9tcI0ZQoNawHMGfOHObMmVPx+5QpUyolp2PHjjFx4kROnTpFUVER/fr1IzY2Vq9whWgwixmGBGvr6TWGuxlu6eGQkIRQhnLJ6XopKSlMn65N0Z4/fz7z588H4L333uPQoUOSmESTNuymxienIcFaghKiOVFqWO96RUVFpKenV3mP0wMPPOCQ600N9cmGV8i9mA1AflEOM18K4fT5E7rFI5qmjq1hZK/qt+cW1rwYbGsPuLWf4+MSQm9Kf97y9vZWZsmipH3LWfLNM3T2DyYnP5OI4DEVU8g/++FVhvefqnOEoqmaOhDyS2DvyRu3XZ0uXhVPN3hoDPh5OS82IfSidHJSSYc2gcQMf4Qu/iHkFZ7hVI52S/62Q6vp130Yh09u0zlC0VQZjTBzBCzfCUl1XM7I30dLTB1bOzc2IfQiyamO0rL3EdQpjIMZWzCb3IgIHg3AgRObKb5cwMGMLdhsVh6Y+ELNBQlRBZMR7h4Mw2+CzUdh63EoreI2qOAOMKIXhHUFs8n1cQrhKpKc6ujE6RRG9J/K9sOr2X9iE1OGPgxQkYzeX5PA+EEP6BihaA46+8G0wTD5Zu2rMAovQbkNvCzQpY22XYiWQKmbcPVS35twE7/4DXOmvNyguuQmXCGEqJ3Ss/VU1dDEJIQQom4kOQFmC5gszq/HZNHqEkIIUTMZ1ruirATKS51bh9kCbp7OrUMIIZoDSU5CCCGUI8N6QgghlCPJSQghhHIkOQkhhFCOJCchhBDKkeQkhBBCOZKchBBCKEeSkxBCCOVIchJCCKEcSU5CCCGUI8lJCCGEciQ5CSGEUI4kJyGEEMqR5CSEEEI5kpyEEEIoR5KTEEII5UhyEkIIoRxJTkIIIZQjyUkIIYRy/j+h4aN3yA3zPAAAAABJRU5ErkJggg==\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 523.664x270.9 with 1 Axes>"
+       "<Figure size 473.498x270.9 with 1 Axes>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -164,18 +194,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "id": "1c3a5712",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:08.918049Z",
+     "iopub.status.busy": "2024-04-19T17:42:08.917818Z",
+     "iopub.status.idle": "2024-04-19T17:42:09.173131Z",
+     "shell.execute_reply": "2024-04-19T17:42:09.172454Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 623.998x270.9 with 1 Axes>"
+       "<Figure size 473.498x270.9 with 1 Axes>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -206,9 +243,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "id": "5d1fb2ca",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:09.177033Z",
+     "iopub.status.busy": "2024-04-19T17:42:09.176313Z",
+     "iopub.status.idle": "2024-04-19T17:42:09.180279Z",
+     "shell.execute_reply": "2024-04-19T17:42:09.179623Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from qiskit_ibm_runtime import QiskitRuntimeService  # noqa: F401\n",
@@ -234,9 +278,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "id": "3cc622d9",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:09.183537Z",
+     "iopub.status.busy": "2024-04-19T17:42:09.182884Z",
+     "iopub.status.idle": "2024-04-19T17:42:09.188106Z",
+     "shell.execute_reply": "2024-04-19T17:42:09.187164Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from qiskit_ibm_runtime import Options\n",
@@ -258,10 +309,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "id": "2ae5160c",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:09.191528Z",
+     "iopub.status.busy": "2024-04-19T17:42:09.191111Z",
+     "iopub.status.idle": "2024-04-19T17:42:09.420212Z",
+     "shell.execute_reply": "2024-04-19T17:42:09.419296Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2967/4196057829.py:3: DeprecationWarning: The function ``circuit_knitting.cutting.cutqc.wire_cutting.evaluate_subcircuits()`` is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. \n",
+      "  subcircuit_instance_probabilities = evaluate_subcircuits(cuts)\n"
+     ]
+    }
+   ],
    "source": [
     "from circuit_knitting.cutting.cutqc import evaluate_subcircuits\n",
     "\n",
@@ -269,7 +336,7 @@
     "\n",
     "# Uncomment the following lines to instead use Qiskit Runtime Service as configured above.\n",
     "# subcircuit_instance_probabilities = evaluate_subcircuits(cuts,\n",
-    "#                                                          service_args=service.active_account(),\n",
+    "#                                                          service=service,\n",
     "#                                                          backend_names=backend_names,\n",
     "#                                                          options=options,\n",
     "#                                                         )"
@@ -287,9 +354,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "id": "fa22661e",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:09.424642Z",
+     "iopub.status.busy": "2024-04-19T17:42:09.423925Z",
+     "iopub.status.idle": "2024-04-19T17:42:09.428782Z",
+     "shell.execute_reply": "2024-04-19T17:42:09.427942Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -316,9 +390,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "id": "7e57f303",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:09.432758Z",
+     "iopub.status.busy": "2024-04-19T17:42:09.432111Z",
+     "iopub.status.idle": "2024-04-19T17:42:09.436940Z",
+     "shell.execute_reply": "2024-04-19T17:42:09.436250Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -348,9 +429,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "id": "3ec4d42c",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:09.440852Z",
+     "iopub.status.busy": "2024-04-19T17:42:09.440554Z",
+     "iopub.status.idle": "2024-04-19T17:42:09.445443Z",
+     "shell.execute_reply": "2024-04-19T17:42:09.444807Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -384,9 +472,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "id": "5aceecc0",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:09.449575Z",
+     "iopub.status.busy": "2024-04-19T17:42:09.449007Z",
+     "iopub.status.idle": "2024-04-19T17:42:10.686860Z",
+     "shell.execute_reply": "2024-04-19T17:42:10.685710Z"
+    }
+   },
    "outputs": [],
    "source": [
     "%%capture\n",
@@ -410,9 +505,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "id": "919958cb",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:10.691821Z",
+     "iopub.status.busy": "2024-04-19T17:42:10.691074Z",
+     "iopub.status.idle": "2024-04-19T17:42:10.697156Z",
+     "shell.execute_reply": "2024-04-19T17:42:10.696348Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -440,10 +542,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "id": "5353b0c8",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:10.700976Z",
+     "iopub.status.busy": "2024-04-19T17:42:10.700196Z",
+     "iopub.status.idle": "2024-04-19T17:42:10.712174Z",
+     "shell.execute_reply": "2024-04-19T17:42:10.711251Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2967/3419935587.py:3: DeprecationWarning: The function ``circuit_knitting.cutting.cutqc.wire_cutting_verification.verify()`` is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. \n",
+      "  metrics, exact_probabilities = verify(circuit, reconstructed_probabilities)\n"
+     ]
+    }
+   ],
    "source": [
     "from circuit_knitting.cutting.cutqc import verify\n",
     "\n",
@@ -462,26 +580,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "id": "673d3cb3",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:10.716298Z",
+     "iopub.status.busy": "2024-04-19T17:42:10.715820Z",
+     "iopub.status.idle": "2024-04-19T17:42:10.721924Z",
+     "shell.execute_reply": "2024-04-19T17:42:10.721135Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'nearest': {'chi2': 0,\n",
-       "  'Mean Squared Error': 8.13178352181795e-35,\n",
-       "  'Mean Absolute Percentage Error': 4.4880309854901524e-10,\n",
-       "  'Cross Entropy': 3.564551116068219,\n",
-       "  'HOP': 0.9945381353717198},\n",
+       "  'Mean Squared Error': 1.8155290178685046e-34,\n",
+       "  'Mean Absolute Percentage Error': 7.550336449277775e-10,\n",
+       "  'Cross Entropy': 3.5645511160682197,\n",
+       "  'HOP': 0.9945381353717202},\n",
        " 'naive': {'chi2': 0,\n",
-       "  'Mean Squared Error': 3.7794080473092745e-35,\n",
-       "  'Mean Absolute Percentage Error': 4.4880563544629694e-10,\n",
-       "  'Cross Entropy': 3.564551116068219,\n",
-       "  'HOP': 0.99453813537172}}"
+       "  'Mean Squared Error': 2.9965472816547853e-34,\n",
+       "  'Mean Absolute Percentage Error': 7.550351082551326e-10,\n",
+       "  'Cross Entropy': 3.5645511160682206,\n",
+       "  'HOP': 0.9945381353717199}}"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -502,18 +627,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "id": "c8cc97e9",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:10.725981Z",
+     "iopub.status.busy": "2024-04-19T17:42:10.725441Z",
+     "iopub.status.idle": "2024-04-19T17:42:11.074622Z",
+     "shell.execute_reply": "2024-04-19T17:42:11.073833Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABfkAAAJOCAYAAAAESA02AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAADAdElEQVR4nOzdeXjM5/7/8ddksjWrJbJYIsRWpWjsVVsRa1G1tIfU0qD9VmnUQSnRnnLUUopWi5Ie7WmopYpamlqqoqV2tYSKLQlCJRJrkvn94Zc5nWaRjEQMz8d15brM/bk/9+d9j5HEaz5z3waTyWQSAAAAAAAAAACwOXZFXQAAAAAAAAAAALAOIT8AAAAAAAAAADaKkB8AAAAAAAAAABtFyA8AAAAAAAAAgI0i5AcAAAAAAAAAwEYR8gMAAAAAAAAAYKMI+QEAAAAAAAAAsFH2RV3AwywjI0NxcXFyd3eXwWAo6nIAAAAAAAAAm2IymXT16lWVLl1adnb5u185PT1dt2/fLqTKgMLl4OAgo9GYp76E/IUoLi5O5cqVK+oyAAAAAAAAAJt25swZlS1bNk99TSaTEhISdOXKlcItCihkxYoVk6+v711vICfkL0Tu7u6S7nwT8vDwKOJqAAAAAAAAANuSnJyscuXKmXO2vMgM+L29veXi4sIKG7A5JpNJ165d04ULFyRJfn5+ufYn5C9Emd9APDw8CPkBAAAAAAAAK+U1qE9PTzcH/CVLlizkqoDC89hjj0mSLly4IG9v71yX7mHjXQAAAAAAAAAPhcw1+F1cXIq4EuDeZb6O77a3BCE/AAAAAAAAgIcKS/TgYZDX1zEhPwAAAAAAAAAANoqQHwAAAAAAAAAAG0XIDwAAAAAAAACAjSLkBwAAAAAAAADARtkXdQEAAAAAAAB49PTo0UPLly9Xenq6XF1dNWvWLPXr1++u573xxhuaNWuWfH19FR8fb24/ePCgunTpopMnTyojI0MlS5bUf//7X7Vu3VqSdOLECXXt2lVHjhzR7du3ZTQa9cQTT+i7776Tv7//Xa87Z84cTZkyRQkJCapVq5ZmzZql+vXr3/W8r7/+Wi+++KI6d+6slStXmtvPnz+vkSNHasOGDbpy5YqaNm2qWbNmqXLlypKky5cva/z48dqwYYNOnz6tUqVKqUuXLnrvvffk6el51+sie6EziroCad4w689dtGhRrv9OoqOj1bBhQ+svcA8mTpyo6tWrq0uXLkVy/UcZd/IDAAAAAADgvho6dKiWLl2qkJAQffvttypXrpwGDBigQ4cO5Xretm3bNGfOHHl4eFi0Z2RkqFGjRrp48aI+//xzrV27VqVKlVK7du104cIFSdL+/ft16dIljRw5UlFRUZo8ebIOHz6sxo0b37XeyMhIhYWFafz48dq9e7dq1aql4OBg89g5iY2N1VtvvaVnnnnGot1kMqlLly76448/9O2332rPnj0qX768WrVqpdTUVElSXFyc4uLiNHXqVB08eFCLFi3SunXrNGDAgLvWi4ffu+++q//85z9ZvipVqlRkNU2cONHijSzcPwaTyWQq6iIeVsnJyfL09FRSUlKWHz4AAAAAAACPKjc3N1WsWFH79++XJKWlpcnJyUmtW7fWunXrsj3n1q1b8vLy0vPPP69t27YpNTXVfCf/+vXr1bZtW61cuVKdO3c2j+no6Kg+ffooIiIi2zHDwsL04Ycf6vr163J2ds6x3gYNGqhevXqaPXu2pDtvKpQrV05DhgzRqFGjsj0nPT1dTZs2Vf/+/fXTTz/pypUr5gD02LFjqlq1qg4ePKgnnnjCPKavr68mTpyoV155Jdsxly5dqt69eys1NVX29o/GAh35zddu3LihkydPqkKFCtn+nT4sd/Lv3LlTdevWLbCaCoKbm5teeOEFLVq0qKhLeWjc7fWciTv5AQAAAAAAcN+kpKQoNTVVHTp0MLfZ29srICBA+/bty/G81q1by83NLdsAMSUlRZLk7u5uMaadnZ1+/vnnHMe8dOmSDAZDruHZrVu39Ntvv6lVq1bmNjs7O7Vq1UrR0dE5nvfuu+/K29s72zvvb968KUkW17Wzs5OTk5O2bduW45iZQfejEvDDOuPHj5ednZ2ioqIs2gcOHChHR0fzv7Nbt25p3LhxCgoKkqenp1xdXfXMM89o06ZNWcbMyMjQzJkzVbNmTTk7O6tUqVJq27atdu3aJUkyGAxKTU1VRESEDAaDDAaD+vbtW+hzxR2E/AAAAAAAALhvjh07JkmqUKGCRXvJkiV19erVbM/5+OOPtW3btmzDR0lq166djEajXn75ZZ08eVIpKSlq166d0tPTdeXKlWzPOXr0qL788ks1atQo13oTExOVnp4uHx8fi3YfHx8lJCRke862bdu0YMECzZs3L9vj1apVk7+/v0aPHq0///xTt27d0uTJk3X27FmLfQb+Xsd7772ngQMH5lovHg1JSUlKTEy0+Lp06ZIkaezYsapdu7YGDBhg/je1fv16zZs3T+PGjVOtWrUk3fmUxPz589W8eXNNnjxZ4eHhunjxooKDg7V3716L6w0YMEDDhg1TuXLlNHnyZI0aNUrOzs7asWOHJOk///mPnJyc9Mwzz5iXDho0aND9e0IecbztBwAAAAAAgAdWXFychg4dqvHjx6tq1arZ9nFxcdGiRYsUGhqqihUrSrrzpkGpUqWU3UrVZ8+eVVBQkIoXL66NGzcWaL1Xr15Vnz59NG/ePHl5eWXbx8HBQcuXL9eAAQNUokQJGY1GtWrVSu3atcu23uTkZHXo0EHVq1dXeHh4gdYL2/TXT5ZkcnJy0o0bN+Tg4KAvvvhCQUFBCgsL05QpUzRgwADVrVvXYnmp4sWLKzY2Vo6Ojua20NBQVatWTbNmzdKCBQskSZs2bdKiRYv0xhtvaObMmea+w4cPN79ee/furcGDB6tixYrq3bt3YU0bOSDkBwAAAAAAwH1TpUoVSdLJkyct2i9dumSx3E6mn376SWlpaRo/frzGjx9vccxgMCgqKkotW7ZU79691bt3b50+fVqpqal6/PHH5ebmluUTA3FxcXr88cfl6OiomJgYubi45Fqvl5eXjEajzp8/b9F+/vx5+fr6Zul/4sQJxcbGqlOnTua2jIwMSXeWEDp69KgCAwMVFBSkvXv3KikpSbdu3VKpUqXUoEGDLOusX716VW3btpW7u7tWrFghBweHXOvFo2HOnDnmf0uZjEaj+c81atTQhAkTNHr0aO3fv1+JiYnasGGDxVJPRqPRfE5GRoauXLmijIwM1a1bV7t37zb3W7ZsmQwGQ5Z/f9Kdf4MoeoT8AAAAAAAAuG/c3Nzk6uqqNWvWaNKkSZLubJIbGxur1q1bZ+kfHBys5cuXW7S99tprunnzphYsWKD69etbHPP395ckbdy4UampqXrppZfMx86ePavHH39cRqNRx44dU7Fixe5ar6Ojo4KCghQVFaUuXbpIuhOIRkVF6fXXX8/Sv1q1ajpw4IBF29ixY3X16lXNnDlT5cqVszjm6ekpSYqJidGuXbv03nvvmY8lJycrODhYTk5OWrVqVa57B+DRUr9+/btuvDtixAh9/fXX+vXXXzVx4kRVr149S5+IiAhNmzZNR44c0e3bt83tf31z7MSJEypdurRKlChRcBNAgSLkBwAAAAAAwH01YMAAffTRRwoNDVXnzp01YsQImUwmTZ06VZIUGBgob29vRUdHq1ixYuratavF+SNGjJAki/awsDD5+/urdu3a2rBhgz744AOVLl1ao0ePlnQn4K9WrZrS09P17bffKi4uTnFxcZLuBPN/XbLk78LCwvTyyy+rbt26ql+/vmbMmKHU1FT169dPkhQSEqIyZcpo0qRJcnZ2Vo0aNSzOz3wz4a/tS5cuValSpeTv768DBw5o6NCh6tKli9q0aSPpTsDfpk0bXbt2TYsXL1ZycrKSk5MlSaVKlbK4axvIzh9//KGYmBhJyvLGkyQtXrxYffv2VZcuXTRixAh5e3vLaDRq0qRJOnHixP0uF/eAkB8AAAAAAAD31cyZMxUXF6eFCxdq/vz5cnV11bx588wh+KVLl/K9DMipU6f00UcfKT09XUajUY0aNdL3339vPv7NN98oNTVVkvTss89anPvTTz+pSZMmOY7ds2dPXbx4UePGjVNCQoJq166tdevWmTfjPX36tOzs7PJVb3x8vMLCwnT+/Hn5+fkpJCRE77zzjvn47t279csvv0iSKlWqZHHuyZMnFRAQkK/r4dGSkZGhvn37ysPDQ8OGDdPEiRP1wgsv6Pnnnzf3+eabb1SxYkUtX77c4t/b35flCQwM1Pr163X58uVc7+Zn6Z6iYzBlt5sHCkRycrI8PT2VlJQkDw+Poi4HAAAAAAAAsCn5zddu3LihkydPqkKFCtkubxQ6oxCKzKd5w6w/d9GiRerXr5927tyZ63I9U6dO1YgRI7Rq1Sp16NBBzzzzjI4fP65Dhw6ZN4Tu1q2b9u7dq5iYGPObVL/88osaNWokf39/xcbGSrqz8W7Lli2zbLwrSSaTyRzu+/r6qmHDhlq5cqX1E4SFu72eM3EnPwAAAAAAAADYkO+//15HjhzJ0t64cWPdvHlT77zzjvr27WveAHrRokWqXbu2XnvtNS1ZskSS1LFjRy1fvlxdu3ZVhw4ddPLkSc2dO1fVq1dXSkqKecwWLVqoT58++uijjxQTE6O2bdsqIyNDP/30k1q0aGHemyIoKEg//PCDpk+frtKlS6tChQpq0KDBfXg2QMgPAAAAAAAA4JFwL3fRP0jGjRuXbfv8+fP16aefysvLSzNmzDC3V65cWZMmTdLQoUO1ZMkS9ejRQ3379lVCQoI+/fRTrV+/XtWrV9fixYu1dOlSbd682WLchQsX6sknn9SCBQs0YsQIeXp6qm7dumrcuLG5z/Tp0zVw4ECNHTtW169f18svv0zIf5+wXE8hYrkeAAAAAAAAwHoFvVwPYEvy+nrO344gAAAAAAAAAADggUHIDwAAAAAAAACAjSLkBwAAAAAAAADARhHyAwAAAAAAAABgowj5AQAAAAAAAACwUYT8AAAAAAAAAADYKEJ+AAAAAAAAAABsFCE/AAAAAAAAAAA2ipAfAAAAAAAAAAAbRcgPAAAAAAAAAICNIuQHAAAAAAAAAMBGEfIDAAAAAAAAAHCfxMbGymAwaNGiRQUynn2BjAIAAAAAAAAAD7gTVcOLugQFHrW+hkWLFqlfv37mx0ajUT4+PmrdurXef/99lSlTpgAqfDB8/PHHcnFxUd++fR/pGvKCkB8AAAAAAAAAbMi7776rChUq6MaNG9qxY4cWLVqkbdu26eDBg3J2di7q8grExx9/LC8vryIP+Yu6hrwg5AcAAAAAAAAAG9KuXTvVrVtXkvTKK6/Iy8tLkydP1qpVq9SjR48iru7+S01Nlaura1GXUWRYkx8AAAAAAAAAbNgzzzwjSTpx4oS57ciRI3rhhRdUokQJOTs7q27dulq1alWWc69cuaI333xTAQEBcnJyUtmyZRUSEqLExERznwsXLmjAgAHy8fGRs7OzatWqpYiICItxMteZnzp1qj777DMFBgbKyclJ9erV086dOy36JiQkqF+/fipbtqycnJzk5+enzp07KzY2VpIUEBCgQ4cOacuWLTIYDDIYDGrevLmkO0sWGQwGbdmyRa+99pq8vb1VtmxZSVLfvn0VEBCQZY7h4eEyGAxZ2hcvXqz69evLxcVFxYsXV9OmTbVhw4a71pD5vA0bNkzlypWTk5OTKlWqpMmTJysjIyPL89u3b195enqqWLFievnll3XlypUstdwL7uQHAAAAAAAAABuWGY4XL15cknTo0CE9/fTTKlOmjEaNGiVXV1ctWbJEXbp00bJly9S1a1dJUkpKip555hkdPnxY/fv311NPPaXExEStWrVKZ8+elZeXl65fv67mzZvr+PHjev3111WhQgUtXbpUffv21ZUrVzR06FCLWr766itdvXpVgwYNksFg0AcffKDnn39ef/zxhxwcHCRJ3bp106FDhzRkyBAFBATowoUL2rhxo06fPq2AgADNmDFDQ4YMkZubm8aMGSNJ8vHxsbjOa6+9plKlSmncuHFKTU3N93M2YcIEhYeHq3Hjxnr33Xfl6OioX375RT/++KPatGmTaw3Xrl1Ts2bNdO7cOQ0aNEj+/v7avn27Ro8erfj4eM2YMUOSZDKZ1LlzZ23btk2DBw/W448/rhUrVujll1/Od725IeQHAAAAAAAAABuSlJSkxMRE3bhxQ7/88osmTJggJycndezYUZI0dOhQ+fv7a+fOnXJycpJ0JxRv0qSJRo4caQ75p0yZooMHD2r58uXmNkkaO3asTCaTJOmzzz7T4cOHtXjxYv3jH/+QJA0ePFjNmjXT2LFj1b9/f7m7u5vPPX36tGJiYsxvOFStWlWdO3fW+vXr1bFjR125ckXbt2/XlClT9NZbb5nPGz16tPnPXbp00dixY+Xl5aXevXtn+xyUKFFCUVFRMhqN+X7+jh8/rnfffVddu3bVN998Izu7/y14kznv3GqYPn26Tpw4oT179qhy5cqSpEGDBql06dKaMmWKhg8frnLlymnVqlXaunWrPvjgA40YMUKS9Oqrr6pFixb5rjk3LNcDAAAAAAAAADakVatWKlWqlMqVK6cXXnhBrq6uWrVqlcqWLavLly/rxx9/VI8ePXT16lUlJiYqMTFRly5dUnBwsGJiYnTu3DlJ0rJly1SrVi2LgD9T5vI2a9eula+vr1588UXzMQcHB73xxhtKSUnRli1bLM7r2bOnOeCX/reU0B9//CFJeuyxx+To6KjNmzfrzz//tPo5CA0NtSrgl6SVK1cqIyND48aNswj4JWW7rM/fLV26VM8884yKFy9ufn4TExPVqlUrpaena+vWrZLuPHf29vZ69dVXzecajUYNGTLEqrpzwp38AAAAAAAAKHShM4q6grwb9Ul4UZeQZ4FHw4u6BBSBOXPmqEqVKkpKStLnn3+urVu3mu/YP378uEwmk9555x2988472Z5/4cIFlSlTRidOnFC3bt1yvdapU6dUuXLlLGH4448/bj7+V/7+/haPMwP/zEDfyclJkydP1vDhw+Xj46OGDRuqY8eOCgkJka+vbx6fAalChQp57vt3J06ckJ2dnapXr27V+TExMdq/f79KlSqV7fELFy5IuvPc+Pn5yc3NzeJ41apVrbpuTgj5AQAAAAAAAMCG1K9fX3Xr1pV0Z1mZJk2a6KWXXtLRo0fNG7++9dZbCg4Ozvb8SpUqFVptOd1dn7kMjiQNGzZMnTp10sqVK7V+/Xq98847mjRpkn788UfVqVMnT9d57LHHsrTldBd+enp6nsbMq4yMDLVu3Vr//Oc/sz1epUqVAr3e3RDyAwAAAAAAAICNMhqNmjRpklq0aKHZs2erf//+ku4sqdOqVatczw0MDNTBgwdz7VO+fHnt379fGRkZFnfzHzlyxHzcGoGBgRo+fLiGDx+umJgY1a5dW9OmTdPixYsl5W3ZnL8rXry4rly5kqX97582CAwMVEZGhn7//XfVrl07x/FyqiEwMFApKSl3fX7Lly+vqKgopaSkWNzNf/To0VzPyy/W5AcAAAAAAAAAG9a8eXPVr19fM2bMkIeHh5o3b65PP/1U8fHxWfpevHjR/Odu3bpp3759WrFiRZZ+mXfet2/fXgkJCYqMjDQfS0tL06xZs+Tm5qZmzZrlq9Zr167pxo0bFm2BgYFyd3fXzZs3zW2urq7ZBva5CQwMVFJSkvbv329ui4+PzzK/Ll26yM7OTu+++675kw+Z/vqJg5xq6NGjh6Kjo7V+/fosx65cuaK0tDRJd567tLQ0ffLJJ+bj6enpmjVrVr7mdTfcyQ8AAAAAAAAANm7EiBHq3r27Fi1apDlz5qhJkyaqWbOmQkNDVbFiRZ0/f17R0dE6e/as9u3bZz7nm2++Uffu3dW/f38FBQXp8uXLWrVqlebOnatatWpp4MCB+vTTT9W3b1/99ttvCggI0DfffKOff/5ZM2bMkLu7e77qPHbsmJ599ln16NFD1atXl729vVasWKHz58+rV69e5n5BQUH65JNP9K9//UuVKlWSt7e3WrZsmevYvXr10siRI9W1a1e98cYbunbtmj755BNVqVJFu3fvNverVKmSxowZo/fee0/PPPOMnn/+eTk5OWnnzp0qXbq0Jk2alGsNI0aM0KpVq9SxY0f17dtXQUFBSk1N1YEDB/TNN98oNjZWXl5e6tSpk55++mmNGjVKsbGxql69upYvX66kpKR8PWd3Q8gPAAAAAAAA4JHwMG9U/PzzzyswMFBTp05VaGiodu3apQkTJmjRokW6dOmSvL29VadOHY0bN858jpubm3766SeNHz9eK1asUEREhLy9vfXss8+qbNmyku6sfb9582aNGjVKERERSk5OVtWqVbVw4UL17ds333WWK1dOL774oqKiovSf//xH9vb2qlatmpYsWWKxCfC4ceN06tQpffDBB7p69aqaNWt215C/ZMmSWrFihcLCwvTPf/5TFSpU0KRJkxQTE2MR8kvSu+++qwoVKmjWrFkaM2aMXFxc9OSTT6pPnz53rcHFxUVbtmzRxIkTtXTpUn3xxRfy8PBQlSpVNGHCBHl6ekqS7OzstGrVKg0bNkyLFy+WwWDQc889p2nTpuV574G8MJj++vkDFKjk5GR5enoqKSlJHh4eRV0OAAAAAABAkQmdUdQV5N2oT8KLuoQ8e5hDayn/+dqNGzd08uRJVahQQc7OzvehQqDw5PX1zJr8AAAAAAAAAADYKEJ+AAAAAAAAAABsFCE/AAAAAAAAAAA2ipAfAAAAAAAAAAAbRcgPAAAAAAAAAICNIuQHAAAAAAAA8FAxmUxFXQJwz/L6OibkBwAAAAAAAPBQsLe3lySlpaUVcSXAvct8HWe+rnNCyA8AAAAAAADgoWA0GmU0GpWcnFzUpQD3LDk52fyazk3ubwEAAAAAAAAAgI0wGAzy9vZWfHy8nJyc5OrqKoPBUNRlAfliMpmUmpqq5ORk+fn53fU1TMgPAAAAAAAA4KHh6emp69evKzExURcvXizqcgCrGAwGFStWTJ6ennftS8gPAAAAAAAA4KFhMBjk5+cnb29v3b59u6jLAazi4OBw12V6MhHyAwAAAAAAAHjo5GUtc+BhwMa7AAAAAAAAAADYKEJ+AAAAAAAAAABsFCE/AAAAAAAAAAA2ipAfAAAAAAAAAAAbRcgPAAAAAAAAAICNIuQHAAAAAAAAAMBGEfIDAAAAAAAAAGCjCPkBAAAAAAAAALBRhPwAAAAAAAAAANgoQn4AAAAAAAAAAGwUIT8AAAAAAAAAADbqgQ35d+7cqfbt26tYsWJydXVVw4YNtWTJEqvH+/PPP1WmTBkZDAa1bds22z4GgyHHr759+1p9bQAAAAAAAAAACoN9UReQnU2bNik4OFjOzs7q1auX3N3dtWzZMvXs2VNnzpzR8OHD8z3m66+/rqSkpLv2K1++fLaBfu3atfN9TQAAAAAAAAAACtMDF/KnpaUpNDRUdnZ22rp1qzlcHzdunOrXr6+3335bL7zwgsqXL5/nMZctW6avvvpKs2fP1uuvv55r34CAAIWHh9/DDAAAAAAAAAAAuD8euOV6fvzxR504cUIvvfSSxd3znp6eevvtt3Xr1i1FRETkebyLFy/q1VdfVZ8+fdShQ4dCqBgAAAAAAAAAgKLxwN3Jv3nzZklSmzZtshwLDg6WJG3ZsiXP4w0ePFhGo1EzZ87M03I9V65c0WeffabExESVKFFCTz/9tGrWrJnn6wEAAAAAAAAAcL88cCF/TEyMJKly5cpZjvn6+srNzc3c524WL16s5cuXa+XKlSpevHieQv59+/Zp0KBBFm1t27ZVRESEvL29cz335s2bunnzpvlxcnKyJOn27du6ffu2JMnOzk5Go1Hp6enKyMgw981sT0tLk8lkMrcbjUbZ2dnl2J45biZ7+zt/pWlpaXlqd3BwUEZGhtLT081tBoNB9vb2ObbnVDtzYk7MiTkxJ+bEnJgTc2JOzIk5MSfmxJyYU87tDkLheJhfe3+fA4CsHriQPzOI9/T0zPa4h4dHnsL6uLg4vfHGG3rxxRfVuXPnPF17+PDh6tatm6pUqSJHR0cdPHhQ7733nr7//nt17NhR0dHRMhqNOZ4/adIkTZgwIUv7hg0b5OLiIkny9/dXnTp1tH//fp0+fdrcp2rVqqpWrZp+/fVXXbx40dxeu3ZtlS9fXlu3btXVq1fN7Y0aNZK3t7c2bNhg8Q24RYsWeuyxx7R27VqLGtq3b6/r169r06ZN5jZ7e3t16NBBiYmJio6ONre7u7urZcuWOnPmjPbu3WtuL1WqlBo3bqyYmBgdPXrU3M6cmBNzYk7MiTkxJ+bEnJgTc2JOzIk5MSfmdPc55S2fQf49zK+9a9eu3duTAzwCDKa/vlX2AGjTpo02btyomJgYVapUKcvxMmXKKCUl5a5Bf/v27fXbb7/p0KFD8vLykiTFxsaqQoUKCg4O1rp16/JUT0ZGhlq2bKktW7Zo2bJlev7553Psm92d/OXKlVNiYqI8PDwkPSjvnP/Pg/COLHNiTsyJOTEn5sScmBNzYk7MiTkxJ+bEnB7+Ob02x3bu5B/1SXhRl5BngUfDH+rXXnJysry8vJSUlGTO1wBYeuDu5M+8gz+nED85OVnFixfPdYyIiAh9//33Wrp0qTngt5adnZ1CQ0O1ZcsW/fzzz7mG/E5OTnJycsrS7uDgIAcHyx9kRqMx208FZH5TzWv738e1pt3Ozk52dln3YM6pPafamRNzym87c2JOEnPKqcb8tjMn5iQxp5xqzG87c2JOEnPKqcb8tjMn5iQxp5xqzG/7wzgnFJyH+bXHawe4u6z/EotY5lr82a27n5CQoJSUlGzX6/+rPXv2SJK6d+8ug8Fg/qpQoYIkaf369TIYDKpdu3aeasp8oyA1NTWv0wAAAAAAAAAAoNA9cHfyN2vWTJMmTdKGDRvUq1cvi2Pr168398lNo0aNlJKSkqU9JSVFkZGRKlu2rIKDg+Xv75+nmn755RdJUkBAQJ76AwAAAAAAAABwPzxwa/KnpaWpatWqOnfunHbs2GG+2z4pKUn169dXbGysjh49ag7c4+PjlZSUJD8/vxw3682U25r8Bw4cULVq1bJ8BGj79u1q3bq1bt++rcOHDyswMDDPc0lOTpanpydrhgEAAAAAgEde6IyiriDvbG1N/ocZ+Rpwdw/cnfz29vaaP3++goOD1bRpU/Xq1Uvu7u5atmyZTp06palTp1rcUT969GhFRERo4cKF6tu3r9XXnTZtmtasWaMmTZqoXLlycnBw0KFDh7RhwwYZDAbNmTMnXwE/AAAAAAAAAACF7YEL+SWpRYsW2rZtm8aPH6/IyEjdvn1bNWvW1OTJk9WzZ89CuWbnzp115coV7du3Txs3btStW7fk6+urXr16adiwYapfv36hXBcAAAAAAAAAAGs9cMv1PEz4OBEAAAAAAMAdLNdTOFiuB4BdURcAAAAAAAAAAACsQ8gPAAAAAAAAAICNIuQHAAAAAAAAAMBGEfIDAAAAAAAAAGCjCPkBAAAAAAAAALBRhPwAAAAAAAAAANgoQn4AAAAAAAAAAGwUIT8AAAAAAAAAADaKkB8AAAAAAAAAABtFyA8AAAAAAAAAgI0i5AcAAAAAAAAAwEYR8gMAAAAAAAAAYKMI+QEAAAAAAAAAsFGE/AAAAAAAAAAA2ChCfgAAAAAAAAAAbBQhPwAAAAAAAAAANoqQHwAAAAAAAAAAG0XIDwAAAAAAAACAjSLkBwAAAAAAAADARhHyAwAAAAAAAABgowj5AQAAAAAAAACwUYT8AAAAAAAAAADYKEJ+AAAAAAAAAABsFCE/AAAAAAAAAAA2ipAfAAAAAAAAAAAbRcgPAAAAAAAAAICNIuQHAAAAAAAAAMBGEfIDAAAAAAAAAGCjCPkBAAAAAAAAALBRhPwAAAAAAAAAANgoQn4AAAAAAAAAAGwUIT8AAAAAAAAAADaKkB8AAAAAAAAAABtFyA8AAAAAAAAAgI0i5AcAAAAAAAAAwEYR8gMAAAAAAAAAYKMI+QEAAAAAAAAAsFGE/AAAAAAAAAAA2ChCfgAAAAAAAAAAbBQhPwAAAAAAAAAANoqQHwAAAAAAAAAAG0XIDwAAAAAAAACAjSLkBwAAAAAAAADARhHyAwAAAAAAAABgowj5AQAAAAAAAACwUYT8AAAAAAAAAADYKEJ+AAAAAAAAAABsFCE/AAAAAAAAAAA2ipAfAAAAAAAAAAAbRcgPAAAAAAAAAICNIuQHAAAAAAAAAMBGEfIDAAAAAAAAAGCjCPkBAAAAAAAAALBRhPwAAAAAAAAAANgoQn4AAAAAAAAAAGwUIT8AAAAAAAAAADaKkB8AAAAAAAAAABtFyA8AAAAAAAAAgI0i5AcAAAAAAAAAwEYR8gMAAAAAAAAAYKMI+QEAAAAAAAAAsFGE/AAAAAAAAAAA2ChCfgAAAAAAAAAAbBQhPwAAAAAAAAAANoqQHwAAAAAAAAAAG2V1yF+9enV9+OGHunTpUkHWAwAAAAAAAAAA8sjqkP/06dN66623VLZsWb344ov68ccfC7IuAAAAAAAAAABwF1aH/AkJCfr4449Vo0YNRUZGqnXr1qpUqZL+/e9/KyEhoSBrBAAAAAAAAAAA2bA65Hdzc9OgQYO0c+dO7du3T6+99pr+/PNPvf322/L399fzzz+v77//XiaTqSDrBQAAAAAAAAAA/1+BbLxbs2ZNzZo1S3FxcfrPf/6jJk2a6Ntvv1XHjh1Vvnx5TZgwQefOnSuISwEAAAAAAAAAgP+vQEL+TE5OTgoODlb79u3l6+srk8mks2fPasKECapYsaL+7//+T9euXSvISwIAAAAAAAAA8MgqsJB/w4YN6tGjh8qWLauRI0fKYDDonXfe0fHjx7VkyRI99dRTmjt3rv7v//6voC4JAAAAAAAAAMAjzf5eTj537pw+//xzLVy4UKdOnZIktWnTRoMGDVKnTp1kNBolSRUrVtQLL7ygTp066dtvv733qgEAAAAAAAAAgPUhf8eOHbV+/Xqlp6fLx8dHI0eO1MCBAxUQEJDjOY0bN9batWutvSQAAAAAAAAAAPgLq0P+tWvXqmXLlho0aJC6du0qe/u7D9WpUyeVLl3a2ksCAAAAAAAAAIC/sDrkP3bsmCpVqpSvc2rUqKEaNWpYe0kAAAAAAAAAAPAXVm+8O3HiRK1atSrXPqtXr1b//v2tvQQAAAAAAAAAAMiF1SH/okWLtHfv3lz77Nu3TxEREdZeAgAAAAAAAAAA5MLqkD8vbty4kae1+gEAAAAAAAAAQP7dUwJvMBiybTeZTDpz5oy+//57NtoFAAAAAAAAAKCQ5OtOfjs7OxmNRhmNRklSeHi4+fFfv+zt7VWhQgXt3r1bvXr1KpTCAQAAAAAAAAB41OXrTv6mTZua797funWr/P39FRAQkKWf0WhUiRIl1LJlS4WGhhZIoQAAAAAAAAAAwFK+Qv7Nmzeb/2xnZ6d+/fpp3LhxBV0TAAAAAAAAAADIA6vX5M/IyCjIOgAAAAAAAAAAQD7la01+AAAAAAAAAADw4Mjznfz9+/eXwWDQxIkT5ePjo/79++fpPIPBoAULFlhdIAAAAAAAAAAAyF6eQ/5FixbJYDBo5MiR8vHx0aJFi/J0HiE/AAAAAAAAAACFI88h/8mTJyVJZcqUsXgMAAAAAAAAAACKRp5D/vLly+f6GAAAAAAAAAAA3F9svAsAAAAAAAAAgI3K8538p0+ftvoi/v7+Vp8LAAAAAAAAAACyl+eQPyAgQAaDId8XMBgMSktLy/d5AAAAAAAAAAAgd3kO+UNCQqwK+QEAAAAAAAAAQOHIc8i/aNGiQiwDAAAAAAAAAADkFxvvAgAAAAAAAABgowj5AQAAAAAAAACwUXlerqd///4yGAyaOHGifHx81L9//zydZzAYtGDBAqsLBAAAAAAAAAAA2cvXmvwGg0EjR46Uj49PntfoJ+QHAAAAAAAAAKBw5DnkP3nypCSpTJkyFo8BAAAAAAAAAEDRyHPIX758+VwfAwAAAAAAAACA+4uNdwEAAAAAAAAAsFH3HPKvWLFCnTt3lr+/vzw9PeXv768uXbpo5cqVBVAeAAAAAAAAAADISZ6X6/m7tLQ0vfTSS1q2bJlMJpPs7e1VsmRJJSQkaNWqVfruu+/UrVs3ffXVV7K3t/oyAAAAAAAAAAAgB1bfyT9p0iR98803euaZZ/TTTz/pxo0bio+P140bN7R161Y1adJEy5Yt07///e+CrBcAAAAAAAAAAPx/Vof8CxcuVLVq1fTDDz/o6aeflp3dnaHs7OzUpEkT/fDDD6pSpYo+//zzAisWAAAAAAAAAAD8j9Uhf3x8vDp16pTjUjwODg7q1KmT4uPjrS4OAAAAAAAAAADkzOqQv1y5ckpJScm1T2pqqvz9/a29BAAAAAAAAAAAyIXVIf8rr7yiJUuW5Hin/rlz5xQZGalXXnnF6uIAAAAAAAAAAEDOsl9rJxunT5+2eNyjRw/9/PPPqlOnjoYNG6YmTZrIx8dH58+f108//aSZM2eqSZMm6t69e4EXDQAAAAAAAAAA8hHyBwQEyGAwZGk3mUwaM2ZMtu2rVq3S6tWrlZaWdm9VAgAAAAAAAACALPIc8oeEhGQb8gMAAAAAAAAAgKKR55B/0aJFhVgGAAAAAAAAAADIL6s33gUAAAAAAAAAAEXrgQ35d+7cqfbt26tYsWJydXVVw4YNtWTJkjyf//3336tXr16qVq2aihUrJhcXF1WrVk0DBgzQsWPHcjxv/fr1atasmdzd3eXh4aEWLVooKiqqIKYEAAAAAAAAAECByvNyPdm5evWqZs+erR9++EFxcXG6efNmlj4Gg0EnTpzI17ibNm1ScHCwnJ2d1atXL7m7u2vZsmXq2bOnzpw5o+HDh991jLVr12rHjh1q0KCB2rVrJwcHBx0+fFgRERH68ssvtXbtWrVs2dLinMWLF6tPnz4qVaqU+vbtK0mKjIxU69attWTJEr3wwgv5mgcAAAAAAAAAAIXJYDKZTNacePHiRTVu3FgnTpyQh4eHkpOT5enpqVu3bun69euSpNKlS8vBwUEnT57M87hpaWmqVq2azp49qx07dqh27dqSpKSkJNWvX1+xsbE6duyYypcvn+s4N27ckLOzc5b2qKgotWrVSnXr1tXOnTvN7X/++acqVqwoe3t77dmzR2XLlpUknT17VnXq1JEk/fHHH3J3d8/zXDKfk6SkJHl4eOT5PAAAAAAAgIdN6IyiriDvRn0SXtQl5Fng0fCiLqFQka8Bd2f1cj3h4eE6ceKEvvjiC/3555+SpDfffFOpqan65ZdfVL9+fQUEBOjQoUP5GvfHH3/UiRMn9NJLL5kDfkny9PTU22+/rVu3bikiIuKu42QX8EvSs88+q+LFi+v48eMW7UuXLtWVK1c0ZMgQc8AvSWXLltXrr7+uxMRErVixIl9zAQAAAAAAAACgMFkd8q9du1bPPvusevfuLYPBYHGsXr16+v777xUbG6sJEybka9zNmzdLktq0aZPlWHBwsCRpy5Yt1hUtKTo6Wn/++adq1KhxX68LAAAAAAAAAEBBs3pN/vj4eHXv3t382Gg0mpfpkaTixYurXbt2WrJkiSZPnpzncWNiYiRJlStXznLM19dXbm5u5j55sWHDBm3fvl03b95UTEyMVq9eLS8vL3344Yd5vm5m292ue/PmTYt9CZKTkyVJt2/f1u3btyVJdnZ2MhqNSk9PV0ZGhrlvZntaWpr+uoKS0WiUnZ1dju2Z42ayt7/zV5qWlpandgcHB2VkZCg9Pd3cZjAYZG9vn2N7TrUzJ+bEnJgTc2JOzIk5MSfmxJyYE3NiTsyJOeXc7iAUjof5tff3OQDIyuqQ39PT0+IfWfHixXX27FmLPh4eHjp//ny+xk1KSjKPnx0PDw9zn7zYsGGDpk2bZn5cqVIlff311woKCsrzdTPX+7rbdSdNmpTtJxc2bNggFxcXSZK/v7/q1Kmj/fv36/Tp0+Y+VatWVbVq1fTrr7/q4sWL5vbatWurfPny2rp1q65evWpub9Sokby9vbVhwwaLb8AtWrTQY489prVr11rU0L59e12/fl2bNm0yt9nb26tDhw5KTExUdHS0ud3d3V0tW7bUmTNntHfvXnN7qVKl1LhxY8XExOjo0aPmdubEnJgTc2JOzIk5MSfmxJyYE3NiTsyJOTGnu8+ps1A4HubX3rVr1+7tyQEeAVZvvNuoUSP5+vqa16kPDg7Wvn37dOjQIZUsWVLXr19XrVq1ZGdnpyNHjuR53DZt2mjjxo2KiYlRpUqVshwvU6aMUlJS8hX0S1JKSop+//13vfvuu/rhhx/0+eef66WXXjIfr1KlimJiYnT79m3zu5eZbt++LUdHRz355JPat29fjtfI7k7+cuXKKTEx0fxGwYPxzvn/PAjvyDIn5sScmBNzYk7MiTkxJ+bEnJgTc2JOzOnhn9Nrc2znTn5b23j3YX7tJScny8vLi413gVxYHfKPHz9eH374oRISEuTi4qLly5frhRdeUOnSpdWoUSPt3r1bsbGxev/99zVq1Kg8j9u9e3d988032rVrV5a77aU77xYWL17c4p2//EhLS1PdunV1/PhxnTx5UqVKlZJ0Zx+BXbt2KTExUSVLlrQ459KlS/Ly8tIzzzyjrVu35vla7P4NAAAAAABwR+iMoq4g72wt5H+Yka8Bd2f1xruDBw/WvHnzzB+Zef755zVlyhSlpqZq2bJlSkhIUFhYmEaMGJGvcXNb/z4hIUEpKSnZrpufV/b29mrRooVSU1O1a9euPF03t/X6AQAAAAAAAAAoKlaH/H5+furZs6e8vLzMbcOHD1diYqLi4+OVkpKiKVOmyGg05mvcZs2aSbqzjv3frV+/3qKPteLi4iTd+cjR/bwuAAAAAAAAAAAFyeqQPydGo1E+Pj4yGAxWnf/ss8+qYsWK+uqrryw270hKStLEiRPl6OiokJAQc3t8fLyOHDmSZY3+v96l/1fr16/XihUrVKxYMTVq1Mjc3qNHD3l6emrWrFkWGwifPXtWs2fPlpeXl7p27WrVnAAAAAAAAAAAKAz2d++Su/j4eH399dfas2ePkpKS5OnpqTp16qhXr17y8/PLf0H29po/f76Cg4PVtGlT9erVS+7u7lq2bJlOnTqlqVOnKiAgwNx/9OjRioiI0MKFC9W3b19ze7169VSjRg09+eSTKlu2rFJTU7V//3799NNPcnBw0Oeffy5XV1dz/+LFi2v27Nnq06ePnnrqKfXs2VOSFBkZqUuXLikyMlLu7u5WP08AAAAAAAAAABS0ewr558yZoxEjRujmzZsWu2AvXrxYY8aM0dSpU/Xaa6/le9wWLVpo27ZtGj9+vCIjI3X79m3VrFlTkydPNofvdzNx4kRt2rRJW7Zs0cWLF2VnZyd/f38NHDhQw4YN0+OPP57lnN69e8vLy0sTJ07UwoULZTAYFBQUpLFjx6pVq1b5ngcAAAAAAAAAAIXJYPprOp8PX3/9tV566SV5eXlp6NCheuaZZ+Tj46Pz589r69atmjlzpi5fvqz//ve/6tGjR0HXbRPY/RsAAAAAAOCO0BlFXUHejfokvKhLyLPAo+FFXUKhIl8D7s7qO/k/+OADeXl5ae/evSpdurS5vWrVqmratKn69u2rOnXqaPLkyY9syA8AAAAAAAAAQGGyeuPdw4cPq0ePHhYB/1+VLVtW3bt31+HDh60uDgAAAAAAAAAA5MzqkL9YsWIWG9dmx83NTcWKFbP2EgAAAAAAAAAAIBdWh/zPPfecvvvuO6WlpWV7/Pbt2/ruu+/UuXNnq4sDAAAAAAAAAAA5szrk/+CDD+Tq6qo2bdpox44dFseio6PVpk0bubu769///vc9FwkAAAAAAAAAALLK88a7FStWzNJ269Yt7d69W08//bTs7e3l5eWlxMRE8939fn5+euqpp3TixImCqxgAAAAAAAAAAEjKR8ifkZEhg8Fg0ebg4CB/f3+Ltr9vxJuRkXEP5QEAAAAAAAAAgJzkOeSPjY0txDIAAAAAAAAAAEB+Wb0mPwAAAAAAAAAAKFp5vpM/N2lpaTp69KiSk5Pl4eGhqlWryt6+QIYGAAAAAAAAAAA5uKc7+S9fvqzQ0FB5enrqySefVJMmTfTkk0+qWLFiGjhwoC5dulRQdQIAAAAAAAAAgL+x+nb7y5cvq2HDhjp+/LhKlCihZ555Rn5+fkpISNCuXbs0f/58bdmyRdHR0SpRokRB1gwAAAAAAAAAAHQPd/K/9957On78uEaMGKFTp05p3bp1Wrhwob7//nudOnVKI0eOVExMjN5///2CrBcAAAAAAAAAAPx/Vof83377rZo3b67JkyfL1dXV4piLi4smTZqk5s2ba8WKFfdcJAAAAAAAAAAAyMrqkD8uLk6NGjXKtU+jRo0UFxdn7SUAAAAAAAAAAEAurA75PT09derUqVz7nDp1Sp6entZeAgAAAAAAAAAA5MLqkL9Zs2ZaunSpfvjhh2yPR0VFaenSpWrevLm1lwAAAAAAAAAAALmwt/bE8ePHa82aNQoODlb79u3VrFkz+fj46Pz589q8ebO+//57ubi4aNy4cQVZLwAAAAAAAAAA+P+sDvmfeOIJrV+/Xn379tWaNWu0Zs0aGQwGmUwmSVJgYKAWLVqkJ554osCKBQAAAAAAAAAA/2N1yC9JTZo0UUxMjH7++Wft2bNHycnJ8vDwUJ06dfT000/LYDAUVJ0AAAAAAAAAAOBvrA75+/fvr5o1a+rNN99UkyZN1KRJk4KsCwAAAAAAAAAA3IXVG+9+9dVXunDhQkHWAgAAAAAAAAAA8sHqkD8wMFDx8fEFWQsAAAAAAAAAAMgHq0P+/v37a82aNTp37lxB1gMAAAAAAAAAAPLI6jX5u3Xrpk2bNqlx48b65z//qXr16snHxyfbzXb9/f3vqUgAAAAAAAAAAJCV1SF/xYoVZTAYZDKZ9MYbb+TYz2AwKC0tzdrLAAAAAAAAAACAHFgd8oeEhGR71z4AAAAAAAAAALg/rA75Fy1aVIBlAAAAAAAAAACA/LJ6410AAAAAAAAAAFC0rL6TP9PNmze1du1a7dmzR0lJSfL09FSdOnXUvn17OTk5FUSNAAAAAAAAAAAgG/cU8q9atUoDBw7UxYsXZTKZzO0Gg0He3t767LPP1KlTp3suEgAAAAAAAAAAZGV1yB8VFaVu3brJaDSqf//+euaZZ+Tj46Pz589r69atWrx4sZ5//nmtX79eLVu2LMiaAQAAAAAAAACA7iHkHz9+vB577DFt375dNWrUsDgWEhKiN954Q08//bTGjx9PyA8AAAAAAAAAQCGweuPdPXv2qGfPnlkC/kxPPvmkevTood27d1tdHAAAAAAAAAAAyJnVIb+Li4tKlSqVax9vb2+5uLhYewkAAAAAAAAAAJALq0P+Vq1a6Ycffsi1zw8//KDWrVtbewkAAAAAAAAAAJALq0P+qVOn6sKFCwoJCdGZM2csjp05c0Z9+vRRYmKipk6des9FAgAAAAAAAACArKzeeLdPnz4qXry4vvzyS3399dfy9/eXj4+Pzp8/r9OnTys9PV1PPvmkevfubXGewWBQVFTUPRcOAAAAAAAAAMCjzuqQf/PmzeY/p6Wl6Y8//tAff/xh0Wffvn1ZzjMYDNZeEgAAAAAAAAAA/IXVIX9GRkZB1gEAAAAAAAAAAPLJ6jX5AQAAAAAAAABA0SqwkP/06dPaunVrQQ0HAAAAAAAAAADuosBC/oULF6pFixYFNRwAAAAAAAAAALgLlusBAAAAAAAAAMBGEfIDAAAAAAAAAGCjCPkBAAAAAAAAALBRBRbye3p6yt/fv6CGAwAAAAAAAAAAd1FgIf+wYcN08uTJghoOAAAAAAAAAADcBcv1AAAAAAAAAABgo+zz2nHr1q2SpPr168vZ2dn8OC+aNm2a/8oAAAAAAAAAAECu8hzyN2/eXAaDQYcPH1aVKlXMj/MiPT3d6gIBAAAAAAAAAED28hzyjxs3TgaDQV5eXhaPAQAAAAAAAABA0chzyB8eHp7rYwAAAAAAAAAAcH+x8S4AAAAAAAAAADbK6pD/6tWr+uOPP3T79m2L9sjISP3jH//QgAEDtHv37nsuEAAAAAAAAAAAZC/Py/X83T//+U8tXrxY58+fl4ODgyTpk08+0euvvy6TySRJ+vrrr/Xbb7+pWrVqBVMtAAAAAAAAAAAws/pO/i1btqhVq1ZycXExt/373/9WmTJltHXrVi1ZskQmk0lTpkwpkEIBAAAAAAAAAIAlq+/kj4+PV9u2bc2PDx8+rDNnzuiDDz5QkyZNJEnffPONtm7deu9VAgAAAAAAAACALKy+k//mzZtydHQ0P96yZYsMBoPatGljbqtYsaLOnTt3bxUCAAAAAAAAAIBsWR3yly1bVvv37zc/Xr16tUqUKKEnn3zS3Hbp0iW5ubndW4UAAAAAAAAAACBbVi/X065dO82ZM0dvvfWWnJ2dtW7dOoWEhFj0OXbsmPz9/e+5SAAAAAAAAAAAkJXVIf/o0aP13Xffafr06ZIkPz8/vfvuu+bjFy5c0M8//6zXX3/93qsEAAAAAAAAAABZWB3y+/r66tChQ4qKipIkNW3aVB4eHubjiYmJmjJlioKDg++9SgAAAAAAAAAAkIXVIb8kPfbYY+rYsWO2x6pXr67q1avfy/AAAAAAAAAAACAXVm+8CwAAAAAAAAAAitY93cmfnp6uJUuW6IcfflBcXJxu3ryZpY/BYDAv6QMAAAAAAAAAAAqO1SF/amqq2rRpox07dshkMslgMMhkMpmPZz42GAwFUigAAAAAAAAAALBk9XI9//rXvxQdHa0JEyYoMTFRJpNJ4eHhio+PV2RkpCpWrKju3btne3c/AAAAAAAAAAC4d1aH/MuXL1fDhg01duxYlShRwtzu4+Oj7t27a9OmTfrhhx80ZcqUAikUAAAAAAAAAABYsjrkP336tBo2bPi/gezsLO7aL1u2rDp06KCIiIh7qxAAAAAAAAAAAGTL6pDf1dVVdnb/O93T01Px8fEWfXx9fXX69GnrqwMAAAAAAAAAADmyOuQvX768RYBfo0YN/fjjj+a7+U0mk6KiouTn53fvVQIAAAAAAAAAgCysDvmfffZZbdq0SWlpaZKkl19+WadPn1ajRo00YsQINWnSRHv37lW3bt0KrFgAAAAAAAAAAPA/9taeGBoaqpIlS+rixYvy8/NT//79tWfPHn388cfau3evJKlbt24KDw8voFIBAAAAAAAAAMBfWR3yV65cWSNHjrRomzVrlsaNG6c//vhD5cuXl6+v7z0XCAAAAAAAAAAAsmd1yJ+TUqVKqVSpUgU9LAAAAAAAAAAA+Bur1+QHAAAAAAAAAABFy+o7+StWrJinfgaDQSdOnLD2MgAAAAAAAAAAIAdWh/wZGRkyGAxZ2pOSknTlyhVJkp+fnxwdHa0uDgAAAAAAAAAA5MzqkD82NjbXY2FhYTp//rw2btxo7SUAAAAAAAAAAEAuCmVN/oCAAEVGRurPP//UmDFjCuMSAAAAAAAAAAA88gpt410HBwe1bt1aS5YsKaxLAAAAAAAAAADwSCu0kF+Srl27psuXLxfmJQAAAAAAAAAAeGQVWsj/008/6b///a+qVq1aWJcAAAAAAAAAAOCRZvXGuy1btsy2PS0tTefOnTNvzDtu3DhrLwEAAAAAAAAAAHJhdci/efPmbNsNBoOKFy+uNm3aKCwsTK1bt7b2EgAAAAAAAAAAIBdWh/wZGRkFWQcAAAAAAAAAAMgnq0P+TBcuXNC5c+eUkZGhMmXKyNfXtyDqAgAAAAAAAAAAd2HVxrs3b97UBx98oMqVK8vPz09169ZV/fr1VaZMGXl5eenNN980r8kPAAAAAAAAAAAKR75D/jNnzqhevXoaPXq0Tpw4IT8/P9WvX1/169eXn5+fLl++rJkzZ6pu3br64YcfzOfFx8dryZIlBVo8AAAAAAAAAACPsnyF/Ldv31b79u118OBBvfjiizp8+LDOnj2r6OhoRUdH6+zZszp8+LD+8Y9/6PLly+rSpYtiY2N14sQJNWnSREeOHCmseQAAAAAAAAAA8MjJ15r8n376qQ4dOqTx48dr/Pjx2fapWrWq/vOf/6hKlSoaP368/vGPfyg2NlaJiYkKCgoqkKIBAAAAAAAAAEA+7+RfsmSJKlWqpHHjxt2179ixY1W5cmVFR0frxo0bWr9+vTp06GB1oQAAAAAAAAAAwFK+Qv7ff/9dbdq0kcFguGtfg8Fg7vvLL7+oefPm1tYIAAAAAAAAAACyka+QPyUlRZ6ennnu7+HhIXt7e1WqVCnfhQEAAAAAAAAAgNzlK+T39vbW8ePH89z/xIkT8vb2zndRAAAAAAAAAADg7vIV8jdq1Ejff/+9EhIS7to3ISFBa9asUZMmTawuDgAAAAAAAAAA5CxfIf/gwYOVkpKirl27KjExMcd+ly5dUteuXXXt2jUNGjTonosEAAAAAAAAAABZ2eenc4sWLRQaGqp58+bp8ccf16BBg9SyZUuVK1dOknTmzBlFRUVp3rx5SkxM1MCBA9lwFwAAAAAAAACAQpKvkF+SPv74Y3l4eOjDDz/UpEmTNGnSJIvjJpNJdnZ2euutt7IcAwAAAAAAAAAABSffIb/RaNSUKVM0cOBALVq0SNHR0eY1+n19fdW4cWO9/PLLqly5coEXCwAAAAAAAAAA/iffIX+mypUr6/333y/IWgAAAAAAAAAAQD7ka+NdAAAAAAAAAADw4CDkBwAAAAAAAADARhHyAwAAAAAAAABgowj5AQAAAAAAAACwUYT8AAAAAAAAAADYKEJ+AAAAAAAAAABsFCE/AAAAAAAAAAA2ipAfAAAAAAAAAAAbRcgPAAAAAAAAAICNIuQHAAAAAAAAAMBGEfIDAAAAAAAAAGCjHtiQf+fOnWrfvr2KFSsmV1dXNWzYUEuWLMnz+SdOnFB4eLiee+45lSlTRgaDQQEBAbmeYzAYcvzq27fvvU0IAAAAAAAAAIACZl/UBWRn06ZNCg4OlrOzs3r16iV3d3ctW7ZMPXv21JkzZzR8+PC7jvHTTz9pwoQJMhqNevzxx5WQkJCna5cvXz7bQL927dr5nAUAAAAAAAAAAIXrgQv509LSFBoaKjs7O23dutUcro8bN07169fX22+/rRdeeEHly5fPdZymTZsqOjpatWrV0mOPPSZnZ+c8XT8gIEDh4eH3OAsAAAAAAAAAAArfA7dcz48//qgTJ07opZdesrh73tPTU2+//bZu3bqliIiIu45TsWJFNWzYUI899lghVgsAAAAAAAAAQNF54O7k37x5sySpTZs2WY4FBwdLkrZs2VJo179y5Yo+++wzJSYmqkSJEnr66adVs2bNQrseAAAAAAAAAADWeuBC/piYGElS5cqVsxzz9fWVm5ubuU9h2LdvnwYNGmTR1rZtW0VERMjb2zvXc2/evKmbN2+aHycnJ0uSbt++rdu3b0uS7OzsZDQalZ6eroyMDHPfzPa0tDSZTCZzu9FolJ2dXY7tmeNmsre/81ealpaWp3YHBwdlZGQoPT3d3GYwGGRvb59je061MyfmxJyYE3NiTsyJOTEn5sScmBNzYk7MiTnl3O4gFI6H+bX39zkAyOqBC/mTkpIk3VmeJzseHh7mPgVt+PDh6tatm6pUqSJHR0cdPHhQ7733nr7//nt17NhR0dHRMhqNOZ4/adIkTZgwIUv7hg0b5OLiIkny9/dXnTp1tH//fp0+fdrcp2rVqqpWrZp+/fVXXbx40dxeu3ZtlS9fXlu3btXVq1fN7Y0aNZK3t7c2bNhg8Q24RYsWeuyxx7R27VqLGtq3b6/r169r06ZN5jZ7e3t16NBBiYmJio6ONre7u7urZcuWOnPmjPbu3WtuL1WqlBo3bqyYmBgdPXrU3M6cmBNzYk7MiTkxJ+bEnJgTc2JOzIk5MSfmdPc5dRYKx8P82rt27dq9PTnAI8Bg+utbZQ+ANm3aaOPGjYqJiVGlSpWyHC9TpoxSUlLyHfQ7OzvL19dXsbGx+TovIyNDLVu21JYtW7Rs2TI9//zzOfbN7k7+cuXKKTExUR4eHpIelHfO/+dBeEeWOTEn5sScmBNzYk7MiTkxJ+bEnJgTc2JOD/+cXptjO3fyj/okvKhLyLPAo+EP9WsvOTlZXl5eSkpKMudrACw9cCF/9+7d9c0332jXrl0KCgrKctzd3V3Fixe3eOcvL6wN+SXpyy+/VO/evRUWFqZp06bl+bzk5GR5enryTQgAAAAAADzyQmcUdQV5Z2sh/8OMfA24O7uiLuDvMtfiz27d/YSEBKWkpGS7Xn9h8vLykiSlpqbe1+sCAAAAAAAAAJCbBy7kb9asmaQ769j/3fr16y363C+//PKLJCkgIOC+XhcAAAAAAAAAgNw8cCH/s88+q4oVK+qrr76y2LwjKSlJEydOlKOjo0JCQszt8fHxOnLkyD1vxnvgwIFsd+vevn27Jk+eLAcHB3Xv3v2ergEAAAAAAAAAQEGyL+oC/s7e3l7z589XcHCwmjZtql69esnd3V3Lli3TqVOnNHXqVIs76kePHq2IiAgtXLhQffv2NbcnJibqrbfeMj++ffu2EhMTLfpMnTrVvBTPtGnTtGbNGjVp0kTlypWTg4ODDh06pA0bNshgMGjOnDkKDAws7OkDAAAAAAAAAJBnD1zIL0ktWrTQtm3bNH78eEVGRur27duqWbOmJk+erJ49e+ZpjJSUFEVERFi0paamWrSFh4ebQ/7OnTvrypUr2rdvnzZu3Khbt27J19dXvXr10rBhw1S/fv2CmyAAAAAAAAAAAAXAYDKZTEVdxMOK3b8BAAAAAADuCJ1R1BXk3ahPwou6hDwLPBpe1CUUKvI14O4euDX5AQAAAAAAAABA3hDyAwAAAAAAAABgowj5AQAAAAAAAACwUYT8AAAAAAAAAADYKEJ+AAAAAAAAAABsFCE/AAAAAAAAAAA2ipAfAAAAAAAAAAAbRcgPAAAAAAAAAICNIuQHAAAAAAAAAMBGEfIDAAAAAAAAAGCjCPkBAAAAAAAAALBRhPwAAAAAAAAAANgoQn4AAAAAAAAAAGwUIT8AAAAAAAAAADaKkB8AAAAAAAAAABtFyA8AAAAAAAAAgI0i5AcAAABwX/To0UP29vYyGAxyc3PTwoULc+z77bffqkyZMub+Xbt2zfeYJ06c0JNPPilHR0cZDAbZ29urVq1aOn36dJ7qnTNnjgICAuTs7KwGDRro119/zbHvoUOH1K1bNwUEBMhgMGjGjBn5HvPy5csaMmSIqlatqscee0z+/v564403lJSUlKd6AQAA8Ggi5AcAAABQ6IYOHaqlS5cqJCRE3377rcqVK6cBAwbo0KFD2fa/cuWKSpcurddee012dtn/t+VuY+7fv1+XLl3SyJEjFRUVpcmTJ+vw4cNq3LjxXeuNjIxUWFiYxo8fr927d6tWrVoKDg7WhQsXsu1/7do1VaxYUf/+97/l6+tr1ZhxcXGKi4vT1KlTdfDgQS1atEjr1q3TgAED7lovAAAAHl0Gk8lkKuoiHlbJycny9PRUUlKSPDw8irocAAAAoMi4ubmpYsWK2r9/vyQpLS1NTk5Oat26tdatW5frufb29urUqZNWrFhxz2OGhYXpww8/1PXr1+Xs7JzjNRs0aKB69epp9uzZkqSMjAyVK1dOQ4YM0ahRo3KtNyAgQMOGDdOwYcPuecylS5eqd+/eSk1Nlb29fa7XBYAHXeiMoq4g70Z9El7UJeRZ4NHwoi6hUJGvAXfHnfwAAAAAClVKSopSU1PVoUMHc5u9vb0CAgK0b9+++zrmpUuXZDAYcg34b926pd9++02tWrUyt9nZ2alVq1aKjo62ql5rx8wMNAj4AQAAkBNCfgAAAACF6tixY5KkChUqWLSXLFlSV69evW9jHj16VF9++aUaNWqU69iJiYlKT0+Xj4+PRbuPj48SEhKsqteaMRMTE/Xee+9p4MCBVl0TAAAAjwZCfgAAAAAPvbNnzyooKEjFixfXxo0bi7qcu0pOTlaHDh1UvXp1hYeHF3U5AAAAeIAR8gMAAAAoVFWqVJEknTx50qL90qVLcnd3L/Qx4+Li9Pjjj8vR0VExMTFycXHJdWwvLy8ZjUadP3/eov38+fM5bqp7N/kZ8+rVq2rbtq3c3d21YsUKOTg4WHVNAAAAPBoI+QEAAAAUKjc3N7m6umrNmjXmtrS0NMXGxqpWrVqFOubZs2dVtWpVGY1GHTt2TMWKFbvr2I6OjgoKClJUVJS5LSMjQ1FRUXdd6udex0xOTlabNm3k6OioVatW5bp3AAAAACAR8gMAAAC4DwYMGKADBw4oNDRUq1evVs2aNWUymTR16lRJUmBgoEXYnZKSosjISEVGRspkMuns2bOKjIy0CMnvNubZs2dVrVo1paWlafny5YqLi9P+/fu1f/9+3bp1K9d6w8LCNG/ePEVEROjw4cN69dVXlZqaqn79+kmSQkJCNHr0aHP/W7duae/evdq7d69u3bqlc+fOae/evTp+/Hiex8wM+FNTU7VgwQIlJycrISFBCQkJSk9Pv8e/AQAAADysCPkBAIWmR48esre3l8FgkJubmxYuXJhr/7CwMDk5OclgMMjZ2VkTJkywOH7w4EFVqlRJRqNRBoNBXl5eWdZV7t27t4oVKyaDwSCDwaBTp07lqdY5c+YoICBAzs7OatCggX799ddc+y9dulTVqlWTs7OzatasqbVr11ocP3/+vPr27avSpUvLxcVFbdu2VUxMjEWfzz77TM2bN5eHh4cMBoOuXLmSp1oBwBbNnDlTL7zwghYuXKhOnTrpzJkzmjdvnmrUqCHpzjI7Fy9eNPffu3evevXqpV69eikjI0O7du1Sr1691K1btzyP+c033yg1NVU3btzQs88+q1q1apm/7vZ9vmfPnpo6darGjRun2rVra+/evVq3bp1549zTp08rPj7e3D8uLk516tRRnTp1FB8fr6lTp6pOnTp65ZVX8jzm7t279csvv+jAgQOqVKmS/Pz8zF9nzpy5x78BAAAAPKwMJpPJVNRFPKySk5Pl6emppKQkeXh4FHU5AHBfDR06VB999JH69eunLl26aOTIkTp69KgOHDigJ554Ikv/Tz/9VIMHD1b79u316quvavLkydq2bZuWL1+url27KiMjQ56enrKzs9NHH30kb29vhYWFKSYmRnFxcfL29pYkde3aVdevX5ckrV+/XrGxsSpfvnyutUZGRiokJERz585VgwYNNGPGDC1dulRHjx41j/tX27dvV9OmTTVp0iR17NhRX331lSZPnqzdu3erRo0aMplMaty4sRwcHDRt2jR5eHho+vTpWrdunX7//Xe5urpKkmbMmKEbN25IkkaPHq0///wzT8tIAAAAALYodEZRV5B3oz4JL+oS8izwaHhRl1CoyNeAuyPkL0R8EwLwKHNzc1PFihW1f/9+SXfWSXZyclLr1q21bt26LP39/f118+ZNiw0J3dzc5O/vr99//13r169X27ZttXLlSnXu3Nk8pqOjo/r06aOIiAiL8WbMmKE333wzTyF/gwYNVK9ePc2ePVvSnTWSy5UrpyFDhmjUqFFZ+vfs2VOpqalavXq1ua1hw4aqXbu25s6dq2PHjqlq1ao6ePCg+Q2NjIwM+fr6auLEiRZ3dUrS5s2b1aJFC0J+AAAAPNQI+QsHIT8AlusBABS4lJQUpaamqkOHDuY2e3t7BQQEaN++fdmeExcXp8aNG1u0BQUF6eTJk+YxJcnd3d1iTDs7O/38889W13rr1i399ttvatWqlbnNzs5OrVq1UnR0dLbnREdHW/SXpODgYHP/mzdvSpLFZol2dnZycnLStm3brK4VAAAAAADg7wj5AQAF7tixY5KkChUqWLSXLFlSV69ezfac9PR0lS1b1qLNz8/PHJi3a9dORqNRL7/8sk6ePKmUlBS1a9dO6enp97SWfWJiotLT083rIWfy8fFRQkJCtuckJCTk2r9atWry9/c3L8Fz69YtTZ48WWfPnrVYvxkAAAAAAOBeEfIDAGyCi4uLFi1apMTERFWsWFHu7u7auXOnSpUqJYPBUNTlWXBwcNDy5ct17NgxlShRQi4uLtq0aZPatWsnOzt+9AIAAAAAgIJD0gAAKHBVqlSRJPNSO5kuXbpksdzOXxmNRp09e9aiLT4+Xk5OTubHvXv31vXr13Xq1Cn9/vvvSkxM1LVr1+Tr62t1rV5eXjIajRZ7AUjS+fPncxzX19f3rv2DgoK0d+9eXblyRfHx8Vq3bp0uXbqkihUrWl0rAAAAAADA39kXdQEAgIePm5ubXF1dtWbNGk2aNEnSnU1yY2Nj1bp162zPKV26tLZv327Rtnv37ixL/kh3NumVpI0bNyo1NVUvvfSS1bU6OjoqKChIUVFR6tKli6Q7m+RGRUXp9ddfz/acRo0aKSoqSsOGDTO3bdy4UY0aNcrS19PTU5IUExOjXbt26b333rO6VgCwJba0uaLEBosAAACwXdzJDwAoFAMGDNCBAwcUGhqq1atXq2bNmjKZTJo6daokKTAw0CIUHzNmjC5cuKBOnTpp7dq1at68uVJSUvT++++b+4SFhWnGjBnavHmz3n77bbVr106lS5fW6NGjzX3279+vyMhI7dmzR5L03XffKTIyUidOnMix1rCwMM2bN08RERE6fPiwXn31VaWmpqpfv36SpJCQEItrDB06VOvWrdO0adN05MgRhYeHa9euXRZvCixdulSbN2/WH3/8oW+//VatW7dWly5d1KZNG3OfhIQE7d27V8ePH5ckHThwQHv37tXly5etes4BAAAAAMCjh5AfAFAoZs6cqRdeeEELFy5Up06ddObMGc2bN081atSQdGfpnosXL5r7Dxo0SG+++abWr1+vDh06aMeOHQoPD1fXrl3NfU6dOqW33npLLVq00AcffKBGjRrp6NGjFtd944031KtXL33xxReSpCFDhqhXr17mTxRkp2fPnpo6darGjRun2rVra+/evVq3bp15c93Tp09bbJjbuHFjffXVV/rss89Uq1YtffPNN1q5cqV5btKdpYb69OmjatWq6Y033lCfPn303//+1+K6c+fOVZ06dRQaGipJatq0qerUqaNVq1bl67kGAMCW9OjRQ/b29jIYDHJzc9PChQtz7R8WFiYnJycZDAY5OztrwoQJFscTEhL05JNPymg0ymAwyMnJKcun/Hr37q1ixYrJYDDIYDDo1KlTea53zpw5CggIkLOzsxo0aKBff/011/5Lly5VtWrV5OzsrJo1a2rt2rUWx1NSUvT666+rbNmyeuyxx1S9enXNnTvXos9nn32m5s2by8PDQwaDQVeuXMlzvQAA4NFjMJlMpqIu4mGVnJwsT09PJSUlycPDo6jLAQAAAO4bluspPLa8XM/QoUP10UcfqV+/furSpYtGjhypo0eP6sCBA3riiSey9P/00081ePBgtW/fXq+++qomT56sbdu2afny5eYbAR5//HEdP35cU6ZMUd26dTV37lx9+eWXevvtt82fCOzatauuX78uSVq/fr1iY2NVvnz5u9YbGRmpkJAQzZ07Vw0aNNCMGTO0dOlSHT16VN7e3ln6b9++XU2bNtWkSZPUsWNHffXVV5o8ebJ2795tvhlg4MCB+vHHHzV//nwFBARow4YNeu2117R8+XI999xzkqQZM2boxo0bkqTRo0frzz//VLFixfL/hAMPGFv62cDPhQcH+Rpwd4T8hYhvQgAAAHhU2VKQIxHm3C9ubm6qWLGi9u/fL+nOnj1OTk5q3bq11q1bl6W/v7+/bt68abHhvZubm/z9/fX7779LkpydnfX0008rKirK3MfFxUVPPfWUtm3bZjHejBkz9Oabb+Y55G/QoIHq1aun2bNnS7qzb0+5cuU0ZMgQjRo1Kkv/nj17KjU1VatXrza3NWzYULVr1zbfrV+jRg317NlT77zzjrlPUFCQ2rVrp3/9618W423evFktWrQg5MdDw5Z+NvBz4cFBvgbcHcv1AAAAAAAKXUpKilJTU9WhQwdzm729vQICArRv375sz4mLi1Pjxo0t2oKCgnTy5Enz4woVKmjHjh3atWuXMjIyNH36dF2/fl09e/a8p3pv3bql3377Ta1atTK32dnZqVWrVoqOjs72nOjoaIv+khQcHGzRv3Hjxlq1apXOnTsnk8mkTZs26dixYxb79gAAAOSHfVEXAAAAAAB4+B07dkzSnVD+r0qWLGlxp/5fpaenq2zZshZtfn5+unnzpvnxL7/8ojp16qhevXrmttDQUA0ZMuSe6k1MTFR6erp5j55MPj4+OnLkSLbnJCQkZNs/ISHB/HjWrFkaOHCgypYtK3t7e9nZ2WnevHlq2rTpPdULAAAeXYT8AAAAAACb9dJLL+nMmTMaO3asatWqpcjISM2bN0+VKlXSP//5z6IuL4tZs2Zpx44dWrVqlcqXL6+tW7fq//7v/1S6dOksnwIAAADIC0J+AAAAAEChq1KliiRZLLUjSZcuXZK7u3u25xiNRp09e9aiLT4+Xk5OTpKky5cva82aNRo/frzCw8MlSS+88IKqVq2qDz744J5Cfi8vLxmNxiyfMjh//rx8fX2zPcfX1zfX/tevX9fbb7+tFStWmJctevLJJ7V3715NnTqVkB8AAFiFNfkBAAAAAIXOzc1Nrq6uWrNmjbktLS1NsbGxqlWrVrbnlC5dWtu3b7do2717t3nJn2vXrkm6s1b+X9nZ2clkMt1TvY6OjgoKCrLY0DcjI0NRUVFq1KhRtuc0atTIor8kbdy40dz/9u3bun37dpZ6jUajMjIy7qleAADw6OJOfgBAgQidUdQV5M+oT8KLuoQ8CzwaXtQlAABQIAYMGKCPPvpIoaGh6ty5s0aMGCGTyaSpU6dKkgIDA+Xt7W3eqHbMmDEaPHiwOnXqpFdffVUffPCBUlJS9P7770uSypYtK09PT02cOFEeHh566qmn9MUXX+jIkSPq1auX+br79+/X4cOHtWfPHknSd999p1KlSqlu3boKDAzMsd6wsDC9/PLLqlu3rurXr68ZM2YoNTVV/fr1kySFhISoTJkymjRpkiRp6NChatasmaZNm6YOHTro66+/1q5du/TZZ59Jkjw8PNSsWTONGDFCjz32mMqXL68tW7boiy++0PTp083XTUhIUEJCgo4fPy5JOnDggNzd3eXv768SJUoUyN8FAAB4eBDyAwAAAADui5kzZyouLk4LFy7U/Pnz5erqqnnz5qlGjRqS7izdYzAYzP0HDRqko0ePavbs2Vq9erWcnJwUHh6url27mvts3bpV3bp10/Dhw5WRkSFHR0c999xz+vLLL8193njjDW3ZssX8OHNT3gEDBmj+/Pk51tuzZ09dvHhR48aNU0JCgmrXrq1169aZN9c9ffq0xV35jRs31ldffaWxY8fq7bffVuXKlbVy5Urz/CTp66+/1ujRo/WPf/xDly9fVvny5fX+++9r8ODB5j5z587VhAkTzI8zN+VduHCh+vbtm7cnGwAAPDIMpnv9DCNylJycLE9PTyUlJcnDw6OoywGAQsWd/IWHO/kB2CJ+LhQefi4AsFW29LOBnwsPDvI14O5Ykx8AAAAAAAAAABtFyA8AAAAAAAAAgI0i5AcAAAAAAAAAwEYR8gMAAAAAAAAAYKMI+QEAAAAAAAAAsFH2RV0AAAAAAODhFDqjqCvIn1GfhBd1CXkWeDS8qEsAAAAPCO7kBwAAAAAAAADARhHyAwAAAAAAAABgowj5AQAAAAAAAACwUYT8AAAAAAAAAADYKEJ+AAAAAAAAAABsFCE/ClyPHj1kb28vg8EgNzc3LVy4MNf+YWFhcnJyksFgkLOzsyZMmGBxvFKlSjIYDBZfXl5e5uMzZszIcjzzKyIiItdrz5kzRwEBAXJ2dlaDBg3066+/5tp/6dKlqlatmpydnVWzZk2tXbvW4njfvn2z1NC2bVvz8c2bN+dY686dO3O9NgAAtsyWfj+Q+B0BAAAAgO0g5EeBGjp0qJYuXaqQkBB9++23KleunAYMGKBDhw5l2//TTz/Vhx9+qFatWum7775TvXr1FB4erhUrVlj08/Ly0r59+8xff/3P7iuvvGJxbN++fapatars7e3Vp0+fHGuNjIxUWFiYxo8fr927d6tWrVoKDg7WhQsXsu2/fft2vfjiixowYID27NmjLl26qEuXLjp48KBFv7Zt2yo+Pt789d///td8rHHjxhbH4uPj9corr6hChQqqW7fuXZ9fAABskS39fiDxOwIAAAAA22IwmUymoi7iYZWcnCxPT08lJSXJw8OjqMu5L9zc3FSxYkXt379fkpSWliYnJye1bt1a69aty9Lf399fN2/e1Pnz5y3G8Pf31++//y7pzp16qampio+Pz1MN165dk5ubm1q0aKGoqKgc+zVo0ED16tXT7NmzJUkZGRkqV66chgwZolGjRmXp37NnT6Wmpmr16tXmtoYNG6p27dqaO3eupDt36V25ckUrV67MU623b99WmTJlNGTIEL3zzjt5Ogd4UIXOKOoK8mfUJ+FFXUKeBR4NL+oSgHtiS78fSPyOUFD4uVB4bOnnAq+DwmNLrwMgky19T+D7wYPjUczXgPziTn4UmJSUFKWmpqpDhw7mNnt7ewUEBGjfvn3ZnhMXF6fGjRtbtAUFBenkyZMWbQkJCbKzs5Ojo6OeeOIJxcTE5FjH+PHjZTKZ9O9//zvHPrdu3dJvv/2mVq1amdvs7OzUqlUrRUdHZ3tOdHS0RX9JCg4OztJ/8+bN8vb2VtWqVfXqq6/q0qVLOdaxatUqXbp0Sf369cuxDwAAtsyWfj+Q+B0BAAAAgO2xL+oC8PA4duyYJKlChQoW7SVLlrS4E++v0tPTVbZsWYs2Pz8/3bx50/y4ffv2cnd311NPPaVdu3Zp+vTpqlOnji5fvixHR8csY0ZERKhUqVKqV69ejrUmJiYqPT1dPj4+Fu0+Pj46cuRItuckJCRk2z8hIcH8uG3btnr++edVoUIFnThxQm+//bbatWun6OhoGY3GLGMuWLBAwcHBWZ4DAAAeFrb0+4HE7wgAAAAAbA8hPx54H330kfnP3bp1U3BwsFq0aKGZM2dqxIgRFn137typixcv6q233rrfZUqSevXqZf5zzZo19eSTTyowMFCbN2/Ws88+a9H37NmzWr9+vZYsWXK/ywQAwObZ0u8HEr8jAAAAACg8LNeDAlOlShVJyvJR+kuXLsnd3T3bc4xGo86ePWvRFh8fLycnpxyv07x5cxkMBu3duzfLsVGjRslgMGjChAm51url5SWj0ZjlDsLz58/L19c323N8fX3z1V+SKlasKC8vLx0/fjzLsYULF6pkyZJ67rnncq0VAABbZku/H0j8jgAAAADA9hDyo8C4ubnJ1dVVa9asMbelpaUpNjZWtWrVyvac0qVLa/v27RZtu3fvzvKR/r/auXOnTCaTAgICLNozMjK0detWPfXUU3Jxccm1VkdHRwUFBVlsvJeRkaGoqCg1atQo23MaNWqUZaO+jRs35thfunMn3qVLl+Tn52fRbjKZtHDhQoWEhMjBwSHXWgEAsGW29PuBxO8IAAAAAGwPIT8K1IABA3TgwAGFhoZq9erVqlmzpkwmk6ZOnSpJCgwMtPgP75gxY3ThwgV16tRJa9euVfPmzZWSkqL3339f0p01buvVq6f58+dr27ZtmjJlipo1ayYHBweNHDnS4trTpk1TWlqa3n333TzVGhYWpnnz5ikiIkKHDx/Wq6++qtTUVPMGdyEhIRo9erS5/9ChQ7Vu3TpNmzZNR44cUXh4uHbt2qXXX39d0p2NBUeMGKEdO3YoNjZWUVFR6ty5sypVqqTg4GCLa//44486efKkXnnllXw+wwAA2B5b+v1A4ncEAAAAALaFNflRoGbOnKm4uDgtXLhQ8+fPl6urq+bNm6caNWpIuvPRfIPBYO4/aNAgHT16VLNnz9bq1avl5OSk8PBwde3aVdKdu+lOnjypgQMHymQyyWg0qnLlylq6dKk8PDwsrj1nzhy5u7urffv2eaq1Z8+eunjxosaNG6eEhATVrl1b69atM2+cd/r0adnZ/e99sMaNG+urr77S2LFj9fbbb6ty5cpauXKleW5Go1H79+9XRESErly5otKlS6tNmzZ67733siwvsGDBAjVu3FjVqlXL5zMMAIDtsaXfDyR+RwAAAABgWwwmk8lU1EU8rJKTk+Xp6amkpKQs/+EEgIdN6IyiriB/Rn0SXtQl5Fng0fCiLuGe9OjRQ8uXL1d6erpcXV01a9Ys8x3R2QkLC9OcOXN069YtOTk5afTo0Ro/fny2fatXr67Dhw+rS5cuWrFihcWx8PBwTZ06VampqTIYDPLx8VF8fHyutc6ZM0dTpkxRQkKCatWqpVmzZql+/fo59l+6dKneeecdxcbGqnLlypo8eXKOYfLgwYP16aef6sMPP9SwYcMsjq1Zs0bvvvuu9u/fL2dnZzVr1kwrV67MtVbgQcfPhcJjSz8XeB0UHlt6HQCZbOl7At8PHhzka8DdsVwPAAAoNEOHDtXSpUsVEhKib7/9VuXKldOAAQN06NChbPtnhuCtWrXSd999p3r16ik8PDxLgC9JI0eO1MmTJy3uqM40YsQIvfvuu3ruuee0bt06rVy5Ur169cq11sjISIWFhWn8+PHavXu3atWqpeDgYF24cCHb/tu3b9eLL76oAQMGaM+ePerSpYu6dOmigwcPZum7YsUK7dixQ6VLl85ybNmyZerTp4/69eunffv26eeff9ZLL72Ua60AAAAAAGQi5AcAAIVmwYIFqlmzpj7//HM999xzOnDggAwGg4YPH55t//fff1/e3t5as2aNOnbsqJ9++kmurq4aM2aMRb9du3Zp6tSp+vrrry2WeZGkGzduaPr06QoJCdFXX32l4OBgPffcc/rwww9zrXX69OkKDQ1Vv379VL16dc2dO1cuLi76/PPPs+0/c+ZMtW3bViNGjNDjjz+u9957T0899ZRmz55t0e/cuXMaMmSIvvzyyywbqaalpWno0KGaMmWKBg8erCpVqqh69erq0aNHrrUCAAAAAJCJkB8AABSKlJQUpaamqkOHDuY2e3t7BQQEaN++fdmeExcXp8aNG1u0BQUF6eTJk+bHaWlpat26tTp37qzOnTtnGeO///2vMjIyZGdnJxcXFxmNRpUqVSrbTwNkunXrln777Te1atXK3GZnZ6dWrVopOjo623Oio6Mt+ktScHCwRf+MjAz16dNHI0aM0BNPPJFljN27d+vcuXOys7NTnTp15Ofnp3bt2mX7aQAAAAAAALLDxru4J6ynV3ge9jX1ADz8jh07JkmqUKGCRXvJkiV1/vz5bM9JT09X2bJlLdr8/Px08+ZN8+MOHTrIzs5O33zzTbZj7NmzR5L0xRdf6M0331StWrU0duxYdevWTTExMQoMDMxyTmJiotLT080bq2by8fHRkSNHsr1OQkJCtv0TEhLMjydPnix7e3u98cYb2Y7xxx9/SLqzf8D06dMVEBCgadOmqXnz5jp27JhKlCiR7XkPOlv6/UCyrd8R+P0AwMOiKPbsad26tXbs2KGUlBRJUn62KCyKfXvef/99rVmzRnv37pWjo6OuXLmS53oBAI8W7uQHAAA2Y/Hixfrhhx+0cePGbNfil+68USBJvXr10pQpU9S7d2/t379fkjRu3Lj7Vutvv/2mmTNnatGiRVmWFMqUkZEhSRozZoy6deumoKAgLVy4UAaDQUuXLr1vtQIAcD8V1Z49t27d0rPPPqugoKB81VtU+/bcunVL3bt316uvvpqvegEAjx5CfgAAUCiqVKkiSRZL7UjSpUuX5O7unu05RqNRZ8+etWiLj4+Xk5OTpDub1GZkZCgoKEgGg0EGg0Hp6elauXKl7O3vfEAx8079vy774+HhIRcXF8XGxmZ7XS8vLxmNxiyfMDh//rx8fX2zPcfX1zfX/j/99JMuXLggf39/2dvby97eXqdOndLw4cMVEBAg6c6nFKQ7dxxmcnJyUsWKFXX69OlsrwsAgK0rij17JGnLli1auXKlateuna96i2LfHkmaMGGC3nzzTdWsWTNf9QIAHj2E/AAAoFC4ubnJ1dVVa9asMbelpaUpNjZWtWrVyvac0qVLa/v27RZtu3fvNi/588EHH2j58uUWX3Z2dmrQoIG+++47STJvWvvrr7+ax7h27ZquXbuWZemgTI6OjgoKClJUVJS5LSMjQ1FRUWrUqFG25zRq1MiivyRt3LjR3L9Pnz7av3+/9u7da/4qXbq0RowYofXr10u6s9+Ak5OTjh49ah7j9u3bio2NVfny5bO9LgAAtqyo9uyxVlHt2wMAQH6wJj8AACg0AwYM0EcffaTQ0FB17txZI0aMkMlk0tSpUyXdueve29vb/J/eMWPGaPDgwerUqZNeffVVffDBB0pJSdH7778vSapcubIqV65scQ2DwWDesFaSypYtq9q1a+s///mPqlatqtq1a5vXt33vvfdyrDUsLEwvv/yy6tatq/r162vGjBlKTU01rw8cEhKiMmXKaNKkSZLuLDXQrFkzTZs2TR06dNDXX3+tXbt26bPPPpN0Z++BkiVLWlzDwcFBvr6+qlq1qqQ7nzAYPHiwxo8fr3Llyql8+fKaMmWKJKl79+7WPekAADzAimrPHmsV1b49AADkByE/AAAoNDNnzlRcXJwWLlyo+fPny9XVVfPmzVONGjUk3Vm6568fpx80aJCOHj2q2bNna/Xq1XJyclJ4eLi6du2ar+v+/PPPatq0qcaMGSOTyaQSJUpoxYoVOd7JL0k9e/bUxYsXNW7cOCUkJKh27dpat26d+T/pp0+ftljft3Hjxvrqq680duxYvf3226pcubJWrlxpnlteTZkyRfb29urTp4+uX7+uBg0a6Mcff1Tx4sXzNQ4AAI+qzD17du7cmeOePQ+SzH17du/eneO+PQAA5AchPwAAKFS5bSB75cqVLG3Tp0/X9OnT8zx+WlpaljYXFxft2rUrz2Nkev311/X6669ne2zz5s1Z2rp3756vO+6z2xPAwcFBU6dONX+6AQCAh1lh79nzV5l79mT3u0JeFfa+PZnS09M1fPhwzZgxI8c9hAAAyMmD/xY3AJvUo0cP2dvby2AwyM3NTQsXLsy1f1hYmJycnGQwGOTs7KwJEyZYHG/evLn5uJ2dnUqUKKEFCxZY9PH19TVf02g0qmLFitq9e/dda50zZ44CAgLk7OysBg0aWKzjnZ2lS5eqWrVqcnZ2Vs2aNbV27VqL4+Hh4apWrZpcXV1VvHhxtWrVSr/88otFn+eee07+/v5ydnaWn5+f+vTpo7i4uLvWCgAAANiyotqzx1pFtW8PAAD5QcgPoMANHTpUS5cuVUhIiL799luVK1dOAwYM0KFDh7Lt/+mnn+rDDz9Uq1at9N1336levXoKDw/XihUrzH2eeOIJvffee9q0aZNWrFihUqVK6ZVXXtHhw4fNfRo2bKi5c+dq27Zt+vjjj3Xx4kU1a9Ys11ojIyMVFham8ePHa/fu3apVq5aCg4N14cKFbPtv375dL774ogYMGKA9e/aoS5cu6tKliw4ePGjuU6VKFc2ePVsHDhzQtm3bFBAQoDZt2ujixYvmPi1atNCSJUt09OhRLVu2TCdOnNALL7yQp+cXAAAAsGUDBgzQgQMHFBoaqtWrV6tmzZpZ9uz5a4A+ZswYXbhwQZ06ddLatWvVvHnzLHv2dO3a1eLr73v2SHc2xI2MjNQff/wh6c7/BSIjIy3Wys9OWFiY5s2bp4iICB0+fFivvvpqln17Ro8ebe4/dOhQrVu3TtOmTdORI0cUHh6uXbt2mT8tWLJkSdWoUcPi6+/79kh3lgrcu3evTp8+rfT0dPMbAikpKffy9AMAHkIs1wOgwC1YsEA1a9bU559/Lklq3769nJycNHz4cK1bty5L//fff1/e3t7mu3k6duwoNzc3jRkzxrwO95w5cyzOCQoKUrly5bR69Wo9/vjjku58HDfT008/rdOnT2vixIm6du2aXFxcsq11+vTpCg0NNf+CPnfuXK1Zs0aff/65Ro0alaX/zJkz1bZtW40YMULSnU08N27cqNmzZ2vu3LmSpJdeeinLNRYsWKD9+/fr2WeflSS9+eab5uPly5fXqFGj1KVLF92+fVsODg7Z1goAAAA8DIpqz54+ffroxIkT5se9evWSJH344YcaNmxYjucV1b4948aNU0REhPlxnTp1JEmbNm1S8+bN8zUWAODhRsgPoEClpKQoNTVVHTp0MLfZ29srICBA+/bty/acuLg4derUyaItKCgox2VzUlJSNHjwYEl3lr3JzokTJ/Sf//xH7u7uOQb8t27d0m+//WZx142dnZ1atWql6OjobM+Jjo5WWFiYRVtwcLDFGwx/v8Znn30mT0/PHD9+fPnyZX355Zdq3LgxAT9sXuiMoq4gf0Z9El7UJeRZ4NHwoi4BAIACUxR79hw/fjzP5/9dUezbs2jRIi1atCjPYwAAHl0s1wOgQB07dkySzOtjZipZsqSuXr2a7Tnp6ekqW7asRZufn59u3rxp0TZu3DgZDAa5u7vr+++/16JFiyw+zipJDRo0kMFgUKVKlfTnn3/qt99+y7HWxMREpaenm+/AyeTj45PjR3YTEhLy1H/16tVyc3OTs7OzPvzwQ23cuFFeXl4WfUaOHClXV1eVLFlSp0+f1rfffptjrQAAAAAAAEB2CPkB2IzXX39dP/zwg+bNm6fAwEC98sorWdb5/+KLL7Ru3TpNmjRJBoNBTz/9tDIyMu57rS1atNDevXu1fft2tW3bVj169Miyzv+IESO0Z88ebdiwQUajUSEhITKZTPe9VgAAAAAAANguQn4ABapKlSqSpJMnT1q0X7p0Se7u7tmeYzQadfbsWYu2+Ph4OTk5WbR5e3vr2Wef1SuvvKJjx47JYDBkWTqnatWqCg4O1qhRoxQVFaWLFy9q/vz52V7Xy8tLRqNR58+ft2g/f/68fH19sz3H19c3T/1dXV1VqVIlNWzYUAsWLJC9vb0WLFiQ5fpVqlRR69at9fXXX2vt2rXasWNHttcFAAAAAAAAssOa/AAKlJubm1xdXbVmzRpNmjRJ0p31MGNjY9W6detszyldurS2b99u0bZ79+4sS/78nclkyrKkz19lrsN57dq1bI87OjoqKChIUVFR6tKliyQpIyNDUVFROa632ahRI0VFRVlszLVx40Y1atQo11ozMjJyrTXz0wa59QEAAABslS3t28OePQAAW8Od/AAK3IABA3TgwAGFhoZq9erVqlmzpkwmk6ZOnSpJCgwMtAjFx4wZowsXLqhTp05au3atmjdvrpSUFL3//vuSpAsXLqhx48aaP3++fv75Zy1evFhVqlRRWlqa3nzzTUnSggUL1L17d0VGRurnn3/WtGnTFBwcLHt7e/Xv3z/HWsPCwjRv3jxFRETo8P9r777Do6rWNg7/ZtIgBAhNeq8SuqEYuhSDdBCQYkU6dgVFBKxYsSHlgBQBRURAmvSS0AMiiEBAAgECoYUECElIZtb3x5wZiRT1O0km5bmv61wnmb0T3nGe7PKuvdc+dIghQ4YQHx/Pk08+CcBjjz2W6sG8zz33HKtWreKTTz7h8OHDjBs3jt27d7sGBeLj4xk1ahQ7duwgMjKSPXv28NRTTxEVFeV68NbOnTuZOHEiv/76K5GRkWzYsIHevXvf8t9FRERERERERETk7+hKfhFJc59//jlnzpxh5syZTJ8+nTx58jBt2jRq1KgBOKbusVgsrvUHDRpEeHg4EydOZPny5fj4+DBu3Di6du0KOK64P3HiBIMGDcJut2O1WrnnnnuYNWsWnTt3BsDf359169bx448/YozB09OTKlWqMHv2bPLly3fHWnv16sWFCxcYM2YM0dHR1KlTh1WrVrkernvy5Ems1j/HQ4OCgvj2228ZPXo0o0aNonLlyixZssT13jw8PDh8+DCzZ8/m4sWLFCpUiPr16xMaGkpAQAAAvr6+LFq0iLFjxxIfH0/x4sUJDg5m9OjRt0xRJCIiIiIiIiIicjdq8otIuvjhhx/uuCw2NvaW1yZMmMCECRNuu76/vz9nzpy567/XvXt3unfv/q9qdBo+fPgdp+fZtGnTLa/16NHDdVX+X+XKlYtFixbd9d+rWbMmGzZs+Nd1ioiIiIiIiIiI/JWm6xERERERERERERERyaLU5BcRERERERERERERyaLU5BcRERERERERERERyaI0J7+I/M8GfObuCv65VyePc3cJ/0rF8HHuLkFERERERERERDIxXckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFqckvIiIiIiIiIiIiIpJFZdomf1hYGA899BD+/v7kyZOHRo0asWDBgn/1O5KSknjrrbeoXLkyuXLlokSJEgwcOJDz58/f8WfmzZtHgwYNyJMnDwUKFKBDhw788ssv/+vbERERERERERERERFJc5myyb9x40YaN27Mli1b6NmzJ4MHDyY6OppevXrxySef/KPfYbfb6dy5M2PHjqVw4cI8//zz3H///UyfPp3777+fCxcu3PIz7777Lv369eP8+fMMHjyYHj16EBISQlBQEFu3bk3rtykiIiIiIiIiIiIi8j/xdHcBf5WSksKAAQOwWq2EhIRQp04dAMaMGUODBg0YNWoUDz/8MGXLlr3r75k9ezarV6+md+/ezJs3D4vFAsCUKVMYMmQIo0ePZurUqa71jx49yrhx46hSpQq7du0if/78AAwdOpRGjRoxYMAADhw4gNWaKcdFRERERERERERERCQHynQd6w0bNnDs2DH69OnjavAD5M+fn1GjRnHjxg1mz579t79n2rRpAIwfP97V4AcYNGgQFSpUYN68eSQkJLhenzlzJikpKbz++uuuBj9AnTp16N27N4cOHWLLli1p8A5FRERERERERERERNJGpruSf9OmTQC0bdv2lmUPPvggAJs3b77r70hMTGTnzp1UrVr1liv+LRYLbdq0YerUqezevZumTZv+o3931qxZbN68mWbNmt3x301KSiIpKcn1fVxcHAAxMTEkJycDYLVa8fDwwGazYbfbXes6X09JScEY43rdw8MDq9V6x9edv9fJ09PxkaakpPyj1728vLDb7dhstlT/jTw9Pe/4+s2130j0uuN/j8zmqi3p71fKRC5duuT6+n/9nCB9s6ccpB9nDrLCNiIr5QCyVhacOUivbTmk3TZCOUg/ly5dyjLHETcSM90h5l1ltRxk9PEe/P+2ETcSM931RHeVlXIQExOT4cd78P/bRtxI/B/fbAbLSjmIjY3NFOeE8M+yl5X2DVkpB1euXMkU54Twz7KXlY4Vs1oOMsM5YXpl7+rVqwCp1hGR1DLdXvbo0aMAVK5c+ZZlxYoVw8/Pz7XOnRw7dgy73X7b33Hz7z569KiryX/06FH8/PwoVqzYXde/m/Hjx/Pmm2/e8nr58uXv+nOSMb5xdwH/VuH33V1BtqQciFOWyoJykG6UAwHlQByyVA4KKQfpJUvloIBykF6yVA7yKwfpRTnIfK5evZpq9g0R+VOma/I7r36/0x9tvnz5XOv8L7/j5vWcX99zzz3/eP3bee2113jxxRdd39vtdmJiYihUqFCqKYMk4125coXSpUtz6tQp1+cpOY9yIE7KgoByIA7KgYByIA7KgYByIA7KQebivJq/RIkS7i5FJNPKdE3+rMzHxwcfH59Ur/n7+7unGLmtfPnyaQctyoG4KAsCyoE4KAcCyoE4KAcCyoE4KAeZh67gF7m7TDdRpvOP9k5XzV+5cuVv/7D/ye+4eT3n1/9mfRERERERERERERERd8t0Tf67zX8fHR3NtWvX7jjXvlOFChWwWq13nEP/dvP+V65cmWvXrhEdHf2P1hcRERERERERERERcbdM1+Rv3rw5AGvWrLll2erVq1Otcye5c+emQYMGhIeHExkZmWqZMYa1a9eSJ08eAgMD0/TflczLx8eHsWPH3jKdkuQsyoE4KQsCyoE4KAcCyoE4KAcCyoE4KAciktVYjDHG3UXcLCUlhapVqxIVFcWOHTuoU6cO4Jh6p0GDBpw4cYLw8HDKlSsHwNmzZ4mLi6N48eKpptOZOXMmTz31FL1792bevHmuB99OmTKFIUOGMHDgQKZOnepa/8iRIwQEBFChQgV27drl+l2//vorjRo1okKFChw4cACrNdONi4iIiIiIiIiIiIhIDpXpmvwAGzdu5MEHHyRXrlw88sgj5M2blx9//JHIyEg+/vhjXnrpJde6TzzxBLNnz2bmzJk88cQTrtftdjsPPfQQq1evplGjRjRv3pw//viDRYsWUa5cOXbu3EmRIkVS/bvvvvsuo0ePpmzZsnTv3p2rV68yf/58bty4wfr162ncuHFG/ScQEREREREREREREflbmfKy9JYtW7JlyxYaN27M999/z+TJkylatCjz589P1eC/G6vVyk8//cS4ceO4cOECn376KVu3bqV///5s3779lgY/wOuvv87cuXMpUqQIkydPZsGCBTRt2pRt27apwS8iIiIiIiIiIiIimU6mvJJfRERERERERERERET+Xqa8kl9ERERERERERERERP6emvwiIiIiIiIiIiIiIlmUmvwiIiIiIiIiIiIiIlmUmvwiIiIiIiIiIiJ/Ybfb0aMsRSQrUJNfREREREREREQEXE19YwxWqxWLxeLmikRE/p6a/CIiIv+Q3W53dwkikkloeyAiTrrKVyR7SU5OBmDUqFH07NmTS5cuubkiEZG/pya/iIjIXVy+fJlz584BYLU6dpvGGDX4RHIgbQ9ExCk8PJyTJ08C6CpfkWzG29sbgDlz5nDx4kVX0/9ONNAnIpmBp7sLEBERyWyuXr3Kd999x/fff09cXBxXr16lcOHCBAcH07VrV2rUqOE6oTfG6OQ+B9DnnHNpeyB/pc8557p48SKTJk1i0aJFXLx4kUuXLlGqVCnatm1L+/btadiwIYUKFQIcd/s4BwNFJGvYunUrx44do127dhw8eJAzZ84wbtw4ihUrBtx5+699gohkBhajIUcRjDFERUVRrFgxPD3/3diXTvSyD+VAwHF77qBBg5g1axb58uXj3nvv5eTJk5w9e9a1ToMGDXjhhRfo1q0bXl5ebqxW0ovdbuf333+ncuXK5MqVy/W687Dpbn/v2h5kH9oeCGh7IA43btygd+/e/PTTT1SoUIFq1aoRFRXF2bNniY6OBiAgIIABAwYwePBg15XAkn0ZY1xztkv20LJlSzZv3kyTJk2Ii4sjOjqamTNn8tBDD6Vaz7ltN8YwZ84crl27xpAhQ7S9FxG3UpNfBFi4cCHTp0+nS5cuBAYGUqFCBQoWLJhqnb+epMXHx5MnT56MLlXSkXIgALNmzWLAgAEMHDiQN998k3z58uHt7c3evXtZs2YNK1euJDQ0FIBOnTrx4YcfUqVKFTdXLWlt/vz5vPfee7Rv355GjRpRu3ZtypYtm+rv/+YTPIvFQlxcHPnz53dj1ZLWtD0Q0PZAHGbMmMGgQYN46aWXGDt2LLlz5wYc0/bs2LGDDRs2sGbNGs6dO0etWrX47LPPaNGihXuLljRns9nYsmULgYGBqc4B7HY7Fovlrk1e3d2RudntdlatWsWOHTtYtWoVu3fvxtvbm6CgIDp37kz9+vWpXLkyhQsXdn3OUVFRdO3aldOnT3PmzBk3vwMRyenU5BcB6tevz549e/D09KRkyZK0aNGC1q1bU6dOHcqWLYufn1+q9e12O5999hkRERF8+OGH+Pr6uqlySUvKgQA0a9YMYwyzZs2iYsWKpKSkpLqzIyUlhfXr1/Pxxx+zfv162rVrx7Rp0yhRooQbq5a0dv/997Nz5068vLzInTs3gYGBtGzZkkaNGlGzZk3uueeeVOvb7XbGjh3L3r17+e6778ibN6+bKpe0pO2BgLYH4tCyZUuSkpKYM2cOFStWJCkpCR8fH9dym83Grl27mDJlCnPmzKFhw4bMmTOHSpUqubFqSWvz5s3j+eefp02bNjRp0oQmTZpQo0aNVM17Z8MfHHf6xMTE3HLhkGRuX3/9NQMHDqR58+ZERUXxxx9/ULRoUZo1a8aDDz5I3bp1qVSpEj/99BODBw/mo48+YvDgwe4uW0RyODX5Jce7dOkSAQEBFCxYkO7duxMSEsIvv/xCUlIS9957L61bt6ZFixbUqFGDokWL4uvryx9//EFwcDAFChQgLCzM3W9B0oByIABXrlyhQYMGVKhQgRUrVrhet1gsrgdr3nwS9/LLLzNhwgTeeecdRo0aleH1SvqIiYmhdu3a5M+fnxdeeIF169axefNmoqOjKV68OEFBQTRv3pwGDRpQuXJlChQowIkTJ3jwwQfx9fVl79697n4Lkga0PRDQ9kAcrl69SvPmzfHz8yMkJARIfffGX6dsmTJlCkOHDmXw4MFMmjTJXWVLOmjWrBlbtmzBy8uL5ORkqlSpQtOmTWnRogVBQUGUL18+1fo2m43nnnuOjRs3snXrVvz9/d1TuPwr69atIyQkhIcffhgfHx9CQ0NZu3YtISEhnD9/ngoVKlCmTBl+/fVXfHx8+OOPP3TBl4i4nR68KznewYMHiY+Pp0OHDrz99tscOnSIAwcOsGXLFjZt2sSXX37J1KlTCQwMpHXr1rRt25bQ0FAiIiL4/PPP3V2+pBHlQADy5s1LpUqV+PXXX7ly5UqqqRZuPnm32WxYrVbee+89fvrpJ0JDQ4mNjdWJWzYRHh5OXFwczZo1o3///rRv354TJ06wfft2fv75Z9asWcPixYupVKkSTZo0ITg4mEOHDnH06FFtD7IRbQ8EtD0QRzM/b9683HvvvSxbtowjR45QpUqVVFdqO7+22WwYYxg8eDBff/01+/bt4/z587fc7SFZ0+XLlzl9+jQ1atRgwoQJrFq1imXLlvH1118zZ84catWqRfPmzWnZsiX16tWjWLFinDlzhg0bNuDh4aH9QhbSunVrWrdu7fq+cuXKdO7cmSNHjrBz5042bNhAWFgYFSpUYMyYMWrwi0imoCv5JcdbsWIFHTt25Msvv2TYsGGu1+Pj4/njjz/Ys2cPmzZtYsuWLURGRlKwYEG8vb05e/YscXFxugU7m1AOxGnq1KkMGTKEhx56iHHjxlG7du1bHqZps9nw8PDAGEP79u2JiIjg119/TfVARsm6Nm3aRLdu3XjzzTd55plnXK8nJydz/vx5Dh8+zKZNm1i9ejX79u3Dw8ODXLlyERsbq+1BNqPtgWh7IE4LFiygb9++1K5dm/Hjx9O4ceNUjT1jDHa73bU96NGjB3v27OHgwYOu+fslawsLC6NNmza0atWKH3/8kcTERC5dusTOnTtZsmQJq1ev5sKFC+TPn5/AwECCg4O5ePEiH3zwAV988QXDhw9391uQu3A+M+Ho0aOsWLGCgIAAmjdvfstDtBMTE4mNjaVAgQIkJSWRL18+N1UsIpKamvyS48XExDB9+nSaNWtGo0aNbnmwKjimcjl48CC//fYbCxYsICQkhA4dOrB06VI3VS1pTTkQp8TERHr16sWyZcuoX78+AwYMoHXr1pQoUeKWg/wDBw7w6KOPUrx4cVauXOmmiiWtXblyhYULF1KnTh3q1at323USEhI4deoUERERzJgxg4ULF2p7kA1peyDaHsjNnnnmGb766itKlizJ448/Trt27ahSpUqqB3EC7N+/n379+lGyZEl+/vlnN1YsaWn37t307duXoUOH8txzz6ValpSUxMmTJ9m0aROLFy8mJCSE69ev4+PjQ1JSkgb9sgDn+d/TTz/NzJkzWbBgAd27d3ctv3LlCoCa+iKSaanJL3IXt2v0vvHGG7z77rssWrSILl26uKcwyVDKQc5jt9v56KOPmDhxIlFRUdSoUYN27drRqFEjChcuTOHChbHZbIwYMYKNGzfyww8/0KFDB3eXLW7yzjvvMGbMGG0PsiltD+Tf0PYg+5s3bx4ffPABv//+OyVLlqRp06YEBgZSsmRJKlasyPnz53nnnXfYv38/CxYsoF27du4uWdLI9evXWb9+PRUrVqR69ep3XC8uLo7o6Gi+/PJLJk2apEG/LMB5vnfy5Elq1KhBnz59mDRpkmt6vp9//pnZs2ezY8cO7rvvPt544w3q1Knj3qJFRP5Cc/JLjme327Hb7Xh6et7SzHV+7bx1LzY2lh07duDj46MTt2xGORAn5+c8fPhwAgIC+Omnn9iwYQMff/wxHh4e+Pn5kZiYSGJiIgBjxoxRQy+bud3A3l85c3L58mXWr1+Pl5eXtgfZUHJyMl5eXgwZMsQ1H7e2BzmLzWYDwMPDA7vdnmr+dSdtD3IG576hb9++3Hvvvfz888+sXr2aFStWMH/+fG6+ds7Ly4v3339fDf5sxtfXl44dO/7tevnz5yd//vyULVsWgCeffDK9S5P/kXOqrZkzZ5IrVy66du3qavD//vvv9O3bl+vXr+Pt7c3ixYux2+3Mnz8fHx8fN1cuIvInNfklx7Nara4d+M0nbc4TNuc6ABEREZw5c4Z+/fplfKGSrpQDcXJ+znny5KFDhw60adOG/fv3s2fPHo4dO0Z0dDSRkZE0bNiQ4OBgWrVq5eaKJa39XYMf/sxJdHQ0NptNJ/DZlHP+/Xz58tGpUyfatGnD3r172bdvHxEREdoe5AAeHh6ur51/9zfPvX7z69oeZG837xvq1atHrVq16NmzJ0eOHOHEiROcOHGCkydP0qRJExo2bEiDBg3cWK2kh39yEYBznZiYGJYuXYqnpyddu3bNoArl/8u5PV+3bh0VKlRw3alx9uxZRowYgaenJ3PmzKFHjx48/vjjLF68mMjISKpUqeLOskVEUtF0PZKjXb9+nT179mCz2bDZbOTOnZuqVatSqFCh264fHx/PqlWrCAwMdF2ZIVmfciC349w93nwyl5SUpCt2JJWkpCTCwsKoVKkSxYoVc3c5kkaio6PZvn07SUlJXLt2jcKFC1O/fn1KlizpWichIUEP08zm/pqDokWLUr9+/VR/6zc3/bQ9yLn+SfNXcpbjx4/zwgsvUKpUKSZOnOjucuQfiI2NpUePHpw9e5YDBw4AMG7cON566y1mzZrFww8/jK+vLxMmTGDs2LH8+OOPtG3b1s1Vi4j8SU1+yZGMMaxfv57nn3+egwcPAuDj44O/vz+VKlWiWbNmtGvXjgYNGtzyYD3JPpQD+aduvqPDeSKvE3qR7Mdut7Nw4UJGjBjByZMnUy0rWbIkLVu2pHv37rRp0wZfX183VSnp7e9y8MADD9CjRw9atWqlgZ5s7u/29Tcvt9lsd53WSXKWlJQUIiIiKFKkCAUKFHB3OfI3nH/LL7/8MhMmTGDAgAH4+voyadIkmjVrxtq1awHH/mHcuHF8+eWXHDp0SAO6IpKpqMkvOdKSJUvo378/uXPnpnfv3vj5+ZGcnExYWBihoaEkJiZSvHhxevfuzeDBg6lUqRKgq3SyG+VAAH755ReSkpJo0KBBqmkZJGdRDgRg4cKFPPnkk5QuXZqnnnqKIkWKkJSUxKZNm1i2bBnx8fEAdOvWjWHDhtGyZUs3Vyzp4d/kYPjw4bRo0QLQ8UF2s23bNo4fP06TJk0oXry4LvjIwfS3nXNs3ryZgQMHcvToUQDat2/P6NGjadiwIQAHDhzgqaeewmq1smPHDneWKiJyCzX5JUcKCgoiMTGRyZMnu3bYycnJWCwWTp48yZIlS5gzZw779u2jcePGfPHFF9StW9fNVUtaUw4EoFq1ahw5coRGjRrRo0cPOnXqRMWKFe/6M3v27CFXrlwEBARkUJWS3pQDAbj//vux2WxMmzaN2rVrp1rmvLp70qRJhISEcO+99/Lll1/ywAMPuKlaSS/KgQAEBARw6NAhAgICaNu2LQ899BA1a9akUKFCdxwMXrt2LVarlZYtW7ruAJSsTYM9OY8xhnXr1nHixAn69euX6o6t0aNH89VXX/HVV1/Rp08fN1YpInIrNfklxzl37hwVKlTg2Wef5d13373j7bSRkZFMmzaN9957j3r16rF+/Xry58/vhoolPSgHAo4clCpVigIFCpCYmMi1a9fw8vKidevW9OnTh7Zt21KkSBHgzyl7Dhw4wKOPPkq9evX4+uuv3fwOJC0oBwJw/vx5KlasyMCBA/nwww+xWq23TMMBjikYvvnmG5577jn8/f0JCQmhfPny7ixd0pByIODYL5QtW5bChQuTL18+wsPDsVqtBAYGuh7CXblyZfLly+fKx8GDB3nkkUeoWrUqP/zwg5vfgaQVDfaI0/r162nTpg0dOnRg4cKFGvARkUxHexzJcU6fPo2Pjw83btxIdeL2V2XLluX111/n/fff55dffuGnn37K4EolPSkHArBz505sNht9+/Zl27ZtvPrqq9StW5c1a9bw6KOPcu+99/Lkk0+yZs0a1/QMu3btYt++fdSoUcPN1UtaUQ4E4PLly+TJk4crV67g4eGRar/gbOQYY/D09KRv3758+eWXREVFsXTpUneVLOlAORBw3KmVkpJC165dWbt2LV988QXdu3cnKiqK119/nQceeIC+ffsyffp0fv/9dwC2bt3KgQMHaNKkiZurl7Ry7tw5jh07RokSJbDZbHz22WcEBwfTuXNnPvzwQ3bv3k1cXBw3Xzd58OBBXnrpJaZMmaIGfxazcuVKpkyZwgcffMD06dPZt2+fa1lKSgqFCxfmzTffZOzYsWrwi0impCv5Jce5evUqDzzwAGfPnmXZsmXUrVsXu92OMSbV1RjOqzUvXbpElSpV6N69O1OnTtV8jNmEciAAX375Jc899xwrVqygXbt2AMTExLBlyxZ+/vlnNm3aRHh4OAAVK1akc+fO7N+/n3Xr1hEXF0fevHndWb6kEeVAAG7cuMGDDz5IWFgYixcvpmXLlnh6eqZ68Db8OTdzUlISFSpUoGnTpsybN0/PcsgmlAMBmDp1KkOGDGHx4sV07twZcBw77tu3j9DQUDZv3kxYWBiXL1+mePHitGzZkuPHj7N9+3btF7KRlStX0qlTJ4YMGcKrr77KkiVLCA0NZdu2bZw+fRo/Pz+aNWtG586dCQoKIiAggGnTpjFo0CA+/fRTnnvuOXe/BbkL591ZR48e5eOPP2bGjBnYbDbX8urVq3PgwAFAz2UQkaxBQ8uS4+TNm5c+ffpw5swZRowYwd69e7Fara6TMpvNht1ud+3Ez507R6FChUhISNCOPRtRDsQYQ5kyZShXrhz+/v6uQZ6CBQvSqVMnJk+ezKpVq/j666/p0aMHCQkJTJgwgXXr1tG+fXudwGcTyoE4eXt788wzz3D9+nVefPFF15XZzsau3W5Ptf6xY8dc07epsZt9KAcCUKlSJSpVqkS+fPlc+4W8efPSpEkTXnvtNWbPns0333zDyy+/TLly5ViyZAnbt2/XfiGbOXXqFHa7ndatW1OyZEmGDRvGtGnT+Pbbb3n33XcJCgpi+/btDBo0iLZt29KvXz9mzZoFwFNPPeXe4uVvOc/pRo8ezezZsxk4cCC//PIL33//PVarlXr16gGO88KwsDB2797tznJFRP6eEcmh3n33XePj42MsFovp0qWLWb58uUlOTr5lvfHjxxsPDw+zaNEiN1Qp6U05kNjYWHP9+nXX93a73dhstlvWCw8PN506dTIWi8UsW7YsI0uUDKAciNPMmTNNyZIljcViMUFBQeabb74x8fHxt6w3ZswY7ReyMeVAjDEmKSkp1fe32y9ERUWZvn37GovFYpYuXZpRpUkGWLdunalcubLZsGGDsdlsxm63p1oeHR1tli9fbl555RUTFBRk8uTJYywWi+nQoYObKpZ/yvlZHj9+3FgsFjN8+HDXslmzZhmLxWI2btzoeu3hhx827dq1M3FxcRldqojIP6bpeiTHMf+91e7y5cvMnTuXzz77jOPHjwNQokQJWrRoQfPmzbl27Rq7d+9m/vz5BAUFERoa6ubKJS0pB/JPGGOw2Wx4enqSnJxM165d2bx5M1evXnV3aZKBlIOcJSkpiWXLljF58mQ2btwIgKenJ61atSIwMJBr165x6NAh1qxZQ+vWrVmzZo2bK5b0oBzkbCkpKXh6et5x+c37hcTERLp160ZoaKj2C9nUjRs3Us3B/tfpuwDX3cHffvstP/30Ex07dszoMuVfcH6GEyZMYNy4ccybN4+OHTty8eJFhg4dyubNmzl37pxr/Y4dOxITE8PSpUspVKiQGysXEbkzNfklx7tx4wYLFy5k+vTphISEpLoN22q18thjj/HCCy9Qs2ZNN1Yp6S0pKYkFCxYwY8YMQkNDlYMc5uaTNfOXOTed369cuZKuXbvSt29fZsyY4a5SJR0pB3Kz5ORk1q1bx4IFCwgJCSE6OhpjDImJieTPn58nnniC559/nrJly7q7VElHyoE43a6xC7Bu3Tq6detGt27dXFO1SPagwZ7sb9SoUUycOJE9e/ZQuXJlQkND6d69O3379uXTTz8F4OTJk/Tq1Yt8+fKxevVqN1csInJnd95jiWRzxhiMMXh7e9OnTx/69OnD+fPn2bJlCxcuXKBMmTLky5ePhg0b3vXgTrKmpKQkfHx8AEhMTCRXrlw8+uijPProo5w7d44tW7Zw8eJF5SCbc+bAarW6TuT++swF5/cFChSgZs2aDB482B2lSjpSDuRmzuMDLy8v2rVrR7t27bh8+TL79u0jISGBokWLkidPHqpUqaJntGRjykHOdvXqVfLmzUtiYiI2m408efLctsEPUKRIEdq2bcsLL7yQwVVKervdsf/Ngz0Wi8W1zpYtW9iyZQvdu3fP0Brl/8d58UbVqlW5du0aBw8epHLlyuzYsYOLFy8ycOBA17oHDx7k0KFDjBgxwo0Vi4j8PV3JLzmG82TtdgfoKSkpWK3WOx68S/Zx7do1Jk+eTFhYGBEREdx7771Ur16datWqUbVqVcqXL0/u3LndXaakszvlwPm/cuXK4eXl5e4yJZ0pBwKO4wO73X7bh6babDasVquauDmAciAA586d49NPP2XDhg1cuHCBypUrU758eWrWrEn9+vWpVauWjhNzkNsN9tzJvn37ePvtt3njjTeoXbt2BlYp/4uIiAgaNmxIxYoV+fTTTxkzZgxnz57lwIEDrnUeeeQRlixZwh9//EGpUqXcWK2IyN2pyS85ws1XbQOuqVj+2tS/eSDAbrdjt9t19XY2sn37dl577TVCQkIoVKgQCQkJ+Pj4EBcXR+7cualXrx6dOnWiS5cuVKxYEbh1yg7J+v5pDrp160b58uWBuzd/JGtSDgQgISEhVcPObrdjjLntZ+zcH2i/kP0oBwKwefNmXnzxRfbu3Uu5cuXw8fHBGENkZCRJSUlUrFiR4OBgevbsSdOmTQHtF7IrDfbkDKNGjeKVV15h0aJFDBgwAG9vbzw8POjRowezZs1i//79TJw4kRkzZjBw4EAmTZrk7pJFRO5KTX7JEZ544gm8vb155JFHaNy48S0Nf4vFohO1HCA4OJhff/2VkSNH8sQTT3D16lWOHTvG8ePH2bZtG+vXrycyMpK6desybtw4PTArm1IOBJQDcQgODgZg0KBBBAcHp2ra2Gw2LBaL7vLLAZQDAWjVqhVHjhxh/Pjx9OnThwsXLnDp0iWio6MJDQ1l6dKl7Nu3j7Jly/Laa6/x9NNPu7tkSQca7MnebDYbHh4eLFmyhG7dujFq1CjefPNNvvjiC8aPH8/FixcB8Pb2xhhDcnIyzz33HC+++CKlS5d2c/UiInenJr9ke6dPn6ZMmTKAY2fdoEED2rVrR3BwMHXr1k217o0bN/D29ubw4cPMnTuXRx55hBo1arijbEljp06doly5crzxxhuMGzfuluUxMTEcPnyYpUuX8sUXX5CYmMi3337LI488kvHFSrpRDgSUA3GIiopKdcJeqlQpOnXqRM+ePWnWrFmqdZ13BB4+fJjJkyfTqVMnWrVqldElSzpQDgQc5wvlypVj7NixvPHGG7csT05O5vTp06xevZpPPvmEY8eO8cEHH/DKK6+4oVpJTxrsyd6cd2E9//zzhISEMHv2bGrWrAk47upaunQpS5Ys4cyZM9SpU4eGDRvSp08fN1ctIvIPGZFsburUqcZisZiuXbuaZs2aGYvFYiwWiylYsKDp3LmzmTp1qomIiEj1M1999ZWxWCzmk08+cVPVkta+/fZb4+XlZebNm2eMMcZmsxm73W7sdnuq9RISEszSpUtNuXLlTNGiRc3x48fdUK2kF+VAjFEOxOHrr782FovF9OvXz3Tt2tV1fGCxWEzt2rXN22+/bQ4ePJjqZ5zHBxMmTHBT1ZLWlAMxxpjFixcbT09PM336dGOMY79wOzdu3DAhISGmTp06xtvb2xw4cCAjy5R0durUKePh4WHeeuut2y6/ceOGiYiIMJMnTzaVKlUyFovFfPjhhxlcpaSFBQsWmIoVK5q4uDhjjDGJiYmplt/uuFBEJLPTfaeS7f3xxx8AvPnmm2zevJl9+/bxxhtvULhwYZYuXcrgwYNp2bIlTz75JEuWLOH06dPs2LEDT09PBg4c6ObqJa0UL14cb29v9u/fD+B65oJzmibz35uacuXKRceOHRkzZgznz59n7969bqtZ0p5yIKAciENERAQAr7/+OosWLeLcuXNMnDiRwMBA9u/fz5gxYwgICKBVq1Z8/fXXHD9+3HV8MGDAADdXL2lFORCA0qVL4+fnx44dOwDHfsBms92ynpeXF02bNuWjjz4iOTmZ3bt3Z3Spko52796NxWKhRIkSwJ/PcXPy8vKifPny9O/fnxkzZlC7dm1Gjx7N77//7o5y5V9y/k0fOHCAI0eOEB8fz9GjRwFc0/mmpKSQkpKiqXxFJEtSk1+ytYSEBOLj4/Hy8qJ48eIA1KxZkzfffJODBw+yceNGBg4cyPXr15k9ezbdunWjTZs2zJ07l9atW+Pn5+fmdyBppV69ehQvXpzp06ezceNGPD09XfNmmv8+cBkcB3YA1apVw9/fn19//dVdJUs6UA4ElAOBxMREEhISsFgs+Pr6YrfbKVKkCEOHDmXXrl2Eh4fz+uuvU65cOTZu3MiAAQOoV68ec+fOpU2bNjo+yCaUA3GqUaMGtWrV4uuvv2bWrFl4eHi49gvOgWD4c7+QP39+ChcurOZuNqPBnuzN+Tfdv39/3nvvPS5evEi/fv1YsmQJMTExAHh6euLp6ek6FhQRyUrU5JdszcvLi1atWjF27Fi8vLxcrxtj8PDwoHnz5kyZMoVjx47x/fff07VrV06cOAHAsGHD3FS1pDW73U6+fPn46quv8PDwoFWrVgwaNIjQ0FCSkpJSPVDP+f+HDx/mypUr1K9f352lSxpSDgSUA3Hw8fHh4YcfZsqUKeTPnx+r1YrdbiclJQVjDJUrV+btt98mIiKCkJAQBgwY4GruDR061M3VS1pRDgQc+wUfHx8mTJhAQEAATz31FO3atWP58uUkJCRgtVpd+wNPT08A9u/fT0xMDE2aNHFn6ZLGNNiT/dntdoYOHUrHjh0pVqwY4eHh9O/fn5dffpm5c+dy8OBBbty4gcVi0dX8IpLl6MG7kiPEx8fj6+t72x2180/AYrEQHx9Pu3bt2LdvH3FxcRldpqSz5ORkvvvuO0aOHMm5c+coXrw4zZs3p0mTJjRs2JD77ruPa9euERoayvPPP09iYiKRkZHuLlvSmHIgoByIQ1JSEt7e3rccHzibOR4eHq5lbdq0YefOnVy5csUdpUo6Ug7Eac2aNYwePdp1ZXajRo1o1aoVrVu3JjAwkKioKPbu3ctLL71E7ty5XVN9SNZnt9uxWq3s2bOHJ554gt9//50HH3yQYcOG0apVK3Lnzn3Lz3z99dcMGjSIRYsW0alTJzdULf9fSUlJREREsHHjRhYtWsTWrVux2WzUrFmTBx54gKZNm1KjRg0qVKjg7lJFRP4xNfklWzPGpJpj+U6j8c6DupUrV/LII4/Qs2dPpk+fnpGlSgZKTExk4sSJzJ071zUnt6enJwULFsTPz4+IiAgqVqzImDFjePTRR91craQX5UBAOcip/unxgc1mw8PDg2XLltG3b18dH2QzyoHcyXfffceUKVMIDQ11vebn54fFYuHq1avUqlWLMWPG0K1bNzdWKelFgz05hzGG69evc+DAAVasWMHSpUs5ePAgKSkp9OvXj2+++cbdJYqI/GNq8ku25Wzc/5XzRO123n//fUaNGkVoaCiNGzdO7xIlgxljXFfkAURHR7N37142b97MunXriIuLo2zZspQoUYKRI0dSvXp13aaZDSkHAspBTna344A7+fjjjxkxYoSOD7IR5UBu56+5OHz4MKtXr2bDhg1cv36dokWLUqpUKQYPHky5cuXcV6hkCA325CzGGC5fvkxYWBizZs2iadOmmppNRLIUNfklW7t48SLx8fGcOHGCsmXLpjoY/2uDByAiIoKdO3fSu3dvN1Qr6eFuV+b9VUxMDF5eXuTNmzedq5KMphwIKAfyp6ioKE6cOMHZs2epUaMGFStWvOXZPTdn5fr164SFhdG8eXN3lCvpRDmQO10UdLv9xbVr1/Sw5RxCgz2SkpKS6nkcIiJZgZr8ki1dunSJH3/8kQkTJnD69GlsNhs2m41KlSrRo0cPevfuTbVq1W77s/+mCSRZy50+W7vd7nq4kj7/7E85EFAOcqozZ84we/ZsPv74Y65cuYLNZgOgdOnSBAcH07VrV5o3b+6ae1kZyJ6UA/krYwzGmL+9C/hOgwKStWmwR0REsgM1+SVbeuGFF5g8eTIlS5akadOmeHt7s3PnTo4dO8b169cBeOCBBxgxYgStW7fGarXqoD0bWrlyJSVLlqRatWr4+Pi4Xr/5YcuS/SkHAsqBOAwaNIhvvvmGWrVq0b59e27cuMFvv/3G0aNHOXLkCHa7nXr16jFixAi6d++Oh4eHGrzZkHIgADNnzqRs2bI0atQIX19f1+s3D/ZKzqLBHhERycrU5JdsJzIyksqVK9OtWze+/fZbANdB2L59+/j5559ZsmQJu3btIleuXLz//vs8++yz7ixZ0sHJkyepXr06gYGBNGzYkKCgIOrUqUOZMmVSnbTdPG3TpUuXuHjxIlWrVnVj5ZKWlAMB5UAcIiMjqVSpEo8++igzZsxItezo0aNs3bqVVatW8eOPP2Kz2Xj22Wd59913yZMnj5sqlvSgHAjAqVOnKFeuHFWqVKFWrVq0aNGCpk2bEhAQkGq/YLfbsdvteHp6cu7cOc6ePUudOnU06JONaLBHRESyDSOSzbz//vumQIECZv369cYYY2w2m0lOTk61zo0bN8z8+fNNzZo1jcViMRMnTnRHqZKO3n//fWOxWMw999xjrFarKVCggGnTpo159913zYYNG8y5c+du+Zlp06aZkiVLmlWrVrmhYkkPyoEYoxyIwyeffGLy589v1q5da4wxJjk52aSkpKRaJzk52axevdo0btzYWCwWM2bMGGOMMXa7PcPrlfShHIgxxnz44YfGYrGYcuXKGavVaiwWi6lWrZoZMGCA+fbbb82JEydu+ZmJEycai8VifvjhBzdULOnh5MmTxmq1mmrVqpmePXuaSZMmmd9+++2Wv/Wbzyejo6PN3r17jTHaJoiISObi6e5BBpG0du7cOex2OwULFgQct1Y6H6Jmt9sB8PLyolevXlSpUoUOHTowZcoUHn/8cc2vmI3s378fDw8PJk2aRFJSEkuWLCE0NJR169ZRokQJgoKCaNGiBYGBgdSsWRMfHx/WrVvHmTNnaNKkibvLlzSiHAgoB+IQGxtLSkqKa551u92Ot7c38OcUDZ6enrRt25bAwEDatm3Lf/7zH5555hkKFy7sztIlDSkHAnDw4EGsViuzZs3Cx8eH+fPns2LFCqZPn84333xD7dq1adGiBc2aNaNJkybkz5+f7du3Y7FYaNeunbvLlzQyf/58jDEkJiaycOFCfvjhB6pWrUrTpk1p2bIlQUFBlC1bNtUDWBcuXMgzzzzDggULePjhh938DkRERP6kJr9kO82aNeOzzz5jx44d1KlTx9XgB1LNnZiSkkLdunUZNmwY48ePZ9euXTzwwAPuKFnS2OXLl7lw4QL+/v50794du91OmzZtOHbsmOs2/FWrVrFo0SIqV65Mq1atKFKkCGvWrCE4OFi35GcTyoGAciB/atmyJe+88w4rV66kcePGrsYukGpKhhs3blCwYEGefPJJRo4cyZYtW+jSpYubqpa0phxIbGwsFy5cwNfXl+bNmwMQGBjIK6+8wo4dO1i8eDFr1qwhLCyM//znPwQFBVGuXDmWLl3Kgw8+qP1CNqLBHhERyU7U5Jdsp3HjxtSpU4dhw4Zx7tw5HnvsMcqVK3fLvMtOefPmJSEhIdVJnmRtycnJ+Pr60qRJE9dDsooUKUKRIkW477776NWrF4cOHWLTpk2sWbOGadOm4eHhQWJiIkOHDnV3+ZJGlAMB5UAcjDHUr1+fhx56iPHjx3P27FmGDx9OzZo1U10MYG564KKHhwcJCQkUKFDAXWVLGlMOxKlMmTJ06dKFGzdu4O3tjaenJyVLlqR79+506NCByMhINm7cyJIlS9i0aRNJSUnY7XaGDRvm7tIljWiwR0REshs9eFeypWXLljFgwAAuXLhA586d6d27N40aNaJw4cLkypXL1fC/cOECzz77LKtXryYmJsbNVUtaOnnyJBcuXKB27dp4enre9gFp165dIyYmho0bN/Lqq6+SkJBAbGysewqWdKEcCCgH8qdt27bRv39/wsPDadCgAd27d6dp06aUL1+ewoUL4+HhAUB0dDRDhgwhJCSES5cuublqSWvKgZw7d46YmBiqVKmCh4fHbfcLdrsdm81GaGgo/fv35/Lly9ovZCOxsbGMGjWKa9euMX369Fsu+EpKSko12BMSEuIa7Fm2bBnt27d3U+UiIiK3pyv5JVvq2LEjW7du5e2332bx4sUsWbKEmjVr0qJFC6pXr06ePHnw9fVl7ty5rFixgpdeesndJUsaK1OmDGXKlHF9/9cTNwA/Pz/8/PwoVqwYV69epXfv3hlZomQA5UBAOZA/BQUF8csvv/Dee+8xZ84cRo4cSenSpQkMDKRq1aoUKFAAX19fvvvuO3755RdGjhzp7pIlHSgHUrRoUYoWLer6/nb7BYvFgpeXF8nJyVy6dImePXtmZImSzvz9/Rk7diwxMTGugb2bB3t8fHyoUqUKlSpV4qmnnko12KMGv4iIZEZq8ku2k5KSgoeHBxUrVuStt96iVatWrF27lu3btzN16lRu3LiRav0xY8YwfPhwN1Ur6SUlJQVPT8cmzm63p5pn96/Wr1/P9evXefrppzOyRMkAyoGAciB/stls5M6dm1deeYVWrVqxYcMGNm/eTEhICIsXL3at5+Hhwccff8zjjz/uxmolvSgH4py+zfm11Wq9Zb/g/H7dunVcu3aNAQMGZHidkr402CMiItmJpuuRbMk5v6bT9evX+e233zh27Bjx8fGcPXuWPHnyEBwcTEBAgBsrlfSUnJx8yxy7drvddVIHEB8fz+eff862bdtYvny5O8qUdKYcCCgHcnvJycmcOnWKs2fPEh8fz7FjxyhYsCCNGzemVKlS7i5PMohykDNduXKFfPnyub6/3X4hKSmJb7/9lg0bNjBnzhx3lCnp6J8M9ji98sorfPLJJ2zfvp2GDRtmZJkiIiL/iJr8km1ERESwcuVKfv/9d7y9vfH19SUgIICWLVtSsmRJd5cnGeSvOciTJw81atSgZcuWFC9e/LY/Exsby5UrV1JN5yFZm3IgoBzInd1u/u3/zzqStSkHOYsxhn379jFv3jyOHz9OSkoKfn5+1K9fny5dulC2bNlU6zo/94SEBBISEihYsKC7Spd0pMEeERHJLtTkl2zh+++/Z8SIEZw6dQqLxYKvry/x8fEAFCtWjHbt2tGrVy9atGiBt7f3LVd0Svbwdzl46KGH6N27N82aNcPLy0sn7tmUciCgHIhDQkICuXLluutn6/zsjTEYY7BardjtdqxWawZWKulJORCA//znP4wbN47o6GgKFCiA1WpN9UDlVq1aMWjQIDp06ECuXLncWKmkJw32iIhIdqUmv2R5p06dol69ehQoUIDPP/+cPHnykDdvXiIjI1m0aBE//vgjCQkJFChQgEGDBjFy5Ejy58/v7rIljSkHAsqBOCgHAnDmzBlGjRpF586due+++yhWrFiqqfxu5+bnN0j2oBwIwMmTJ6lduzblypVj8uTJ+Pv7U7RoUcLDw1m8eDE//fQTR44cAaBXr1689dZbVK5c2c1VS3rQYI+IiGRbRiSLe+ONN8w999xjli9fftvlN27cMDNnzjR169Y1VqvVdO/e3Zw/fz6Dq5T0phyIMcqBOCgHYowxr7/+urFYLMbDw8NUq1bNvPTSS2b9+vXm3LlzJiUlJdW6drvdGGPM6tWrzXvvvWeioqLcUbKkA+VAjDFmzJgx5p577jGrVq264zorVqwwLVq0MBaLxbRo0cIcO3YsAyuUjBAZGWn8/f1NnTp1zPbt282hQ4dMTEyM2b59uxkxYoSpWrWqsVgsxmKxmEceecQcOXLE3SWLiIj8Y2ryS5bXqlUrU7t2bdeJWHJysjHGGJvNlurk7fjx4+bRRx81FovFfPLJJ26pVdKPciDGKAfioByIMcY0b97c5M6d2/Tq1csEBAQYi8VivLy8TFBQkHn//fdNWFiYiY2NdWUiMTHRdO7c2eTOndskJCS4uXpJK8qBGGNM+/btzb333mtOnTpljDGuz/uv+4Xk5GTXwNCLL77ollol/WiwR0REsjNN1yNZWnJyMsOGDeO7777jwoULf3tLZXx8PE2aNMEYQ0hISKqHLEnWpRwIKAfioBwIwNmzZwkODgZg37597Nu3j61bt7J582a2bdtGVFQUefPmpXnz5rRv35727dtz9OhR+vTpQ+3atVm1apWb34GkBeVAnEaOHMknn3xCZGQkJUuWvO06zmcwGGNo3bo1UVFRbNiwgRIlSmRwtZJeOnToQEREBGvWrKFUqVLYbDY8PDyw2+0YY1wP201JSWHcuHG89957vPDCC3zyySdurlxEROTv6UlSkqV5eXnRrFkz4uPjGThwIJGRkYDjIUk2m821njEGu91Onjx5aNiwIadPnyY6OtpdZUsaUw4ElANxUA4EHM3d8PBwKlSoAEDt2rUZOnQo06dP59tvv+Wdd96hYcOGbNmyhSFDhtCoUSNeffVVzp07x/Dhw91cvaQV5UCcmjdvjt1u58knn+SXX35JtT9wslgs2O12LBYLderU4ezZs1y+fNkN1Up6CQgI4MiRI66H6Tqb+lar1fW13W7H09OTt99+m5YtW7JixQrOnDnjtppFRET+KTX5JcsLDg6mVatWzJ07lxEjRrBnzx4sFovrQA0cB+1Wq5XY2FiSkpLw8vKiSpUqbqxa0ppyIKAciINyINWqVeO1114jODiYlJQUjGOKSvLmzUvTpk0ZNWoUc+bMYfbs2YwcOZLixYsTFhaGv78/HTp0cHf5kkaUA3Fq3bo1ffv2Zd26dTz33HMsWbKE+Pj4VOs49wtxcXHExMTg4+NDQECAmyqW9KDBHhERydYydnYgkfRx5coV079/f9eDklq0aGHmzp1rLl68aBITE01MTIwxxpjJkyebfPnymSFDhri5YkkPyoEYoxyIg3Igd2Kz2W55bcaMGcZisZhBgwa5oSJxB+UgZ3rzzTdN4cKFjcViMfXq1TMffPCB2b17tzl+/Lg5deqUSUhIMO+//77x8/Mzw4YNc3e5ksaSkpJMv379jMViMU2aNDELFy40165du+26sbGx5oknnjBFihTJ4CpFRET+fzQnv2R5KSkpeHp6cvr0aX744Qdmz57N/v37AfD09KRBgwYUKFCAw4cPc+zYMRo3bsysWbOoWLGimyuXtKQcCCgH4qAciPnv9Eyenp53XQccV22+/vrrjB8/nl27dhEYGJhRZUo6Uw7EyTnfflxcHD///DPz589nw4YNXLt2DQ8PDypXroy3tzdRUVFcunSJ4OBgvvrqK8qXL+/u0iUdvPXWW3z55ZdcunSJunXr0qtXL1q1akWhQoXw9PSkcOHCfP7557zzzjs8/vjjTJw40d0li4iI/C01+SXbSUpKYtWqVSxbtox9+/Zx5coVrl69ire3N3379mXIkCGUKlXK3WVKOlMOBJQDcVAOxMnZ6LvZ2bNn6d27NydPniQiIsJNlUlGUg4kOTmZHTt2sGHDBn777TcuXbrE2bNn8ff3p1evXjz11FPkz5/f3WVKGtNgj4iIZGdq8kuWdv78eS5cuEChQoWIi4ujcOHCFCpUyLX88uXLnDlzxtW8yZcvn+tBS5J9KAcCyoE4KAcCqXNw7do1ChcujL+//23XTUpK4ocffiBXrlw8/PDDGVuopCvlQP7K/Pe5DDcP8sTHx3Pt2jWKFi1KUlISPj4+bqxQMpIGe0REJDtRk1+ypLNnz/L666+zdu1aoqKiyJs3L+XLl6datWo0aNCAoKAgatWqha+vL+A4oFcTJ/tRDgSUA3FQDgTunoNGjRrRpEkTatasqSZeNqcciHMbb7fbsdvtt52yyflwVee+wPkz2j9kfxrsERGR7EhNfslyoqOj6dq1Kzt37iQ4OBg/Pz+sViuRkZHs37+fhIQEqlevTs+ePXn66acpUaKEu0uWdKAcCCgH4qAcCPy7HAwYMIDixYsDt2/2SNalHAjAlStXiI2NpUyZMq7XbDYbAB4eHu4qSzKYBntERCRHSf9n+4qkrTFjxpj8+fObzz77zPXa5cuXzalTp0xISIgZPXq0qV69urFareb+++83W7ZsMcYYY7fb3VWypAPlQIxRDsRBORBjlANxUA7EGGNefPFFY7FYTNOmTc2MGTNMfHx8quXJycnGZrOleu3s2bPm3LlzykI2EhcXZyIjI1O9lpKSYlJSUtxUkYiISPrRlfyS5QQEBFChQgVmzJhBkSJFbrnKIikpifDwcGbPns2nn35K1apV2bx5M/fcc48bq5a0phwIKAfioBwIKAfioBwIQK1atThw4ECq17p06cLTTz/NQw895HrNmY+rV68yZMgQLl68yPLly297xbdkPS+99BKffvopTZo04cknn6RXr16uafsAUlJSsFqtqe7giY6Oxmq1UqRIEV3JLyIiWYruR5Us5dy5cxhjSEpKokiRIgC3HHz5+PhQq1YtPvjgAz7//HPCw8OZMGGCO8qVdKIcCCgH4qAcCCgH4qAcCEBERATR0dE0a9aMzZs3M3jwYEqXLs2SJUvo0KEDBQsWZNiwYezdu9eVj2PHjrFy5UoSExPV4M9G1q5dC8CWLVvo378/fn5+dOvWjZUrVwLg6emJ1WrFed3j1atXefnll3nsscdc0zuJiIhkFWryS5ZhjKFIkSIEBASwc+dOdu3a5Xr9dgdhnp6ePPPMM9SoUYOwsDCuXbuW0SVLOlAOBJQDcVAOBJQDcVAOxOnYsWNcvHiRwMBAmjZtyqRJk9i/fz/z58/n4Ycfxm63M3nyZO677z4qVarEBx98wPz584mNjeWVV15xd/mSRjTYIyIiOY2a/JJlWCwWrFYrbdu2dV1l8fvvv2OxWFwP0LLb7dhsNtfVGFeuXKF06dKcP38ePz8/d5YvaUQ5EFAOxEE5EFAOxEE5ECc/Pz9KlSpF1apVAceULPnz56dnz54sWLCA/fv388UXX9C8eXMiIiJ47bXX+PDDD/H396d9+/Zurl7SigZ7REQkp1GTX7KcAQMGMH78eLZu3UrNmjV54oknWLNmDYmJiVitVteJHEBYWBj79u3j/vvvd2PFkh6UAwHlQByUAwHlQByUAwkMDGTVqlV06tQJcNy1cfMdHWXKlGH48OFs3LiR8PBw+vbtC0CfPn3cVrOkPQ32iIhITqMH70qW4nw4VmxsLDNmzOCDDz7gwoULeHh4cN9999G4cWNatmxJ/vz5CQsLY+LEiVy9epUNGzZQs2ZNd5cvaUQ5EFAOxEE5EFAOxEE5kH/CGIPdbncN+Lz11luMGzeOsLAw7rvvPjdXJ2klOTmZo0ePUqhQIYoWLQrc+tk7HT16lLfeeot58+YxdOhQJk6c6I6SRURE/idq8kuW4jx5c0pMTGT27Nl88803bN++/Zb1q1evzmuvvea6QkeyB+VAQDkQB+VAQDkQB+VAwDEtk9VqxWaz3dLMdXJm5ciRI3Ts2JGUlBSOHTuWwZWKO2mwR0REshs1+SXbOHnyJOvWrePAgQMUK1aMe+65hyZNmlCpUiV3lyYZSDkQUA7EQTkQUA7EQTmQ2wkPD6dLly507NiRDz/80N3lSBrSYI+IiOQ0avJLlrFq1SoOHDjAr7/+StGiRQkMDKRSpUqULl2aQoUK4eXl5e4SJQMoBwLKgTgoBwLKgTgoBwKpc3DPPfdQv359KlWqRNmyZSlUqBAeHh633PEBjvnaPT093VS1uJsGe0REJDtQk18yvdjYWMaPH89HH32Eh4eH66FZAAULFqRx48Z07dqVTp06UbBgQdey2x3AS9alHAgoB+KgHAgoB+KgHAj88xx06dIFf39/17K7XeUtWZcGe0REJCdSk18yvY8++ohx48bx4IMP8uyzz1KiRAn27t1LeHg4YWFh7Ny5k4sXL1K3bl3eeOMNunTp4u6SJR0oBwLKgTgoBwLKgTgoBwLKgThosEdERHIyNfkl0ytXrhw1atRg9uzZFCpUKNWyM2fOsHfvXpYuXcqMGTOw2Wz85z//4emnn3ZTtZJelAMB5UAclAMB5UAclAMB5UAcNNgjIiI5mhHJxA4dOmT8/PzMqFGjXK/ZbDZjs9lSrZeUlGRWrFhhKlSoYAoWLGi2bduW0aVKOlIOxBjlQByUAzFGORAH5UCMUQ7kT2XLljXt27c3Fy9evGVZVFSUWb58uRk4cKDx9PQ0FovFTJs2zQ1VioiIpA+ruwcZRO7GGIO/vz/Hjh0DHPMkAlitVtdyYwze3t489NBDTJgwgcuXLxMaGuq2miXtKQcCyoE4KAcCyoE4KAcCyoE4HD58mEuXLlG7dm3X3Rx2ux273Q5AiRIlaN++PV9++SU//fQT5cuXZ+TIkWzfvt2dZYuIiKQZNfklU7v33nspWbIkK1eu5Oeff8bT09N1wO5ksVhcB29NmzalXLlyhIWFuaNcSSfKgYByIA7KgYByIA7KgYByIA4a7BERkZxOTX7JtMx/HxfxxRdfkC9fPtq3b88LL7zArl27SExMBBwH7ADJyckAhIeHk5SURIkSJdxTtKQ55UBAORAH5UBAORAH5UBAOZA/abBHRERyvAyYEkjkf5KSkmJmzZplihcvbiwWiwkICDAvvPCC+eGHH8zvv//umm/z9OnTpnfv3sbT09Ps2bPHzVVLWlMOxBjlQByUAzFGORAH5UCMUQ5yOrvdbowxZufOnaZkyZLGYrGY559/3uzcudMkJCSkWjcxMdEYY8y2bdtMiRIlzLPPPpvh9YqIiKQHizH/vfxBJJO7cOECEydOZMGCBRw5cgRfX19KliyJn58fBQsW5PDhw1y4cIEnn3ySSZMmubtcSSfKgYByIA7KgYByIA7KgYBykNPZbDbmzp3La6+9RnR0NNWrV6dt27YEBQVRvXp1qlWrhtVqJSoqildeeYUffviBnTt3Uq9ePXeXLiIi8j9Tk18yPWMMdrsdDw8PEhISOHr0KGFhYWzdupWdO3dy+PBhihQpQunSpXn66afp168fefLkcXfZksaUAwHlQByUAwHlQByUAwHlQFLTYI+IiOREavJLlmS320lMTMTb25u4uDiio6MJCAhwd1mSwZQDAeVAHJQDAeVAHJQDAeUgJ9Jgj4iI5GRq8kumk5CQwMmTJylTpgy5c+dOtcxut2OxWFwP0DLGuL52Lv/rA5Yka1IOBJQDcVAOBJQDcVAOBJQD+ec02CMiIjmFjm4k0/n888/p168fn332GRs3buTMmTPYbDYArFYrFosFY0yqA/YLFy6QkpKiA/ZsRDkQUA7EQTkQUA7EQTkQUA7EISEhgfDwcBISEm5ZZrfbMcZgtVrx9fXFw8ODQoUKuRr8drs9o8sVERFJV7qSXzKdUqVKcebMGTw8PMifPz9BQUG0bduWhg0bUqFCBQoVKpRq/fj4eMaNG8elS5eYPn26DtyzCeVAQDkQB+VAQDkQB+VAQDkQh/fff58ff/yRbt260ahRI6pWrUrRokXx8PBwreNsd9w82FOgQAE8PT3dUrOIiEh60Z5NMpUjR44QFxfH/fffT58+fVi7di3bt29n+fLllClThhYtWtC6dWvq1q1LyZIl8ff358CBA0ybNo0WLVrogD2bUA4ElANxUA4ElANxUA4ElAP508SJEzlz5gy//vrrHQd7bp6qKT4+ng8//FCDPSIiki2pyS+ZypEjR0hMTKRt27YMGzaMDh06EB4ezvbt29mwYQM//vgj8+bNo3r16jzwwAMEBwezfv16rly5woABA9xdvqQR5UBAORAH5UBAORAH5UBAORAHDfaIiIikpul6JFNZuHAhPXv2ZP78+fTs2dP1enJyMpGRkezbt4/Q0FA2bdrEoUOH8PLywhiDj48PMTExbqxc0pJyIKAciINyIKAciINyIKAciMPy5cvp2rUro0ePZuzYsURGRqYa7Pnll19ITEy8ZbDn448/ZtmyZbRv397db0FERCRNqckvmYoxhsOHD5MrVy7Kly+f6mFZTvHx8Rw5coTw8HBmzpzJ2rVrGT58OF988YWbqpa0phwIKAfioBwIKAfioBwIKAfioMEeERGR1NTklyzjdgfwzz77LBMnTmTPnj3UrVvXTZVJRlIOBJQDcVAOBJQDcVAOBJSDnESDPSIiIqmpyS9Zjt1ux2q1cuLECTp37szly5c5efKku8uSDKYcCCgH4qAcCCgH4qAcCCgHOZ0Ge0REJCfS02Yky3E+JCkqKork5GSGDh3q5orEHZQDAeVAHJQDAeVAHJQDAeUgp3M2+O12OwAnTpxg8+bNlCpVSg1+ERHJtnQlv2RZxhhOnz5NwYIFyZMnj7vLETdRDgSUA3FQDgSUA3FQDgSUA3HYunUrAwYM4LHHHuPVV191dzkiIiLpQk1+EREREREREcmWNNgjIiI5gZr8IiIiIiIiIiIiIiJZlObkFxERERERERERERHJotTkFxERERERERERERHJotTkFxERERERERERERHJotTkFxERERERERERERHJotTkFxERERERERERERHJotTkFxERERERERERERHJotTkFxERERERERERERHJotTkFxERERERERERERHJov4PWX7hfOxe09gAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -585,7 +717,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.html b/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.html
index b53fd3f56..e5791294c 100644
--- a/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.html
+++ b/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" /><link rel="next" title="Circuit Knitting Toolbox API References" href="../../../apidocs/index.html" /><link rel="prev" title="CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding" href="tutorial_1_automatic_cut_finding.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -146,7 +146,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -173,7 +173,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -190,6 +190,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../../tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../explanation/index.html">Cutting Explanatory Material</a></li>
@@ -219,6 +220,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -306,6 +310,7 @@
         <article role="main">
           <section id="CutQC-Tutorial-2:-Circuit-Cutting-with-Manual-Wire-Cutting">
 <h1>CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting<a class="headerlink" href="#CutQC-Tutorial-2:-Circuit-Cutting-with-Manual-Wire-Cutting" title="Link to this heading">¶</a></h1>
+<p><strong>NOTE: CutQC is deprecated and will be removed no sooner than Circuit Knitting Toolbox v0.8.0. The circuit cutting workflow in ``circuit_knitting.cutting`` now implements similar and improved functionalities, which will be maintained going forward.</strong></p>
 <p>Circuit cutting is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output.</p>
 <p>The circuit knitting toolbox implements a wire cutting method presented in <a class="reference external" href="https://doi.org/10.1145/3445814.3446758">CutQC</a> (Tang et al.). This method allows a circuit wire to be cut such that the generated subcircuits are amended by measurements in the Pauli bases and by state preparation of four Pauli eigenstates (see Fig. 4 of <a class="reference external" href="https://doi.org/10.1145/3445814.3446758">CutQC</a>).</p>
 <p>This wire cutting technique is comprised of the following basic steps:</p>
@@ -318,7 +323,7 @@ <h1>CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting<a class="headerli
 <h2>Create a quantum circuit with Qiskit<a class="headerlink" href="#Create-a-quantum-circuit-with-Qiskit" title="Link to this heading">¶</a></h2>
 <p>In this tutorial, we’ll use the example circuit shown in <a class="reference external" href="https://dl.acm.org/doi/10.1145/3445814.3446758">CutQC</a>.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -349,7 +354,7 @@ <h2>Create a quantum circuit with Qiskit<a class="headerlink" href="#Create-a-qu
 </div>
 </div>
 <div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
@@ -361,7 +366,7 @@ <h2>Create a quantum circuit with Qiskit<a class="headerlink" href="#Create-a-qu
 <h2>Set up the Qiskit Runtime Service<a class="headerlink" href="#Set-up-the-Qiskit-Runtime-Service" title="Link to this heading">¶</a></h2>
 <p>The Qiskit Runtime Service provides access to IBM Runtime Primitives and quantum backends. Alternatively, a local statevector simulator can be used with the Qiskit primitives.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_ibm_runtime</span> <span class="kn">import</span> <span class="p">(</span>
@@ -380,7 +385,7 @@ <h2>Set up the Qiskit Runtime Service<a class="headerlink" href="#Set-up-the-Qis
 <p>The wire cutter tool uses a <code class="docutils literal notranslate"><span class="pre">Sampler</span></code> primitive to evaluate the probabilities of each subcircuit. Here, we configure the options for the Runtime Sampler and specify the backend(s) to be used to evaluate the subcircuits.</p>
 <p>If no service was set up, the <code class="docutils literal notranslate"><span class="pre">backend_names</span></code> argument will be ignored, and Qiskit primitives will be used with statevector simulator.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set the Sampler and runtime options</span>
@@ -400,7 +405,7 @@ <h2>Decompose the circuit with wire cutting<a class="headerlink" href="#Decompos
 <li><p><code class="docutils literal notranslate"><span class="pre">subcircuit_vertices</span></code>: A list of lists containing the two-qubit gate indices appearing on either side of the cut(s) <img alt="how-to-manual-cut.png" src="data:image/png;base64,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" /></p></li>
 </ul>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%capture</span>
@@ -415,7 +420,7 @@ <h2>Decompose the circuit with wire cutting<a class="headerlink" href="#Decompos
 </div>
 <p><strong>The two subcircuits produced</strong></p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># visualize the first subcircuit</span>
@@ -424,7 +429,7 @@ <h2>Decompose the circuit with wire cutting<a class="headerlink" href="#Decompos
 </div>
 </div>
 <div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
@@ -432,7 +437,7 @@ <h2>Decompose the circuit with wire cutting<a class="headerlink" href="#Decompos
 </div>
 </div>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># visualize the second subcircuit</span>
@@ -441,7 +446,7 @@ <h2>Decompose the circuit with wire cutting<a class="headerlink" href="#Decompos
 </div>
 </div>
 <div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
@@ -454,7 +459,7 @@ <h2>Evaluate the subcircuits<a class="headerlink" href="#Evaluate-the-subcircuit
 <p><strong>Set up the Qiskit Runtime Service</strong></p>
 <p>The Qiskit Runtime Service provides access to Qiskit Runtime Primitives and quantum backends. See the <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit-ibm-runtime/">Qiskit Runtime documentation</a> for more information. Alternatively, if a Qiskit Runtime Service is not passed, then a local statevector simulator will be used with the <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/primitives">Qiskit Primitives</a>.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Use local versions of the primitives by default.</span>
@@ -469,7 +474,7 @@ <h2>Evaluate the subcircuits<a class="headerlink" href="#Evaluate-the-subcircuit
 <p>The wire cutter tool uses a <code class="docutils literal notranslate"><span class="pre">Sampler</span></code> primitive to evaluate the probabilities of each subcircuit. Here, we configure the options for the Qiskit Runtime Sampler and specify the backend(s) to be used to evaluate the subcircuits. Backends could be <a class="reference external" href="https://quantum-computing.ibm.com/services/resources?tab=systems">simulator(s) and/or quantum device(s)</a>. In this tutorial, two local cores will be used to support each of the parallel backend threads we’ll specify below.</p>
 <p>If no service was set up, the <code class="docutils literal notranslate"><span class="pre">backend_names</span></code> argument will be ignored, and Qiskit Primitives will be used with statevector simulator.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set the Sampler and runtime options</span>
@@ -481,8 +486,8 @@ <h2>Evaluate the subcircuits<a class="headerlink" href="#Evaluate-the-subcircuit
 </div>
 </div>
 <p><strong>Evaluate the subcircuits on the backend(s)</strong></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">circuit_knitting.cutting.cutqc</span> <span class="kn">import</span> <span class="n">evaluate_subcircuits</span>
@@ -491,20 +496,29 @@ <h2>Evaluate the subcircuits<a class="headerlink" href="#Evaluate-the-subcircuit
 
 <span class="c1"># Uncomment the following lines to instead use Qiskit Runtime Service as configured above.</span>
 <span class="c1"># subcircuit_instance_probabilities = evaluate_subcircuits(cuts,</span>
-<span class="c1">#                                                          service_args=service.active_account(),</span>
+<span class="c1">#                                                          service=service,</span>
 <span class="c1">#                                                          backend_names=backend_names,</span>
 <span class="c1">#                                                          options=options,</span>
 <span class="c1">#                                                         )</span>
 </pre></div>
 </div>
 </div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/tmp/ipykernel_3259/4196057829.py:3: DeprecationWarning: The function ``circuit_knitting.cutting.cutqc.wire_cutting.evaluate_subcircuits()`` is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package.
+  subcircuit_instance_probabilities = evaluate_subcircuits(cuts)
+</pre></div></div>
+</div>
 <p><strong>See Tutorial 1 for more info about the subcircuit results.</strong></p>
 </section>
 <section id="Reconstruct-the-full-circuit-output">
 <h2>Reconstruct the full circuit output<a class="headerlink" href="#Reconstruct-the-full-circuit-output" title="Link to this heading">¶</a></h2>
 <p>Next, the results of the subcircuit experiments are classically postprocessed to reconstruct the original circuit’s full probability distribution.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%capture</span>
@@ -523,8 +537,8 @@ <h2>Reconstruct the full circuit output<a class="headerlink" href="#Reconstruct-
 <section id="Verify-the-results">
 <h2>Verify the results<a class="headerlink" href="#Verify-the-results" title="Link to this heading">¶</a></h2>
 <p>If the original circuit is small enough, we can use a statevector simulator to check the results of cutting against the original circuit’s exact probability distribution (ground truth).</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">circuit_knitting.cutting.cutqc</span> <span class="kn">import</span> <span class="n">verify</span>
@@ -533,10 +547,19 @@ <h2>Verify the results<a class="headerlink" href="#Verify-the-results" title="Li
 </pre></div>
 </div>
 </div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/tmp/ipykernel_3259/3419935587.py:3: DeprecationWarning: The function ``circuit_knitting.cutting.cutqc.wire_cutting_verification.verify()`` is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package.
+  metrics, exact_probabilities = verify(circuit, reconstructed_probabilities)
+</pre></div></div>
+</div>
 <p><strong>The verify step includes several metrics</strong></p>
 <p>For example, the chi square loss is computed. Since we’re using the Qiskit Sampler with statevector simulator, we expect the reconstructed distributed to exactly match the ground truth. More info about each metric can be found in the <a class="reference external" href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/main/circuit_knitting/utils/metrics.py">utils metrics file</a>.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">metrics</span>
@@ -544,27 +567,27 @@ <h2>Verify the results<a class="headerlink" href="#Verify-the-results" title="Li
 </div>
 </div>
 <div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 {&#39;nearest&#39;: {&#39;chi2&#39;: 0,
-  &#39;Mean Squared Error&#39;: 1.554441697306333e-32,
-  &#39;Mean Absolute Percentage Error&#39;: 4.748059826691265e-14,
+  &#39;Mean Squared Error&#39;: 2.2429332936235563e-32,
+  &#39;Mean Absolute Percentage Error&#39;: 1.1722866042107947e-13,
   &#39;Cross Entropy&#39;: 2.599681088367844,
   &#39;HOP&#39;: 0.9004283905932716},
  &#39;naive&#39;: {&#39;chi2&#39;: 0,
-  &#39;Mean Squared Error&#39;: 6.5760386594463524e-34,
-  &#39;Mean Absolute Percentage Error&#39;: 5.260720927719812e-14,
+  &#39;Mean Squared Error&#39;: 2.033924079765218e-33,
+  &#39;Mean Absolute Percentage Error&#39;: 1.212151701573308e-13,
   &#39;Cross Entropy&#39;: 2.5996810883678423,
-  &#39;HOP&#39;: 0.9004283905932736}}
+  &#39;HOP&#39;: 0.9004283905932735}}
 </pre></div></div>
 </div>
 <p><strong>Visualize both distributions</strong></p>
 <p>If we calculated the ground truth above, we can visualize a comparison to the reconstructed probabilities</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit.visualization</span> <span class="kn">import</span> <span class="n">plot_histogram</span>
@@ -601,7 +624,7 @@ <h2>Verify the results<a class="headerlink" href="#Verify-the-results" title="Li
 </div>
 </div>
 <div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
@@ -695,7 +718,7 @@ <h2>Verify the results<a class="headerlink" href="#Verify-the-results" title="Li
   </div>
 </div><script src="../../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb b/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb
index 7518e53ff..c2cfd384f 100644
--- a/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb
+++ b/circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb
@@ -7,6 +7,8 @@
    "source": [
     "# CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting\n",
     "\n",
+    "**NOTE: CutQC is deprecated and will be removed no sooner than Circuit Knitting Toolbox v0.8.0.  The circuit cutting workflow in `circuit_knitting.cutting` now implements similar and improved functionalities, which will be maintained going forward.**\n",
+    "\n",
     "Circuit cutting is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output. \n",
     "\n",
     "The circuit knitting toolbox implements a wire cutting method presented in [CutQC](https://doi.org/10.1145/3445814.3446758) (Tang et al.). This method allows a circuit wire to be cut such that the generated subcircuits are amended by measurements in the Pauli bases and by state preparation of four Pauli eigenstates (see Fig. 4 of [CutQC](https://doi.org/10.1145/3445814.3446758)).\n",
@@ -30,18 +32,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "eb859bde",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:17.192774Z",
+     "iopub.status.busy": "2024-04-19T17:42:17.192467Z",
+     "iopub.status.idle": "2024-04-19T17:42:18.587956Z",
+     "shell.execute_reply": "2024-04-19T17:42:18.587209Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 466.456x338.625 with 1 Axes>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -86,9 +95,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "5d1fb2ca",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:18.591613Z",
+     "iopub.status.busy": "2024-04-19T17:42:18.590975Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.254006Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.253128Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from qiskit_ibm_runtime import (\n",
@@ -115,9 +131,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "d409553d",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.259579Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.258036Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.264089Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.263354Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Set the Sampler and runtime options\n",
@@ -148,9 +171,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "8c11457a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.267512Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.266907Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.297080Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.296320Z"
+    }
+   },
    "outputs": [],
    "source": [
     "%%capture\n",
@@ -172,18 +202,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "5816c27f",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.300182Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.299635Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.506657Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.505891Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 423.331x170.567 with 1 Axes>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -195,18 +232,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "5f605d57",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.511147Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.510046Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.665574Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.664903Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 373.164x170.567 with 1 Axes>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -237,9 +281,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "7fd84c6e",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.671114Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.669747Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.684118Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.683353Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Use local versions of the primitives by default.\n",
@@ -263,9 +314,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "id": "aafc01ef",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.689377Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.688046Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.693864Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.693141Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Set the Sampler and runtime options\n",
@@ -285,10 +343,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "id": "2ae5160c",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.698916Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.697617Z",
+     "iopub.status.idle": "2024-04-19T17:42:19.743023Z",
+     "shell.execute_reply": "2024-04-19T17:42:19.742257Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3259/4196057829.py:3: DeprecationWarning: The function ``circuit_knitting.cutting.cutqc.wire_cutting.evaluate_subcircuits()`` is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. \n",
+      "  subcircuit_instance_probabilities = evaluate_subcircuits(cuts)\n"
+     ]
+    }
+   ],
    "source": [
     "from circuit_knitting.cutting.cutqc import evaluate_subcircuits\n",
     "\n",
@@ -296,7 +370,7 @@
     "\n",
     "# Uncomment the following lines to instead use Qiskit Runtime Service as configured above.\n",
     "# subcircuit_instance_probabilities = evaluate_subcircuits(cuts,\n",
-    "#                                                          service_args=service.active_account(),\n",
+    "#                                                          service=service,\n",
     "#                                                          backend_names=backend_names,\n",
     "#                                                          options=options,\n",
     "#                                                         )"
@@ -322,9 +396,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "id": "5aceecc0",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:19.746149Z",
+     "iopub.status.busy": "2024-04-19T17:42:19.745620Z",
+     "iopub.status.idle": "2024-04-19T17:42:20.976210Z",
+     "shell.execute_reply": "2024-04-19T17:42:20.975201Z"
+    }
+   },
    "outputs": [],
    "source": [
     "%%capture\n",
@@ -350,10 +431,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "id": "5353b0c8",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:20.980252Z",
+     "iopub.status.busy": "2024-04-19T17:42:20.979628Z",
+     "iopub.status.idle": "2024-04-19T17:42:20.987941Z",
+     "shell.execute_reply": "2024-04-19T17:42:20.987149Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3259/3419935587.py:3: DeprecationWarning: The function ``circuit_knitting.cutting.cutqc.wire_cutting_verification.verify()`` is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the ``circuit_knitting.cutting`` package. \n",
+      "  metrics, exact_probabilities = verify(circuit, reconstructed_probabilities)\n"
+     ]
+    }
+   ],
    "source": [
     "from circuit_knitting.cutting.cutqc import verify\n",
     "\n",
@@ -372,26 +469,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "id": "8d54b767",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:20.991193Z",
+     "iopub.status.busy": "2024-04-19T17:42:20.990628Z",
+     "iopub.status.idle": "2024-04-19T17:42:20.995648Z",
+     "shell.execute_reply": "2024-04-19T17:42:20.995000Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'nearest': {'chi2': 0,\n",
-       "  'Mean Squared Error': 1.554441697306333e-32,\n",
-       "  'Mean Absolute Percentage Error': 4.748059826691265e-14,\n",
+       "  'Mean Squared Error': 2.2429332936235563e-32,\n",
+       "  'Mean Absolute Percentage Error': 1.1722866042107947e-13,\n",
        "  'Cross Entropy': 2.599681088367844,\n",
        "  'HOP': 0.9004283905932716},\n",
        " 'naive': {'chi2': 0,\n",
-       "  'Mean Squared Error': 6.5760386594463524e-34,\n",
-       "  'Mean Absolute Percentage Error': 5.260720927719812e-14,\n",
+       "  'Mean Squared Error': 2.033924079765218e-33,\n",
+       "  'Mean Absolute Percentage Error': 1.212151701573308e-13,\n",
        "  'Cross Entropy': 2.5996810883678423,\n",
-       "  'HOP': 0.9004283905932736}}"
+       "  'HOP': 0.9004283905932735}}"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -412,18 +516,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "id": "c8cc97e9",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:20.998203Z",
+     "iopub.status.busy": "2024-04-19T17:42:20.997817Z",
+     "iopub.status.idle": "2024-04-19T17:42:21.418473Z",
+     "shell.execute_reply": "2024-04-19T17:42:21.417908Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -495,7 +606,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/circuit_cutting/explanation/index.html b/circuit_cutting/explanation/index.html
index 081032a2a..7d3ff6f01 100644
--- a/circuit_cutting/explanation/index.html
+++ b/circuit_cutting/explanation/index.html
@@ -3,10 +3,10 @@
   <head><meta charset="utf-8"/>
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
-<link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="Circuit Cutting How-To Guides" href="../how-tos/index.html" /><link rel="prev" title="Wire Cutting Phrased as a Two-Qubit Move Instruction" href="../tutorials/03_wire_cutting_via_move_instruction.html" />
+<link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="Circuit Cutting How-To Guides" href="../how-tos/index.html" /><link rel="prev" title="Automatically find cuts using CKT" href="../tutorials/04_automatic_cut_finding.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>Explanatory material for the circuit cutting module - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>Explanatory material for the circuit cutting module - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current current-page"><a class="current reference internal" href="#">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -497,14 +501,14 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
               </div>
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
             </a>
-          <a class="prev-page" href="../tutorials/03_wire_cutting_via_move_instruction.html">
+          <a class="prev-page" href="../tutorials/04_automatic_cut_finding.html">
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
               <div class="page-info">
                 <div class="context">
                   <span>Previous</span>
                 </div>
                 
-                <div class="title">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</div>
+                <div class="title">Automatically find cuts using CKT</div>
                 
               </div>
             </a>
@@ -569,7 +573,7 @@ <h2>References<a class="headerlink" href="#references" title="Link to this headi
   </div>
 </div><script src="../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.html b/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.html
index 9b4471226..d9646bd5e 100644
--- a/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.html
+++ b/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="How to generate exact sampling coefficients" href="how_to_generate_exact_sampling_coefficients.html" /><link rel="prev" title="Circuit Cutting How-To Guides" href="index.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>How to generate exact quasiprobability distributions from Sampler - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>How to generate exact quasiprobability distributions from Sampler - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -146,7 +146,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -173,7 +173,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -190,6 +190,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../explanation/index.html">Cutting Explanatory Material</a></li>
@@ -219,6 +220,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -312,7 +316,7 @@ <h1>How to generate exact quasiprobability distributions from Sampler<a class="h
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit</span> <span class="kn">import</span> <span class="n">QuantumCircuit</span>
-<span class="kn">from</span> <span class="nn">qiskit.quantum_info</span> <span class="kn">import</span> <span class="n">PauliList</span>
+<span class="kn">from</span> <span class="nn">qiskit.quantum_info</span> <span class="kn">import</span> <span class="n">SparsePauliOp</span>
 
 <span class="kn">from</span> <span class="nn">circuit_knitting.cutting</span> <span class="kn">import</span> <span class="p">(</span>
     <span class="n">partition_problem</span><span class="p">,</span>
@@ -329,9 +333,9 @@ <h1>How to generate exact quasiprobability distributions from Sampler<a class="h
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">circuit</span> <span class="o">=</span> <span class="n">QuantumCircuit</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
 <span class="n">circuit</span><span class="o">.</span><span class="n">h</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
 <span class="n">circuit</span><span class="o">.</span><span class="n">cx</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">observables</span> <span class="o">=</span> <span class="n">PauliList</span><span class="p">([</span><span class="s2">&quot;ZZ&quot;</span><span class="p">])</span>
+<span class="n">observable</span> <span class="o">=</span> <span class="n">SparsePauliOp</span><span class="p">([</span><span class="s2">&quot;ZZ&quot;</span><span class="p">])</span>
 <span class="n">partitioned_problem</span> <span class="o">=</span> <span class="n">partition_problem</span><span class="p">(</span>
-    <span class="n">circuit</span><span class="o">=</span><span class="n">circuit</span><span class="p">,</span> <span class="n">partition_labels</span><span class="o">=</span><span class="s2">&quot;AB&quot;</span><span class="p">,</span> <span class="n">observables</span><span class="o">=</span><span class="n">observables</span>
+    <span class="n">circuit</span><span class="o">=</span><span class="n">circuit</span><span class="p">,</span> <span class="n">partition_labels</span><span class="o">=</span><span class="s2">&quot;AB&quot;</span><span class="p">,</span> <span class="n">observables</span><span class="o">=</span><span class="n">observable</span><span class="o">.</span><span class="n">paulis</span>
 <span class="p">)</span>
 <span class="n">subcircuits</span> <span class="o">=</span> <span class="n">partitioned_problem</span><span class="o">.</span><span class="n">subcircuits</span>
 <span class="n">subobservables</span> <span class="o">=</span> <span class="n">partitioned_problem</span><span class="o">.</span><span class="n">subobservables</span>
@@ -433,7 +437,7 @@ <h1>How to generate exact quasiprobability distributions from Sampler<a class="h
   </div>
 </div><script src="../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb b/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb
index 425b8b458..e807caf00 100644
--- a/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb
+++ b/circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb
@@ -14,11 +14,18 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "072055cb",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:26.666751Z",
+     "iopub.status.busy": "2024-04-19T17:42:26.666047Z",
+     "iopub.status.idle": "2024-04-19T17:42:27.037654Z",
+     "shell.execute_reply": "2024-04-19T17:42:27.037075Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from qiskit import QuantumCircuit\n",
-    "from qiskit.quantum_info import PauliList\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
     "from circuit_knitting.cutting import (\n",
     "    partition_problem,\n",
@@ -38,15 +45,22 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "dc4af922",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:27.040845Z",
+     "iopub.status.busy": "2024-04-19T17:42:27.040384Z",
+     "iopub.status.idle": "2024-04-19T17:42:27.049464Z",
+     "shell.execute_reply": "2024-04-19T17:42:27.048947Z"
+    }
+   },
    "outputs": [],
    "source": [
     "circuit = QuantumCircuit(2)\n",
     "circuit.h(0)\n",
     "circuit.cx(0, 1)\n",
-    "observables = PauliList([\"ZZ\"])\n",
+    "observable = SparsePauliOp([\"ZZ\"])\n",
     "partitioned_problem = partition_problem(\n",
-    "    circuit=circuit, partition_labels=\"AB\", observables=observables\n",
+    "    circuit=circuit, partition_labels=\"AB\", observables=observable.paulis\n",
     ")\n",
     "subcircuits = partitioned_problem.subcircuits\n",
     "subobservables = partitioned_problem.subobservables"
@@ -64,7 +78,14 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "d095701f",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:27.051994Z",
+     "iopub.status.busy": "2024-04-19T17:42:27.051624Z",
+     "iopub.status.idle": "2024-04-19T17:42:27.130549Z",
+     "shell.execute_reply": "2024-04-19T17:42:27.129845Z"
+    }
+   },
    "outputs": [],
    "source": [
     "subexperiments, coefficients = generate_cutting_experiments(\n",
@@ -86,7 +107,14 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "7a74f709",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:27.133708Z",
+     "iopub.status.busy": "2024-04-19T17:42:27.133304Z",
+     "iopub.status.idle": "2024-04-19T17:42:27.137290Z",
+     "shell.execute_reply": "2024-04-19T17:42:27.136791Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.utils.simulation import ExactSampler\n",
@@ -106,7 +134,14 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "7019d781",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:27.139638Z",
+     "iopub.status.busy": "2024-04-19T17:42:27.139267Z",
+     "iopub.status.idle": "2024-04-19T17:42:27.151495Z",
+     "shell.execute_reply": "2024-04-19T17:42:27.150902Z"
+    }
+   },
    "outputs": [],
    "source": [
     "results = {\n",
@@ -132,7 +167,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.html b/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.html
index 55cc0fb84..b114d7159 100644
--- a/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.html
+++ b/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="How to place wire cuts using a single-qubit CutWire instruction" href="how_to_specify_cut_wires.html" /><link rel="prev" title="How to generate exact quasiprobability distributions from Sampler" href="how_to_generate_exact_quasi_dists_from_sampler.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>How to generate exact sampling coefficients - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>How to generate exact sampling coefficients - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -146,7 +146,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -173,7 +173,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -190,6 +190,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../explanation/index.html">Cutting Explanatory Material</a></li>
@@ -219,6 +220,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -314,7 +318,7 @@ <h1>How to generate exact sampling coefficients<a class="headerlink" href="#How-
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">from</span> <span class="nn">qiskit.circuit.library</span> <span class="kn">import</span> <span class="n">EfficientSU2</span>
-<span class="kn">from</span> <span class="nn">qiskit.quantum_info</span> <span class="kn">import</span> <span class="n">PauliList</span>
+<span class="kn">from</span> <span class="nn">qiskit.quantum_info</span> <span class="kn">import</span> <span class="n">SparsePauliOp</span>
 
 <span class="kn">from</span> <span class="nn">circuit_knitting.cutting</span> <span class="kn">import</span> <span class="p">(</span>
     <span class="n">partition_problem</span><span class="p">,</span>
@@ -329,7 +333,7 @@ <h1>How to generate exact sampling coefficients<a class="headerlink" href="#How-
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">circuit</span> <span class="o">=</span> <span class="n">EfficientSU2</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">entanglement</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">decompose</span><span class="p">()</span>
 <span class="n">circuit</span><span class="o">.</span><span class="n">assign_parameters</span><span class="p">([</span><span class="mf">0.8</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">circuit</span><span class="o">.</span><span class="n">parameters</span><span class="p">),</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">observables</span> <span class="o">=</span> <span class="n">PauliList</span><span class="p">([</span><span class="s2">&quot;ZZZZ&quot;</span><span class="p">])</span>
+<span class="n">observable</span> <span class="o">=</span> <span class="n">SparsePauliOp</span><span class="p">([</span><span class="s2">&quot;ZZZZ&quot;</span><span class="p">])</span>
 <span class="n">circuit</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="s2">&quot;mpl&quot;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -348,7 +352,7 @@ <h1>How to generate exact sampling coefficients<a class="headerlink" href="#How-
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">partitioned_problem</span> <span class="o">=</span> <span class="n">partition_problem</span><span class="p">(</span>
-    <span class="n">circuit</span><span class="o">=</span><span class="n">circuit</span><span class="p">,</span> <span class="n">partition_labels</span><span class="o">=</span><span class="s2">&quot;AABB&quot;</span><span class="p">,</span> <span class="n">observables</span><span class="o">=</span><span class="n">observables</span>
+    <span class="n">circuit</span><span class="o">=</span><span class="n">circuit</span><span class="p">,</span> <span class="n">partition_labels</span><span class="o">=</span><span class="s2">&quot;AABB&quot;</span><span class="p">,</span> <span class="n">observables</span><span class="o">=</span><span class="n">observable</span><span class="o">.</span><span class="n">paulis</span>
 <span class="p">)</span>
 <span class="n">subcircuits</span> <span class="o">=</span> <span class="n">partitioned_problem</span><span class="o">.</span><span class="n">subcircuits</span>
 <span class="n">bases</span> <span class="o">=</span> <span class="n">partitioned_problem</span><span class="o">.</span><span class="n">bases</span>
@@ -638,7 +642,7 @@ <h3>Observe the coefficient weights returned from <code class="docutils literal
   </div>
 </div><script src="../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb b/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb
index 72d59cd6a..7e4a31d1e 100644
--- a/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb
+++ b/circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb
@@ -16,12 +16,19 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "dc54656b",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:28.788507Z",
+     "iopub.status.busy": "2024-04-19T17:42:28.787885Z",
+     "iopub.status.idle": "2024-04-19T17:42:29.225050Z",
+     "shell.execute_reply": "2024-04-19T17:42:29.224317Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "from qiskit.circuit.library import EfficientSU2\n",
-    "from qiskit.quantum_info import PauliList\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
     "from circuit_knitting.cutting import (\n",
     "    partition_problem,\n",
@@ -33,11 +40,18 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "dd147239",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:29.228363Z",
+     "iopub.status.busy": "2024-04-19T17:42:29.228093Z",
+     "iopub.status.idle": "2024-04-19T17:42:29.982364Z",
+     "shell.execute_reply": "2024-04-19T17:42:29.981635Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApQAAAD2CAYAAABobBdEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA49ElEQVR4nO3deXhU9dn/8fdMdrKxJ2FfNCAQJXEBFKjEKrKIRaVQnrrVVitCRShWq4+i9WfVp/76gNpIUcHHqrRXfwLVCqhh8SlSQQ0QBJWdBAh7WEJCtvn9MRIIZJnkzOSc8+Xzuq5chjMzZ+7b+d4n95zvWTw+n8+HiIiIiEgjee0OQERERETcTQ2liIiIiFiihlJERERELFFDKSIiIiKWqKEUEREREUvUUIqIiIiIJWooRURERMQSNZQiIiIiYokaShERERGxRA2liIiIiFiihlJERERELFFDKSIiIiKWqKEUEREREUvUUIqIiIiIJWooRURERMQSNZQiIiIiYokaShERERGxRA2liIiIiFiihlJERERELFFDKSIiIiKWqKEUEREREUvUUIqIiIiIJWooRURERMQSNZQiIiIiYokaShERERGxRA2liIiIiFiihlJERERELFFDKSIiIiKWqKEUEREREUvUUIqIiIiIJWooRURERMQSNZQiIiIiYkm43QGYZu18KC60OwqIaQ59R1tbh1NygeDkYxLTPhun5KNxZjanjDNQ3Yh51FAGWXEhFB2yO4rgMCkX05j22ZiWjziTaePMtHzE3TTlLSIiIiKWqKEUEREREUvUUIqIiIiIJWooRURERMQSnZRjkxfm3cXHX74JgNfjpWVCCn27Z3LP8N/TOrG9zdE1nGn5mMSkz8akXMTZTBprJuUizqU9lDZK6zqIv/7nXt5+bBePjn+HLXty+N1bY+wOq9FMy8ckJn02JuUizmbSWDMpF3EmNZQ2Cg+LpGVCMq0T23Npt8GM6HcvG3euoqjkmN2hNYpp+ZjEpM/GpFzE2UwaayblIs6khtIhDh7dw6e5f8frDcPrDbM7HMtMy8ckJn02JuUizmbSWDMpF3EOHUNpo3XblnPTY3H4fJWcKisG4LbBU4mJjAXg6f+5jctTb2BE/3sB2LI7h2ffGc+rk3OIjIi2Le7a1JfPv3Ln89bHT1V7za79G5kwagY3XX1/k8d7ITFprGmcSVNR3ahuJHCubijXrVvHE088wfLly/H5fGRmZpKVlUVqaiojRoxg3rx5dodYp54d+/HwuDcpLS9hxbq/kbP5E+6+8ZmqxyfcPIOHXhnIwLRbiI9pyYz37mfij1523IbqtPryGZg2moFpZ+7PtXLDAt5Y9Fuuv+JOO8JtEJ8PCo7C8RKICocOLSHMRfv3TRprJo8z06hunEN1I6Hm2oYyOzubkSNH0rlzZx5//HFiYmKYO3cuw4YN48SJE/Tt29fuEOsVFRFD+9YXAdA1uQ97D23l5QWTmDJmNgCtE9tz6+Ap/PmDafTs1I8OrVPJuPg6O0OuU335nO1AYT4vzX+AZ+9ZRHRks6YONWA+H6zZDss3wZ7CM8sTYmDgxZDZC8JdMGNk0lgzcZyZxueDL3fAso2wu/DM8oQYuPpiuK4XRKhumpTqRkLNRd8Vzzhw4ABjx44lIyODnJwcpk2bxsSJE8nOzmbXrl0Armgoz3X79dNZ8sUcvs37omrZqKsfYOe+r/nrsue476YXbYyu4WrKB6CyspLn3v0p44Y8Qrd2l9oUXf18PljwFbyzqnozCXCsGD5cD68uhdJyW8KzxKSx5vZxZhqfD/6RA3/5rHozCf66WbwesrJVN3ZT3UiwubKhfP755zly5Ahz5swhJiamanliYiIZGRmAOxvKDm0uZsAlNzFn8WNVy7xeLyP7/5Kreg6neVwbG6NruJryAXg7+xmaRSfwo4GTbIosMF/ugBXf1P2cLfvh/ZwmCSeoTBprbh9npsnZCcs21f2cbQdgwZdNE08wqW5EaufKhnLevHkMGjSI1NTUGh9PSkoiOTkZgPLych588EFatmxJ8+bNueeeeygpKWnKcBtkzLXT+PK7j1i3dXnVMo/Hi8fjyo/qvHw2bF/J4tWvM+3Hc+wNrB4+n3+aOxD/3grFpaGNJxRMGmtuHWcmWl7Pl7DTVm+DolOhjSUUVDciNXPdMZQFBQXs3r2bsWPHnvdYZWUlubm5pKenVy179tlnWbZsGbm5uURGRjJq1CgefvhhZs6cGdD7lZeXU1BQEHB8ZWVJQES9z3t43Nwal/fucjUf/5cv4PerPY4y8vP3WVxHYLlAYPmcKC7k+Xm3M23sXBJiWzUwFuv5NMThk+HkH0kO6LllFfDp+sP0Tj4Z4qjOes8gfzbWYmm6sWbaODNNYXEYuw6lBPTc8kp/3aSlqG4avw7VjQRfcnIy4eENbw9d11AWFRUB4PF4znts4cKF7N+/v9p092uvvcYLL7xA+/b+20tNnz6dMWPG8Mc//pGwsPqPCi8oKKBjx44Bxzd76ga6JPcO+Pmh8t133/GD+/pYWkewc3l/VRaHj+0l6x8PVVt+wxV3cuvgh2p5lV8w8mmIdqnXMOaJfwX8/Eef+D/kLPq/IYyoOqeMM3DeWHPTODNNcvd+jH3q3wE//4nfvcAX7z8fwoiqU93UTnUjp+Xl5dGhQ4cGv851DWXHjh0JCwtjxYoV1Zbv3LmTSZP8x3ycbigLCwvJy8ur1mBmZGRw/PhxduzYQffu3ZsqbEuGXnkXQ6+8y+4wLPtJ5qP8JPNRu8MISGlxw+4eUVZyPESRNC0TxpqbxplpGlo3pcWqG6dQ3YhVHp/PZ33ffRP72c9+xpw5cxg1ahQjRowgLy+P2bNnk5SUxPr169m0aRM9e/YkLy+PTp06sXfv3qpjKsvKyoiMjCQnJyegE3caOuW97cMkSo8FNqUSSpEJZXQbbm0Kwim5QHDyaQifD15bnczRkjDg/L3hZ/Pg477+e4mLqmya4DDvs3FKPk09zkzj88Eba5I4UhxOIHXzi34FJERXNE1wOGecgepGnOuCmfIGmDlzJhERESxcuJClS5cyYMAA5s+fz9NPP82WLVuqTtaJj48H4OjRo1UNZWFhYbXH6hMeHt6gXb95EeCE8zMiIiIatcv6bE7JBYKTT0NdewIWflX/8y7r5KFn93ahD+gspn02TsnHjnFmmmuLYH4AZ3D36eCh10WBHW8ZLE4ZZ6C6EfO477Q0IC4ujlmzZlFQUMDx48f56KOPGDBgABs2bCAtLQ2v159W8+bN6dixI2vXrq16bU5ODvHx8XTp0sWe4MU1BveAXvX0ia3i4NYrmyYeETcYmAp96uktWsbCmKuaJh4RaRqubChrUlhYSH5+/nnT2D//+c/5/e9/z549ezhw4ADTp0/nrrvuCuiEHLmwhXnhZ4P9d8OJPmdWyeuB9M4weSjEO+8uayK2CfPC3YP8d8OpqW76doKHhvrvmiMi5nDllHdNcnNzgfMvaP7b3/6WgwcP0rt3byorK7ntttt4/vmmO6tQ3C08DEalw9A0WLMN/r7Gv/yhG6FjS3tjE3GqMC/clA43nFM3k4dCp4ZdkUZEXML4hjI8PJyZM2cGfN3Jpvbh56+xZM0beDxeHrwli64paVWPrf5mEW8ueYLwsAguap/BpNEv2xhpYEzL57SocP803uk/jG7cK2nSZ2NSLiY7t27cuFfSpLFmUi7iPMZMeU+YMAGfz0f//v3tDiVgx04e5oNVWbx4/wqmjnmdPy18sNrjb300nSfvfI8ZEz9j76GtbNuz3qZIA2NaPiYx6bMxKRdxNpPGmkm5iDMZ01C60be7VnNp92sJD4ugY9seHC06SGXlmUvPdEnuQ1FxIRWVFZwqKyauWQsbo62fafmYxKTPxqRcxNlMGmsm5SLOZMyUtxsdLz5MfMyZoo2Jiqeo5Cjx3xfykPTxPPLaUKIjY0nvnknb5oHfsccOpuVjEpM+G5NyEWczaayZlIs4k/ZQ2igupgUnigur/l186jix0YlV/35p/gRe+dUa3vzNZvB4WLlhQdMH2QCm5WMSkz4bk3IRZzNprJmUiziTGkob9ezUj9ztn1JRUc7ug1tIjG1ddQ1NAK8njNiY5gA0j23DsZOHbIo0MKblYxKTPhuTchFnM2msmZSLOJOmvG2U0Kwlw676OVOyBuPxeJk0+hXWfLOY48WHyUwfzx03PMXDr2YSER5FXEwLxjn8Pqum5WMSkz4bk3IRZzNprJmUiziTK+/l7WSr5kCRA77YxbaCAXdbW4dTcoHg5GNV4UmYPt//+/TR0LyZfbGY9tk4JR8njDPTqG5qproR02jKW0REREQsUUMpIiIiIpboGMog+/6YZtsFIw6n5ALOisUJnPT/w6Sx5pQ4JDSc9PmqbsQ0aiiDrO9ouyMIHpNyMY1pn41p+YgzmTbOTMtH3E1T3iIiIiJiiRpKEREREbFEDaWIiIiIWKKGUkREREQsUUMpIiIiIpaooRQRERERS9RQioiIiIglaihFRERExBI1lCIiIiJiiRpKEREREbFEDaWIiIiIWKKGUkREREQsUUMpIiIiIpaooRQRERERS9RQioiIiIgl4XYHYJq186G40O4oIKY59B1tbR1OyQWCk484l1PGmupG3MQpY011I6CGMuiKC6HokN1RBIdJuYizmTTWTMpFnM2ksWZSLhcqTXmLiIiIiCVqKEVERETEEjWUIiIiImKJjqEUqcOJEti8D/IOQf6RM8v/uRYuSoLubaF1vG3hiTjSiRLYsg92HYb8w2eWf7D2TN20Ud2IGEUNpUgN8g7B8m9g7S6oqDz/8TXb/T8AqckwuAf0bg8eT9PGKeIkeYdhxSbIqaVuvtju/wG4OAkG9YC0DqobEROoobTJC/Pu4uMv3wTA6/HSMiGFvt0zuWf472md2N7m6BrOlHxKy2HReli+CXwBvua7Av9Pnw4w5ipIjAlpiBc0U8bZaabkU1bhr5tlm8AXYOFs3uf/6dUOxvaDxGahjfFCZso4O820fEyhYyhtlNZ1EH/9z728/dguHh3/Dlv25PC7t8bYHVajuT2fY8Xw30u+/6PYiNdvyIcX/gm7dOmLkHL7ODuX2/M5XgwzlsDSjYE3k2fbuAee/yfsOBj82OQMt4+zc5mWjwnUUNooPCySlgnJtE5sz6XdBjOi371s3LmKopJjdofWKG7O50QJvPwJ7Cms/Tlej3/vY2KM//eaFJ2CP2X7p/4kNNw8zmri5nyKTsEr2dWPLz5XIHVzshSysvVlLJTcPM5qYlo+JlBD6RAHj+7h09y/4/WG4fWG2R2OZW7Kx+eDd1bB/nq2Q/HR8NQt/p/46NqfV1IGb3zq/6+ElpvGWSDclI/PB+/+GwqO1v28QOvmVLm/bopLgxunnM9N4ywQpuXjVjqG0kbrti3npsfi8PkqOVVWDMBtg6cSExkLwNP/cxuXp97AiP73ArBldw7PvjOeVyfnEBlRx5bZJvXl86/c+bz18VPVXrNr/0YmjJrBTVff3+TxnrZ6m3/aLZiOFME/cuDHVwV3vaK6AWfUzZc7/Id5BFPhSVj4FYzrH9z1iuoGnFE3JnN1Q7lu3TqeeOIJli9fjs/nIzMzk6ysLFJTUxkxYgTz5s2zO8Q69ezYj4fHvUlpeQkr1v2NnM2fcPeNz1Q9PuHmGTz0ykAGpt1CfExLZrx3PxN/9LIjixvqz2dg2mgGpp25QerKDQt4Y9Fvuf6KO+0IF4DyCv+lTELhs81wbU9omxCa9TfWkSJ/bDsPQaUP2sbDgIuhY0u7IwuM6sb+uqmohPdzQrPuf2+Fay+B5MTQrL+xCk/662bHQX/dtImHARdBp1Z2RxYY1Y39dWM61zaU2dnZjBw5ks6dO/P4448TExPD3LlzGTZsGCdOnKBv3752h1ivqIgY2re+CICuyX3Ye2grLy+YxJQxswFondieWwdP4c8fTKNnp350aJ1KxsXX2RlynerL52wHCvN5af4DPHvPIqIj7Tu9c30eHC8J3fpXbobRl4du/Q3h88GH6+CTr6ufdLRlH3y2xX+W+u1XQ1SEbSEGRHVjf91syIejxaFb/8rNcOsVoVt/Q/h8sDgXPtpQ/aSjLftg1Rb/5cJuvwaiVTdNyo11YzpXHkN54MABxo4dS0ZGBjk5OUybNo2JEyeSnZ3Nrl27AFzRUJ7r9uuns+SLOXyb90XVslFXP8DOfV/z12XPcd9NL9oYXcPVlA9AZWUlz737U8YNeYRu7S61KTq/09fEC+X6G3PmaygsWg8ff137Gewb8uGN/4XKGq4f6GSqm6a3pgnqptIhdbNkAyzJrb2Ov94Nr6+o+bqbTqa6kWBzZUP5/PPPc+TIEebMmUNMzJmL/iUmJpKRkQG4s6Hs0OZiBlxyE3MWP1a1zOv1MrL/L7mq53Cax7WxMbqGqykfgLezn6FZdAI/GjjJpsj8fD7/tG8oFZ2CQydC+x6BKDzpbybr8+1e/x9IN1HdNL1dIb7ET3EpHDge2vcIxLFi+Ci3/udt3ge5QT6eNNRUNxJsrpzynjdvHoMGDSI1NbXGx5OSkkhOTgbgb3/7GzNnzmTt2rW0bt2aHTt2NOi9ysvLKSgoCPj5ZWVJQOPnPsZcO43Jr1zDuq3Luaz7tQB4PF48nob1/mVlZeTn72t0HP51WMsFzs9nw/aVLF79OlmTv2pgLNbzOdexkjCKTqVUW+b11H4makJMzb+f7XjJ+XtW1m05RI82IZwfDMDKHQn4fIEczOkjO/cULTxNe1FA1U11Tq6bE6e8HCtpV21ZKOpm/eZDXJJkb938e2c8lb5ADub0101rr+qmsUyvGzdJTk4mPLzh7aHH53PKhFxgCgoKSElJYcqUKbz4YvVd8pWVlaSkpJCens7ixYsB+Pjjjzl06BD79u3jj3/8Y4Mbyvz8fDp27Bjw82dP3UCX5N4Neo/6LFkzl+/yv2DS6JcDfs2Ogq/5xYt9LL1vsHM5UVzI/f+dwdQxr9P3oiENem0w8jlX266X85PfVZ8eSYzxX96ksZ587/xjy5bNfYD1n/yp8SsNglG//oAulw0L6A9FSdERZt3XtGfoqG5q57S6ad3pMv7j2bXVloWibla8NZm1S2Y0fqVBMHLyfLpdfjOeAO4NWVpygqyfN+0NylU3tXNa3bhJXl4eHTp0aPDrXLeHsqioCKDGAl+4cCH79++vNt19/fXXA7BgwYKmCE/q8P6qLA4f20vWPx6qtvyGK+7k1sEP1fKq0Ankj0Rw3sf+I0s8Dbg2m67j5iwXbN14VTfSeE6rmwuB6/ZQlpaW0qxZM9LT01mzZk3V8p07d3LNNdewe/du3n33XcaNG1ftdQsWLGDy5Mkhn/Le9mESpcfsP90vMqGMbsOt7bJ3Si4QnHzOdeRkOK+vSa62rL6pu6nD/L+/uMh/fNW5apq6G97zML2STgYh4sZbtiWRL3cHsvfER3J8GT/N2B/ymM7mlLGmuqlfYXEYr61u2KEijamboT0Ok5Zsb90s35rIF/mB1U3buDLuuFx101hOyQVCUzdu0tgpb9ftoYyMjOSOO+5gzpw53HzzzYwYMYK8vDxmz55NUlISu3fvDuoJOeHh4Q3a9ZsXAU640UNERESjdlmfzSm5QHDyOVc7H0Tl+O/QcVqlL7DLoRwrDvyyKWndW5LS3N6LPF4fD18GdLKNhyG9I4P+/7o+Thlrqpv6tfdBdE71O0GFpG66taRDS3vr5oYE+CKgk208XNtLdWOFU3KB0NTNhcD+OYVGmDlzJvfeey+ff/45U6dO5fPPP2f+/Pm0a9eOZs2a1XqyjsjZvB7oEOK/V5FhzriweXIiXN6l/uclJUB655CHIy7m8YT+Yt4RYZDSPLTvEYi2CXBlt/qf1yYeLu8a+nhEnMx1eygB4uLimDVrFrNmzaq2fMOGDaSlpeF1wLE34g7pnWFrCGepLusMYQ4ZjuP6Q2l57Zc3SUqE+zMh0pVbBWlK6Z3hu8CPBGqwSzs6p27GXgWlZbAur+bH2ybAL4dAlOpGLnDGlEBhYSH5+fmMGDGi2vKKigrKysooKyvD5/NRUlKCx+MhKirKpkjFSa7o6r+F3NnT3sE08OLQrLcxIsLg7sH+RmDZRvj2+4agY0sY1AP6dlIzKYHJ6OK/5/bZ097BNNBBk0zhYXDnINhcAMs2wTd7/cs7tIRBqf7mWnUjYlBDmZvrv/rsucdPvvXWW9x9991V/46JiaFz584NPjknVD78/DWWrHkDj8fLg7dk0TUlreqx1d8s4s0lTxAeFsFF7TMadBkHu7gtn+gIGNILFq8P/rp7tXPefX69HuiZ4p8Cnz7fv+yeH0Bzl92NzG3jrD5uyycqHK7rBf9cF/x190iBLq2Dv14rvB5/XEln1c3PVTe2My0ft3PIpIJ1tTWUd911Fz6fr9qPU5rJYycP88GqLF68fwVTx7zOnxY+WO3xtz6azpN3vseMiZ+x99BWtu0JQdcTRG7N5/re0L5FcNcZHQE/7uc/3kyCy63jrDZuzSezl3/vdjBFhcM41U1IuHWc1ca0fExgTEM5YcIEfD4f/fv3tzuUgH27azWXdr+W8LAIOrbtwdGig1SedSPlLsl9KCoupKKyglNlxcQ1C3LXE2RuzSfMC3dcA7H1HAVxvMR/AeYn3/P/XhuvB/5jgPv2XriFW8dZbdyaz+m6iQtS3Xg8MH4AtIgNbpzi59ZxVhvT8jGBMVPebnS8+DDxMWcGeUxUPEUlR4n/fuAPSR/PI68NJToylvTumbRtHvgde+zg5nxOn5Dy6lI4carm5wRyaRSvB26/BtKck5px3DzOauLmfNokwITrIGtp7c1ioHUzfgBc1in4MYqfm8dZTUzLxwTG7KF0o7iYFpwoLqz6d/Gp48RGn7lv7EvzJ/DKr9bw5m82g8fDyg0Lmj7IBnB7Ph1a+i/A3DOl/ufWJCkBfnWDLrsTam4fZ+dyez7tWvjr5pJ29T+3Jm3iYdL1/hPkJHTcPs7OZVo+JlBDaaOenfqRu/1TKirK2X1wC4mxratd8sjrCSM2pjkAzWPbcOzkIZsiDYwJ+bSIhfuG+KesA70OXmIMDL8Mfj3ceScTmMiEcXY2E/Jp3gzuvRZ+erW/wQxEQgwMuxSmDYeubUIanmDGODubafmYQFPeNkpo1pJhV/2cKVmD8Xi8TBr9Cmu+Wczx4sNkpo/njhue4uFXM4kIjyIupgXjMh+1O+Q6mZKPx+O/mPEVXWH7Af9lQvIOw76jUFoB4V5oFeffo9m9LfRu75xr5l0ITBlnp5mSj8fjr5nLu8COg7BpD+QfhoLv6ybM46+bjq1UN3YwZZydZlo+JnDdvbydbtUcKHLAF6HYVjDg7vqfVxen5ALByUfOKDx55vIn00fbfwKRU8aa6kbqorqpmepGQFPeIiIiImKRGkoRERERsUQNpYiIiIhYopNyguz7k8psF4w4nJILOCsWCT6nfL6qG3ETp3y+qhsBNZRB13e03REEj0m5iLOZNNZMykWczaSxZlIuFypNeYuIiIiIJWooRURERMQSNZQiIiIiYokaShERERGxRA2liIiIiFiihlJERERELFFDKSIiIiKWqKEUEREREUvUUIqIiIiIJWooRURERMQSNZQiIiIiYokaShERERGxRA2liIiIiFiihlJERERELFFDKSIiIiKWhNsdgGnWzofiQrujgJjm0He0tXU4JRcITj4iTcG0unFKPtoGmM0p4wxUN42lhjLIiguh6JDdUQSHSbmINBXT6sa0fMSZTBtnpuUTCE15i4iIiIglaihFRERExBI1lCIXGJ8PjhSd+feBY1BRaV88Im5wbt3sV92IVKNjKEUuAOUVsD4PVm+DXYfgZOmZx17JhogwaN8C+naGq7pCsyj7YhVxivIKyM33183Og9Xr5k/f1027FtC3E1zVDWJVN3IBU0Npkxfm3cXHX74JgNfjpWVCCn27Z3LP8N/TOrG9zdE1nGn5mMLn8/8x/GAtHC+p/XllFbDjoP/nn2vhBz3hxjQID2uqSC9MJtWNSbn4fPDFdng/B47VUzc7D/p/PlwHg3rAsEv9jaaEjkljzaRcNOVto7Sug/jrf+7l7cd28ej4d9iyJ4ffvTXG7rAazbR83O5ECcxeDu/+u+5m8lxlFfDJ1/CHRbDnSMjCk++ZVDcm5FJ0Cl7/FN5eVXczea6yCli6Ef7rQ8g/HLr4xM+EsXaaKbmoobRReFgkLROSaZ3Ynku7DWZEv3vZuHMVRSXH7A6tUUzLx82OF8NLH8PGPY1fR8FRmPmxf6+lhI5JdeP2XE6UwMsfw4b8xq9j/zF/7W0/ELy45HxuH2tnMyUXNZQOcfDoHj7N/Ttebxher/vnS0zLx03KKuDVZbCvjm2R1wOJMf4fr6f255WUwZ+XwcHjwY9TzmdS3bgtl/IKmLUM9h6t/TmB1s2pcv+69rurH3Att421urg5Fx1DaaN125Zz02Nx+HyVnCorBuC2wVOJiYwF4On/uY3LU29gRP97AdiyO4dn3xnPq5NziIyIti3u2tSXz79y5/PWx09Ve82u/RuZMGoGN119f5PHa6rF62F3PVPV8dHw1C3+3598D44W1/7ck6X+afMHflj3H1FpHJO2A27eBizJhbx6pqobUjclZf66mfRD8GrXTdCpbpxRN2dzdUO5bt06nnjiCZYvX47P5yMzM5OsrCxSU1MZMWIE8+bNszvEOvXs2I+Hx71JaXkJK9b9jZzNn3D3jc9UPT7h5hk89MpABqbdQnxMS2a8dz8Tf/Sy44rhtPryGZg2moFpZ+4BtXLDAt5Y9Fuuv+JOO8I10u4jsHRT8Ne7dT/8ewtcfXHw122Vz+ffG3u8BKLD/WfdhrnoD7hJ2wG3bgP2FkL2xuCvd/sB+GwLDEwN/rqDYd8xOFYMUeH+qzyobuzh1ro5l2sbyuzsbEaOHEnnzp15/PHHiYmJYe7cuQwbNowTJ07Qt29fu0OsV1REDO1bXwRA1+Q+7D20lZcXTGLKmNkAtE5sz62Dp/DnD6bRs1M/OrROJePi6+wMuU715XO2A4X5vDT/AZ69ZxHRkc2aOlRjrfjG32CFwrJNMOAi8DhkL6XPB1/ugOWbIP+sPbKJMXBNKmRe4o6z1E3aDrh1G7DiG6gMUd0s3+T/Iuakvfun6+bsPbIJMXDNxZDZyx1nqatu7K+bc7no+8gZBw4cYOzYsWRkZJCTk8O0adOYOHEi2dnZ7Nq1C8AVDeW5br9+Oku+mMO3eV9ULRt19QPs3Pc1f132HPfd9KKN0TVcTfkAVFZW8ty7P2XckEfo1u5Sm6IzT9EpyNkZuvUfOA6b94Vu/Q3h8/kv6fKXz6o3k+Cfhvxwnf840tJye+KzwqTtgBu2ASdL/Q1WqBw8Ad/uDd36G+qDtfDWyvOn948Vw6L1kLVUdWM3N9RNTVzZUD7//PMcOXKEOXPmEBMTU7U8MTGRjIwMwJ0NZYc2FzPgkpuYs/ixqmVer5eR/X/JVT2H0zyujY3RNVxN+QC8nf0MzaIT+NHASTZFZqat+/0n5ITSNxbOGg+mnJ31T+1v2Qf/+Kpp4gkmk7YDbtgGbG+KunFIQ7lul/+SYHXZth/mf9k08QST6sZ+rmwo582bx6BBg0hNrfnAlKSkJJKTkzl16hS/+MUv6NatG/Hx8aSmpvLSSy81cbQNM+baaXz53Ues27q8apnH48XjceVHdV4+G7avZPHq15n24zn2BmagvENN8B4Oub7e8m8Ce96/t1W/u4lbmLQdcPo2oCnGdFPUZiACrZs12/wzHm6jurGX646hLCgoYPfu3YwdO/a8xyorK8nNzSU9PR2A8vJykpOT+eijj+jWrRvr169n6NChJCUl8eMf/zig9ysvL6egoCDg+MrKkoCIep/38Li5NS7v3eVqPv4v6wfzlJWVkZ9vbX4y0FwgsHxOFBfy/LzbmTZ2LgmxrRoYi/V8TLdjX0vgzDE1Xo//rNSaJMTU/Pu5jpdUP7Zsz5EK8vPt3d1ypDiMXYdSAnpueQV8uv4wfZJPhjiqM4JdN9ZiabrtgFu3AdsLQl83ewvtr5ujJWFsPxBg3VTCivWHuTRFddP4dbi3bpKTkwkPb3h76LqGsqioCABPDWcGLFy4kP3791dNd8fGxvK73/2u6vG+ffsyatQo/vWvfwXcUBYUFNCxY8eA45s9dQNdknsH/PxQ+e677/jBfX0srSPYuby/KovDx/aS9Y+Hqi2/4Yo7uXXwQ7W8yi8Y+Zhu1K8/oGvfEVX/PvsSJ3WZOqz2x869NMqhI8caVA+hkHxRf8ZOXxXw8x+f/hxf/vO/QhhRdU7ZBoDztgNO3AaMfGgB3S+/uerfoaibo8eLba+bpG5XMO7pNQE/f/ozf2DNP34fwoiqU93UrqnrJi8vjw4dOjQ4Ttc1lB07diQsLIwVK1ZUW75z504mTfIfV1Db8ZNlZWX87//+L7/+9a9DHWZQDb3yLoZeeZfdYVj2k8xH+Unmo3aHYazK8rImeA/754/LShp2lfXSYjOuLm3CdsCJ24DKigujbk41sA5Ki824m4Hqpul4fL5QXWQkdH72s58xZ84cRo0axYgRI8jLy2P27NkkJSWxfv16Nm3aRM+ePc973X333cdXX33FypUriYyMDOi9Gjrlve3DJEqPBbbbPpQiE8roNtzaLnun5ALBycd0y7Ym8mV+fNW/65u6O72H5cVF/jM8a3Lu1F27hFOMT7f3nnI+H7y2OpmjJWFA3ddi8eDj3v4FxEeF+KyLs5hWN07JJ1TbgBXbElmTF9q6SYov5faM/UGKuHF8PnhjTRJHisOpr27Axy/6FZAYrbppLKfk05hcLpgpb4CZM2cSERHBwoULWbp0KQMGDGD+/Pk8/fTTbNmypcaTdaZMmcKqVatYunRpwM0kQHh4eIN2/eZFgP3fRSEiIqJRu6zP5pRcIDj5mK5XGXx51j2IK31138njtGPFgT0PoHtylCM+hyEnYEEAZ3CndfRwSffAjhsLFtPqxin5hGob0KsC1uSd+XdI6iYp0hF1k3kS/t8X9T+vd3sPvS9S3VjhlHya8m+nKxvKuLg4Zs2axaxZs6ot37BhA2lpaXjPuc/V5MmTyc7OZunSpbRu3bopQxVpMt3a+vc7hHLKoXtSCFfeAIN6wHcFsLGOyxi1jIXbrmy6mMSdurXxX6w/lHN1Tqmbqy+GbwtgQ37tz2neDH58VdPFJOZw37n0tSgsLCQ/P/+84yd/9atf8cknn7B06VLatHHPdahEGqpFLPRqH7r1x0dDnxCuvyHCvPCzwf674USd87XY64G+nWDy0LrPxBUBSGwW2nEdGwWX2Xs+TpUwL9w9CK7rBdHnzMZ6PP44H7rR//9EpKFcuYeyJrm5uUD1E3J27tzJSy+9RFRUFF27dq1aPmjQIBYtWtTUIYqE3OAe8PXu0Kz76ouddSvD8DAYlQFD02D1dvh/35/AOnkodGrYlTXkAje4B+TWsdfOiqsvclbdhHnhpnS4Ic1/vcm/f183D6luxCKjG8rOnTvj9HOOPvz8NZaseQOPx8uDt2TRNSWt6rHV3yzizSVPEB4WwUXtM5g0+mUbIw2Mafm4TY8USO8c/Fswton379VwoqgISOtwpqF0415Jk+rGjblcnAyXdwn+LRhbx8EPHXq1s6hw6NPhTEOpurGXCbkYM+U9YcIEfD4f/fv3tzuUgB07eZgPVmXx4v0rmDrmdf608MFqj7/10XSevPM9Zkz8jL2HtrJtz3qbIg2Mafm41a1XQEItZ6medrzEf628J9/z/14XrwfGD4BIY75+OotJdePmXG65AhLraaoaWjc/6X/+IRkSHG4ea+cyJRdjGko3+nbXai7tfi3hYRF0bNuDo0UHqaysrHq8S3IfiooLqais4FRZMXHNWtgYbf1My8et4qLhl5nQrI6LGZw+k/VocfXLm5zL44GfXg1ddfhxyJhUN27OJTbKXzexUbU/J+C6wd9MOuVkHBO5eaydy5Rc9N3JRseLDxMfc2ZgxETFU1RylPjvB8uQ9PE88tpQoiNjSe+eSdvmDjmyuxam5eNm7VrApOvh9U/hYCOvTxwd4d8zeak+ppAyqW7cnktK8+/rZgUcsFA34/r7TwyT0HH7WDubKbloD6WN4mJacKK4sOrfxaeOExudWPXvl+ZP4JVfreHN32wGj4eVGxY0fZANYFo+bpfSHB4eDj/oWf9ljM/Vqx08MlLNZFMwqW5MyCU5EaYNhyGX+PfQN0TPFPjNCDWTTcGEsXaaKbmoobRRz079yN3+KRUV5ew+uIXE2NbVrqHp9YQRG9McgOaxbTh28pBNkQbGtHxMEBkOoy+Hx2/2n1RT17GVUeFwVTeYciPcO8R/PToJPZPqxpRcIsPh5gz4z1Fwfe+6T1iJDIcru/rPkr5viP/yXRJ6pow1MCcXTXnbKKFZS4Zd9XOmZA3G4/EyafQrrPlmMceLD5OZPp47bniKh1/NJCI8iriYFoxz+L08TcvHJK3i/JcKGdkXCk9C3mH/SQU+H8REQvsW0DYevPqK2eRMqhuTcgFoGQcj+sLwy/zHTeYdOnNbRdWNvUwaa6bk4sp7eTvZqjlQ5IAvD7GtYMDd1tbhlFwgOPmIuQpPwvT5/t+nj7Z376ppdeOUfLQNCD7VTc1UN42j71UiIiIiYokaShERERGxRMdQBtn3x83aLhhxOCUXcFYsInVx0lg1aTvglDgkNJz0+apuGkcNZZD1HW13BMFjUi4iTcW0ujEtH3Em08aZafkEQlPeIiIiImKJGkoRERERsUQNpYiIiIhYooZSRERERCxRQykiIiIilqihFBERERFL1FCKiIiIiCVqKEVERETEEjWUIiIiImKJGkoRERERsUQNpYiIiIhYooZSRERERCxRQykiIiIilqihFBERERFL1FCKiIiIiCXhdgdgmrXzobjQ7iggpjn0HW1tHU7JBYKTj0hTMK1unJKPtgFmc8o4A9VNY6mhDLLiQig6ZHcUwWFSLiJNxbS6MS0fcSbTxplp+QRCU94iIiIiYokaShERERGxRA2liIiIiFiiYyhFxJWKTsHmfZB3CPKPnFn+4Tro3tb/0zrevvhEnKjoFGzZB3mH/T+n/XMdXNQWurWFNqobaQQ1lCLiKvmHYfk3sHYnlFee//jqbf4fgB7JMKgH9G4PHk/TxiniJLuP+OsmZ0fNdbNmm/8HIDUZBqVCnw6qGwmcGkqbvDDvLj7+8k0AvB4vLRNS6Ns9k3uG/57Wie1tjq7hTMtHnKesAhath2WbwOcL7DXfFvh/+nSAH18FCTGhjbGhTKobk3IxSXkFLMmF7I1QGWDdfFfg/+nd3l83ic1CG2NDmTTWTMpFx1DaKK3rIP76n3t5+7FdPDr+HbbsyeF3b42xO6xGMy0fcY7jJTBjCSzdGHgzebYN+fD8P2GXAy/jYVLdmJSLCU6UwIyP4OOvA28mz/b1bn/d7DgY/NisMmmsmZKLGkobhYdF0jIhmdaJ7bm022BG9LuXjTtXUVRyzO7QGsW0fMQZik7BK59UP07yXF4PJMb4f7y1TNEVnYI/ZVc/bswJTKobk3Jxu5MBjPdA6uZkKWRlO+/LmEljzZRc1FA6xMGje/g09+94vWF4vWF2h2OZafmIPXw+eGcVFByt+3nx0fDULf6f+Ojan1dSBm986v+vE5lUNybl4jY+H8z7HPYU1v28QOvmVLm/bopLgxpm0Jg01tyci46htNG6bcu56bE4fL5KTpUVA3Db4KnERMYC8PT/3MblqTcwov+9AGzZncOz74zn1ck5REbUUf02qS+ff+XO562Pn6r2ml37NzJh1Axuuvr+Jo9XnO/LHf5pt2A6UgT/yPEfG+YEJm0HtA1whpydsD4vuOssPAkLv4Jx/YO73sZS3TivblzdUK5bt44nnniC5cuX4/P5yMzMJCsri9TUVEaMGMG8efPsDrFOPTv24+Fxb1JaXsKKdX8jZ/Mn3H3jM1WPT7h5Bg+9MpCBabcQH9OSGe/dz8Qfvey4YjitvnwGpo1mYNqZm4qu3LCANxb9luuvuNOOcMXhKirh/ZzQrPuzzXBtT2ibEJr1N4RJ2wFtA+xXWen/whQK/94K114CyYmhWX9DqG6cVzeubSizs7MZOXIknTt35vHHHycmJoa5c+cybNgwTpw4Qd++fe0OsV5RETG0b30RAF2T+7D30FZeXjCJKWNmA9A6sT23Dp7Cnz+YRs9O/ejQOpWMi6+zM+Q61ZfP2Q4U5vPS/Ad49p5FREc67BRCcYQN+XC0OHTrX7kZRl8euvUHyqTtgLYB9tu4x783MVRWfge3Xhm69QdKdeO8unHlMZQHDhxg7NixZGRkkJOTw7Rp05g4cSLZ2dns2rULwBUN5bluv346S76Yw7d5X1QtG3X1A+zc9zV/XfYc9930oo3RNVxN+QBUVlby3Ls/ZdyQR+jW7lKbohOnO30tyVBZs61xZ76GmknbAW0Dml7I62a7fy+o06hu7OfKhvL555/nyJEjzJkzh5iYMxeWS0xMJCMjA3BnQ9mhzcUMuOQm5ix+rGqZ1+tlZP9fclXP4TSPa2NjdA1XUz4Ab2c/Q7PoBH40cJJNkYnT+XywM8RnlZ4shUMnQvsejWHSdkDbgKa3M8SX+Ckpg/3HQ/sejaG6sZ8rp7znzZvHoEGDSE1NrfHxpKQkkpOTAZgwYQLvv/8+R48eJT4+njFjxvDCCy8QGRkZ0HuVl5dTUFAQcGxlZUlARMDPP9eYa6cx+ZVrWLd1OZd1vxYAj8eLx9Ow3r+srIz8/H2NjsO/Dmu5wPn5bNi+ksWrXydr8lcNjMV6PuIex095OVHSrtoyr6f2M1HPvmB5bRcvP15y/h7JdZsP0bNtcOfVQ1E34N7tgLYBTaeo1MvR4tDXzfothylPCu68uuqmOjvrJjk5mfDwhreHHp+vMZcJtk9BQQEpKSlMmTKFF1+svgu7srKSlJQU0tPTWbx4MQAbN26kc+fOxMbGcvDgQcaMGcMPfvADpk+fHtD75efn07Fjx4Djmz11A12Sewf8/EAsWTOX7/K/YNLolwN+zY6Cr/nFi30svW+wczlRXMj9/53B1DGv0/eiIQ16bTDyEfdo0yWd8c9U33Amxvgvb9JYT753/jGZy9+cxLqPA6+rQIRiGwBmbAe0DQitVh368NPncqstC0XdfPr2FHIW/bHxK62B6qZ2TV03eXl5dOjQoUGvARfuoSwqKgLAU8MNRhcuXMj+/furTXf36tWr6nefz4fX62Xz5s0hj1PO9/6qLA4f20vWPx6qtvyGK+7k1sEP1fIquRB5aJobCHu8rjzqx7W0DQitmv4uhuZ9VDdNyS1147o9lKWlpTRr1oz09HTWrFlTtXznzp1cc8017N69m3fffZdx48ZVPfbcc8/xzDPPUFRURKtWrVi0aBFXXhnYaWoNnfLe9mESpces7bYPhsiEMroNt7bL3im5QHDyEfcoLA7jtdUp1ZbVN3U3dZj/9xcXwbEaZrFrmrq7scdh+iQHd+rOtLpxSj7aBtTvWEkYf/489HUzNPUwaSmqm7o4JZ/G5NLYKW/X7aGMjIzkjjvuYM6cOdx8882MGDGCvLw8Zs+eTVJSErt37z7vhJxHHnmERx55hE2bNvH222+TkpJS88prEB4e3qBdv3kR4ISbCURERDRql/XZnJILBCcfcY92Poj6yn+HjtMqfYFdRuhYceCXG0rr3pL2LVo2LshamFY3TslH24D6+XzQLMd/wtlpoaibPt1a0qGV6qYuTsmnKevGlfutZ86cyb333svnn3/O1KlT+fzzz5k/fz7t2rWjWbNmtZ6sc8kll3DZZZdx++23N3HEItIQXg90bBXa94gIc8YFmkWCxeOBjsHt884T5oWU5qF9D3En1+2hBIiLi2PWrFnMmjWr2vINGzaQlpaGt47josrKyvjuu+9CHaKIWJTeCbaEcIbzso7+P44iJknvDN8GfpRWg13aEcLddYtpaSLGbE4LCwvJz8+vNt199OhR5s6dS2FhIT6fj/Xr1/PMM88wdOhQ+wIVkYBc3hWiQviV95qaJzJEXC2jC0SH8NC9gReHbt3ibq7cQ1mT3Fz/pRLObig9Hg9/+ctfmDJlCqWlpbRt25ZbbrmFp556qpa1NL0PP3+NJWvewOPx8uAtWXRNSat6bPU3i3hzyROEh0VwUfuMBl32wC6m5SP2iY6A63rBh+uDv+6eKdCldfDX21gm1Y1JubhRZDj8sDd8sDb4605Nhm5tg7/exjJprJmQizF7KGtqKBMSEvjkk084fPgwJ06cYNu2bfzhD38gNjbWpiirO3byMB+syuLF+1cwdczr/Gnhg9Uef+uj6Tx553vMmPgZew9tZdueEPxlDSLT8hH7XdcbOrQI7jqjI2BsP//xZk5gUt2YlIubDbkEOgX5GOSocNVNqJiSizEN5YQJE/D5fPTv39/uUAL27a7VXNr9WsLDIujYtgdHiw5SedZNUrsk96GouJCKygpOlRUT1yzIf1mDzLR8xH5hXrj9GoiNqvt5x0v8F2B+8j3/77XxeOAn/aGFM75TAmbVjUm5uFmYF26/GuKCVTfAuP7QKi6oYVpi0lgzJRdjprzd6HjxYeJjzgyMmKh4ikqOEv/9YBmSPp5HXhtKdGQs6d0zads88Dv22MG0fMQZkhLh/kx4dSmcOFXzcwK5NIrXA/8xAC7rFPwYrTCpbkzKxe3aJMCE6yBrae3NYqB1M66//2QfJzFprJmSizF7KN0oLqYFJ4oLq/5dfOo4sdFnrmPy0vwJvPKrNbz5m83g8bByw4KmD7IBTMtHnKNDS/8FmHskN+71beJh0vX+E32cxqS6MSkXE7Rr4a+bnoFferma1vHwwA/hqm7BjSsYTBprpuSihtJGPTv1I3f7p1RUlLP74BYSY1tXu+SR1xNGbExzAJrHtuHYyUM2RRoY0/IRZ2kRC7/MhPEDICXA60cmRMONaTBtOHRtE9r4GsukujEpF1M0bwb3DYGfXg3tmgf2mvhoGJoGDw+H7g46CedsJo01U3LRlLeNEpq1ZNhVP2dK1mA8Hi+TRr/Cmm8Wc7z4MJnp47njhqd4+NVMIsKjiItpwbjMR+0OuU6m5SPO4/H495Zc2RW2HYBv9kDeYdh3FEorINwLLeP8F3fu3hb6dHD+tSZNqhuTcjGJxwNXdIXLu8D2A7DpnLoJ80KrWOjQ6vu6ae/8a02aNNZMycV19/J2ulVzoMgBXx5iW8GAu62twym5QHDyEWkKptWNU/LRNsBsThlnoLppLId/dxcRERERp1NDKSIiIiKWqKEUEREREUt0Uk6QfX8ilu2CEYdTcgFnxSJSFyeNVZO2A06JQ0LDSZ+v6qZxdFKOiIiIiFiiKW8RERERsUQNpYiIiIhYooZSRERERCxRQykiIiIilqihFBERERFL1FCKiIiIiCVqKEVERETEEjWUIiIiImKJGkoRERERsUQNpYiIiIhYooZSRERERCxRQykiIiIilqihFBERERFL1FCKiIiIiCVqKEVERETEEjWUIiIiImKJGkoRERERsUQNpYiIiIhYooZSRERERCxRQykiIiIilqihFBERERFL1FCKiIiIiCX/H2FBjzkJfhTMAAAAAElFTkSuQmCC",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 831.997x294.311 with 1 Axes>"
       ]
@@ -50,7 +64,7 @@
    "source": [
     "circuit = EfficientSU2(4, entanglement=\"linear\", reps=2).decompose()\n",
     "circuit.assign_parameters([0.8] * len(circuit.parameters), inplace=True)\n",
-    "observables = PauliList([\"ZZZZ\"])\n",
+    "observable = SparsePauliOp([\"ZZZZ\"])\n",
     "circuit.draw(\"mpl\", scale=0.8)"
    ]
   },
@@ -66,11 +80,18 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "d4ccf5b8",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:29.986565Z",
+     "iopub.status.busy": "2024-04-19T17:42:29.985900Z",
+     "iopub.status.idle": "2024-04-19T17:42:30.190473Z",
+     "shell.execute_reply": "2024-04-19T17:42:30.189786Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 684.861x120.4 with 1 Axes>"
       ]
@@ -82,7 +103,7 @@
    ],
    "source": [
     "partitioned_problem = partition_problem(\n",
-    "    circuit=circuit, partition_labels=\"AABB\", observables=observables\n",
+    "    circuit=circuit, partition_labels=\"AABB\", observables=observable.paulis\n",
     ")\n",
     "subcircuits = partitioned_problem.subcircuits\n",
     "bases = partitioned_problem.bases\n",
@@ -94,11 +115,18 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "44956cbb",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:30.193586Z",
+     "iopub.status.busy": "2024-04-19T17:42:30.193094Z",
+     "iopub.status.idle": "2024-04-19T17:42:30.438300Z",
+     "shell.execute_reply": "2024-04-19T17:42:30.437592Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 684.982x120.4 with 1 Axes>"
       ]
@@ -126,7 +154,14 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "8c56282f",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:30.441244Z",
+     "iopub.status.busy": "2024-04-19T17:42:30.441021Z",
+     "iopub.status.idle": "2024-04-19T17:42:30.714499Z",
+     "shell.execute_reply": "2024-04-19T17:42:30.713655Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -199,7 +234,14 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "78539fcc",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:30.717844Z",
+     "iopub.status.busy": "2024-04-19T17:42:30.717338Z",
+     "iopub.status.idle": "2024-04-19T17:42:30.722870Z",
+     "shell.execute_reply": "2024-04-19T17:42:30.722190Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -225,7 +267,14 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "f07a6cc3",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:30.725747Z",
+     "iopub.status.busy": "2024-04-19T17:42:30.725290Z",
+     "iopub.status.idle": "2024-04-19T17:42:30.731306Z",
+     "shell.execute_reply": "2024-04-19T17:42:30.730545Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -268,7 +317,14 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "43d32869",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:30.734094Z",
+     "iopub.status.busy": "2024-04-19T17:42:30.733692Z",
+     "iopub.status.idle": "2024-04-19T17:42:30.983163Z",
+     "shell.execute_reply": "2024-04-19T17:42:30.982415Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -342,7 +398,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/circuit_cutting/how-tos/how_to_specify_cut_wires.html b/circuit_cutting/how-tos/how_to_specify_cut_wires.html
index 8d72927df..9af869405 100644
--- a/circuit_cutting/how-tos/how_to_specify_cut_wires.html
+++ b/circuit_cutting/how-tos/how_to_specify_cut_wires.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="CutQC (legacy circuit cutting implementation)" href="../cutqc/index.html" /><link rel="prev" title="How to generate exact sampling coefficients" href="how_to_generate_exact_sampling_coefficients.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>How to place wire cuts using a single-qubit CutWire instruction - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>How to place wire cuts using a single-qubit CutWire instruction - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -146,7 +146,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -173,7 +173,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -190,6 +190,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../explanation/index.html">Cutting Explanatory Material</a></li>
@@ -219,6 +220,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -313,8 +317,8 @@ <h1>How to place wire cuts using a single-qubit <code class="docutils literal no
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">from</span> <span class="nn">qiskit</span> <span class="kn">import</span> <span class="n">QuantumCircuit</span>
-<span class="kn">from</span> <span class="nn">qiskit.quantum_info</span> <span class="kn">import</span> <span class="n">PauliList</span>
-<span class="kn">from</span> <span class="nn">qiskit_aer.primitives</span> <span class="kn">import</span> <span class="n">Estimator</span><span class="p">,</span> <span class="n">Sampler</span>
+<span class="kn">from</span> <span class="nn">qiskit.quantum_info</span> <span class="kn">import</span> <span class="n">SparsePauliOp</span>
+<span class="kn">from</span> <span class="nn">qiskit_aer.primitives</span> <span class="kn">import</span> <span class="n">EstimatorV2</span><span class="p">,</span> <span class="n">SamplerV2</span>
 
 <span class="kn">from</span> <span class="nn">circuit_knitting.cutting</span> <span class="kn">import</span> <span class="p">(</span>
     <span class="n">partition_problem</span><span class="p">,</span>
@@ -381,14 +385,14 @@ <h2>Recover the uncut circuit<a class="headerlink" href="#Recover-the-uncut-circ
 </div>
 </div>
 </section>
-<section id="Specify-some-observables">
-<h2>Specify some observables<a class="headerlink" href="#Specify-some-observables" title="Link to this heading">¶</a></h2>
-<p>These observables have 7 qubits, just like the original circuit.</p>
+<section id="Specify-an-observable">
+<h2>Specify an observable<a class="headerlink" href="#Specify-an-observable" title="Link to this heading">¶</a></h2>
+<p>This observable has 7 qubits, just like the original circuit.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">observables_0</span> <span class="o">=</span> <span class="n">PauliList</span><span class="p">([</span><span class="s2">&quot;ZIIIIII&quot;</span><span class="p">,</span> <span class="s2">&quot;IIIZIII&quot;</span><span class="p">,</span> <span class="s2">&quot;IIIIIIZ&quot;</span><span class="p">])</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">observable</span> <span class="o">=</span> <span class="n">SparsePauliOp</span><span class="p">([</span><span class="s2">&quot;ZIIIIII&quot;</span><span class="p">,</span> <span class="s2">&quot;IIIZIII&quot;</span><span class="p">,</span> <span class="s2">&quot;IIIIIIZ&quot;</span><span class="p">])</span>
 </pre></div>
 </div>
 </div>
@@ -415,16 +419,16 @@ <h2>Transform cuts to moves<a class="headerlink" href="#Transform-cuts-to-moves"
 </div>
 </div>
 </section>
-<section id="Update-the-observables">
-<h2>Update the observables<a class="headerlink" href="#Update-the-observables" title="Link to this heading">¶</a></h2>
-<p>The transformed circuit contains additional qubits (one for each <code class="docutils literal notranslate"><span class="pre">CutWire</span></code> instruction), so the observables must be updated for the new circuit. This can be done using the <code class="docutils literal notranslate"><span class="pre">expand_observables</span></code> function.</p>
+<section id="Update-the-observable-terms-to-account-for-the-extra-qubit">
+<h2>Update the observable terms to account for the extra qubit<a class="headerlink" href="#Update-the-observable-terms-to-account-for-the-extra-qubit" title="Link to this heading">¶</a></h2>
+<p>The transformed circuit contains additional qubits (one for each <code class="docutils literal notranslate"><span class="pre">CutWire</span></code> instruction), so the observables must be updated for the new circuit. This can be done using the <code class="docutils literal notranslate"><span class="pre">expand_observables</span></code> function. Since the observable coefficients are ignored until reconstruction of the final expectation value, the input and output types of the observables for <code class="docutils literal notranslate"><span class="pre">expand_observables</span></code> are <code class="docutils literal notranslate"><span class="pre">PauliList</span></code>s.</p>
 <p>The resulting observables have 9 qubits, just like the transformed circuit.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">observables_1</span> <span class="o">=</span> <span class="n">expand_observables</span><span class="p">(</span><span class="n">observables_0</span><span class="p">,</span> <span class="n">qc_0</span><span class="p">,</span> <span class="n">qc_1</span><span class="p">)</span>
-<span class="n">observables_1</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">observable_expanded_paulis</span> <span class="o">=</span> <span class="n">expand_observables</span><span class="p">(</span><span class="n">observable</span><span class="o">.</span><span class="n">paulis</span><span class="p">,</span> <span class="n">qc_0</span><span class="p">,</span> <span class="n">qc_1</span><span class="p">)</span>
+<span class="n">observable_expanded_paulis</span>
 </pre></div>
 </div>
 </div>
@@ -445,7 +449,9 @@ <h2>Separate the circuit and observables<a class="headerlink" href="#Separate-th
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">partitioned_problem</span> <span class="o">=</span> <span class="n">partition_problem</span><span class="p">(</span><span class="n">circuit</span><span class="o">=</span><span class="n">qc_1</span><span class="p">,</span> <span class="n">observables</span><span class="o">=</span><span class="n">observables_1</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">partitioned_problem</span> <span class="o">=</span> <span class="n">partition_problem</span><span class="p">(</span>
+    <span class="n">circuit</span><span class="o">=</span><span class="n">qc_1</span><span class="p">,</span> <span class="n">observables</span><span class="o">=</span><span class="n">observable_expanded_paulis</span>
+<span class="p">)</span>
 <span class="n">subcircuits</span> <span class="o">=</span> <span class="n">partitioned_problem</span><span class="o">.</span><span class="n">subcircuits</span>
 <span class="n">subobservables</span> <span class="o">=</span> <span class="n">partitioned_problem</span><span class="o">.</span><span class="n">subobservables</span>
 </pre></div>
@@ -525,9 +531,9 @@ <h2>Generate and run the cutting experiments; reconstruct and compare against un
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="p">(</span><span class="n">run_options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;shots&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="o">**</span><span class="mi">12</span><span class="p">})</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sampler</span> <span class="o">=</span> <span class="n">SamplerV2</span><span class="p">()</span>
 <span class="n">results</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">label</span><span class="p">:</span> <span class="n">sampler</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">subexperiment</span><span class="p">)</span><span class="o">.</span><span class="n">result</span><span class="p">()</span>
+    <span class="n">label</span><span class="p">:</span> <span class="n">sampler</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">subexperiment</span><span class="p">,</span> <span class="n">shots</span><span class="o">=</span><span class="mi">2</span><span class="o">**</span><span class="mi">12</span><span class="p">)</span><span class="o">.</span><span class="n">result</span><span class="p">()</span>
     <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">subexperiment</span> <span class="ow">in</span> <span class="n">subexperiments</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
 <span class="p">}</span>
 </pre></div>
@@ -542,6 +548,7 @@ <h2>Generate and run the cutting experiments; reconstruct and compare against un
     <span class="n">coefficients</span><span class="p">,</span>
     <span class="n">subobservables</span><span class="p">,</span>
 <span class="p">)</span>
+<span class="n">final_expval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">reconstructed_expvals</span><span class="p">,</span> <span class="n">observable</span><span class="o">.</span><span class="n">coeffs</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -549,21 +556,15 @@ <h2>Generate and run the cutting experiments; reconstruct and compare against un
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">estimator</span> <span class="o">=</span> <span class="n">Estimator</span><span class="p">(</span><span class="n">run_options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;shots&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">},</span> <span class="n">approximation</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">exact_expvals</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">estimator</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">qc_0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">observables_0</span><span class="p">),</span> <span class="nb">list</span><span class="p">(</span><span class="n">observables_0</span><span class="p">))</span><span class="o">.</span><span class="n">result</span><span class="p">()</span><span class="o">.</span><span class="n">values</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Reconstructed expectation values: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">reconstructed_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Exact expectation values: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Errors in estimation: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">reconstructed_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">-</span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">estimator</span> <span class="o">=</span> <span class="n">EstimatorV2</span><span class="p">()</span>
+<span class="n">exact_expval</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">estimator</span><span class="o">.</span><span class="n">run</span><span class="p">([(</span><span class="n">qc_0</span><span class="o">.</span><span class="n">decompose</span><span class="p">(</span><span class="s2">&quot;cut_wire&quot;</span><span class="p">),</span> <span class="n">observable</span><span class="p">)])</span><span class="o">.</span><span class="n">result</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">evs</span>
 <span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Reconstructed expectation value: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">final_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">))</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Exact expectation value: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">exact_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Error in estimation: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">final_expval</span><span class="o">-</span><span class="n">exact_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">))</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Relative errors in estimation: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">((</span><span class="n">reconstructed_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">-</span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Relative error in estimation: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">((</span><span class="n">final_expval</span><span class="o">-</span><span class="n">exact_expval</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">exact_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">))</span><span class="si">}</span><span class="s2">&quot;</span>
 <span class="p">)</span>
 </pre></div>
 </div>
@@ -573,10 +574,10 @@ <h2>Generate and run the cutting experiments; reconstruct and compare against un
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Reconstructed expectation values: [0.17901284, 0.70971423, 0.68885177]
-Exact expectation values: [0.1767767, 0.70710678, 0.70710678]
-Errors in estimation: [0.00223614, 0.00260745, -0.01825501]
-Relative errors in estimation: [0.01264952, 0.00368749, -0.02581648]
+Reconstructed expectation value: 1.6174469
+Exact expectation value: 1.59099026
+Error in estimation: 0.02645664
+Relative error in estimation: 0.01662904
 </pre></div></div>
 </div>
 </section>
@@ -646,9 +647,9 @@ <h2>Generate and run the cutting experiments; reconstruct and compare against un
 <li><a class="reference internal" href="#">How to place wire cuts using a single-qubit <code class="docutils literal notranslate"><span class="pre">CutWire</span></code> instruction</a><ul>
 <li><a class="reference internal" href="#Prepare-a-circuit-for-cutting">Prepare a circuit for cutting</a></li>
 <li><a class="reference internal" href="#Recover-the-uncut-circuit">Recover the uncut circuit</a></li>
-<li><a class="reference internal" href="#Specify-some-observables">Specify some observables</a></li>
+<li><a class="reference internal" href="#Specify-an-observable">Specify an observable</a></li>
 <li><a class="reference internal" href="#Transform-cuts-to-moves">Transform cuts to moves</a></li>
-<li><a class="reference internal" href="#Update-the-observables">Update the observables</a></li>
+<li><a class="reference internal" href="#Update-the-observable-terms-to-account-for-the-extra-qubit">Update the observable terms to account for the extra qubit</a></li>
 <li><a class="reference internal" href="#Separate-the-circuit-and-observables">Separate the circuit and observables</a></li>
 <li><a class="reference internal" href="#Visualize-the-decomposed-problem">Visualize the decomposed problem</a></li>
 <li><a class="reference internal" href="#Generate-and-run-the-cutting-experiments;-reconstruct-and-compare-against-uncut-expectation-values">Generate and run the cutting experiments; reconstruct and compare against uncut expectation values</a></li>
@@ -665,7 +666,7 @@ <h2>Generate and run the cutting experiments; reconstruct and compare against un
   </div>
 </div><script src="../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb b/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb
index f91e143ab..44a0bb67d 100644
--- a/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb
+++ b/circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb
@@ -14,13 +14,20 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "1aa871cb",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:32.984407Z",
+     "iopub.status.busy": "2024-04-19T17:42:32.983921Z",
+     "iopub.status.idle": "2024-04-19T17:42:33.359403Z",
+     "shell.execute_reply": "2024-04-19T17:42:33.358807Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "from qiskit import QuantumCircuit\n",
-    "from qiskit.quantum_info import PauliList\n",
-    "from qiskit_aer.primitives import Estimator, Sampler\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "from qiskit_aer.primitives import EstimatorV2, SamplerV2\n",
     "\n",
     "from circuit_knitting.cutting import (\n",
     "    partition_problem,\n",
@@ -46,11 +53,18 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "0ae22516",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:33.362341Z",
+     "iopub.status.busy": "2024-04-19T17:42:33.361898Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.014510Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.013813Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1290.83x618.722 with 1 Axes>"
       ]
@@ -92,11 +106,18 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "631286a6",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.017159Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.016897Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.260375Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.259691Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 956.385x618.722 with 1 Axes>"
       ]
@@ -115,19 +136,26 @@
    "id": "dcd8dea0",
    "metadata": {},
    "source": [
-    "### Specify some observables\n",
+    "### Specify an observable\n",
     "\n",
-    "These observables have 7 qubits, just like the original circuit."
+    "This observable has 7 qubits, just like the original circuit."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "id": "847a3205",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.262953Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.262735Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.266201Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.265626Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "observables_0 = PauliList([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])"
+    "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])"
    ]
   },
   {
@@ -146,11 +174,18 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "e4ee1559",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.268783Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.268324Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.594163Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.593481Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1290.83x785.944 with 1 Axes>"
       ]
@@ -170,9 +205,9 @@
    "id": "c22b9572",
    "metadata": {},
    "source": [
-    "### Update the observables\n",
+    "### Update the observable terms to account for the extra qubit\n",
     "\n",
-    "The transformed circuit contains additional qubits (one for each `CutWire` instruction), so the observables must be updated for the new circuit.  This can be done using the `expand_observables` function.\n",
+    "The transformed circuit contains additional qubits (one for each `CutWire` instruction), so the observables must be updated for the new circuit.  This can be done using the `expand_observables` function. Since the observable coefficients are ignored until reconstruction of the final expectation value, the input and output types of the observables for ``expand_observables`` are ``PauliList``s.\n",
     "\n",
     "The resulting observables have 9 qubits, just like the transformed circuit."
    ]
@@ -181,7 +216,14 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "95fbeda0",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.596688Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.596481Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.601335Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.600793Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -195,8 +237,8 @@
     }
    ],
    "source": [
-    "observables_1 = expand_observables(observables_0, qc_0, qc_1)\n",
-    "observables_1"
+    "observable_expanded_paulis = expand_observables(observable.paulis, qc_0, qc_1)\n",
+    "observable_expanded_paulis"
    ]
   },
   {
@@ -213,10 +255,19 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "99bef123",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.603720Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.603374Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.609287Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.608662Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "partitioned_problem = partition_problem(circuit=qc_1, observables=observables_1)\n",
+    "partitioned_problem = partition_problem(\n",
+    "    circuit=qc_1, observables=observable_expanded_paulis\n",
+    ")\n",
     "subcircuits = partitioned_problem.subcircuits\n",
     "subobservables = partitioned_problem.subobservables"
    ]
@@ -243,7 +294,14 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "abeee650",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.612002Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.611552Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.615983Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.615292Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -265,13 +323,20 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "aaef5b3d",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.618472Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.618111Z",
+     "iopub.status.idle": "2024-04-19T17:42:34.917676Z",
+     "shell.execute_reply": "2024-04-19T17:42:34.916967Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 974.414x451.5 with 1 Axes>"
+       "<Figure size 974.616x451.5 with 1 Axes>"
       ]
      },
      "execution_count": 9,
@@ -287,13 +352,20 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "975a3ca9",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:34.920262Z",
+     "iopub.status.busy": "2024-04-19T17:42:34.919872Z",
+     "iopub.status.idle": "2024-04-19T17:42:35.089754Z",
+     "shell.execute_reply": "2024-04-19T17:42:35.089072Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 723.783x367.889 with 1 Axes>"
+       "<Figure size 723.984x367.889 with 1 Axes>"
       ]
      },
      "execution_count": 10,
@@ -317,7 +389,14 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "459dcee8",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:35.092542Z",
+     "iopub.status.busy": "2024-04-19T17:42:35.092086Z",
+     "iopub.status.idle": "2024-04-19T17:42:35.426066Z",
+     "shell.execute_reply": "2024-04-19T17:42:35.425092Z"
+    }
+   },
    "outputs": [],
    "source": [
     "subexperiments, coefficients = generate_cutting_experiments(\n",
@@ -331,12 +410,19 @@
    "cell_type": "code",
    "execution_count": 12,
    "id": "dc9fe287",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:35.429165Z",
+     "iopub.status.busy": "2024-04-19T17:42:35.428747Z",
+     "iopub.status.idle": "2024-04-19T17:42:36.349289Z",
+     "shell.execute_reply": "2024-04-19T17:42:36.348638Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "sampler = Sampler(run_options={\"shots\": 2**12})\n",
+    "sampler = SamplerV2()\n",
     "results = {\n",
-    "    label: sampler.run(subexperiment).result()\n",
+    "    label: sampler.run(subexperiment, shots=2**12).result()\n",
     "    for label, subexperiment in subexperiments.items()\n",
     "}"
    ]
@@ -345,49 +431,58 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "e317a998",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:36.352558Z",
+     "iopub.status.busy": "2024-04-19T17:42:36.352328Z",
+     "iopub.status.idle": "2024-04-19T17:42:38.830508Z",
+     "shell.execute_reply": "2024-04-19T17:42:38.829874Z"
+    }
+   },
    "outputs": [],
    "source": [
     "reconstructed_expvals = reconstruct_expectation_values(\n",
     "    results,\n",
     "    coefficients,\n",
     "    subobservables,\n",
-    ")"
+    ")\n",
+    "final_expval = np.dot(reconstructed_expvals, observable.coeffs)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "id": "5ae568ca",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:38.833471Z",
+     "iopub.status.busy": "2024-04-19T17:42:38.833025Z",
+     "iopub.status.idle": "2024-04-19T17:42:38.844321Z",
+     "shell.execute_reply": "2024-04-19T17:42:38.843788Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reconstructed expectation values: [0.17901284, 0.70971423, 0.68885177]\n",
-      "Exact expectation values: [0.1767767, 0.70710678, 0.70710678]\n",
-      "Errors in estimation: [0.00223614, 0.00260745, -0.01825501]\n",
-      "Relative errors in estimation: [0.01264952, 0.00368749, -0.02581648]\n"
+      "Reconstructed expectation value: 1.6174469\n",
+      "Exact expectation value: 1.59099026\n",
+      "Error in estimation: 0.02645664\n",
+      "Relative error in estimation: 0.01662904\n"
      ]
     }
    ],
    "source": [
-    "estimator = Estimator(run_options={\"shots\": None}, approximation=True)\n",
-    "exact_expvals = (\n",
-    "    estimator.run([qc_0] * len(observables_0), list(observables_0)).result().values\n",
-    ")\n",
-    "print(\n",
-    "    f\"Reconstructed expectation values: {[np.round(reconstructed_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Exact expectation values: {[np.round(exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Errors in estimation: {[np.round(reconstructed_expvals[i]-exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
+    "estimator = EstimatorV2()\n",
+    "exact_expval = (\n",
+    "    estimator.run([(qc_0.decompose(\"cut_wire\"), observable)]).result()[0].data.evs\n",
     ")\n",
+    "print(f\"Reconstructed expectation value: {np.real(np.round(final_expval, 8))}\")\n",
+    "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n",
+    "print(f\"Error in estimation: {np.real(np.round(final_expval-exact_expval, 8))}\")\n",
     "print(\n",
-    "    f\"Relative errors in estimation: {[np.round((reconstructed_expvals[i]-exact_expvals[i]) / exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
+    "    f\"Relative error in estimation: {np.real(np.round((final_expval-exact_expval) / exact_expval, 8))}\"\n",
     ")"
    ]
   }
@@ -408,7 +503,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/circuit_cutting/how-tos/index.html b/circuit_cutting/how-tos/index.html
index 3477112f4..bbf92c504 100644
--- a/circuit_cutting/how-tos/index.html
+++ b/circuit_cutting/how-tos/index.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="How to generate exact quasiprobability distributions from Sampler" href="how_to_generate_exact_quasi_dists_from_sampler.html" /><link rel="prev" title="Explanatory material for the circuit cutting module" href="../explanation/index.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>Circuit Cutting How-To Guides - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>Circuit Cutting How-To Guides - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -146,7 +146,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -173,7 +173,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -190,6 +190,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../explanation/index.html">Cutting Explanatory Material</a></li>
@@ -219,6 +220,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -390,7 +394,7 @@ <h1>Circuit Cutting How-To Guides<a class="headerlink" href="#circuit-cutting-ho
   </div>
 </div><script src="../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html b/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html
index 41299abb2..55b97a411 100644
--- a/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html
+++ b/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="Gate Cutting to Reduce Circuit Depth" href="02_gate_cutting_to_reduce_circuit_depth.html" /><link rel="prev" title="Circuit Cutting Tutorials" href="index.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>Gate Cutting to Reduce Circuit Width - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>Gate Cutting to Reduce Circuit Width - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -146,7 +146,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -173,7 +173,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -190,6 +190,7 @@
 <li class="toctree-l2 current current-page"><a class="current reference internal" href="#">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../explanation/index.html">Cutting Explanatory Material</a></li>
@@ -219,6 +220,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -306,7 +310,7 @@
         <article role="main">
           <section id="Gate-Cutting-to-Reduce-Circuit-Width">
 <h1>Gate Cutting to Reduce Circuit Width<a class="headerlink" href="#Gate-Cutting-to-Reduce-Circuit-Width" title="Link to this heading">¶</a></h1>
-<p>In this tutorial we will simulate expectation values of a four-qubit circuit using only two-qubit experiments by cutting gates in the circuit.</p>
+<p>In this tutorial we will simulate the expectation value of a four-qubit circuit using only two-qubit experiments by cutting gates in the circuit.</p>
 <p>Like any circuit knitting technique, gate cutting can be described as three consecutive steps:</p>
 <ul class="simple">
 <li><p><strong>cut</strong> some non-local gates in the circuit and possibly separate the circuit into subcircuits</p></li>
@@ -337,23 +341,23 @@ <h2>Create a circuit to cut<a class="headerlink" href="#Create-a-circuit-to-cut"
 </div>
 </div>
 </section>
-<section id="Specify-some-observables">
-<h2>Specify some observables<a class="headerlink" href="#Specify-some-observables" title="Link to this heading">¶</a></h2>
-<p>Currently, only <code class="docutils literal notranslate"><span class="pre">Pauli</span></code> observables with phase equal to 1 are supported. Full support for <code class="docutils literal notranslate"><span class="pre">SparsePauliOp</span></code> is expected in CKT v0.7.0.</p>
+<section id="Specify-an-observable">
+<h2>Specify an observable<a class="headerlink" href="#Specify-an-observable" title="Link to this heading">¶</a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit.quantum_info</span> <span class="kn">import</span> <span class="n">PauliList</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit.quantum_info</span> <span class="kn">import</span> <span class="n">SparsePauliOp</span>
 
-<span class="n">observables</span> <span class="o">=</span> <span class="n">PauliList</span><span class="p">([</span><span class="s2">&quot;ZZII&quot;</span><span class="p">,</span> <span class="s2">&quot;IZZI&quot;</span><span class="p">,</span> <span class="s2">&quot;IIZZ&quot;</span><span class="p">,</span> <span class="s2">&quot;XIXI&quot;</span><span class="p">,</span> <span class="s2">&quot;ZIZZ&quot;</span><span class="p">,</span> <span class="s2">&quot;IXIX&quot;</span><span class="p">])</span>
+<span class="n">observable</span> <span class="o">=</span> <span class="n">SparsePauliOp</span><span class="p">([</span><span class="s2">&quot;ZZII&quot;</span><span class="p">,</span> <span class="s2">&quot;IZZI&quot;</span><span class="p">,</span> <span class="s2">&quot;-IIZZ&quot;</span><span class="p">,</span> <span class="s2">&quot;XIXI&quot;</span><span class="p">,</span> <span class="s2">&quot;ZIZZ&quot;</span><span class="p">,</span> <span class="s2">&quot;IXIX&quot;</span><span class="p">])</span>
 </pre></div>
 </div>
 </div>
 </section>
-<section id="Separate-the-circuit-and-observables-according-to-a-specified-qubit-partitioning">
-<h2>Separate the circuit and observables according to a specified qubit partitioning<a class="headerlink" href="#Separate-the-circuit-and-observables-according-to-a-specified-qubit-partitioning" title="Link to this heading">¶</a></h2>
+<section id="Separate-the-circuit-and-observable-according-to-a-specified-qubit-partitioning">
+<h2>Separate the circuit and observable according to a specified qubit partitioning<a class="headerlink" href="#Separate-the-circuit-and-observable-according-to-a-specified-qubit-partitioning" title="Link to this heading">¶</a></h2>
 <p>Each label in <code class="docutils literal notranslate"><span class="pre">partition_labels</span></code> corresponds to the <code class="docutils literal notranslate"><span class="pre">circuit</span></code> qubit in the same index. Qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut.</p>
+<p><strong>Note:</strong> The <code class="docutils literal notranslate"><span class="pre">observables</span></code> kwarg to <code class="docutils literal notranslate"><span class="pre">partition_problem</span></code> is of type <code class="docutils literal notranslate"><span class="pre">PauliList</span></code>. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
@@ -361,7 +365,7 @@ <h2>Separate the circuit and observables according to a specified qubit partitio
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">circuit_knitting.cutting</span> <span class="kn">import</span> <span class="n">partition_problem</span>
 
 <span class="n">partitioned_problem</span> <span class="o">=</span> <span class="n">partition_problem</span><span class="p">(</span>
-    <span class="n">circuit</span><span class="o">=</span><span class="n">qc</span><span class="p">,</span> <span class="n">partition_labels</span><span class="o">=</span><span class="s2">&quot;AABB&quot;</span><span class="p">,</span> <span class="n">observables</span><span class="o">=</span><span class="n">observables</span>
+    <span class="n">circuit</span><span class="o">=</span><span class="n">qc</span><span class="p">,</span> <span class="n">partition_labels</span><span class="o">=</span><span class="s2">&quot;AABB&quot;</span><span class="p">,</span> <span class="n">observables</span><span class="o">=</span><span class="n">observable</span><span class="o">.</span><span class="n">paulis</span>
 <span class="p">)</span>
 <span class="n">subcircuits</span> <span class="o">=</span> <span class="n">partitioned_problem</span><span class="o">.</span><span class="n">subcircuits</span>
 <span class="n">subobservables</span> <span class="o">=</span> <span class="n">partitioned_problem</span><span class="o">.</span><span class="n">subobservables</span>
@@ -463,97 +467,109 @@ <h2>Generate the subexperiments to run on the backend<a class="headerlink" href=
 </div>
 </div>
 </section>
-<section id="Run-the-subexperiments-using-the-Qiskit-Sampler-primitive">
-<h2>Run the subexperiments using the Qiskit Sampler primitive<a class="headerlink" href="#Run-the-subexperiments-using-the-Qiskit-Sampler-primitive" title="Link to this heading">¶</a></h2>
+<section id="Choose-a-backend">
+<h2>Choose a backend<a class="headerlink" href="#Choose-a-backend" title="Link to this heading">¶</a></h2>
+<p>Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator).</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_aer.primitives</span> <span class="kn">import</span> <span class="n">Sampler</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_ibm_runtime.fake_provider</span> <span class="kn">import</span> <span class="n">FakeManilaV2</span>
 
-<span class="c1"># Set up a Qiskit Aer Sampler primitive for each circuit partition</span>
-<span class="n">samplers</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">label</span><span class="p">:</span> <span class="n">Sampler</span><span class="p">(</span><span class="n">run_options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;shots&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="o">**</span><span class="mi">12</span><span class="p">})</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">subexperiments</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
-<span class="p">}</span>
-
-<span class="c1"># Retrieve results from each partition&#39;s subexperiments</span>
-<span class="n">results</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">label</span><span class="p">:</span> <span class="n">sampler</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">subexperiments</span><span class="p">[</span><span class="n">label</span><span class="p">])</span><span class="o">.</span><span class="n">result</span><span class="p">()</span>
-    <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">sampler</span> <span class="ow">in</span> <span class="n">samplers</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
+<span class="n">backend</span> <span class="o">=</span> <span class="n">FakeManilaV2</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
-<p>To use the Qiskit Runtime Sampler, replace the code above with this commented block.</p>
+</section>
+<section id="Prepare-the-subexperiments-for-the-backend">
+<h2>Prepare the subexperiments for the backend<a class="headerlink" href="#Prepare-the-subexperiments-for-the-backend" title="Link to this heading">¶</a></h2>
+<p>We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># from qiskit_ibm_runtime import Session, Options, Sampler, QiskitRuntimeService</span>
-<span class="c1"># from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager</span>
-<span class="c1">#</span>
-<span class="c1"># service = QiskitRuntimeService()</span>
-<span class="c1"># backend = service.least_busy(operational=True, simulator=False)</span>
-<span class="c1">#</span>
-<span class="c1"># # Prepare transpiler for target backend</span>
-<span class="c1"># pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)</span>
-<span class="c1">#</span>
-<span class="c1"># with Session(backend=backend) as session:</span>
-<span class="c1">#     # Set up Qiskit Runtime Sampler primitives.</span>
-<span class="c1">#     samplers = {</span>
-<span class="c1">#         label: Sampler(Options(execution={&quot;shots&quot;: 2**12})) for label in subexperiments.keys()</span>
-<span class="c1">#     }</span>
-<span class="c1">#</span>
-<span class="c1">#     # Retrieve results from each subexperiment</span>
-<span class="c1">#     results = {</span>
-<span class="c1">#         label: sampler.run(pass_manager.run(subexperiments[label])).result()</span>
-<span class="c1">#         for label, sampler in samplers.items()</span>
-<span class="c1">#     }</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit.transpiler.preset_passmanagers</span> <span class="kn">import</span> <span class="n">generate_preset_pass_manager</span>
+
+<span class="c1"># Transpile the subexperiments to ISA circuits</span>
+<span class="n">pass_manager</span> <span class="o">=</span> <span class="n">generate_preset_pass_manager</span><span class="p">(</span><span class="n">optimization_level</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="n">backend</span><span class="p">)</span>
+<span class="n">isa_subexperiments</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="n">label</span><span class="p">:</span> <span class="n">pass_manager</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">partition_subexpts</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">partition_subexpts</span> <span class="ow">in</span> <span class="n">subexperiments</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Run-the-subexperiments-using-the-Qiskit-Runtime-Sampler-primitive">
+<h2>Run the subexperiments using the Qiskit Runtime Sampler primitive<a class="headerlink" href="#Run-the-subexperiments-using-the-Qiskit-Runtime-Sampler-primitive" title="Link to this heading">¶</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_ibm_runtime</span> <span class="kn">import</span> <span class="n">SamplerV2</span><span class="p">,</span> <span class="n">Batch</span>
+
+<span class="c1"># Set up a Qiskit Runtime Sampler primitive for each circuit partition</span>
+<span class="n">samplers</span> <span class="o">=</span> <span class="p">{</span><span class="n">label</span><span class="p">:</span> <span class="n">SamplerV2</span><span class="p">(</span><span class="n">backend</span><span class="o">=</span><span class="n">backend</span><span class="p">)</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">subexperiments</span><span class="o">.</span><span class="n">keys</span><span class="p">()}</span>
+
+<span class="c1"># Submit each partition&#39;s subexperiments as a single batch</span>
+<span class="k">with</span> <span class="n">Batch</span><span class="p">(</span><span class="n">backend</span><span class="o">=</span><span class="n">backend</span><span class="p">):</span>
+    <span class="n">jobs</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="n">label</span><span class="p">:</span> <span class="n">sampler</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">isa_subexperiments</span><span class="p">[</span><span class="n">label</span><span class="p">],</span> <span class="n">shots</span><span class="o">=</span><span class="mi">2</span><span class="o">**</span><span class="mi">12</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">sampler</span> <span class="ow">in</span> <span class="n">samplers</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+    <span class="p">}</span>
+
+<span class="c1"># Retrive results</span>
+<span class="n">results</span> <span class="o">=</span> <span class="p">{</span><span class="n">label</span><span class="p">:</span> <span class="n">job</span><span class="o">.</span><span class="n">result</span><span class="p">()</span> <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
 </pre></div>
 </div>
 </div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/home/runner/work/circuit-knitting-toolbox/circuit-knitting-toolbox/.tox/docs/lib/python3.9/site-packages/qiskit_ibm_runtime/session.py:157: UserWarning: Session is not supported in local testing mode or when using a simulator.
+  warnings.warn(
+</pre></div></div>
+</div>
 </section>
 <section id="Reconstruct-the-expectation-values">
 <h2>Reconstruct the expectation values<a class="headerlink" href="#Reconstruct-the-expectation-values" title="Link to this heading">¶</a></h2>
-<p>Use the subexperiment results, subobservables, and sampling coefficients to reconstruct the expectation value of the original circuit.</p>
+<p>Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">circuit_knitting.cutting</span> <span class="kn">import</span> <span class="n">reconstruct_expectation_values</span>
 
+<span class="c1"># Get expectation values for each observable term</span>
 <span class="n">reconstructed_expvals</span> <span class="o">=</span> <span class="n">reconstruct_expectation_values</span><span class="p">(</span>
     <span class="n">results</span><span class="p">,</span>
     <span class="n">coefficients</span><span class="p">,</span>
     <span class="n">subobservables</span><span class="p">,</span>
 <span class="p">)</span>
+
+<span class="c1"># Reconstruct final expectation value</span>
+<span class="n">final_expval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">reconstructed_expvals</span><span class="p">,</span> <span class="n">observable</span><span class="o">.</span><span class="n">coeffs</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 </section>
-<section id="Compare-the-reconstructed-expectation-values-with-the-exact-expectation-values-from-the-original-circuit">
-<h2>Compare the reconstructed expectation values with the exact expectation values from the original circuit<a class="headerlink" href="#Compare-the-reconstructed-expectation-values-with-the-exact-expectation-values-from-the-original-circuit" title="Link to this heading">¶</a></h2>
+<section id="Compare-the-reconstructed-expectation-values-with-the-exact-expectation-value-from-the-original-circuit-and-observable">
+<h2>Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable<a class="headerlink" href="#Compare-the-reconstructed-expectation-values-with-the-exact-expectation-value-from-the-original-circuit-and-observable" title="Link to this heading">¶</a></h2>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_aer.primitives</span> <span class="kn">import</span> <span class="n">Estimator</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_aer.primitives</span> <span class="kn">import</span> <span class="n">EstimatorV2</span>
 
-<span class="n">estimator</span> <span class="o">=</span> <span class="n">Estimator</span><span class="p">(</span><span class="n">run_options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;shots&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">},</span> <span class="n">approximation</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">exact_expvals</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">estimator</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">qc</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">observables</span><span class="p">),</span> <span class="nb">list</span><span class="p">(</span><span class="n">observables</span><span class="p">))</span><span class="o">.</span><span class="n">result</span><span class="p">()</span><span class="o">.</span><span class="n">values</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Reconstructed expectation values: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">reconstructed_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Exact expectation values: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Errors in estimation: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">reconstructed_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">-</span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="p">)</span>
+<span class="n">estimator</span> <span class="o">=</span> <span class="n">EstimatorV2</span><span class="p">()</span>
+<span class="n">exact_expval</span> <span class="o">=</span> <span class="n">estimator</span><span class="o">.</span><span class="n">run</span><span class="p">([(</span><span class="n">qc</span><span class="p">,</span> <span class="n">observable</span><span class="p">)])</span><span class="o">.</span><span class="n">result</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">evs</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Reconstructed expectation value: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">final_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">))</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Exact expectation value: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">exact_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Error in estimation: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">final_expval</span><span class="o">-</span><span class="n">exact_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">))</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Relative errors in estimation: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">((</span><span class="n">reconstructed_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">-</span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Relative error in estimation: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">((</span><span class="n">final_expval</span><span class="o">-</span><span class="n">exact_expval</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">exact_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">))</span><span class="si">}</span><span class="s2">&quot;</span>
 <span class="p">)</span>
 </pre></div>
 </div>
@@ -563,10 +579,10 @@ <h2>Compare the reconstructed expectation values with the exact expectation valu
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Reconstructed expectation values: [0.37551016, 0.52859873, 0.58741736, 0.12230408, 0.29614246, -0.1243059]
-Exact expectation values: [0.36916216, 0.52511814, 0.59161991, 0.10927476, 0.28001606, -0.12940509]
-Errors in estimation: [0.00634799, 0.00348059, -0.00420255, 0.01302933, 0.01612639, 0.00509918]
-Relative errors in estimation: [0.01719568, 0.0066282, -0.00710346, 0.11923456, 0.05759096, -0.0394048]
+Reconstructed expectation value: 0.70903873
+Exact expectation value: 0.56254612
+Error in estimation: 0.14649261
+Relative error in estimation: 0.26040996
 </pre></div></div>
 </div>
 </section>
@@ -635,14 +651,16 @@ <h2>Compare the reconstructed expectation values with the exact expectation valu
             <ul>
 <li><a class="reference internal" href="#">Gate Cutting to Reduce Circuit Width</a><ul>
 <li><a class="reference internal" href="#Create-a-circuit-to-cut">Create a circuit to cut</a></li>
-<li><a class="reference internal" href="#Specify-some-observables">Specify some observables</a></li>
-<li><a class="reference internal" href="#Separate-the-circuit-and-observables-according-to-a-specified-qubit-partitioning">Separate the circuit and observables according to a specified qubit partitioning</a></li>
+<li><a class="reference internal" href="#Specify-an-observable">Specify an observable</a></li>
+<li><a class="reference internal" href="#Separate-the-circuit-and-observable-according-to-a-specified-qubit-partitioning">Separate the circuit and observable according to a specified qubit partitioning</a></li>
 <li><a class="reference internal" href="#Visualize-the-decomposed-problem">Visualize the decomposed problem</a></li>
 <li><a class="reference internal" href="#Calculate-the-sampling-overhead-for-the-chosen-cuts">Calculate the sampling overhead for the chosen cuts</a></li>
 <li><a class="reference internal" href="#Generate-the-subexperiments-to-run-on-the-backend">Generate the subexperiments to run on the backend</a></li>
-<li><a class="reference internal" href="#Run-the-subexperiments-using-the-Qiskit-Sampler-primitive">Run the subexperiments using the Qiskit Sampler primitive</a></li>
+<li><a class="reference internal" href="#Choose-a-backend">Choose a backend</a></li>
+<li><a class="reference internal" href="#Prepare-the-subexperiments-for-the-backend">Prepare the subexperiments for the backend</a></li>
+<li><a class="reference internal" href="#Run-the-subexperiments-using-the-Qiskit-Runtime-Sampler-primitive">Run the subexperiments using the Qiskit Runtime Sampler primitive</a></li>
 <li><a class="reference internal" href="#Reconstruct-the-expectation-values">Reconstruct the expectation values</a></li>
-<li><a class="reference internal" href="#Compare-the-reconstructed-expectation-values-with-the-exact-expectation-values-from-the-original-circuit">Compare the reconstructed expectation values with the exact expectation values from the original circuit</a></li>
+<li><a class="reference internal" href="#Compare-the-reconstructed-expectation-values-with-the-exact-expectation-value-from-the-original-circuit-and-observable">Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable</a></li>
 </ul>
 </li>
 </ul>
@@ -656,7 +674,7 @@ <h2>Compare the reconstructed expectation values with the exact expectation valu
   </div>
 </div><script src="../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb b/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb
index 7b0d38c4f..129cd3812 100644
--- a/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb
+++ b/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "## Gate Cutting to Reduce Circuit Width\n",
     "\n",
-    "In this tutorial we will simulate expectation values of a four-qubit circuit using only two-qubit experiments by cutting gates in the circuit.\n",
+    "In this tutorial we will simulate the expectation value of a four-qubit circuit using only two-qubit experiments by cutting gates in the circuit.\n",
     "\n",
     "Like any circuit knitting technique, gate cutting can be described as three consecutive steps:\n",
     "\n",
@@ -28,11 +28,18 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "96f5b72a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:40.888918Z",
+     "iopub.status.busy": "2024-04-19T17:42:40.888715Z",
+     "iopub.status.idle": "2024-04-19T17:42:41.860986Z",
+     "shell.execute_reply": "2024-04-19T17:42:41.860261Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApQAAAD2CAYAAABobBdEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABDdElEQVR4nO3deXhM9/4H8PfMZEKSSYQgE0kIItYoUT9CY6e2oguqLdrb3VJbtXRRLdXqguqiiobSWtp7LXVRRGm1pFSoWBK7JDKRXfZl5vz+yJWIbDM5c+acGe/X83gec+bMdz7hvL/5zJxNJQiCACIiIiKiWlLLXQARERER2Tc2lEREREQkChtKIiIiIhKFDSURERERicKGkoiIiIhEYUNJRERERKKwoSQiIiIiUdhQEhEREZEobCiJiIiISBQ2lEREREQkChtKIiIiIhKFDSURERERicKGkoiIiIhEYUNJRERERKKwoSQiIiIiUdhQEhEREZEobCiJiIiISBQ2lEREREQkChtKIiIiIhKFDSURERERicKGkoiIiIhEYUNJRERERKKwoSQiIiIiUdhQEhEREZEobCiJiIiISBQ2lEREREQkChtKIiIiIhKFDSURERERicKGkoiIiIhEYUNJRERERKKwoSQiIiIiUdhQEhEREZEoTnIX4GgiJn6IrKsGucuAe4Ae/dfNETXGjEggIddKBYnk6wos7SZ3FcqhlO0McKxtjduZY2NupMHcEMCG0uqyrhqQERsvdxlWkZALXM6SuwqqjCNtZwC3NbIN5oZIOtzlTURERESisKEkIiIiIlHYUBIRERGRKGwoiYiIiEgUnpQjkweWTUbg2L4AAJPRiLykDCT+EY0Ti75HriFN5uosd/Wzp5F6YF3JA7Ua2vo+cA/uB98JH8DZy1fe4u5xjrStcTsjW2FuiCzDbyhlZDh6Fps7Poef7n8Zv01eBq8OAejzzSy5y6o1XbswdFybiODV19F81g/IvRKFy4tHy10WwbG2NW5nZCvMDZH52FDKyFRYjLzkDOQa0pB09BxiNuxH466todW5yF1araicnKGtr4ezly/c2/dCo0EvICfmCIy5t+Qu7Z7nSNsatzOyFeaGyHxsKBXCxbs+AoZ3h6nYCMFokrsc0QpTbyD9z58AtabkDymGI21r3M7IVpgbourxGEoZ6Xu0x5MX10OlVsPJpQ4AIHrFDhTnFQAA+qyahRuHTiF2w34AQIMOzdHrq2n4eeBsGAuKZKu7KlnRBxE1VgfBZIJQmAcA8B41C5q6bgCA9CNbkbj53XKvyY87C//nPkOjIS/bvN57iSNta9zOyFaYG+aGzGfXDeWpU6cwb948HDx4EIIgoF+/flixYgWCgoIwbNgwbNq0Se4Sq5V84gIOT/sCmjpaBIzogSZhHRG1eGPp83+9HY4h2xfg2q5IFKRnI/TD5xH5xhrFTVS3uQV1Q8D0dRAK85F+eAtundqPJk8uLH2+fujDqB/6cOnjjKPbkLD+DXj1myhHuRYRBAFnLqbjZlo+dK5ahLT1gpOT/XzB70jbmiNvZ45GEAScvZSBpNQ86Fy16NzGC1otcyMH5oakZrcNZUREBIYPH45mzZrhrbfegouLC9auXYshQ4YgOzsbnTp1krvEGhnzC0vvK3vy481wD9Cj2/vP4s9XvwYA5BrScGblTtz/9nikRF1E5uVEJB4+LWfJ1VI7u6CuTyAAwKVZBxQYLiHum6loNmVVhXULU+JxfeVkBL6zG+o6rrYu1WyCIOC7HRexZH00/oktO7OzSSNXvDy2DWY/3RF1nJW/y8iRtjVH3M4cjSAI+P6/l/DputM4GVOWG5+GLnhpTFu89kww6tZR/q8f5oa5IfPZz0fFOyQnJ2Ps2LEICQlBVFQUZs+ejSlTpiAiIgLXr18HALtoKO928pPNCBzbF173tSxddj58Dzxb+yN4yigce3edjNVZzmfcfKREhCPnwvFyywWTCVeWPgX9o3PgGtBRpupqJggCZn4ciaff/g2nL5S/TEhiSi7e/uIEBr+0B3n5xTJVWHuOtK3Z+3bmaARBwGtLjmH8G4dwKrZ8bgypeXjnqxMY9OIe5OYxN3Jibsja7LKhXLx4MdLT0xEeHg4Xl7Kz7erVq4eQkBAA9tlQZl0xIG7fcYTMGVe2UBAQ890+xEecQEGqfZ2NV7dJK3h2fQg3NrxZbnniloXQuHig8fCpMlVmnh92XcKyDWcAAIJQ/rnbjw8eN+C1pcdsXJl4jrSt2ft25mi2/HIFn6wr+Zauqtz8fiIJMz+JtHFl4jE3RFWzy4Zy06ZNCAsLQ1BQUKXPe3t7Q6/XAwCKi4sxbdo0NGjQAJ6ennj22WeRn59vy3ItEv3VDvj26QR9aPuyhSYTBJNQ9YsUzPvh2bh1ci+yTh8EAGSf+wOp+9cg4JVwWeuqiSAIWPJdNFSqmtdd858YZGYVSl+UlTnStmav25kjWrrevNys3R6LtMwC6QuyMuaGqHLKP4jlLgaDAQkJCRg7dmyF50wmE06fPo3OnTuXLlu0aBF+/fVXnD59Gs7OzhgxYgRee+01LF++3Kz3Ky4uhsFgMLu+oiLzduMcnv5lpcuTj8dgrc9jZr9fdXXEx8eLHMMbgNasdQOmra10ua5tD3TZXjLRFmdn4MrS8Qh4ZS2cPLwsrKUI8fFJFr1GjMvxOThxLtWsdfMKjAj/TxQe62+7O06Yu50BjrWtOdp25miuJuYi8nSyWesWFJrw7U9RePxBP4mrKsPclMfcUGX0ej2cnCxvD+2uoczJyQEAqCr5CLx9+3bcvHmz3O7u1atX46OPPoKvb8kv+/nz52P06NFYunQpNJqaT6YwGAzw9/c3u76FXgPhq/Uwe32pxMbGYowFdVem3efRcGnavuYVzZS8ZwWK0hMR9+2Mcsu9+k6E98gZVbyqRGxsLPwf7GC1WmrkGgi0nGP26jNenYcZKXslLKg8pWxngPK2NbvazhyNSwsg8A2zV5899z3Mfm63hAWVx9xUjbmh2+Li4uDnZ/kHPbtrKP39/aHRaHDo0KFyy69du4apU0uO+bjdUGZkZCAuLq5cgxkSEoKsrCxcvXoVLVu2hD24uOUgLm45KHcZovk8Nhc+j82VuwzzmCw8LMKo3MMoLOEI25pdbWeOxpRn2frMjWIwNySW3TWUzs7OmDBhAsLDwzFy5EgMGzYMcXFxWLVqFby9vZGQkFDaQGZlZQEAPD09S19/+++3n6uJXq9HXFyc2fUdGfMhcq6Yv4tcKkFBQYjb8q2oMaae9UacQub7oKAg/GLB/4NYJpOAB579HfE38yqcWHA3tRqI/HUN9F51bVMclLOdAY61rdl6O3M0JpOAPi8cxtXE3JpzowL+3LcSvo1tdxtD5kYazI1juX0OiqXsrqEEgOXLl0Or1WL79u04cOAAQkNDsXXrVrz33nu4ePFi6ck67u7uAIDMzMzSf6CMjIxyz9XEycnJoq9+tVpl/JNqtZbVXekYFwAoYLICAK1WK/rnsdT08cGY9clfNa736IDmuP++QBtUVEYp2xngWNuaHNuZo5k+viOmLT5a43oj+jZDt5BWNqioDHMjDeaGADs9y1un02HlypUwGAzIysrC3r17ERoaiujoaAQHB0OtLvmxPD094e/vj5MnT5a+NioqCu7u7ggICJCneLIbU8e1x9Cw6ifJln7u+GJuqI0qIlK+SWPbYkSfptWu09xXhxVv9bBRRURkC3bZUFYmIyMD8fHxFa4/+dxzz+GDDz7AjRs3kJycjPnz5+Ppp58264QcurdptWpsXTYArz0TDA+38mdSajQqPD64Bf5c/xAae9lulx2R0jk5qfHTp/0x59mOFXOjVmHMg81xZMMI6BvyDixEjsRhGsrTp0supHt3Q/nGG2+gV69eaN++PQIDA9G2bVssXrxYhgrJHjlrNVg84/9wI2Icvnqz7BuVv74fgY0f9WUzSVQJrVaND6Z1rZCbo9+PwOaP+8GbuSFyOA7fUDo5OWH58uVIT09HZmYm1qxZU+7uOnJr9UR/DN3xPoZsXwDPNpXvJhr873cRuvgFG1dWOyl7V+P8az1wfs4DyLta+T1tY97sg2tfvWTjysRxc9Xiod5l/z+NGyhnGzKXI21rjrqdOZq7c6O3w0aSuSEyj8M0lJMmTYIgCOjevbvcpZjN2VOH1hMHYfcj8/DHzBXotuCZCuv4DeiComwLL8Uhk+KsNCTvWYHWiw4hYMoaxK2eVmGdjGM7oXEx74Qosh5H2ta4nZGtMDdE5nOYhtIeNeocCMOfZyAUG3Hr0g3UaeCBcvcsU6nQ5pnBOL92j3xFWiDnwl/QdegDlZMWdf1ao/hWCgSTqfR5wWRC8q4v0WjoZBmrvDc50rbG7YxshbkhMh8bShk5e+pQmJlT+rgoOw/OHmUHqgeO6YNruyJhzC+SozyLGbPS4KSrX/pY7eIOY25m6ePUA+vgGfoI1FrbXa+RSjjStsbtjGyFuSEyHxtKGRVm5sDZw630sVbngsJbuQAATR0tWjwShoubDshVnsU0uvow5mSUPjblZUHjWq/k74X5SDv0PRr2r7jLiKTnSNsatzOyFeaGyHzKucrrPSj5xAV0enUMVBo1dP6NUZB2C7dvL6Fr2hjO9dwwYP1cOHvq4NLYEy1H98alHw/VMKp83IK64cbGdyAYi1Fw8yqcPBpC9b9rghYkXYExJwMXFwxHcXYaitINSD3wHbz6TZC56nuDI21r3M7IVpgb5obMx4ZSRoUZ2bjwQwSGbF0AQTDh6NzV8O3bCc6eOlzZehg7B78OANCHtkfzUT0VO1Hd5uTeAA0HPoeYub0AtRpNX/wSmSf2wJiVhga9n0DbJccBAFmnDyLt902crGzIkbY1bmdkK8wNkflUglDTHVfJEtt6T0dGbLzcZcAzyA+jDi0TNcaYX4HL5t3yXHIt3IEtfeWtId6QA/9BmwAAcXsfh5/erYZXSEcp2xngWNuaErYzR8PcVI65IUfDYyiJiIiISBQ2lEREREQkCo+htDL3AL3cJQCwTh2+CrrVrpJqUQKlbGeAY21rSqmDpMHcSEMpdZC8eAwlkZmUdCwYkb1gbojuDdzlTURERESisKEkIiIiIlHYUBIRERGRKGwoiYiIiEgUNpREREREJAobSiIiIiIShQ0lEREREYnChpKIiIiIRGFDSURERESisKEkIiIiIlHYUBIRERGRKGwoiYiIiEgUNpREREREJAobSiIiIiIShQ0lEREREYniJHcBjiZi4ofIumqQuwy4B+jRf90cUWPMiAQScq1UkEi+rsDSbnJXQVJhbqTB3Dg25kYazE3tsKG0sqyrBmTExstdhlUk5AKXs+Sugu4FzA2R5ZgbUhLu8iYiIiIiUdhQEhEREZEobCiJiIiISBQeQ0lUjeS0PPx6LBHHz6Qg6lxq6fI3Pz+OPl190KuLHi39PWSskEh5UtLz/5ebZJw4W5abN5aX5SawKXND5EjYUBJV4u+zKVjyXTR+3HsFRcWmCs9/9/NFfPfzRQDAgO5NMHVcOzzUpylUKpWtSyVSjBNnU7B0QzS2/HIFhUUVc7N+50Ws31mSm37/54OpT7TDyL7NmBsiB8CGUiYPLJuMwLF9AQAmoxF5SRlI/CMaJxZ9j1xDmszVWe7qZ08j9cC6kgdqNbT1feAe3A++Ez6As5evvMVZIC+/GG9/8TeWrI+GIJj3mv1Hb2D/0RsY0acpvn67J3wauUpb5D2MuVGm/IJivPPVCXyyLhomk3nBOfBXIg78lYhhvfzxzbyeaNLYTeIq713MDdkCj6GUkeHoWWzu+Bx+uv9l/DZ5Gbw6BKDPN7PkLqvWdO3C0HFtIoJXX0fzWT8g90oULi8eLXdZZjOk5KL7Uz/j0+/MbybvtOPgdQQ/+h8ci062fnFUirlRlqTUPPQYvxMfhZ82u5m8039/i0Pwo1tx9NRNCaqj25gbkhobShmZCouRl5yBXEMako6eQ8yG/WjctTW0Ohe5S6sVlZMztPX1cPbyhXv7Xmg06AXkxByBMfeW3KXVKCU9H32f3YV/Yqv+tK7RqODr7Qpfb1doNJXvokvNKMCAF3bjxNkUqUq95zE3ypGWWYD+z+1C1PnUKtcxJzdpmQUY+OJufhiTEHNDUmNDqRAu3vURMLw7TMVGCMaKxx7Zm8LUG0j/8ydArSn5o2CCIGDiW4dw/kpmtevpG7ogft84xO8bB33DqifhW9lFeGRmBLJyCq1dKt2FuZGPIAj417zfcOZSRrXrmZub7NxiPDozAplZzI3UmBuSAo+hlJG+R3s8eXE9VGo1nFzqAACiV+xAcV4BAKDPqlm4cegUYjfsBwA06NAcvb6ahp8HzoaxoEi2uquSFX0QUWN1EEwmCIV5AADvUbOgqVtybFT6ka1I3Pxuudfkx52F/3OfodGQl21e723rdlzArt+te7eJazey8dqSY1jxdk+rjkvMDaCM3Pyw6xK2/3rdqmPGGXLw6qeRWDU/zKrjEnMDKCM3jsyuG8pTp05h3rx5OHjwIARBQL9+/bBixQoEBQVh2LBh2LRpk9wlViv5xAUcnvYFNHW0CBjRA03COiJq8cbS5/96OxxDti/AtV2RKEjPRuiHzyPyjTWKDDcAuAV1Q8D0dRAK85F+eAtundqPJk8uLH2+fujDqB/6cOnjjKPbkLD+DXj1myhHuQCAwiIj5iw7LsnYX/94HjPGd0BQQD1Jxq+t64nZ+Oan8zj6TzKMJgGtA+rhhUdbI6RdQ7lLMwtzI39uiotNeH3pMUnGXv2fWMwY3wHtWtaXZPzaijfk4Jt/n8eRUzdRbBTQqqkHXnisNe5v30ju0szC3MifG0dnt7u8IyIi0L17d8TExOCtt97CokWLEB8fjyFDhiA7OxudOnWSu8QaGfMLS+7FGhOHkx9vRlbcTXR7/9nS53MNaTizcifuf3s8Wo8fiMzLiUg8fFrGiqundnZBXZ9AuDTrgCZPvoc63s0R983UStctTInH9ZWT0Xz2JqjryHdW9NaIa0hKzZNs/K9/PCfZ2JYSBAFvfX4czQdvxvurTuHAXzdw6HgiVv54Hl0e345R0/YhO1eZvzzuxNzIn5sdB68j4WauZON//eN5yca2lCAImP/VCQQM3owFK08i4mhJblb9OwZdx+3AQ1P22sXhLcyN/LlxdHbZUCYnJ2Ps2LEICQlBVFQUZs+ejSlTpiAiIgLXr5fsgrGHhvJuJz/ZjMCxfeF1X8vSZefD98CztT+Cp4zCsXfXyVid5XzGzUdKRDhyLpT/BlAwmXBl6VPQPzoHrgEdZaquxHc/X5B4/IsQanPKuATe+eoE3l91CrdPxBUElDubffuv1/HozAgY7eyYKubG9qTOzfqfL9bqjHEpLFh5Eu9+HQXj/+oRUD43O3+Lw6hp+1FUyXU3lYy5IWuzy4Zy8eLFSE9PR3h4OFxcyg7yrlevHkJCQgDYZ0OZdcWAuH3HETJnXNlCQUDMd/sQH3ECBan2dfZa3Sat4Nn1IdzY8Ga55YlbFkLj4oHGwyv/NGkrgiAg8rS0Z5WmZhTgcnyWpO9hjoSkHCxadarG9fb+mYCdv8XZoCLrYW5sT+rcZGQV4sK16k+SswVDSi4WfBNV43oH/krE9l+v2aAi62FuyNrs8hjKTZs2ISwsDEFBQZU+7+3tDb1eDwDYsmULli9fjpMnT6Jhw4a4evWqRe9VXFwMg8Fg9vpFRcUWjX+36K92YNjP70Mf2h6GI2dKFppMECz8tF5UVIz4eHEnmhQVeQPQihrD++HZiJnTE1mnD8I9uA+yz/2B1P1r0HbJCQtrKUJ8fJKoWu6WcDMPqRkF5ZZpNKoqz0T1uWO5TxXrGFLyYDSW/7/adzgWdcL0IqsVZ8mGi6XfsNS47roodGll2zMlmZvylJybpLQCGFLKHyYiRW72Ho6Fm7aJyGrFWb7pEoqLzduGln53Et3bift/txRzU56Sc2NP9Ho9nJwsbw9VglL2x5nJYDDAx8cHM2fOxKefflruOZPJBB8fH3Tu3Bl79uwBAOzbtw+pqalISkrC0qVLLW4o4+Pj4e/vb/b6C70Gwldr3XvUBo7pA6/7WiLyzTVmvyah6BbeSt0n6n3bfR4Nl6btRY1xp+LsDJybGYKAKWvg3rGvRa/Nu34GZ6d2sFotAACXZkDg2+UW+Xq7In7fuCpeUDO/gRuRkHTXsWUJ3wNpv9Z6TKto9grgHgyYc4s7Yy5w9hXpa7oDc1M1xeWmrj/Q6p1yiyTJzY1NQOr+Wo9pFU0nAx6dzMxNPnB2iuQl3Ym5qZricmNH4uLi4OfnZ/Hr7O4bypycHACo9N6v27dvx82bN8vt7h44cCAAYNu2bbYoj6qRvGcFitITEfftjHLLvfpOhPfIGVW8Sko2un+wEu5TrLLk6BYF1EullJcbG7G33FiUMZLaPZsbGdndN5SFhYVwdXVF586dcexY2WUrrl27hp49eyIhIQEbN27E448/Xu5127Ztw/Tp0yXf5X1kzIfIuWL++lJxa65H6JY5osaYetYbcfm23YVTFf+6Rfi8nXV3QVxJyEGv5w+XW1bTrrtjG0cBALqO24bElIpnh1e26+6zV4PxSD95d93N/+Y81myr+RgvFYCOQR7YuSxU+qLuwNxIQ4rcXDfkoue/fi+3TIrcfDK9A8YOkve+zAvXxGDlv6/WuJ5KBbRr7o49X/SQvqg7MDfSkCI39qS2u7zt7htKZ2dnTJgwAeHh4Rg5ciSGDRuGuLg4rFq1Ct7e3khISLDqCTlOTk4WffWr1Srjn1SrtazuSse4ACDfOvWIpdVqRf88d2vSRIDONbLcpXKMRqHirrdKJKbkmbUeAPQLDYSfX4Na12kNs57WmdVQCgCmPXWf1f+ta8LcSEOK3Pj6CqjnHlnujjZS5KZ/j0D4+XnVuk5rmDnR3ayGUhCAV57qyNyIGcPBc3MvsMvv6JcvX44XXngBkZGRmDVrFiIjI7F161Y0adIErq6uVZ6sQ3QntVqFkLbS/sJyreuENs09JX0Pc7Rt4Yknh7U0a72xD7awQUVkr1QqFe6X+CL4deto0F4BFzYPCqiHiSNa1bxeMw88ObTmfBE5MrtsKHU6HVauXAmDwYCsrCzs3bsXoaGhiI6ORnBwMNRqu/yxSAaPD5a2eRo9qDmcnJSxPa6e/wBG9WtWYfntI9XatfDE3q8Hw9VFGd96kHJJnZtHBwRAq1VGblbO64lHBwRUWH47N60D6mHvysFwc1XG7loiuSgjsVaQkZGB+Pj4Cru7jUYj8vPzUVRUBEEQkJ+fj4KCgsoHoXvOU8NbQifhL4JJY9tKNral6tZxwr+X9McvXz+IgaFlx6aFtPPCuoW9cHzTSPjp3WSskOzFuCEtUM/dWbLxlZSbOs4abPmkH/Z9MxgP9ijLTee2XghfEIaoLaPQrIm7jBUSKYPDNJSnT5fcIuruhnL9+vVwcXHBmDFjcP36dbi4uKB169YyVFi5Vk/0x9Ad72PI9gXwbNO00nUG//tdhC5+wcaV1U7K3tU4/1oPnJ/zAPKuVn7brpg3++DaVy/ZuLLKubs5Y/bTwZKMPTTMD107KOv+2Gq1CoN6+OHbd8NKl21bNhATRrSCS137+WaSuZGXm6sWrz8jzV1HBvXwReh9jSUZu7bUahUGdPfF6vlludn+2UA8PTKIuZGRveXG0Tl8Q/n0009DEIRyfyw901sqzp46tJ44CLsfmYc/Zq5AtwXPVFjHb0AXFGVLd69payrOSkPynhVovegQAqasQdzqaRXWyTi2ExoXZX2an/vsfejUxronzdRzd8Y38x6o9PJWJA5zowyznw7G/e2t+4HJ3U2LVe8wN1JgbkhqDtNQTpo0CYIgoHv37nKXYrZGnQNh+PMMhGIjbl26gToNPMpfe02lQptnBuP82j3yFWmBnAt/QdehD1ROWtT1a43iWykQTGX3txVMJiTv+hKNhk6WscqKtFo1Ni7uCy/POtWuZ0jJg9/AjfAbuLHCnULupNGosG5hL/h6c/exFJgbZXByUuOHD/ugUf261a5nbm7UahXC3wtDUx+dtUslMDckPYdpKO2Rs6cOhZk5pY+LsvPg7OFa+jhwTB9c2xUJY35RZS9XHGNWGpx0ZWdmql3cYcwtux9v6oF18Ax9BGpt9b+A5NCmuSf2fzOk2l+Oty+NkpCUW+Gaebc5aVT44cM+GNm34skvZB3MjXK0alYP+1cNgbdX5degBMzLjUajwnfv98KjA5tLVeo9j7khqbGhlFFhZg6cPcq+xdLqXFB4q+QabZo6WrR4JAwXNx2QqzyLaXT1YczJKH1sysuCxrVeyd8L85F26Hs07F9xN4tSdGrjhb83jyx34L0l2rbwxOF1wzGGl92RFHOjLB2DGuD4xpEY8kDtrtsX1KwefgsfhieHBVq5MroTc0NSY0Mpo+QTF+DdvS1UGjXcA/QoSLtVcoVcALqmjeFczw0D1s9Fl7efgm//zmg5urfMFVfPLagbss78BsFYjPzEi3DyaAjV/y7hVJB0BcacDFxcMBzx615D5t+7kHrgO5krrshfr8PuFQ/iu/d7IbiVedfBa9LYFQundMGJzSPRraOyTiZwRMyN8nLjp3fDf78chA0f9MZ9rc07HtmnkSvemxyCkz+OQo9O3hJXSMyN8nLjaOzn9DQHVJiRjQs/RGDI1gUQBBOOzl0N376d4Oypw5Wth7Fz8OsAAH1oezQf1ROXfjwkc8XVc3JvgIYDn0PM3F6AWo2mL36JzBN7YMxKQ4PeT6DtkuMAgKzTB5H2+yZ49Zsgb8FVUKlUGP9QKzw1PBB/nryJPX/E4++zKTh3OQO5+cVw1mrQws8dXdp5oVcXPYaFNVXMNfPuBcyNcnPz5LBAPDG0JY7+cxO7D5fk5uylktxondT/y01D9Oqix/BezI0tMTfKzI0jsbt7eSvdtt7TkREbL3cZ8Azyw6hDy0SNMeZX4HKWdeoRq4U7sKWv3FU4jnhDDvwHbQIAxO19XPbrTzI30mBurIu5qRxzQwB3eRMRERGRSGwoiYiIiEgUNpREREREJApPyrEy9wC93CUAsE4dvq41r2MrSqqFrI+5kYaSaiHrY26koaRa7AlPyiG6Bynt5AIie8DcEFWNu7yJiIiISBQ2lEREREQkChtKIiIiIhKFDSURERERicKGkoiIiIhEYUNJRERERKKwoSQiIiIiUdhQEhEREZEobCiJiIiISBQ2lEREREQkChtKIiIiIhKFDSURERERicKGkoiIiIhEYUNJRERERKKwoSQiIiIiUZzkLsDRREz8EFlXDXKXAfcAPfqvmyNqjBmRQEKulQoSydcVWNpN7iqIaqaUOQBwrHmAc4BjY26kYcvcsKG0sqyrBmTExstdhlUk5AKXs+Sugsi+ONIcAHAeINtgbuwfd3kTERERkShsKImIiIhIFDaURPcYQRAQZ8gufXzheiaKi00yVkSkfHfnJvYac0N0Jx5DSXQPKCwy4j/7r2LdjouIPH0T6bcKS5/r99xuuNTVoFNrL4wZ1BwTR7ZCfY86MlZLpAyFRUZsO3ANa7dfQOTpZKRlFpQ+1//53ahbR4NOrRtg9KDmeHpkEBrUY27o3sWGUiYPLJuMwLF9AQAmoxF5SRlI/CMaJxZ9j1xDmszVWe7qZ08j9cC6kgdqNbT1feAe3A++Ez6As5evvMXdwwRBwLodFzBn2XEkpeZVuV5evhFHTt3EkVM38cbnxzH9yQ545+XOqOOssWG19x5HmgccaQ4QBAEbdl7Ea0uPwZBSdW7yC4w4+k8yjv6TjDc//xuvPNEO704KQd06/NUqJeZGmbjLW0aGo2exueNz+On+l/Hb5GXw6hCAPt/MkrusWtO1C0PHtYkIXn0dzWf9gNwrUbi8eLTcZd2zUtLzMXzKXjzz9u/VNpN3y8s34oM1pxAydhtOx9rX5GyPHGkecIQ5IC2zAKOm7ceEN3+rtpm8W36BER+Fn0bnMdtw8nyqhBUSwNwoERtKGZkKi5GXnIFcQxqSjp5DzIb9aNy1NbQ6F7lLqxWVkzO09fVw9vKFe/teaDToBeTEHIEx95bcpd1zklLz0OuZ/2LX77W/DMfZSxkIe+a/iPznphUro7s50jxg73NAcloeej/zX+w4eL3WY5y/kolez/wXf55MsmJldDfmRnnYUCqEi3d9BAzvDlOxEYLR/g/0Lky9gfQ/fwLUmpI/ZDP5BcUY/PIenLucUeU6Go0Kvt6u8PV2hUajqnK9zKxCDJn0Cy7F2dfEZq8caR6wtzmgoNCIoZP3IvpiepXrmJubrJwiDJ28F7FXM6Uole7C3CgDD/SQkb5Hezx5cT1UajWcXEoO5o5esQPFeSUHfvdZNQs3Dp1C7Ib9AIAGHZqj11fT8PPA2TAWFMlWd1Wyog8iaqwOgskEobBkV5H3qFnQ1HUDAKQf2YrEze+We01+3Fn4P/cZGg152eb1Oqr5K6Jw8nz1u6r1DV0Qv28cAMBv4EYkJFV9S4f0W4X417zf8euaoVCrq/4lSrXjSPOAPc8BC1ZG4fiZlGrXsSQ3mVmF+Nc7v+PQt0Oh0fC7G2tjbpSRmzvZdUN56tQpzJs3DwcPHoQgCOjXrx9WrFiBoKAgDBs2DJs2bZK7xGoln7iAw9O+gKaOFgEjeqBJWEdELd5Y+vxfb4djyPYFuLYrEgXp2Qj98HlEvrFGcWG4zS2oGwKmr4NQmI/0w1tw69R+NHlyYenz9UMfRv3Qh0sfZxzdhoT1b8Cr30Q5ynVIp2JS8fHa01Yf97e/DVj9nxi88Fgbq48tliAIOH8lE0mpeXB30+K+oAZwcrKfX+CONA/Y6xwQfSENH377j9XH/SMqCSt/PI9Jj7ez+tjWEHMlA4kpedC5OqFTay/mRib2mpu72W1DGRERgeHDh6NZs2Z466234OLigrVr12LIkCHIzs5Gp06d5C6xRsb8wtJ7l578eDPcA/To9v6z+PPVrwEAuYY0nFm5E/e/PR4pUReReTkRiYet3yxYi9rZBXV9AgEALs06oMBwCXHfTEWzKasqrFuYEo/rKycj8J3dUNdxtXWpDmvZhjMwmQRJxv50XTSef7Q1VCplfEspCAJ+2HUJS76LxolzZSdB+DZ2xctj2uLVp4Pt4ix1R5oH7HUO+Oz7MzAapcnNkvXReGlMW0V9u7/xf7k5frbsG9kmjVzx0pg2mP10sF2cpc7cyJ+bu9nPx5E7JCcnY+zYsQgJCUFUVBRmz56NKVOmICIiAtevlxxMbQ8N5d1OfrIZgWP7wuu+lqXLzofvgWdrfwRPGYVj766TsTrL+Yybj5SIcORcOF5uuWAy4crSp6B/dA5cAzrKVJ3jScsswKY9lyUbP/ZaJg5EJko2viUEQcDrS4/hqbmHEHXXGbU3knPx1hd/Y8jLvyAvv1imCmvPkeYBe5gDMm4V4PtdlyQb/1JcFvb+mSDZ+JZ647PjeGLOQfx9rvzu/cSUXMz78gQefOkX5OYxN3Kyh9xUxi4bysWLFyM9PR3h4eFwcSk7o6tevXoICQkBYJ8NZdYVA+L2HUfInHFlCwUBMd/tQ3zECRSk2teJEXWbtIJn14dwY8Ob5ZYnblkIjYsHGg+fKlNljunQ8UTkFxglfY9f/qz9WePWtHnP5dJd+8JdXyzdfvzrsUS8+ulfNq5MPEeaB+xhDjgclYS8/HsjN//edwUfrDkFoOrc/Pa3ATM+PmrjysRjbuRnlw3lpk2bEBYWhqCgoEqf9/b2hl6vR0FBAZ5//nm0aNEC7u7uCAoKwueff27jai0T/dUO+PbpBH1o+7KFJhMEiXZjSs374dm4dXIvsk4fBABkn/sDqfvXIOCVcFnrckR/n63+hAJ7eQ9zLF0fDXP2vH+7LRYZtwpqXlFhHGkeUPocwNxUtG7HBaRm5EtfkJUxN/JS/oESdzEYDEhISMDYsWMrPGcymXD69Gl07twZAFBcXAy9Xo+9e/eiRYsW+Oeff/Dggw/C29sbY8aMMev9iouLYTAYzK6vqMi8XQWHp39Z6fLk4zFY6/OY2e9XXR3x8eI+FRcVeQPQmrVuwLS1lS7Xte2BLttLwlycnYErS8cj4JW1cPLwsrCWIsTH87pu1Yk6W3471WhU0Des/JpsPncs96liHQAwpOSVO7Ys+mKa6O1KrCs3cvBXtHm/oPMLjAj/z0mMHmC7O06YOwcAjjUP2Osc8PeZ8odxSJGbMwrITXxSHv44ad41ZQsKTfj2p5MYN9hP4qrKMDflyZkbvV4PJyfL20O7ayhzcnIAoNITA7Zv346bN2+W7u52c3PDggULSp/v1KkTRowYgcOHD5vdUBoMBvj7+5td30KvgfDVepi9vlRiY2MxxoK6K9Pu82i4NG1f84pmSt6zAkXpiYj7dka55V59J8J75IwqXlUiNjYW/g92sFotDqnZK4BH2XE1d17ipDrHNo6q8rm7L41yMznNojxIwrUF0PINs1efOXs+ZqbskbCg8pQyBwDKmwcUOQc0mwx4dC59KEVu0tKz5M+NSwAQ+JbZq7/2xgK89vwu6eq5C3NTNVvnJi4uDn5+ln+YsLuG0t/fHxqNBocOHSq3/Nq1a5g6teS4gqqOnywqKsLvv/+OV199VeoyreriloO4uOWg3GWI5vPYXPg8NlfuMhyXIO1xYAAAkw3eoyZGC3fFmcy/fZ6SOcI8oMg5wBa5scV71MRoYQ5M9rfLuzLMje2oBOHuQ3OV71//+hfCw8MxYsQIDBs2DHFxcVi1ahW8vb3xzz//4Ny5c2jTpuL18l588UWcOHECf/zxB5ydnc16L0t3eR8Z8yFyrpi/vlTcmusRumWOqDGmnvVGXL55u7yl5l+3CJ+34y7v6ry36jxWbb1W+rimXXe3v2HpOm4bEqu4Z/Hdu+66tPXEtk+7Wa/oWjCZBIQ99zvikvIqnFhwN7UaOLq2N3wa1rVNcVDOHAA41jwg1Ryw6NsYrPjpauljKXLTsZUH/vtZqNVqrg1BENDnhcO4ciO3xtyoVMAf3/aCv7ftbmPI3EijNrm5Z3Z5A8Dy5cuh1Wqxfft2HDhwAKGhodi6dSvee+89XLx4sdKTdWbOnIkjR47gwIEDZjeTAODk5GTRV79arTL+SbVay+qudIwLABTyIVWr1Yr+eRxdn/8rKNdQGo1CtXfyuC0xJc+s9QAgtFMTRfw/TB/fETM/jqxxvZF9m6Frp0AbVFRGKXMA4FjzgFRzQJ9uheUaSkfOzYwJ92HqB0dqXG94r6YI7dLKBhWVYW6kYcvfnXZ5lrdOp8PKlSthMBiQlZWFvXv3IjQ0FNHR0QgODoZaXf7Hmj59Ovbt24eIiAg0bNhQpqqJpBXWxdusMzjF6N1FL+0bmGnK4+0wNKz6SbK5rw5fvdnDRhWRvXqgs7fkFx1XSm5eGt0GI/o0rXadpno3fP02c0OWs8uGsjIZGRmIj4+vcPzkK6+8gv379+PAgQNo1KiRPMUR2YC/XodhYdId+O/t5YIRfav/ZWQrWq0aW5cNwOyng+HuVn63kkatwuhBzfHn+oegb6isO0mQ8jRp7FZjkyVGw/p18ciAAMnGt4STkxo/fdofr/+rIzzuyo1arcKjAwJw9PsRaNLYTaYKyZ45TEN5+nTJRY7vbCivXbuGzz//HBcvXkTz5s2h0+mg0+kwZMgQmaokktYrT1rvrPy7vTS6DZy1yrmVobNWg49m/h9uRIzDF3PLjk87+v0IbPmkH5tJMtsrT0h3r+0XH2utqFuAarVqfDi9K25EjMOXb5R9E3l0w0P4aUl/+DRibqh2HLqhbNasGQRBQH5+PrKzs0v/7N69W6YqK2r1RH8M3fE+hmxfAM82lX9KHvzvdxG6+AUbV1Y7KXtX4/xrPXB+zgPIu1r5fVNj3uyDa1+9ZOPK7g0DQ30xdnBzq48b1KweXv+X8m71BQA6Vy1G9m1W+ljvZbsTCazFkeYBe5wD+v5fEzw5rGXNK1qopb875j57n9XHtQY3V225b2Z97PADGHOjLA7TUE6aNAmCIKB79+5yl2I2Z08dWk8chN2PzMMfM1eg24JnKqzjN6ALirLt47InxVlpSN6zAq0XHULAlDWIWz2twjoZx3ZC4+IuQ3X3ji/m9qjyLNXbDCl58Bu4EX4DN8JQxZmqtzlpVAhfEAaXuso5aN6RONI8YM9zwPI5ofBtXH1TZUluNBoVvn03DG6u8p/p64iYG+VxmIbSHjXqHAjDn2cgFBtx69IN1GnggXJnVahUaPPMYJxfa7uLMouRc+Ev6Dr0gcpJi7p+rVF8KwWCyVT6vGAyIXnXl2g0dLKMVTq+hvXr4pevB6NBvTpVrnP7TNaEpNxylze5m1qtwvpFvdGjk7cUpRIcax6w5zmgQb062LPiQXh5is+NSgWEvxeGXvf7SFEqgblRIjaUMnL21KEwM6f0cVF2Hpw9yj4hB47pg2u7ImHML5KjPIsZs9LgpKtf+ljt4g5jbmbp49QD6+AZ+gjUWttdE/Be1TGoAQ59OxSBTWt/54l67s7495J+eHyI9XcFUhlHmgfsfQ7o0KoBfl87HEHN6tV6DA+dFls+7ofxD9n2sjv3GuZGedhQyqgwMwfOHmVn02l1Lii8VXJdM00dLVo8EoaLmw7IVZ7FNLr6MOZklD425WVB41oyMZsK85F26Hs07F9xtwRJo0OrBjj148OYMb69xZcTGtbLH2f+8whG9QuQpDYq40jzgCPMAW1beOLkj6Pw6sRgiy8nNLinH6L/8wgeG2T945ipPOZGeXhQlIyST1xAp1fHQKVRQ+ffGAVpt3D7Fga6po3hXM8NA9bPhbOnDi6NPdFydG9c+vFQDaPKxy2oG25sfAeCsRgFN6/CyaMhVP+7JmhB0hUYczJwccFwFGenoSjdgNQD38Gr3wSZq3Zsri5OWDK7O155oj1W/nQea7dfqPLYL52rFo8NDMCksW3RtQMvsWUrjjQPOMoc4FLXCR/P+j9MGdcW3/wUg/DtF5CYXPlFzN1cnPDogABMfrwdunZoCJXUF4MlAMyNEnPDhlJGhRnZuPBDBIZsXQBBMOHo3NXw7dsJzp46XNl6GDsHvw4A0Ie2R/NRPRUbhtuc3Bug4cDnEDO3F6BWo+mLXyLzxB4Ys9LQoPcTaLvkOAAg6/RBpP2+SZGBcFQBvu74YFpXLHrlfsQn5eDvsylISs2HySTA090Zndp4IaiZBzQa7rSwNUeaBxxtDmjWxB3vv3I/Fk7tgoSkXPx9LgVJqSW3VfR0d8Z9rRugdUA95kYGzI3y2OW9vJVsW+/pyIiNl7sMeAb5YdShZaLGGPMrcDnLOvWI1cId2NJX7ipIqeINOfAftAkAELf3cfjp5bsws1LmAMCx5gHOAdbH3FSOuakdfqwiIiIiIlHYUBIRERGRKDyG0srcA/RylwDAOnX4KujGCUqqhag6SpkDAMeaB5RSB0mDuZGGLevgMZREZPeUdCwYkb1gbsiauMubiIiIiERhQ0lEREREorChJCIiIiJR2FASERERkShsKImIiIhIFDaURERERCQKG0oiIiIiEoUNJRERERGJwoaSiIiIiERhQ0lEREREorChJCIiIiJR2FASERERkShsKImIiIhIFDaURERERCQKG0oiIiIiEsVJ7gIcTcTED5F11SB3GXAP0KP/ujmixpgRCSTkWqkgkXxdgaXd5K6CqGZKmQMAx5oHOAc4NuZGGrbMDRtKK8u6akBGbLzcZVhFQi5wOUvuKojsiyPNAQDnAbIN5sb+cZc3EREREYnChpKIiIiIRGFDSURERESi8BhKIrJLqRn5+PWvRPx9LgUnzqaWLn/7y7/R+349enXRo4Wfh4wVEilPWmYBfv3rBv4+m4q/z6aULn/ri5LchIXoEdiUuSHLsaEkIrsSdS4FS9efwZa9V1BQaKzw/NrtF7B2+wUAwMDQJpg6rj2G9/aHSqWydalEinEqJhVL15/Bpj2XK83Nuh0XsG5HSW76d2uCqU+0w4g+TZkbMhsbSpk8sGwyAsf2BQCYjEbkJWUg8Y9onFj0PXINaTJXZ7mrnz2N1APrSh6o1dDW94F7cD/4TvgAzl6+8hZHDiG/oBjvfHUCn6yLhskkmPWafUduYN+RGxjRpylWzusJfUNXiau0jCPNA5wDlKmg0Ij3vo7C4vB/YDSal5uIyBuIiLyB4b38sXJeTzRp7CZxlZZhbpSJx1DKyHD0LDZ3fA4/3f8yfpu8DF4dAtDnm1lyl1VrunZh6Lg2EcGrr6P5rB+QeyUKlxePlrsscgA3U/PQY/xOfBR+2uxm8k47Dl5H8KNbcSw6WYLqxHGkeYBzgLKkpOfjgYk7sWj1KbObyTvt/C0OwY9uxdFTNyWoThzmRnnYUMrIVFiMvOQM5BrSkHT0HGI27Efjrq2h1bnIXVqtqJycoa2vh7OXL9zb90KjQS8gJ+YIjLm35C6N7FhqRj76PbcLUedTq1xHo1HB19sVvt6u0Ggq30WXkp6PAS/sxok7jhtTAkeaBzgHKEf6rQL0f343jp+pens3JzdpmQUY+OJuxX0YY26Uhw2lQrh410fA8O4wFRshGE1ylyNaYeoNpP/5E6DWlPwhqgVBEPDM27/jzKWMatfTN3RB/L5xiN83DvqGVf9CuZVdhEdmRiArp9DKlVqHI80DnAPkIwgCnnvnMP6JrX73r7m5yc4txiMzIpCZxdxIzZ5zw2MoZaTv0R5PXlwPlVoNJ5c6AIDoFTtQnFcAAOizahZuHDqF2A37AQANOjRHr6+m4eeBs2EsKJKt7qpkRR9E1FgdBJMJQmEeAMB71Cxo6pYcf5N+ZCsSN79b7jX5cWfh/9xnaDTkZZvXS8r3/X8v4edD16065rUb2XhtyTGseLunVcetLUeaBzgHKMOWX67gPxFXrTpmfFIOZn0SidXvhll13NpibpSXG7tuKE+dOoV58+bh4MGDEAQB/fr1w4oVKxAUFIRhw4Zh06ZNcpdYreQTF3B42hfQ1NEiYEQPNAnriKjFG0uf/+vtcAzZvgDXdkWiID0boR8+j8g31iguDLe5BXVDwPR1EArzkX54C26d2o8mTy4sfb5+6MOoH/pw6eOMo9uQsP4NePWbKEe5pHBFRSa8vuyYJGN//eN5zBjfAUEB9SQZ3xKONA9wDpCf0WjC7CV/STL2mq2xmDmhA9q1rC/J+JZgbpSXG7vd5R0REYHu3bsjJiYGb731FhYtWoT4+HgMGTIE2dnZ6NSpk9wl1siYX1hy/9KYOJz8eDOy4m6i2/vPlj6fa0jDmZU7cf/b49F6/EBkXk5E4uHTMlZcPbWzC+r6BMKlWQc0efI91PFujrhvpla6bmFKPK6vnIzmszdBXUdZZ96SMuw4eA03buZKNv7XP56TbGxLONI8wDlAfv/9LQ5xhhzJxl+x5bxkY1uCuVFebuyyoUxOTsbYsWMREhKCqKgozJ49G1OmTEFERASuXy/ZPWYPDeXdTn6yGYFj+8Lrvpaly86H74Fna38ETxmFY++uk7E6y/mMm4+UiHDkXDhebrlgMuHK0qegf3QOXAM6ylQdKd26HRclH782Z4xLzZHmAc4Btid1btbvvAijAo9TZG7kZ5cN5eLFi5Geno7w8HC4uJQdSFyvXj2EhIQAsM+GMuuKAXH7jiNkzriyhYKAmO/2IT7iBApS7euMr7pNWsGz60O4seHNcssTtyyExsUDjYdX/gmMSBAERJ6W9lIlaZkFuByfJel71IYjzQOcA2xP6txkZhUi9prytkHmRn52eQzlpk2bEBYWhqCgoEqf9/b2hl6vBwBMmjQJP//8MzIzM+Hu7o7Ro0fjo48+grOzs1nvVVxcDIPBYHZtRUXFZq9bmeivdmDYz+9DH9oehiNnShaaTBAs/CalqKgY8fHxomopKvIGoBU1hvfDsxEzpyeyTh+Ee3AfZJ/7A6n716DtkhMW1lKE+PgkUbWQ/UhMycfNtPxyyzQaVZVnovrcsdyninUMKXkVrsX3y+8xqNvbR2S15YmdAwDHmgc4B9hOSkYBEu46TESK3Ow7HAt35yYiqy2PuSlPztzo9Xo4OVneHqoEQVDePp9qGAwG+Pj4YObMmfj000/LPWcymeDj44POnTtjz549AICzZ8+iWbNmcHNzQ0pKCkaPHo3evXtj/vz5Zr1ffHw8/P39za5voddA+Gqtex/UwDF94HVfS0S+ucbs1yQU3cJbqftEvW+7z6Ph0rS9qDHuVJydgXMzQxAwZQ3cO/a16LV518/g7NQOVquFFK5uU6DVvHKLfL1dEb9vXBUvqJnfwI1ISLrrmMwbPwCpB2o9ZmWkmAMAx5gHOAdIrI4vEFT+bGBJcpO4GUgRt13djbmpmq1zExcXBz8/P4teA9jhN5Q5OSUHG1d2f9Ht27fj5s2b5XZ3t2vXrvTvgiBArVbjwoULktdJFSXvWYGi9ETEfTuj3HKvvhPhPXJGFa+ie5Ot7h/M+xTbEucAidnsvtvMjS3ZS27s7hvKwsJCuLq6onPnzjh2rOySIteuXUPPnj2RkJCAjRs34vHHHy997sMPP8TChQuRk5MDLy8v7N69G127djXr/Szd5X1kzIfIuWL++lJxa65H6JY5osaYetYbcfnidnlbi3/dInzejru77hVXE3MR9uzv5ZbVtOvu2MZRAICu47YhMSWvwjqV7bpbMrMDRg+w7v1ylTIHAI41D3AOqFnCzTx0f/q3csukyM3H09rj8Qct/warOsyNNGqTm9ru8ra7byidnZ0xYcIEhIeHY+TIkRg2bBji4uKwatUqeHt7IyEhocIJOXPmzMGcOXNw7tw5fP/99/DxMf+YKScnJ4u++tVqlfFPqtVaVnelY1wAkF/jajah1WpF/zxkP5o0EeDudhRZOWXXjDMahYq73iqRmJJn1noA0C80EH5+XrWuszJKmQMAx5oHOAfUzNdXQH2Po0i/VXZHGyly0zc0EH5+DWtdZ2WYG2nYMjd2eZb38uXL8cILLyAyMhKzZs1CZGQktm7diiZNmsDV1bXKk3Xatm2L++67D+PHj7dxxURkCbVahS7trPsL625162jQroX8F2gmshaVSoX720ubG2etGh0CmRuqyC4bSp1Oh5UrV8JgMCArKwt79+5FaGgooqOjERwcDLW66h+rqKgIsbGxNqyWiGpj7IPNJR3/sYEB0GrtcgokqtLYB1tIOv7D/ZuhjrN93WOabMNhZtOMjAzEx8eX292dmZmJtWvXIiMjA4Ig4J9//sHChQvx4IMPylcoEZnlyWEt4e4m3TFIk8a2lWxsIrmMG9IS9dzNuyxebUwaw9xQ5RymoTx9uuSWSnc2lCqVChs2bECLFi3g7u6OUaNGYejQofj8889lqrKiVk/0x9Ad72PI9gXwbNO00nUG//tdhC5+wcaV1U7K3tU4/1oPnJ/zAPKuVn6bq5g3++DaVy/ZuDKyN+5uznjtmWBJxn6why+6d2wsydi14UjzAOcAebm6OGHOv6S5i0r/bk0Q1kUvydi1wdwoi0M3lB4eHti/fz/S0tKQnZ2Ny5cv45NPPoGbm5tMVZbn7KlD64mDsPuRefhj5gp0W/BMhXX8BnRBUXbFM++UqDgrDcl7VqD1okMImLIGcaunVVgn49hOaFzcZaiO7NHrz9yHzm2se9KMh06LVfMfqPTSY3JwpHmAc4AyvDoxGF07WPdYSp2rFquZG0k4Sm4cpqGcNGkSBEFA9+7d5S7FbI06B8Lw5xkIxUbcunQDdRp4lL+OmEqFNs8Mxvm1e+Qr0gI5F/6CrkMfqJy0qOvXGsW3UiCYyu75KphMSN71JRoNnSxjlWRPtFo1fljcB16edapdz5CSB7+BG+E3cCMMlVz65Da1WoXw93rBX6+zdqm15kjzAOcAZXByUuP7D/qgUf261a5nbm5UKmDNuw8gwFc5DQ1zozwO01DaI2dPHQozc0ofF2XnwdnDtfRx4Jg+uLYrEsb8osperjjGrDQ46crO/lO7uMOYm1n6OPXAOniGPgK1tvpJjuhObZp7Yv83Q6r95Xj70igJSbkVrpl3m0ajwvr3e+ORAQESVVo7jjQPcA5QjlbN6iFi1RB4e1V+DUrA/NysXdALYyQ+2cdSzI3ysKGUUWFmDpw9yna/a3UuKLxVch0wTR0tWjwShoubrHtbOClpdPVhzMkofWzKy4LGtV7J3wvzkXboezTsX3G3BFFNOrXxwvFNIzEwtHb3Dw5qVg+/hQ/DE8NaWrky8RxpHuAcoCzBQQ1wfONIDO5Zu+sQBjb1wK+rh2LCiFZWrkw85kZ52FDKKPnEBXh3bwuVRg33AD0K0m4B/7txka5pYzjXc8OA9XPR5e2n4Nu/M1qO7i1zxdVzC+qGrDO/QTAWIz/xIpw8GkL1v0s4FSRdgTEnAxcXDEf8uteQ+fcupB74TuaKyZ409dHhl68HY+2CXmZfB0/f0AXzX+6Mkz+OQo9O3hJXWDuONA9wDlAeP70bdn01COsX9UbHoAZmvcbbywXzXuyMUz8+rKiTcO7E3CiPci5Nfw8qzMjGhR8iMGTrAgiCCUfnroZv305w9tThytbD2Dn4dQCAPrQ9mo/qiUs/HpK54uo5uTdAw4HPIWZuL0CtRtMXv0TmiT0wZqWhQe8n0HbJcQBA1umDSPt9E7z6TZC3YLI7KpUKE0e2woQRgTh8Igl7/ojH32dTcO5KBnLzjXDWqtHc1x1d2nqh9/0+eKh3U8Vfa9KR5gHOAcqkUqnw1PBAPDmsJf48eRO7D8fh77OpOHs5Hbn5Rmid1Gjuq0OXdg3RK0SPEX2bwlmr7GtNMjfKY3f38la6bb2nIyM2Xu4y4Bnkh1GHlokaY8yvwOUs69QjVgt3YEtfuasgqplS5gDAseYBzgGOjbmRhi1zo+yP7kRERESkeGwoiYiIiEgUNpREREREJApPyrEy9wBlnBFnjTp8XWtex1aUVAtRdZQyBwCONQ8opQ6SBnMjDVvWwZNyiIiIiEgU7vImIiIiIlHYUBIRERGRKGwoiYiIiEgUNpREREREJAobSiIiIiIShQ0lEREREYnChpKIiIiIRGFDSURERESisKEkIiIiIlHYUBIRERGRKGwoiYiIiEgUNpREREREJAobSiIiIiIShQ0lEREREYnChpKIiIiIRGFDSURERESisKEkIiIiIlHYUBIRERGRKGwoiYiIiEgUNpREREREJAobSiIiIiIShQ0lEREREYny/+nhttSBnktAAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 831.997x294.311 with 1 Axes>"
       ]
@@ -56,21 +63,26 @@
    "id": "8638fdf1",
    "metadata": {},
    "source": [
-    "### Specify some observables\n",
-    "\n",
-    "Currently, only `Pauli` observables with phase equal to 1 are supported. Full support for `SparsePauliOp` is expected in CKT v0.7.0."
+    "### Specify an observable"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "id": "f75e8dd1",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:41.864097Z",
+     "iopub.status.busy": "2024-04-19T17:42:41.863622Z",
+     "iopub.status.idle": "2024-04-19T17:42:41.867665Z",
+     "shell.execute_reply": "2024-04-19T17:42:41.867037Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit.quantum_info import PauliList\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
-    "observables = PauliList([\"ZZII\", \"IZZI\", \"IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])"
+    "observable = SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])"
    ]
   },
   {
@@ -78,22 +90,31 @@
    "id": "162a5629",
    "metadata": {},
    "source": [
-    "### Separate the circuit and observables according to a specified qubit partitioning\n",
+    "### Separate the circuit and observable according to a specified qubit partitioning\n",
     "\n",
-    "Each label in `partition_labels` corresponds to the `circuit` qubit in the same index. Qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut."
+    "Each label in `partition_labels` corresponds to the `circuit` qubit in the same index. Qubits sharing a common partition label will be grouped together, and non-local gates spanning more than one partition will be cut.\n",
+    "\n",
+    "**Note:** The ``observables`` kwarg to `partition_problem` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "id": "30326299",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:41.871005Z",
+     "iopub.status.busy": "2024-04-19T17:42:41.870615Z",
+     "iopub.status.idle": "2024-04-19T17:42:41.898352Z",
+     "shell.execute_reply": "2024-04-19T17:42:41.897640Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.cutting import partition_problem\n",
     "\n",
     "partitioned_problem = partition_problem(\n",
-    "    circuit=qc, partition_labels=\"AABB\", observables=observables\n",
+    "    circuit=qc, partition_labels=\"AABB\", observables=observable.paulis\n",
     ")\n",
     "subcircuits = partitioned_problem.subcircuits\n",
     "subobservables = partitioned_problem.subobservables\n",
@@ -112,7 +133,14 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "6b54be63",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:41.901553Z",
+     "iopub.status.busy": "2024-04-19T17:42:41.901119Z",
+     "iopub.status.idle": "2024-04-19T17:42:41.905691Z",
+     "shell.execute_reply": "2024-04-19T17:42:41.905183Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -134,11 +162,18 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "b7e06fac",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:41.908126Z",
+     "iopub.status.busy": "2024-04-19T17:42:41.907769Z",
+     "iopub.status.idle": "2024-04-19T17:42:42.076871Z",
+     "shell.execute_reply": "2024-04-19T17:42:42.076174Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAACPCAYAAAA4J1eUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAppUlEQVR4nO3de1iUdf7/8ScDg4CgKCgoaFimeSqzzEN5TC3bNpUtKS1zN/O7eUplrTT91ma62XZQXCvL1tR2Nas1y5+VSmprFul6WDXzfAAExQPK+TAzvz/4ihEozMCcbl6P6+q6mnvu+cwbue8X77nvez63j81msyEiIiIiInYzubsAERERERFvpWZaRERERMRBaqZFRERERBykZlpERERExEFqpkVEREREHKRmWkRERETEQWqmRUREREQcpGZaRERERMRBaqZFRERERBykZlpERERExEFqpkVEREREHKRmWkRERETEQWqmRUREREQcpGZaRERERMRBaqZFRERERBykZlpERERExEFqpkVEREREHKRmWkRERETEQWqmRUREREQcpGZaRERERMRBaqZFRERERBykZlpERERExEFqpkVEREREHKRmWkRERETEQWqmRUREREQcpGZaRERERMRBaqZFRERERBykZlpERERExEFqpkVEREREHKRmWkRERETEQWqmRUREREQcpGZaRERERMRBfu4uoDZLfPwVso6nu7sMQmIiuXvJc9UaY1ISpObWUEHVFBUEb3ZxdxW1j6dsz1Az27QraL8Ro/GUHPCWDADPyQFlgOPUTLtR1vF0Mg+muLuMGpGaC0ez3F2FuJORtmdX0X4jRqMcsJ9ywPvpMg8REREREQepmRYRERERcZCaaRERERERB6mZFhERERFxkL6A6AXumjuWlnF9ALBaLOSdziTtu73smP0PctPPu7k6+x2fN5Jz3ywpeWAyYW7QhJAOfYka8Rf8w6LcW5y4hNG2aVfQfiNGogywnzLAc+nItJdI/+EnPrp5FJ/c/hTfjp1LWPsYer8b7+6yHBbctgc3f5BGh0UnaRH/T3KP7eTonIfcXZa4kNG2aVfQfiNGogywnzLAM6mZ9hLWwmLyMjLJTT/P6R/2c+DDDTTu3BpzcKC7S3OIj58/5gaR+IdFEdKuJ40GjCbnwPdYci+5uzRxEaNt066g/UaMRBlgP2WAZ1Iz7YUCIxoQc39XrMUWbBaru8uptsJzp7iw9RMw+Zb8J7WO0bZpV9B+I0aiDLCfMsBzGP6aaavVyrx581i4cCHHjx+nUaNGDB06lJdeeom6deu6u7wqi+zejuGHl+FjMuEXWAeAvW9/TnFeAQC934vn1ObdHPxwAwAN27eg51tP80X/KVgKitxW99Vk7d3EzrhgbFYrtsI8ACIGx+MbUPI7ufD9KtI++nOZ1+Qn/0SzUfNoNPApl9crNc9o27QraL8RI1EG2E8Z4JkM30xPmjSJhIQEhgwZQnx8PPv37ychIYGdO3eyYcMGTCbvODifseMQW57+G751zMQ80J2mPW5m55zlpc//OGMxA1fP5MTaJAouZNPtlSdJmva+xwZO3VZdiJm4BFthPhe2rOTS7g00Hf5y6fMNug2hQbchpY8zf/iM1GXTCOv7uDvKtYvNZmP/0UzOnM+nXl0zt7RuiK+vd2xnrmS0bdoVjLzfGM2l7EL2Hr5AcbGVls3r0bSx9xy8cRVlgP2UAZ7J0M30vn37mD9/PrGxsXz66aely1u0aMGECRNYsWIFw4YNc2OFVWfJLyTreDoAu/76ESExkXSZ9QRb//QOALnp59m3cA23z3iMszsPc/FoGmlb9riz5Gsy+QcS0KQlAIHXtacg/QjJ747nunHvlVu38GwKJxeOpeULX2KqE+TqUqvMZrOx9PPDvLFsL/89eOXb6M0j6/JUXBsmj2iPv1mn4i4z2jbtCkbcb4wmOT2bl9/dxYdrjpCbXwyAyQd+27s500Z15I4OjdxcoedQBthPGeCZvPZw2e7duxk0aBD169enXr16DB48mLS0NEJCQnj44YcBWL58OTabjYkTJ5Z57ZNPPklQUBAffvihGyqvGbte+4iWcX0Iu+WG0mU/L/6K0NbN6DBuMNv+vMSN1dmvySMvcjZxMTmHtpdZbrNaOfbmo0T+7jmCYm52U3WVs9lsTJzzAyNnfMueQ2WndUo+ncPUedv57fj1FBRa3FSh5zPaNu0K3r7fGM3B4xe545HPefeTA6WNNIDVBqs3nuSux9fwxaaTbqzQsykD7KcM8Axe2UwnJibStWtXDhw4wPTp05k9ezYpKSkMHDiQ7OxsOnbsCMC2bdswmUzccccdZV4fEBBAx44d2bZtmxuqrxlZx9JJXr+dTs89cmWhzcaBpetJSdxBwTnv+mZvQNMbCe38W059+HyZ5WkrX8Y3sB6N7x/vpsqqZunnh0n4508A2Gxln7v8eN3WVKYlbEcqZrRt2hW8fb8xEovFygMT1pN+Lu+q6xRbrAyd8g3J6dkurMx7KAPspwzwDF7XTGdkZBAXF0enTp3YuXMnU6ZMYdy4cSQmJnLyZMkn/svN9KlTpwgPD6dOnTrlxomKiuLs2bMUFha6svwatfetz4nq3ZHIbu2uLLRasVltV3+RB4sYMoVLu9aRtWcTANn7v+PchveJmbDYrXVVxmaz8frSPfj4VL7uu5/8THZu7b3erzJG26ZdwVv3G6P5emsqB45fvOY6NhvkF1h495MDLqrK+ygD7KcMcD+vu2Z6zpw5XLhwgcWLFxMYeGUuyvr169OpUycSExNLm+nc3NwKG2koOTp9eR1/f/8aqa24uJj09PQqr19UVFz5SsCWiQsqXJ6x/QAfNHmwyu93rTpSUlKqOUYEYK7SujFPf1Dh8uA23bltdUlgFmdncuzNx4iZ8AF+9cLsrKWIlJTTdr2mOg6cyGbPoQtVWjc7t5glq3YxqFcTJ1flelXdnsE7tmlXqM37jdG893HVruX1AT5YfYD/GRzh3ILcxEh/11ylqjmgDHC+yMhI/Pzsb429rplesWIFPXr0oFWrVhU+HxERQWRkJABBQUGcOXOmwvXy8/NL17msuLiY+Ph4li1bhtVq5Xe/+x0LFiwobbwrk56eTrNmzar8s7wc1p8oc70qr+8sBw8eZKgddVek7fy9BDZvV/mKVZTx1dsUXUgj+e+TyiwP6/M4EYMmXeVVJQ4ePEize9rXWC2Vqtsarp9S5dXHPT2VcecSnViQe3jK9gw1s027Qq3eb4wmZhIEt6WyU1Q2ICUt066/Fd7EU3LAWzIAajYHlAHVk5ycTHR0tN2v86pmOj09ndTUVOLi4so9Z7Va2bNnD7feemvpsqZNm/LTTz9RUFBQ7gh1amoq4eHhZY5Kz549m40bN7Jnzx78/f154IEHeOaZZ0hISHDeD1XDDq/cxOGVm9xdRrU1eXAqTR6c6u4yqsaSb9/6VjvXr+WMsk27glftN0ZT1f3aZrM/M2o5ZUDVKQPcw6ua6ZycHAB8Kvjkv3r1as6cOVN6iQdA586dWbduHT/++CM9evQoXZ6fn8+uXbvo2bNnmTEWLVrEq6++SlRUFAAvvvgiDz30EG+++Sa+vpVPaRYZGUlycnKVf57vh75CzrGqXxbiLK1atSJ55d+rNcb4nyJI9pC/D61ateJrO34P1WWx2Oj++29JO5df7suHv+bn60PSpiU0bljx5UfezFO2Z6iZbdoVavN+YzSfJKYy6fW9la/o48Ojg9rzl/XG/Lf2lBzwlgwAz8kBZQClVzbYy6ua6WbNmuHr68vmzZvLLD9x4gTjx5d8Y/WXzXRcXByzZ89m7ty5ZZrp9957j9zcXIYPH166LDMzk+Tk5DKv79SpE1lZWRw/fpwbbrgyVc/V+Pn52XV6wGz2jH9+s9m+uisc4xDgAWEAYDabq/3z2Gv88PZMnVf5TB0PDWhBp5sr35a8kadsz1Az27Qr1Pb9xkj++HAkLy86xPlLBVf9UO1DyWUeU/5wO9HRDV1Znst4Sg54SwaA5+SAMsBxXjWbh7+/PyNGjGD79u0MGjSId999lxkzZtClSxfCwkoutP9lM9yhQwfGjh3Lv/71L2JjY1m0aBHx8fFMnjyZXr16lblhS1ZWFgChoaGlyy7//+XnRK5m8oj29O/a9Jrr3Ni8HvOe7eqiikTElQLq+LHytb6Y/UwVXjbt41PSSP918h3c3MqYjbRIbeVVzTRAQkICo0ePJikpifj4eJKSkli1ahVNmzYlKCio3BcT586dy2uvvca+ffsYO3YsK1asYPz48axZs6bMrcRDQkIAuHjxytRGmZmZZZ4TuRp/sy9f/G0Akx5rR93AskdmfH19eGTg9Xy39H4aNQy8yggi4u36dmnKxvfvo0sFdzm8rkkwS17uyZ9GdnBDZSLiTJ5xPsYOwcHBLFy4kIULF5ZZvnfvXjp06FCmQQbw9fUlPj6e+Pj4a44bGhpKs2bN2LVrF61btwZg586dhISEEBMTU6M/gxhTHX9f3pjSlT+P6cQHqw8x4ZUfAPjxHw/QqW24m6sTEVfo3jGC7z98gC//ncx9Y9cB8OHsXjxy3w2YTFWYjF5EvI7XHZmuSGZmJikpKWUu8XDEqFGj+Mtf/sKpU6fIyMjgxRdfZOTIkVX68qEr3Djsbu77fBYDV88k9KbmFa5z76d/ptuc0S6uzDFn1y3i52e68/Nzd5F3vOI5Wg8835sTb/3RxZVVT0hdf4b0jSl93FhHoytktO3ZVYy63xhNhxuvXMrR6/YmaqSvQjlgP2WA5zFEM71nT8nGVN1metq0afTs2ZN27drRsmVL2rRpw5w5c2qgwurzDw2m9eMD+DL2f/lu8tt0mfn7cutE97uNouyr38rWkxRnnSfjq7dpPXszMePeJ3nR0+XWydy2Bt9AXWJjREbbnl1F+40YiXLAfsoAz6Rm+hf8/PxISEjgwoULXLx4kffff7/MXRbdqdGtLUnfug9bsYVLR05Rp2G9sjcH8PHhpt/fy88ffOW+Iu2Qc+hHgtv3xsfPTEB0a4ovncVmtZY+b7NayVi7gEb3jXVjleIsRtueXUX7jRiJcsB+ygDPZIhmesyYMdhsNrp2Ne5MCf6hwRRezCl9XJSdh3+9K3dvbDm0NyfWJmHJL3JHeXazZJ3HL7hB6WNTYAiW3Ctf/jz3zRJCu8ViMlft7pPiXYy2PbuK9hsxEuWA/ZQBnskQzXRtUHgxB/96dUsfm4MDKbyUC4BvHTPXx/bg8Ipv3FWe3XyDG2DJySx9bM3Lwjeofsn/F+ZzfvM/CL+7/Ck/MQajbc+uov1GjEQ5YD9lgGfyutk8aquMHYfo+Keh+PiaCG7WmILzl7h8Z4Dg5o3xr1+Xfsum4h8aTGDjUG54qBdHPt5cyajuU7dVF04tfwGbpZiCM8fxqxeOz//NxFJw+hiWnEwOz7yf4uzzFF1I59w3SwnrO8LNVUtNMdr27Crab8RIlAP2UwZ4JjXTXqIwM5tD/0xk4KqZ2GxWfpi6iKg+HfEPDebYqi2sufdZACK7taPF4Ds9PnD8QhoS3n8UB6b2BJOJ5v+zgIs7vsKSdZ6GvYbR5o2Suwlm7dnE+X+vUBgYjNG2Z1fRfiNGohywnzLAM/nYbFe78ak422e9JpJ5MMXdZRDaKprBm+dWa4yhG+Goh9wo8voQWNnHvTWkpOfQbMAKAJLXPUx0ZN1KXuH9PGV7hprZpl1B+41x1cYMAM/JAW/JAPCcHFAGOE7XTIuIiIiIOEjNtIiIiIiIg3TNtBuFxES6uwSgZuqICqp8HVfxpFpqE0/ZnsGzarkWT9pWPakW8V6esu95Sh1V4Sn7nqfU4Y10zbSIE9TW6yVFpIQyQKT20GUeIiIiIiIOUjMtIiIiIuIgNdMiIiIiIg5SMy0iIiIi4iA10yIiIiIiDlIzLSIiIiLiIDXTIiIiIiIOUjMtIiIiIuIgNdMiIiIiIg5SMy0iIiIi4iA10yIiIiIiDlIzLSIiIiLiIDXTIiIiIiIOUjMtIk7Rr18/Ro4c6e4yysjKyuLJJ58kLCyMunXrMnDgQI4cOeLuskQMyxNzYOLEiXTp0oWgoCD8/PzcXY4YgJppEak1HnvsMRITE/nkk0/YsmULNpuN/v37k5eX5+7SRMRFLBYLw4YNY8yYMe4uRQxCH8ncKPHxV8g6nu7uMgiJieTuJc9Va4xJSZCaW0MFVVNUELzZxd1VGMOCBQtYsGABR44coX79+vTo0YNPP/2UmJgYRo0axfTp00vXHTVqFIcPH2bTpk2MHDmSxMREAJYsWQLAxo0b6d279zXfr7i4mFmzZrF06VJSUlIIDw8nNjaW+fPns3XrVnr16sXKlSsZMmRI6ZgDBgxgzZo13HPPPdcc++DBg6xevZqvv/6aPn36ALB8+XIiIyP56KOP3HL0zFMyAIyVA8qAmmWkHACYP38+AB988IED/xo1z1NywEgZAK7NATXTbpR1PJ3MgynuLqNGpObC0Sx3VyE16YUXXuD111/nlVdeYcCAAWRnZ/Pll19W6bXz5s3j6NGjNGnShHnz5gHQsGHDSl/3xBNP8OWXX/L666/TvXt3MjIy+P777wHo3r07L774Ik888QS33XYbgYGBPProo0yaNKlKf0C/++47zGYzd999d+myBg0acMcdd7Blyxa3NNNGygBQDhiR0XLAExkpB2prBqiZFpFycnJyePXVV5k5cybjxo0rXd6pU6cqvb5+/fr4+/sTGBhIZGRklV5z+PBhli5dyscff8yDDz4IwA033EDXrl1L15k6dSobN25k+PDhhISEEBUVxaxZs6o0flpaGuHh4fj6+pZZHhkZSVpaWpXGEKlNjJgDIs6gZlpEytm3bx/5+fkMGDDAZe+5Y8cOgGu+p8lkYtmyZbRp04bi4mL++9//YjabXVWiSK2iHBCpGn0BUUTsZjKZsNlsZZYVFRW55L137dpFTk4O+fn5JCcnV/l1TZo04ezZs1gsljLLT58+TZMmTWq6TBHD88YcEHEGNdMiNSTjfB4frzvGc3O38cQL/y5d/sJbO1j6+SGOp3rPhWRt27YlICCAdevWVfh848aNOXXqVJllO3fuLPPY39+/XON6LZdPHV/tPQHS09N5/PHHef755xk3bhyPPvoo58+fr9L4d955J0VFRXzzzTelyzIzM0lKSuKuu+6qcp0iV2Oz2fhxTwbz/7mPya8llS4fM2srf1m0m/Xfp1JYVPV9wt2MmAMizqDLPESqafu+DN5cto+P1x2jqNha7vm/f3aQv392EIB774xmwrC2DOzRzNVl2iU4OJj4+HhefPFFAgMDS6ePW7t2LVOnTqVfv3689dZbDBkyhOuuu4533nmHEydOlPlyUYsWLdi4cWPpDAD169e/5qnYli1bMnz4cMaMGUN+fj7dunXj/PnzbN26laeffhqbzcaIESO46aabmDFjBhaLhW+//ZY//OEPfPbZZ5X+TK1atWLQoEE89dRTvP/++9SvX59p06YRFRVFXFxcTfyzSS2VX1DMe58e4K2P9vPzsYvlnv9i80m+2HwSgMYNAxgV25oJw9sRERbo6lLtYsQcgJLrsrOzszl5suR3smvXrtL3Dg4OdvjfS2ovNdNe4K65Y2kZVzKVl9ViIe90Jmnf7WXH7H+Qm+59n8aPzxvJuW9KpknCZMLcoAkhHfoSNeIv+IdFubc4O+TmFTNjwX94c9lefnWm86q++i6Fr75L4cH+MSyY1p3GHvzHdObMmTRq1IiEhAQmTZpEgwYN6NmzJwDPPvssJ06cIC4uDrPZzJgxY3jooYc4fPhw6evj4+PZs2cPt9xyCzk5OVWaEmvx4sW89NJLTJ8+nVOnTtG4cePSLyG9+uqrbN++nd27d+Pr64uvry8rVqzgtttuY8GCBYwdO7bSn2nZsmVMnjyZIUOGkJ+fT8+ePVm3bh2BgZ77e7jMSDlglAwASPrvGUbO+LbCJroiZ87nM3vRbt75+GfmP9eNR+67Hh8fHydX6Tgj5sCoUaPYvHlz6eNbb70VqNq0fe5kpAwAY+WAj+3XFzyJy3zWa2KVpsO5a+5Ygq+LYPPoN/DxNRESE0HX2aMoys5n7QPPV7uO0FbRDN48t1pjDN1Y9elwjs8bSUH6Ua5/ZiU2q4WC9COcXDgW34AQbnp1a7XqALg+BFb2qfYw15SWkcs9f/yKPYcuODxG44YBfP3OvXS8KawGKxNvUtUMAGPlgBEyAOCdlfsZO/t7rFbH/4yOim3FOzPuxNdXV13WVuoFvDsHQNdMew1rYTF5GZnkpp/n9A/7OfDhBhp3bo052POPqFXEx88fc4NI/MOiCGnXk0YDRpNz4HssuZfcXVqlzpzLo88Ta6/ZSPv6+hAVEURURBC+vhUfdTpzPp++o9ay56D3HVEQ9zBSDnhzBkBJI/3Uy1uv2khXJQMAFv3rIE+8sKVaDbnUHkbKAPD+HLisVlzmYbVamTdvHgsXLuT48eM0atSIoUOH8tJLL1G3bl13l2e3wIgGxNzfFWuxBZul/DW63qbw3CkubP0ETL4l/3kwm83GiOc3c+D4tU/pRoYHkrL+EQCi+y8n9XTFt4S6cKmQIZM2sPvjIdQNMvbUTmPGjLnmHcc2b95MYWEhH3zwASNHjsTf3/+a43Xu3LnM43bt2nHixIkK13300Ud555137K7ZkxkpB7wpA6Dk0o6xs7+/5jpVzQCAJZ8fonP7cMY+3LZG6/Q027ZtY/HixdfMgfXr1zucAVC7csBIGQDelwO/VCua6UmTJpGQkMCQIUOIj49n//79JCQksHPnTjZs2IDJ5PkH6CO7t2P44WX4mEz4BdYBYO/bn1OcVwBA7/fiObV5Nwc/3ABAw/Yt6PnW03zRfwqWAtdMVWSPrL2b2BkXjM1qxVaYB0DE4Hh8A0o+3Fz4fhVpH/25zGvyk3+i2ah5NBr4lMvrvezvqw7y9dbUGh3zSHIWz8//D3Of7Vr5yl4sNjaWfv36XXOdwsJCFi1axLBhwyr9Q/pra9euveq0XPXq1bNrLE9lpBzw1gzILyjm9//77xo/kvzMG9sYeFc010cbY1u9mspyoDoZAMbPASNlAHhvDvya4Zvpffv2MX/+fGJjY/n0009Ll7do0YIJEyawYsUKhg0b5sYKqyZjxyG2PP03fOuYiXmgO0173MzOOctLn/9xxmIGrp7JibVJFFzIptsrT5I07X2P3HkA6rbqQszEJdgK87mwZSWXdm+g6fCXS59v0G0IDboNKX2c+cNnpC6bRljfx91RLgAFhRamztvulLET/rmPp4e3o0V0iFPG9wSXv8nvLNddd53TxvYURsoBb8wAKPlAvf9oZo2Pm5tfzAtv7WDZ7N41PrYnqSwHsrOzqzW+0XPASBkA3psDv+b5h2SvYffu3QwaNIj69etTr149Bg8eTFpaGiEhITz88MMALF++HJvNxsSJE8u89sknnyQoKIgPP/zQDZXbz5JfSNbxdDIPJLPrrx+RlXyGLrOeKH0+N/08+xau4fYZj9H6sf5cPJpG2pY9bqz42kz+gQQ0aUngde1pOvwl6kS0IPnd8RWuW3g2hZMLx9JiygpMdYJcXOkVn64/TsaFfKeMbbPBu5/+7JSxxTiMlAPemAE2m423PtrvtPFXfn2MjPN5ThtfvJ+RMgC8Mwcq4rXNdGJiIl27duXAgQNMnz6d2bNnk5KSwsCBA8nOzqZjx45AyTVaJpOJO+64o8zrAwIC6NixI9u2bXND9dW367WPaBnXh7Bbbihd9vPirwht3YwO4waz7c9L3Fid/Zo88iJnExeTc6jskV+b1cqxNx8l8nfPERRzs5uqK7Hki0NOHf+D1YfK3U2stvHz8+OBBx7Az8/wJ81qhJFywBsyYMf+c+w7kum08QuLrKz46qjTxvcGygD7GCkDwDtyoCJe2UxnZGQQFxdHp06d2LlzJ1OmTGHcuHEkJiaWTsJ+uZk+deoU4eHh1KlTp9w4UVFRnD17lsLCQleWXyOyjqWTvH47nZ575MpCm40DS9eTkriDgnPe9U3YgKY3Etr5t5z6sOz0PmkrX8Y3sB6N76/4k6qrXL6zmTOln80j5XSOU9/D0wUEBDB9+nQCAgLcXYpXMFIOeHoGQMkXD53tx73OzRlPpwywj5EyALwjByrilR/95syZw4ULF1i8eHGZmy3Ur1+fTp06kZiYWNpM5+bmVthIA6U7a25urkNfdPi14uJi0tPTq7x+UVFxtd5v71uf85svZhHZrR3p3+8rWWi1YrPzizFFRcWkpFRtrturjxEBVG82ioghUzjw3J1k7dlESIfeZO//jnMb3qfNGzvsrKWIlJTT1arl106k5ZKZVfZDl6+vD5HhFU9H1OQXy5tcZZ30s3lYLGV/V19/e4B7u0dUs1rPlJNT+QeFgoICEhISmDBhwlX328uqu816gupmABgrBzw5AwD+vf1kmcfVzQAonwNJu9MNsW1XRBlQMfUCZbkzByIjIx06K+KVN22Jjo6mZcuWbNq0qdxz/fr1Y+/evaVNbYcOHThz5gynT5f/Bx06dCgff/wxBQUFpc30ypUrSUhIYNeuXYSHh3P8+PEq15WSkkKzZlW/TfTLYf2JMtfst4tbDu1N2C03kPT8+1V+TWrRJaafW1+t9207fy+BzdtVa4xfKs7OZP/kTsSMe5+Qm+2bdT3v5D5+Gt++xmoBIOh6uGFamUVREUGlU185osLpslKWwoVvHR7Tk40aNarSdQoLC1m6dCkjRoyo9APuokWLaqo0t3FGBoAxcsDjMgDguvFQ75bSh9XNAKggB4qzYP+kao3pqZQBFVMvcHWuzoHk5GSio6Pteg144ZHp9PR0UlNTiYuLK/ec1Wplz549pbcGBWjatCk//fQTBQUF5T7lpqamEh4eXmaHbdCgAePGjeP06dO8+eabzvtB5JoyvnqbogtpJP+97B+VsD6PEzHIHX9oXHS7Xw++rbCIK3leBriKV159KeIU3pIDXndk+siRI7Rs2ZL4+Hhee+21Ms+tWrWK2NhYnn32WV555RUApk+fzqxZs/j222/p0aNH6br5+fmEhYXRs2dPvvzyy3Lv89lnnzFx4kS7jkzbe5nH90NfIedY1dd3lrotIum28rlqjTH+pwiS8z3jpiPNAoqY37ZmT/EePJnN3X/8rsyyyk7xbls+GIDOj3xG2tny39Cv6DKPBc/ezAO9mtRM0R7m8OHDla6Tk5PD/fffz5o1ayq9oVLLli1rqjS38ZQMAGPlgDMyAGDCX//Lqo1ppY+rmwFQPgeaRwby3d971lzRHkQZUDFPyQEjZQA4lgOOXubhdUemmzVrhq+vL5s3by6z/MSJE4wfX3Jh+uXrpQHi4uKYPXs2c+fOLdNMv/fee+Tm5jJ8+PAaq83Pz8+u0wNms2f885vN9tVd4RiHAOfMGmc3s9lc7Z/n1yIjrQQG/EBevqV0mcViu+ZdzS5LO5tXpfUA7u5+I9HRoY6W6dHS0tIqXcdsNjNq1ChCQ0MrPcVb079jd/CUDABj5YAzMgCg+60XyjTTzsiA29tFGGLbrogyoGKekgNGygBwXg5UxDN+g3bw9/dnxIgRLF68mEGDBvGb3/yG5ORk3nvvPSIiIkhNTS3TTHfo0IGxY8fyt7/9jdjYWO67777SOyD26tXLK27YIu7n52eiY+swvt/tvG/zh9Q1c+N1zrupiTfw9/dn9OjR7i5DpEK3twt3+nvc1jbM6e/hyZQB4o288uKshIQERo8eTVJSEvHx8SQlJbFq1SqaNm1KUFAQrVq1KrP+3Llzee2119i3bx9jx45lxYoVjB8/njVr1njFrcTFM8Td08Kp4w8d0AKTqXZfM52Xl8f48ePJy9ONK8Tz3NkxgqaNnXuziKH3XO/U8T2dMkC8kVd2ksHBwSxcuJD09HSysrJYt24d3bp1Y+/evXTo0KFcg+zr60t8fDwHDhygoKCA1NRU3njjDYKDg930E4g3evyBGwkKcN7JnDFxbZw2trewWCwkJSVhsVgqX1nExcxmE6N/19pp49/TPYqWzWt+dhdvogwQb+SVzXRFMjMzSUlJKXOJhyMsFgv5+fkUFRVhs9nIz8+noKCgZoqsphuH3c19n89i4OqZhN7UvMJ17v30z3Sb4x2nyM6uW8TPz3Tn5+fuIu94xbc7PfB8b0689UcXV1ax0Hp1mDzCCdNtAQ/0bk6nts4/hSzez0g54G0ZADD24bY0alDzNxTx8YEXnrq18hWl1jNSBoB35sCvGaaZ3rOn5BdQ3WZ62bJlBAYGMnToUE6ePElgYCCtWzvvSERV+YcG0/rxAXwZ+798N/ltusz8fbl1ovvdRlG2d5waK846T8ZXb9N69mZixr1P8qKny62TuW0NvoEhbqju6qaP7kj7lg1qdMwG9fx5Z8adNTqmGJORcsBbMyC8QQBvPd+9xsed/Fh7ut1izBs2Sc0xUgaA9+bAr6mZ/pWRI0dis9nK/GfP9HjO0ujWlqRv3Yet2MKlI6eo07Be2TmJfXy46ff38vMHX7mvSDvkHPqR4Pa98fEzExDdmuJLZ7FZraXP26xWMtYuoNF9Y91YZXl1/H1ZPqc3oSHX/pZ5+tk8ovsvJ7r/ctKvMiUWlEyttXRWL5o0cu51mN6iTp06TJs2rdI7n9VWRsoBb80AgAcHtGDsw9e+LKuqGQDQvWNjZo67rSZL9FrKgGszUgaAd+fALxmmmR4zZgw2m42uXbu6uxSn8A8NpvDilVuxFmXn4V/vSgPWcmhvTqxNwpJf5I7y7GbJOo9f8JUjvKbAECy5F0sfn/tmCaHdYjGZa/50anW1v7Eh69+9l4b1rx72l6fMSj2dW24u6cvMfiZWzOnD/b0qPk1XG5nNZgYPHozZ7BnzlHoaI+WAN2cAQMJz3Rj94NXPWlYlAwDuvDWC//e3AQQ68fsY3kQZcG1GygDw/hy4zDDNtNEVXszBv96VCezNwYEUXiqZt9S3jpnrY3tweMU37irPbr7BDbDkZJY+tuZl4RtUMi2ctTCf85v/Qfjd5U9feYrb2zVi+/JB9Ons2A1W2lwfypYl9/PgAOfOEOJtcnNziYuLIze3anPy1jZGygFvzwCTyYd3ZtzJu/97JyF17W/8fHzgT493YP3Cewmtp6OwlykDrs1IGQDenwOXqZn2Ehk7DhHRtQ0+viZCYiIpOH8J/u/mlcHNG+Nfvy79lk3lthmPEnX3rdzwUC83V3xtdVt1IWvft9gsxeSnHcavXjg+/zcLS8HpY1hyMjk8835SljzDxf+s5dw3S91ccXktokPY8N5AFr14F22uD63Sa5o2DuLlcbex46NB3NGhkXML9EJWq5Vjx45h/cVpPrnCSDlghAzw8fHhyQdvYu+/Yhk56EYC6vhW4TUw8K5ovlt6P3+Nv0NHpH9FGXBtRsoAMEYOgBfetKW2KszM5tA/Exm4aiY2m5Ufpi4iqk9H/EODObZqC2vufRaAyG7taDH4To58vLmSEd3LL6Qh4f1HcWBqTzCZaP4/C7i44yssWedp2GsYbd7YDkDWnk2c//cKwvqOcG/BV2Ey+fBEbGv+MKQVm7ens25rKv/Zf5b9RzPJK7DgbzZxQ3QIt7UNp9ftkfymR3PMZn2GFccYKQeMkgEAzZsEs3hmT17/UxdWfn2UH/dm8J+fznH6XB5Wq43Qev50bB3GbW3DeLB/C25oVrunvxPHGSkDwDg54GOz2a5+MZc41We9JpJ5MMXdZRDaKprBm+dWa4yhG+FoVs3UU13Xh8DKPu6uQn5t27Ztla6TnZ1N3759+eabbyqdB75z5841VZrbeEoGgLFyQBngmZQBFfOUHDBSBoBrc0CHyETEYwQEBDBv3jwCAjz7yyYi4hzKAPFGusxDRDyGn58f3bp1c3cZIuImygDxRjoyLSIeIzs7mz59+pCdne3uUkTEDZQB4o10ZNqNQmIi3V0CUDN1RHnQPUc8qRaxX05OTuUrGYSnZAAYKwc8pQ5xTG3KAPCcHDBSBoBra1Ez7UZ3L3nO3SXUmDe7uLsCEe9jpAwA5YCII4yUA7U1A3SZh4iIiIiIg9RMi4jHCAwMZPny5QQGBrq7FBFxA2WAeCM10yLiMUwmExEREZhMiiaR2kgZIN5IW6uIeIycnBz69u1b676AJCIllAHijdRMi4iIiIg4SM20iIiIiIiD1EyLiIiIiDjIx2az2dxdhIgIgM1mIysri5CQEHx8fNxdjoi4mDJAvJGaaRERERERB+kyDxERERERB6mZFhERERFxkJppEREREREHqZkWEREREXGQmmkREREREQepmRYRERERcZCaaRERERERB6mZFhERERFxkJppEREREREHqZkWEREREXGQmmkREREREQepmRYRERERcZCaaRERERERB/1/zF06fXTJ81gAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 913.147x160.533 with 1 Axes>"
       ]
@@ -156,11 +191,18 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "11e45e83",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:42.079604Z",
+     "iopub.status.busy": "2024-04-19T17:42:42.079205Z",
+     "iopub.status.idle": "2024-04-19T17:42:42.296509Z",
+     "shell.execute_reply": "2024-04-19T17:42:42.295819Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 913.309x160.533 with 1 Axes>"
       ]
@@ -197,7 +239,14 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "3d606ef8",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:42.299255Z",
+     "iopub.status.busy": "2024-04-19T17:42:42.298869Z",
+     "iopub.status.idle": "2024-04-19T17:42:42.302431Z",
+     "shell.execute_reply": "2024-04-19T17:42:42.301774Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -229,7 +278,14 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "2029d18e-0e91-4160-b8c9-02cb9e1ba3cb",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:42.304898Z",
+     "iopub.status.busy": "2024-04-19T17:42:42.304537Z",
+     "iopub.status.idle": "2024-04-19T17:42:42.678677Z",
+     "shell.execute_reply": "2024-04-19T17:42:42.677821Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.cutting import generate_cutting_experiments\n",
@@ -241,68 +297,112 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d8870454-2173-4454-90b4-a034779510e0",
+   "id": "444b0038-b3d5-469c-85b2-4c1d9d8fce05",
    "metadata": {},
    "source": [
-    "### Run the subexperiments using the Qiskit Sampler primitive"
+    "### Choose a backend\n",
+    "\n",
+    "Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "7430eef1",
-   "metadata": {},
+   "id": "36c89aa0-70aa-4615-8198-77ec85e8aa93",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:42.682386Z",
+     "iopub.status.busy": "2024-04-19T17:42:42.681780Z",
+     "iopub.status.idle": "2024-04-19T17:42:43.193076Z",
+     "shell.execute_reply": "2024-04-19T17:42:43.192515Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit_aer.primitives import Sampler\n",
-    "\n",
-    "# Set up a Qiskit Aer Sampler primitive for each circuit partition\n",
-    "samplers = {\n",
-    "    label: Sampler(run_options={\"shots\": 2**12}) for label in subexperiments.keys()\n",
-    "}\n",
+    "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n",
     "\n",
-    "# Retrieve results from each partition's subexperiments\n",
-    "results = {\n",
-    "    label: sampler.run(subexperiments[label]).result()\n",
-    "    for label, sampler in samplers.items()\n",
-    "}"
+    "backend = FakeManilaV2()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "68eb0522-8c01-4f56-aacc-0bfc60159896",
+   "id": "85a1d76a-5c47-4210-a742-9b22f02f7200",
    "metadata": {},
    "source": [
-    "To use the Qiskit Runtime Sampler, replace the code above with this commented block."
+    "### Prepare the subexperiments for the backend\n",
+    "\n",
+    "We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "3c9a7e78-4bf9-4dbd-a8f9-3db185591d9c",
-   "metadata": {},
+   "id": "184e0f36-1279-48a3-aab7-b7603bb71f66",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:43.195989Z",
+     "iopub.status.busy": "2024-04-19T17:42:43.195647Z",
+     "iopub.status.idle": "2024-04-19T17:42:45.516007Z",
+     "shell.execute_reply": "2024-04-19T17:42:45.515210Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# from qiskit_ibm_runtime import Session, Options, Sampler, QiskitRuntimeService\n",
-    "# from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
-    "#\n",
-    "# service = QiskitRuntimeService()\n",
-    "# backend = service.least_busy(operational=True, simulator=False)\n",
-    "#\n",
-    "# # Prepare transpiler for target backend\n",
-    "# pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
-    "#\n",
-    "# with Session(backend=backend) as session:\n",
-    "#     # Set up Qiskit Runtime Sampler primitives.\n",
-    "#     samplers = {\n",
-    "#         label: Sampler(Options(execution={\"shots\": 2**12})) for label in subexperiments.keys()\n",
-    "#     }\n",
-    "#\n",
-    "#     # Retrieve results from each subexperiment\n",
-    "#     results = {\n",
-    "#         label: sampler.run(pass_manager.run(subexperiments[label])).result()\n",
-    "#         for label, sampler in samplers.items()\n",
-    "#     }"
+    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
+    "\n",
+    "# Transpile the subexperiments to ISA circuits\n",
+    "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
+    "isa_subexperiments = {\n",
+    "    label: pass_manager.run(partition_subexpts)\n",
+    "    for label, partition_subexpts in subexperiments.items()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8870454-2173-4454-90b4-a034779510e0",
+   "metadata": {},
+   "source": [
+    "### Run the subexperiments using the Qiskit Runtime Sampler primitive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3bc1c07b-b286-4a5f-8b0a-67efd4a6309e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:45.519227Z",
+     "iopub.status.busy": "2024-04-19T17:42:45.518797Z",
+     "iopub.status.idle": "2024-04-19T17:42:54.689525Z",
+     "shell.execute_reply": "2024-04-19T17:42:54.688868Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/runner/work/circuit-knitting-toolbox/circuit-knitting-toolbox/.tox/docs/lib/python3.9/site-packages/qiskit_ibm_runtime/session.py:157: UserWarning: Session is not supported in local testing mode or when using a simulator.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit_ibm_runtime import SamplerV2, Batch\n",
+    "\n",
+    "# Set up a Qiskit Runtime Sampler primitive for each circuit partition\n",
+    "samplers = {label: SamplerV2(backend=backend) for label in subexperiments.keys()}\n",
+    "\n",
+    "# Submit each partition's subexperiments as a single batch\n",
+    "with Batch(backend=backend):\n",
+    "    jobs = {\n",
+    "        label: sampler.run(isa_subexperiments[label], shots=2**12)\n",
+    "        for label, sampler in samplers.items()\n",
+    "    }\n",
+    "\n",
+    "# Retrive results\n",
+    "results = {label: job.result() for label, job in jobs.items()}"
    ]
   },
   {
@@ -312,23 +412,34 @@
    "source": [
     "### Reconstruct the expectation values\n",
     "\n",
-    "Use the subexperiment results, subobservables, and sampling coefficients to reconstruct the expectation value of the original circuit."
+    "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "7d57339c",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:54.692693Z",
+     "iopub.status.busy": "2024-04-19T17:42:54.692460Z",
+     "iopub.status.idle": "2024-04-19T17:42:57.601371Z",
+     "shell.execute_reply": "2024-04-19T17:42:57.600628Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.cutting import reconstruct_expectation_values\n",
     "\n",
+    "# Get expectation values for each observable term\n",
     "reconstructed_expvals = reconstruct_expectation_values(\n",
     "    results,\n",
     "    coefficients,\n",
     "    subobservables,\n",
-    ")"
+    ")\n",
+    "\n",
+    "# Reconstruct final expectation value\n",
+    "final_expval = np.dot(reconstructed_expvals, observable.coeffs)"
    ]
   },
   {
@@ -336,44 +447,43 @@
    "id": "53beaca3",
    "metadata": {},
    "source": [
-    "### Compare the reconstructed expectation values with the exact expectation values from the original circuit"
+    "### Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "e3385ba5",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:57.604408Z",
+     "iopub.status.busy": "2024-04-19T17:42:57.603973Z",
+     "iopub.status.idle": "2024-04-19T17:42:57.617317Z",
+     "shell.execute_reply": "2024-04-19T17:42:57.616698Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reconstructed expectation values: [0.37551016, 0.52859873, 0.58741736, 0.12230408, 0.29614246, -0.1243059]\n",
-      "Exact expectation values: [0.36916216, 0.52511814, 0.59161991, 0.10927476, 0.28001606, -0.12940509]\n",
-      "Errors in estimation: [0.00634799, 0.00348059, -0.00420255, 0.01302933, 0.01612639, 0.00509918]\n",
-      "Relative errors in estimation: [0.01719568, 0.0066282, -0.00710346, 0.11923456, 0.05759096, -0.0394048]\n"
+      "Reconstructed expectation value: 0.70903873\n",
+      "Exact expectation value: 0.56254612\n",
+      "Error in estimation: 0.14649261\n",
+      "Relative error in estimation: 0.26040996\n"
      ]
     }
    ],
    "source": [
-    "from qiskit_aer.primitives import Estimator\n",
+    "from qiskit_aer.primitives import EstimatorV2\n",
     "\n",
-    "estimator = Estimator(run_options={\"shots\": None}, approximation=True)\n",
-    "exact_expvals = (\n",
-    "    estimator.run([qc] * len(observables), list(observables)).result().values\n",
-    ")\n",
-    "print(\n",
-    "    f\"Reconstructed expectation values: {[np.round(reconstructed_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Exact expectation values: {[np.round(exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Errors in estimation: {[np.round(reconstructed_expvals[i]-exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
+    "estimator = EstimatorV2()\n",
+    "exact_expval = estimator.run([(qc, observable)]).result()[0].data.evs\n",
+    "print(f\"Reconstructed expectation value: {np.real(np.round(final_expval, 8))}\")\n",
+    "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n",
+    "print(f\"Error in estimation: {np.real(np.round(final_expval-exact_expval, 8))}\")\n",
     "print(\n",
-    "    f\"Relative errors in estimation: {[np.round((reconstructed_expvals[i]-exact_expvals[i]) / exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
+    "    f\"Relative error in estimation: {np.real(np.round((final_expval-exact_expval) / exact_expval, 8))}\"\n",
     ")"
    ]
   }
@@ -394,7 +504,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html b/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html
index 2b7d4e137..e822fe6b8 100644
--- a/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html
+++ b/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="Wire Cutting Phrased as a Two-Qubit Move Instruction" href="03_wire_cutting_via_move_instruction.html" /><link rel="prev" title="Gate Cutting to Reduce Circuit Width" href="01_gate_cutting_to_reduce_circuit_width.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>Gate Cutting to Reduce Circuit Depth - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>Gate Cutting to Reduce Circuit Depth - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -146,7 +146,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -173,7 +173,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -190,6 +190,7 @@
 <li class="toctree-l2"><a class="reference internal" href="01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2 current current-page"><a class="current reference internal" href="#">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../explanation/index.html">Cutting Explanatory Material</a></li>
@@ -219,6 +220,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -336,43 +340,29 @@ <h2>Create a circuit to run on the backend<a class="headerlink" href="#Create-a-
 </div>
 </div>
 </section>
-<section id="Specify-some-observables">
-<h2>Specify some observables<a class="headerlink" href="#Specify-some-observables" title="Link to this heading">¶</a></h2>
-<p>Currently, only <code class="docutils literal notranslate"><span class="pre">Pauli</span></code> observables with phase equal to 1 are supported. Full support for <code class="docutils literal notranslate"><span class="pre">SparsePauliOp</span></code> is expected in CKT v0.7.0.</p>
+<section id="Specify-an-observable">
+<h2>Specify an observable<a class="headerlink" href="#Specify-an-observable" title="Link to this heading">¶</a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit.quantum_info</span> <span class="kn">import</span> <span class="n">PauliList</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit.quantum_info</span> <span class="kn">import</span> <span class="n">SparsePauliOp</span>
 
-<span class="n">observables</span> <span class="o">=</span> <span class="n">PauliList</span><span class="p">([</span><span class="s2">&quot;ZZII&quot;</span><span class="p">,</span> <span class="s2">&quot;IZZI&quot;</span><span class="p">,</span> <span class="s2">&quot;IIZZ&quot;</span><span class="p">,</span> <span class="s2">&quot;XIXI&quot;</span><span class="p">,</span> <span class="s2">&quot;ZIZZ&quot;</span><span class="p">,</span> <span class="s2">&quot;IXIX&quot;</span><span class="p">])</span>
+<span class="n">observable</span> <span class="o">=</span> <span class="n">SparsePauliOp</span><span class="p">([</span><span class="s2">&quot;ZZII&quot;</span><span class="p">,</span> <span class="s2">&quot;IZZI&quot;</span><span class="p">,</span> <span class="s2">&quot;-IIZZ&quot;</span><span class="p">,</span> <span class="s2">&quot;XIXI&quot;</span><span class="p">,</span> <span class="s2">&quot;ZIZZ&quot;</span><span class="p">,</span> <span class="s2">&quot;IXIX&quot;</span><span class="p">])</span>
 </pre></div>
 </div>
 </div>
 </section>
 <section id="Specify-a-backend">
 <h2>Specify a backend<a class="headerlink" href="#Specify-a-backend" title="Link to this heading">¶</a></h2>
-<p>You can provide either a fake backend from Qiskit or a hardware backend from Qiskit Runtime.</p>
+<p>You can provide either a fake backend or a hardware backend from Qiskit Runtime.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit.providers.fake_provider</span> <span class="kn">import</span> <span class="n">GenericBackendV2</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_ibm_runtime.fake_provider</span> <span class="kn">import</span> <span class="n">FakeManilaV2</span>
 
-<span class="n">backend</span> <span class="o">=</span> <span class="n">GenericBackendV2</span><span class="p">(</span><span class="n">num_qubits</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">coupling_map</span><span class="o">=</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
-</pre></div>
-</div>
-</div>
-<p>To use a hardware backend, replace the code above with the following commented block.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># from qiskit_ibm_runtime import QiskitRuntimeService</span>
-<span class="c1">#</span>
-<span class="c1"># service = QiskitRuntimeService()</span>
-<span class="c1"># # backend = service.backend(&quot;ibm_cairo&quot;)</span>
-<span class="c1"># backend = service.least_busy(operational=True, simulator=False)</span>
+<span class="n">backend</span> <span class="o">=</span> <span class="n">FakeManilaV2</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
@@ -381,7 +371,7 @@ <h2>Specify a backend<a class="headerlink" href="#Specify-a-backend" title="Link
 <h2>Transpile the circuit, visualize the swaps, and note the depth<a class="headerlink" href="#Transpile-the-circuit,-visualize-the-swaps,-and-note-the-depth" title="Link to this heading">¶</a></h2>
 <p>We choose a layout that requires two swaps to execute the gates between qubits 3 and 0 and another two swaps to return the qubits to their initial positions.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit.transpiler.preset_passmanagers</span> <span class="kn">import</span> <span class="n">generate_preset_pass_manager</span>
@@ -391,7 +381,7 @@ <h2>Transpile the circuit, visualize the swaps, and note the depth<a class="head
 <span class="p">)</span>
 
 <span class="n">transpiled_qc</span> <span class="o">=</span> <span class="n">pass_manager</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">circuit</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Transpiled circuit depth: </span><span class="si">{</span><span class="n">transpiled_qc</span><span class="o">.</span><span class="n">depth</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Transpiled circuit depth: </span><span class="si">{</span><span class="n">transpiled_qc</span><span class="o">.</span><span class="n">depth</span><span class="p">(</span><span class="k">lambda</span><span class="w"> </span><span class="n">x</span><span class="p">:</span><span class="w"> </span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="w"> </span><span class="o">&gt;=</span><span class="w"> </span><span class="mi">2</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -400,11 +390,11 @@ <h2>Transpile the circuit, visualize the swaps, and note the depth<a class="head
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Transpiled circuit depth: 101
+Transpiled circuit depth: 30
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">transpiled_qc</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="s2">&quot;mpl&quot;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">idle_wires</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fold</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
@@ -412,11 +402,11 @@ <h2>Transpile the circuit, visualize the swaps, and note the depth<a class="head
 </div>
 </div>
 <div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
-<img alt="../../_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_11_0.png" src="../../_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_11_0.png" />
+<img alt="../../_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_9_0.png" src="../../_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_9_0.png" />
 </div>
 </div>
 </section>
@@ -424,7 +414,7 @@ <h2>Transpile the circuit, visualize the swaps, and note the depth<a class="head
 <h2>Replace distant gates with <code class="docutils literal notranslate"><span class="pre">TwoQubitQPDGate</span></code>s by specifying their indices<a class="headerlink" href="#Replace-distant-gates-with-TwoQubitQPDGates-by-specifying-their-indices" title="Link to this heading">¶</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">cut_gates</span></code> will replace the gates in the specified indices with <code class="docutils literal notranslate"><span class="pre">TwoQubitQPDGate</span></code>s and also return a list of <code class="docutils literal notranslate"><span class="pre">QPDBasis</span></code> instances – one for each gate decomposition.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">circuit_knitting.cutting</span> <span class="kn">import</span> <span class="n">cut_gates</span>
@@ -444,11 +434,11 @@ <h2>Replace distant gates with <code class="docutils literal notranslate"><span
 </div>
 </div>
 <div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
-<img alt="../../_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_13_0.png" src="../../_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_13_0.png" />
+<img alt="../../_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_11_0.png" src="../../_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_11_0.png" />
 </div>
 </div>
 </section>
@@ -456,8 +446,9 @@ <h2>Replace distant gates with <code class="docutils literal notranslate"><span
 <h2>Generate the subexperiments to run on the backend<a class="headerlink" href="#Generate-the-subexperiments-to-run-on-the-backend" title="Link to this heading">¶</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">generate_cutting_experiments</span></code> accepts a circuit containing <code class="docutils literal notranslate"><span class="pre">TwoQubitQPDGate</span></code> instances and observables as a <code class="docutils literal notranslate"><span class="pre">PauliList</span></code>.</p>
 <p>To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates’ joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by <code class="docutils literal notranslate"><span class="pre">num_samples</span></code>, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the <a class="reference internal" href="../explanation/index.html"><span class="doc">explanatory material</span></a>.</p>
+<p><strong>Note:</strong> The <code class="docutils literal notranslate"><span class="pre">observables</span></code> kwarg to <code class="docutils literal notranslate"><span class="pre">generate_cutting_experiments</span></code> is of type <code class="docutils literal notranslate"><span class="pre">PauliList</span></code>. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -465,7 +456,7 @@ <h2>Generate the subexperiments to run on the backend<a class="headerlink" href=
 
 <span class="c1"># Generate the subexperiments and sampling coefficients</span>
 <span class="n">subexperiments</span><span class="p">,</span> <span class="n">coefficients</span> <span class="o">=</span> <span class="n">generate_cutting_experiments</span><span class="p">(</span>
-    <span class="n">circuits</span><span class="o">=</span><span class="n">qpd_circuit</span><span class="p">,</span> <span class="n">observables</span><span class="o">=</span><span class="n">observables</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span>
+    <span class="n">circuits</span><span class="o">=</span><span class="n">qpd_circuit</span><span class="p">,</span> <span class="n">observables</span><span class="o">=</span><span class="n">observable</span><span class="o">.</span><span class="n">paulis</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span>
 <span class="p">)</span>
 </pre></div>
 </div>
@@ -476,7 +467,7 @@ <h2>Calculate the sampling overhead for the chosen cuts<a class="headerlink" hre
 <p>Here we cut three CNOT gates, resulting in a sampling overhead of <span class="math notranslate nohighlight">\(9^3\)</span>.</p>
 <p>For more on the sampling overhead incurred by circuit cutting, refer to the <a class="reference internal" href="../explanation/index.html"><span class="doc">explanatory material</span></a>.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Sampling overhead: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">([</span><span class="n">basis</span><span class="o">.</span><span class="n">overhead</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">basis</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="n">bases</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
@@ -496,14 +487,18 @@ <h2>Calculate the sampling overhead for the chosen cuts<a class="headerlink" hre
 <h2>Demonstrate that the QPD subexperiments will be shallower after cutting distant gates<a class="headerlink" href="#Demonstrate-that-the-QPD-subexperiments-will-be-shallower-after-cutting-distant-gates" title="Link to this heading">¶</a></h2>
 <p>Here is an example of an arbitrarily chosen subexperiment generated from the QPD circuit. Its depth has been reduced by more than half. Many of these probabilistic subexperiments must be generated and evaluated in order to reconstruct an expectation value of the deeper circuit.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Transpile the decomposed circuit to the same layout</span>
 <span class="n">transpiled_qpd_circuit</span> <span class="o">=</span> <span class="n">pass_manager</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">subexperiments</span><span class="p">[</span><span class="mi">100</span><span class="p">])</span>
 
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Original circuit depth after transpile: </span><span class="si">{</span><span class="n">transpiled_qc</span><span class="o">.</span><span class="n">depth</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;QPD subexperiment depth after transpile: </span><span class="si">{</span><span class="n">transpiled_qpd_circuit</span><span class="o">.</span><span class="n">depth</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;Original circuit depth after transpile: </span><span class="si">{</span><span class="n">transpiled_qc</span><span class="o">.</span><span class="n">depth</span><span class="p">(</span><span class="k">lambda</span><span class="w"> </span><span class="n">x</span><span class="p">:</span><span class="w"> </span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="w"> </span><span class="o">&gt;=</span><span class="w"> </span><span class="mi">2</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;QPD subexperiment depth after transpile: </span><span class="si">{</span><span class="n">transpiled_qpd_circuit</span><span class="o">.</span><span class="n">depth</span><span class="p">(</span><span class="k">lambda</span><span class="w"> </span><span class="n">x</span><span class="p">:</span><span class="w"> </span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="w"> </span><span class="o">&gt;=</span><span class="w"> </span><span class="mi">2</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="p">)</span>
 <span class="n">transpiled_qpd_circuit</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="s2">&quot;mpl&quot;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">idle_wires</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fold</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -513,55 +508,47 @@ <h2>Demonstrate that the QPD subexperiments will be shallower after cutting dist
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Original circuit depth after transpile: 101
-QPD subexperiment depth after transpile: 26
+Original circuit depth after transpile: 30
+QPD subexperiment depth after transpile: 7
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
-<img alt="../../_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_19_1.png" src="../../_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_19_1.png" />
+<img alt="../../_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_17_1.png" src="../../_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_17_1.png" />
 </div>
 </div>
 </section>
-<section id="Run-the-subexperiments-using-the-Qiskit-Sampler-primitive">
-<h2>Run the subexperiments using the Qiskit Sampler primitive<a class="headerlink" href="#Run-the-subexperiments-using-the-Qiskit-Sampler-primitive" title="Link to this heading">¶</a></h2>
+<section id="Prepare-subexperiments-for-the-backend-and-run-them-using-the-Qiskit-Runtime-Sampler-primitive">
+<h2>Prepare subexperiments for the backend and run them using the Qiskit Runtime Sampler primitive<a class="headerlink" href="#Prepare-subexperiments-for-the-backend-and-run-them-using-the-Qiskit-Runtime-Sampler-primitive" title="Link to this heading">¶</a></h2>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_aer.primitives</span> <span class="kn">import</span> <span class="n">Sampler</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_ibm_runtime</span> <span class="kn">import</span> <span class="n">SamplerV2</span>
 
-<span class="c1"># Set up Qiskit Aer Sampler primitive.</span>
-<span class="n">sampler</span> <span class="o">=</span> <span class="n">Sampler</span><span class="p">(</span><span class="n">run_options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;shots&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="o">**</span><span class="mi">12</span><span class="p">})</span>
+<span class="c1"># Transpile the subeperiments to the backend&#39;s instruction set architecture (ISA)</span>
+<span class="n">isa_subexperiments</span> <span class="o">=</span> <span class="n">pass_manager</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">subexperiments</span><span class="p">)</span>
 
-<span class="c1"># Retrieve results from each subexperiment</span>
-<span class="n">results</span> <span class="o">=</span> <span class="n">sampler</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">subexperiments</span><span class="p">)</span><span class="o">.</span><span class="n">result</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<p>To use the Qiskit Runtime Sampler, replace the code above with this commented block, and be sure to specify a hardware backend (above) when specifying a backend.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># from qiskit_ibm_runtime import Session, Options, Sampler</span>
-<span class="c1">#</span>
-<span class="c1"># with Session(backend=backend) as session:</span>
-<span class="c1">#     sampler = Sampler(options=Options(execution={&quot;shots&quot;: 2**12}))</span>
-<span class="c1">#</span>
-<span class="c1">#     results = sampler.run(pass_manager.run(subexperiments)).result()</span>
+<span class="c1"># Set up the Qiskit Runtime Sampler primitive.  For a fake backend, this will use a local simulator.</span>
+<span class="n">sampler</span> <span class="o">=</span> <span class="n">SamplerV2</span><span class="p">(</span><span class="n">backend</span><span class="o">=</span><span class="n">backend</span><span class="p">)</span>
+
+<span class="c1"># Submit the subexperiments</span>
+<span class="n">job</span> <span class="o">=</span> <span class="n">sampler</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">isa_subexperiments</span><span class="p">)</span>
+
+<span class="c1"># Retrieve the results</span>
+<span class="n">results</span> <span class="o">=</span> <span class="n">job</span><span class="o">.</span><span class="n">result</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
 </section>
 <section id="Reconstruct-the-expectation-values">
 <h2>Reconstruct the expectation values<a class="headerlink" href="#Reconstruct-the-expectation-values" title="Link to this heading">¶</a></h2>
-<p>Use the subexperiment results, subobservables, and sampling coefficients to reconstruct the expectation value of the original circuit.</p>
+<p>Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">circuit_knitting.cutting</span> <span class="kn">import</span> <span class="n">reconstruct_expectation_values</span>
@@ -569,35 +556,29 @@ <h2>Reconstruct the expectation values<a class="headerlink" href="#Reconstruct-t
 <span class="n">reconstructed_expvals</span> <span class="o">=</span> <span class="n">reconstruct_expectation_values</span><span class="p">(</span>
     <span class="n">results</span><span class="p">,</span>
     <span class="n">coefficients</span><span class="p">,</span>
-    <span class="n">observables</span><span class="p">,</span>
+    <span class="n">observable</span><span class="o">.</span><span class="n">paulis</span><span class="p">,</span>
 <span class="p">)</span>
+<span class="c1"># Reconstruct final expectation value</span>
+<span class="n">final_expval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">reconstructed_expvals</span><span class="p">,</span> <span class="n">observable</span><span class="o">.</span><span class="n">coeffs</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 </section>
-<section id="Compare-reconstructed-expectation-values-to-exact-expectation-values-from-the-original-circuit">
-<h2>Compare reconstructed expectation values to exact expectation values from the original circuit<a class="headerlink" href="#Compare-reconstructed-expectation-values-to-exact-expectation-values-from-the-original-circuit" title="Link to this heading">¶</a></h2>
+<section id="Compare-the-reconstructed-expectation-values-with-the-exact-expectation-value-from-the-original-circuit-and-observable">
+<h2>Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable<a class="headerlink" href="#Compare-the-reconstructed-expectation-values-with-the-exact-expectation-value-from-the-original-circuit-and-observable" title="Link to this heading">¶</a></h2>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_aer.primitives</span> <span class="kn">import</span> <span class="n">Estimator</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_aer.primitives</span> <span class="kn">import</span> <span class="n">EstimatorV2</span>
 
-<span class="n">estimator</span> <span class="o">=</span> <span class="n">Estimator</span><span class="p">(</span><span class="n">run_options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;shots&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">},</span> <span class="n">approximation</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">exact_expvals</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">estimator</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">circuit</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">observables</span><span class="p">),</span> <span class="nb">list</span><span class="p">(</span><span class="n">observables</span><span class="p">))</span><span class="o">.</span><span class="n">result</span><span class="p">()</span><span class="o">.</span><span class="n">values</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Reconstructed expectation values: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">reconstructed_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Exact expectation values: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Errors in estimation: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">reconstructed_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">-</span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="p">)</span>
+<span class="n">estimator</span> <span class="o">=</span> <span class="n">EstimatorV2</span><span class="p">()</span>
+<span class="n">exact_expval</span> <span class="o">=</span> <span class="n">estimator</span><span class="o">.</span><span class="n">run</span><span class="p">([(</span><span class="n">circuit</span><span class="p">,</span> <span class="n">observable</span><span class="p">)])</span><span class="o">.</span><span class="n">result</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">evs</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Reconstructed expectation value: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">final_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">))</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Exact expectation value: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">exact_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Error in estimation: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">final_expval</span><span class="o">-</span><span class="n">exact_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">))</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Relative errors in estimation: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">((</span><span class="n">reconstructed_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">-</span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Relative error in estimation: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">((</span><span class="n">final_expval</span><span class="o">-</span><span class="n">exact_expval</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">exact_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">))</span><span class="si">}</span><span class="s2">&quot;</span>
 <span class="p">)</span>
 </pre></div>
 </div>
@@ -607,10 +588,10 @@ <h2>Compare reconstructed expectation values to exact expectation values from th
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Reconstructed expectation values: [0.52972412, 0.5692749, 0.37542725, -0.20349121, 0.25061035, -0.24511719]
-Exact expectation values: [0.50983039, 0.56127511, 0.36167086, -0.23006544, 0.23416169, -0.20855487]
-Errors in estimation: [0.01989373, 0.00799979, 0.01375639, 0.02657423, 0.01644866, -0.03656231]
-Relative errors in estimation: [0.03902028, 0.01425288, 0.03803565, -0.11550725, 0.07024487, 0.17531269]
+Reconstructed expectation value: 0.66918945
+Exact expectation value: 0.50497603
+Error in estimation: 0.16421342
+Relative error in estimation: 0.32519053
 </pre></div></div>
 </div>
 </section>
@@ -679,16 +660,16 @@ <h2>Compare reconstructed expectation values to exact expectation values from th
             <ul>
 <li><a class="reference internal" href="#">Gate Cutting to Reduce Circuit Depth</a><ul>
 <li><a class="reference internal" href="#Create-a-circuit-to-run-on-the-backend">Create a circuit to run on the backend</a></li>
-<li><a class="reference internal" href="#Specify-some-observables">Specify some observables</a></li>
+<li><a class="reference internal" href="#Specify-an-observable">Specify an observable</a></li>
 <li><a class="reference internal" href="#Specify-a-backend">Specify a backend</a></li>
 <li><a class="reference internal" href="#Transpile-the-circuit,-visualize-the-swaps,-and-note-the-depth">Transpile the circuit, visualize the swaps, and note the depth</a></li>
 <li><a class="reference internal" href="#Replace-distant-gates-with-TwoQubitQPDGates-by-specifying-their-indices">Replace distant gates with <code class="docutils literal notranslate"><span class="pre">TwoQubitQPDGate</span></code>s by specifying their indices</a></li>
 <li><a class="reference internal" href="#Generate-the-subexperiments-to-run-on-the-backend">Generate the subexperiments to run on the backend</a></li>
 <li><a class="reference internal" href="#Calculate-the-sampling-overhead-for-the-chosen-cuts">Calculate the sampling overhead for the chosen cuts</a></li>
 <li><a class="reference internal" href="#Demonstrate-that-the-QPD-subexperiments-will-be-shallower-after-cutting-distant-gates">Demonstrate that the QPD subexperiments will be shallower after cutting distant gates</a></li>
-<li><a class="reference internal" href="#Run-the-subexperiments-using-the-Qiskit-Sampler-primitive">Run the subexperiments using the Qiskit Sampler primitive</a></li>
+<li><a class="reference internal" href="#Prepare-subexperiments-for-the-backend-and-run-them-using-the-Qiskit-Runtime-Sampler-primitive">Prepare subexperiments for the backend and run them using the Qiskit Runtime Sampler primitive</a></li>
 <li><a class="reference internal" href="#Reconstruct-the-expectation-values">Reconstruct the expectation values</a></li>
-<li><a class="reference internal" href="#Compare-reconstructed-expectation-values-to-exact-expectation-values-from-the-original-circuit">Compare reconstructed expectation values to exact expectation values from the original circuit</a></li>
+<li><a class="reference internal" href="#Compare-the-reconstructed-expectation-values-with-the-exact-expectation-value-from-the-original-circuit-and-observable">Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable</a></li>
 </ul>
 </li>
 </ul>
@@ -702,7 +683,7 @@ <h2>Compare reconstructed expectation values to exact expectation values from th
   </div>
 </div><script src="../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb b/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb
index 3f69d56ca..143a39dad 100644
--- a/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb
+++ b/circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb
@@ -28,11 +28,18 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "54ed0f13",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:00.038466Z",
+     "iopub.status.busy": "2024-04-19T17:43:00.038247Z",
+     "iopub.status.idle": "2024-04-19T17:43:01.073530Z",
+     "shell.execute_reply": "2024-04-19T17:43:01.072796Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1434x294.311 with 1 Axes>"
       ]
@@ -55,21 +62,26 @@
    "id": "452cd19e",
    "metadata": {},
    "source": [
-    "### Specify some observables\n",
-    "\n",
-    "Currently, only `Pauli` observables with phase equal to 1 are supported. Full support for `SparsePauliOp` is expected in CKT v0.7.0."
+    "### Specify an observable"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "id": "105e858d",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:01.076216Z",
+     "iopub.status.busy": "2024-04-19T17:43:01.075949Z",
+     "iopub.status.idle": "2024-04-19T17:43:01.079736Z",
+     "shell.execute_reply": "2024-04-19T17:43:01.079184Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit.quantum_info import PauliList\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
-    "observables = PauliList([\"ZZII\", \"IZZI\", \"IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])"
+    "observable = SparsePauliOp([\"ZZII\", \"IZZI\", \"-IIZZ\", \"XIXI\", \"ZIZZ\", \"IXIX\"])"
    ]
   },
   {
@@ -79,41 +91,26 @@
    "source": [
     "### Specify a backend\n",
     "\n",
-    "You can provide either a fake backend from Qiskit or a hardware backend from Qiskit Runtime."
+    "You can provide either a fake backend or a hardware backend from Qiskit Runtime."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "id": "756f2130-6947-479a-9fe7-97443c87a904",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:01.082117Z",
+     "iopub.status.busy": "2024-04-19T17:43:01.081912Z",
+     "iopub.status.idle": "2024-04-19T17:43:01.558662Z",
+     "shell.execute_reply": "2024-04-19T17:43:01.558071Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit.providers.fake_provider import GenericBackendV2\n",
+    "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n",
     "\n",
-    "backend = GenericBackendV2(num_qubits=4, coupling_map=[[0, 1], [1, 2], [2, 3]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b5debc22-7e92-465e-b37d-05763946b6e6",
-   "metadata": {},
-   "source": [
-    "To use a hardware backend, replace the code above with the following commented block."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "322fb191-6b85-4887-9883-71f94bb0be87",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from qiskit_ibm_runtime import QiskitRuntimeService\n",
-    "#\n",
-    "# service = QiskitRuntimeService()\n",
-    "# # backend = service.backend(\"ibm_cairo\")\n",
-    "# backend = service.least_busy(operational=True, simulator=False)"
+    "backend = FakeManilaV2()"
    ]
   },
   {
@@ -128,15 +125,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "b394da7a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:01.561886Z",
+     "iopub.status.busy": "2024-04-19T17:43:01.561340Z",
+     "iopub.status.idle": "2024-04-19T17:43:01.596696Z",
+     "shell.execute_reply": "2024-04-19T17:43:01.596116Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Transpiled circuit depth: 101\n"
+      "Transpiled circuit depth: 30\n"
      ]
     }
    ],
@@ -148,23 +152,30 @@
     ")\n",
     "\n",
     "transpiled_qc = pass_manager.run(circuit)\n",
-    "print(f\"Transpiled circuit depth: {transpiled_qc.depth()}\")"
+    "print(f\"Transpiled circuit depth: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "4fe4af43",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:01.598991Z",
+     "iopub.status.busy": "2024-04-19T17:43:01.598785Z",
+     "iopub.status.idle": "2024-04-19T17:43:02.317434Z",
+     "shell.execute_reply": "2024-04-19T17:43:02.316773Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 3447.07x147.156 with 1 Axes>"
+       "<Figure size 2008.96x147.156 with 1 Axes>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -185,18 +196,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "12e73c69",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:02.320250Z",
+     "iopub.status.busy": "2024-04-19T17:43:02.319784Z",
+     "iopub.status.idle": "2024-04-19T17:43:02.631638Z",
+     "shell.execute_reply": "2024-04-19T17:43:02.630944Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1634.66x294.311 with 1 Axes>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -226,14 +244,23 @@
     "\n",
     "`generate_cutting_experiments` accepts a circuit containing `TwoQubitQPDGate` instances and observables as a `PauliList`.\n",
     "\n",
-    "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst)."
+    "To simulate the expectation value of the full-sized circuit, many subexperiments are generated from the decomposed gates' joint quasiprobability distribution and then executed on one or more backends. The number of samples taken from the distribution is controlled by `num_samples`, and one combined coefficient is given for each unique sample. For more information on how the coefficients are calculated, refer to the [explanatory material](../explanation/index.rst).\n",
+    "\n",
+    "**Note:** The ``observables`` kwarg to `generate_cutting_experiments` is of type `PauliList`. Observable term coefficients and phases are ignored during decomposition of the problem and execution of the subexperiments. They may be re-applied during reconstruction of the expectation value."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "83b1efed-bafa-48c4-bbf0-cf7eb9027ac5",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:02.634186Z",
+     "iopub.status.busy": "2024-04-19T17:43:02.633980Z",
+     "iopub.status.idle": "2024-04-19T17:43:04.963805Z",
+     "shell.execute_reply": "2024-04-19T17:43:04.962860Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -241,7 +268,7 @@
     "\n",
     "# Generate the subexperiments and sampling coefficients\n",
     "subexperiments, coefficients = generate_cutting_experiments(\n",
-    "    circuits=qpd_circuit, observables=observables, num_samples=np.inf\n",
+    "    circuits=qpd_circuit, observables=observable.paulis, num_samples=np.inf\n",
     ")"
    ]
   },
@@ -259,9 +286,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "c7b28e2c",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:04.967500Z",
+     "iopub.status.busy": "2024-04-19T17:43:04.966963Z",
+     "iopub.status.idle": "2024-04-19T17:43:04.970995Z",
+     "shell.execute_reply": "2024-04-19T17:43:04.970300Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -287,26 +321,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "70e2f1b6",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:04.973543Z",
+     "iopub.status.busy": "2024-04-19T17:43:04.973161Z",
+     "iopub.status.idle": "2024-04-19T17:43:05.463994Z",
+     "shell.execute_reply": "2024-04-19T17:43:05.463230Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Original circuit depth after transpile: 101\n",
-      "QPD subexperiment depth after transpile: 26\n"
+      "Original circuit depth after transpile: 30\n",
+      "QPD subexperiment depth after transpile: 7\n"
      ]
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 2320.28x428.089 with 1 Axes>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -315,8 +356,12 @@
     "# Transpile the decomposed circuit to the same layout\n",
     "transpiled_qpd_circuit = pass_manager.run(subexperiments[100])\n",
     "\n",
-    "print(f\"Original circuit depth after transpile: {transpiled_qc.depth()}\")\n",
-    "print(f\"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth()}\")\n",
+    "print(\n",
+    "    f\"Original circuit depth after transpile: {transpiled_qc.depth(lambda x: len(x[1]) >= 2)}\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"QPD subexperiment depth after transpile: {transpiled_qpd_circuit.depth(lambda x: len(x[1]) >= 2)}\"\n",
+    ")\n",
     "transpiled_qpd_circuit.draw(\"mpl\", scale=0.8, idle_wires=False, fold=-1)"
    ]
   },
@@ -325,46 +370,36 @@
    "id": "fd9a126c",
    "metadata": {},
    "source": [
-    "### Run the subexperiments using the Qiskit Sampler primitive"
+    "### Prepare subexperiments for the backend and run them using the Qiskit Runtime Sampler primitive"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "80dd2a66",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:05.466804Z",
+     "iopub.status.busy": "2024-04-19T17:43:05.466360Z",
+     "iopub.status.idle": "2024-04-19T17:43:37.025494Z",
+     "shell.execute_reply": "2024-04-19T17:43:37.024692Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit_aer.primitives import Sampler\n",
+    "from qiskit_ibm_runtime import SamplerV2\n",
     "\n",
-    "# Set up Qiskit Aer Sampler primitive.\n",
-    "sampler = Sampler(run_options={\"shots\": 2**12})\n",
+    "# Transpile the subeperiments to the backend's instruction set architecture (ISA)\n",
+    "isa_subexperiments = pass_manager.run(subexperiments)\n",
     "\n",
-    "# Retrieve results from each subexperiment\n",
-    "results = sampler.run(subexperiments).result()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "878c9d46-53b9-4ac5-8170-f4abd1072a1c",
-   "metadata": {},
-   "source": [
-    "To use the Qiskit Runtime Sampler, replace the code above with this commented block, and be sure to specify a hardware backend (above) when specifying a backend."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "a437de20-2042-4e62-87a7-804058cff5db",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from qiskit_ibm_runtime import Session, Options, Sampler\n",
-    "#\n",
-    "# with Session(backend=backend) as session:\n",
-    "#     sampler = Sampler(options=Options(execution={\"shots\": 2**12}))\n",
-    "#\n",
-    "#     results = sampler.run(pass_manager.run(subexperiments)).result()"
+    "# Set up the Qiskit Runtime Sampler primitive.  For a fake backend, this will use a local simulator.\n",
+    "sampler = SamplerV2(backend=backend)\n",
+    "\n",
+    "# Submit the subexperiments\n",
+    "job = sampler.run(isa_subexperiments)\n",
+    "\n",
+    "# Retrieve the results\n",
+    "results = job.result()"
    ]
   },
   {
@@ -374,14 +409,21 @@
    "source": [
     "### Reconstruct the expectation values\n",
     "\n",
-    "Use the subexperiment results, subobservables, and sampling coefficients to reconstruct the expectation value of the original circuit."
+    "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "ace12f7f",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:37.029345Z",
+     "iopub.status.busy": "2024-04-19T17:43:37.028881Z",
+     "iopub.status.idle": "2024-04-19T17:43:39.308072Z",
+     "shell.execute_reply": "2024-04-19T17:43:39.307244Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.cutting import reconstruct_expectation_values\n",
@@ -389,8 +431,10 @@
     "reconstructed_expvals = reconstruct_expectation_values(\n",
     "    results,\n",
     "    coefficients,\n",
-    "    observables,\n",
-    ")"
+    "    observable.paulis,\n",
+    ")\n",
+    "# Reconstruct final expectation value\n",
+    "final_expval = np.dot(reconstructed_expvals, observable.coeffs)"
    ]
   },
   {
@@ -398,44 +442,43 @@
    "id": "3fc2327f",
    "metadata": {},
    "source": [
-    "### Compare reconstructed expectation values to exact expectation values from the original circuit"
+    "### Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "id": "4928e703",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:43:39.311175Z",
+     "iopub.status.busy": "2024-04-19T17:43:39.310719Z",
+     "iopub.status.idle": "2024-04-19T17:43:39.324322Z",
+     "shell.execute_reply": "2024-04-19T17:43:39.323777Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reconstructed expectation values: [0.52972412, 0.5692749, 0.37542725, -0.20349121, 0.25061035, -0.24511719]\n",
-      "Exact expectation values: [0.50983039, 0.56127511, 0.36167086, -0.23006544, 0.23416169, -0.20855487]\n",
-      "Errors in estimation: [0.01989373, 0.00799979, 0.01375639, 0.02657423, 0.01644866, -0.03656231]\n",
-      "Relative errors in estimation: [0.03902028, 0.01425288, 0.03803565, -0.11550725, 0.07024487, 0.17531269]\n"
+      "Reconstructed expectation value: 0.66918945\n",
+      "Exact expectation value: 0.50497603\n",
+      "Error in estimation: 0.16421342\n",
+      "Relative error in estimation: 0.32519053\n"
      ]
     }
    ],
    "source": [
-    "from qiskit_aer.primitives import Estimator\n",
+    "from qiskit_aer.primitives import EstimatorV2\n",
     "\n",
-    "estimator = Estimator(run_options={\"shots\": None}, approximation=True)\n",
-    "exact_expvals = (\n",
-    "    estimator.run([circuit] * len(observables), list(observables)).result().values\n",
-    ")\n",
-    "print(\n",
-    "    f\"Reconstructed expectation values: {[np.round(reconstructed_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Exact expectation values: {[np.round(exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Errors in estimation: {[np.round(reconstructed_expvals[i]-exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
+    "estimator = EstimatorV2()\n",
+    "exact_expval = estimator.run([(circuit, observable)]).result()[0].data.evs\n",
+    "print(f\"Reconstructed expectation value: {np.real(np.round(final_expval, 8))}\")\n",
+    "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n",
+    "print(f\"Error in estimation: {np.real(np.round(final_expval-exact_expval, 8))}\")\n",
     "print(\n",
-    "    f\"Relative errors in estimation: {[np.round((reconstructed_expvals[i]-exact_expvals[i]) / exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
+    "    f\"Relative error in estimation: {np.real(np.round((final_expval-exact_expval) / exact_expval, 8))}\"\n",
     ")"
    ]
   }
@@ -456,7 +499,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html b/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html
index f527677c3..28b6481f2 100644
--- a/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html
+++ b/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html
@@ -3,10 +3,10 @@
   <head><meta charset="utf-8"/>
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
-<link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="Explanatory material for the circuit cutting module" href="../explanation/index.html" /><link rel="prev" title="Gate Cutting to Reduce Circuit Depth" href="02_gate_cutting_to_reduce_circuit_depth.html" />
+<link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="Automatically find cuts using CKT" href="04_automatic_cut_finding.html" /><link rel="prev" title="Gate Cutting to Reduce Circuit Depth" href="02_gate_cutting_to_reduce_circuit_depth.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>Wire Cutting Phrased as a Two-Qubit Move Instruction - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>Wire Cutting Phrased as a Two-Qubit Move Instruction - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -146,7 +146,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -173,7 +173,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -190,6 +190,7 @@
 <li class="toctree-l2"><a class="reference internal" href="01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2 current current-page"><a class="current reference internal" href="#">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../explanation/index.html">Cutting Explanatory Material</a></li>
@@ -219,6 +220,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -344,7 +348,7 @@ <h2>Create a circuit to cut<a class="headerlink" href="#Create-a-circuit-to-cut"
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-&lt;qiskit.circuit.instructionset.InstructionSet at 0x7fb6f8b361f0&gt;
+&lt;qiskit.circuit.instructionset.InstructionSet at 0x7f8640f91340&gt;
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
@@ -364,16 +368,15 @@ <h2>Create a circuit to cut<a class="headerlink" href="#Create-a-circuit-to-cut"
 </div>
 </div>
 </section>
-<section id="Specify-some-observables">
-<h2>Specify some observables<a class="headerlink" href="#Specify-some-observables" title="Link to this heading">¶</a></h2>
-<p>Next, we specify a list of observables whose expectation values we would like to determine.</p>
+<section id="Specify-an-observable">
+<h2>Specify an observable<a class="headerlink" href="#Specify-an-observable" title="Link to this heading">¶</a></h2>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit.quantum_info</span> <span class="kn">import</span> <span class="n">PauliList</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit.quantum_info</span> <span class="kn">import</span> <span class="n">SparsePauliOp</span>
 
-<span class="n">observables_0</span> <span class="o">=</span> <span class="n">PauliList</span><span class="p">([</span><span class="s2">&quot;ZIIIIII&quot;</span><span class="p">,</span> <span class="s2">&quot;IIIZIII&quot;</span><span class="p">,</span> <span class="s2">&quot;IIIIIIZ&quot;</span><span class="p">])</span>
+<span class="n">observable</span> <span class="o">=</span> <span class="n">SparsePauliOp</span><span class="p">([</span><span class="s2">&quot;ZIIIIII&quot;</span><span class="p">,</span> <span class="s2">&quot;IIIZIII&quot;</span><span class="p">,</span> <span class="s2">&quot;IIIIIIZ&quot;</span><span class="p">])</span>
 </pre></div>
 </div>
 </div>
@@ -418,14 +421,14 @@ <h2>Create a new circuit where <code class="docutils literal notranslate"><span
 </div>
 </div>
 </section>
-<section id="Create-observables-to-go-with-the-new-circuit">
-<h2>Create observables to go with the new circuit<a class="headerlink" href="#Create-observables-to-go-with-the-new-circuit" title="Link to this heading">¶</a></h2>
-<p>These observables correspond with <code class="docutils literal notranslate"><span class="pre">observables_0</span></code>, but we must account correctly for the extra qubit wire that has been added (i.e., we insert an “I” at index 4). Note that in Qiskit, the string representation qubit-0 corresponds to the right-most Pauli character.</p>
+<section id="Create-observable-to-go-with-the-new-circuit">
+<h2>Create observable to go with the new circuit<a class="headerlink" href="#Create-observable-to-go-with-the-new-circuit" title="Link to this heading">¶</a></h2>
+<p>This observable corresponds with <code class="docutils literal notranslate"><span class="pre">observable</span></code>, but we must account correctly for the extra qubit wire that has been added (i.e., we insert an “I” at index 4). Note that in Qiskit, the string representation qubit-0 corresponds to the right-most Pauli character.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">observables_1</span> <span class="o">=</span> <span class="n">PauliList</span><span class="p">([</span><span class="s2">&quot;ZIIIIIII&quot;</span><span class="p">,</span> <span class="s2">&quot;IIIIZIII&quot;</span><span class="p">,</span> <span class="s2">&quot;IIIIIIIZ&quot;</span><span class="p">])</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">observable_expanded</span> <span class="o">=</span> <span class="n">SparsePauliOp</span><span class="p">([</span><span class="s2">&quot;ZIIIIIII&quot;</span><span class="p">,</span> <span class="s2">&quot;IIIIZIII&quot;</span><span class="p">,</span> <span class="s2">&quot;IIIIIIIZ&quot;</span><span class="p">])</span>
 </pre></div>
 </div>
 </div>
@@ -440,7 +443,7 @@ <h2>Separate the circuit and observables<a class="headerlink" href="#Separate-th
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">circuit_knitting.cutting</span> <span class="kn">import</span> <span class="n">partition_problem</span>
 
 <span class="n">partitioned_problem</span> <span class="o">=</span> <span class="n">partition_problem</span><span class="p">(</span>
-    <span class="n">circuit</span><span class="o">=</span><span class="n">qc_1</span><span class="p">,</span> <span class="n">partition_labels</span><span class="o">=</span><span class="s2">&quot;AAAABBBB&quot;</span><span class="p">,</span> <span class="n">observables</span><span class="o">=</span><span class="n">observables_1</span>
+    <span class="n">circuit</span><span class="o">=</span><span class="n">qc_1</span><span class="p">,</span> <span class="n">partition_labels</span><span class="o">=</span><span class="s2">&quot;AAAABBBB&quot;</span><span class="p">,</span> <span class="n">observables</span><span class="o">=</span><span class="n">observable_expanded</span><span class="o">.</span><span class="n">paulis</span>
 <span class="p">)</span>
 <span class="n">subcircuits</span> <span class="o">=</span> <span class="n">partitioned_problem</span><span class="o">.</span><span class="n">subcircuits</span>
 <span class="n">subobservables</span> <span class="o">=</span> <span class="n">partitioned_problem</span><span class="o">.</span><span class="n">subobservables</span>
@@ -540,61 +543,78 @@ <h2>Generate the subexperiments to run on the backend<a class="headerlink" href=
 </div>
 </div>
 </section>
-<section id="Run-the-subexperiments-using-the-Qiskit-Sampler-primitive">
-<h2>Run the subexperiments using the Qiskit Sampler primitive<a class="headerlink" href="#Run-the-subexperiments-using-the-Qiskit-Sampler-primitive" title="Link to this heading">¶</a></h2>
+<section id="Choose-a-backend">
+<h2>Choose a backend<a class="headerlink" href="#Choose-a-backend" title="Link to this heading">¶</a></h2>
+<p>Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator).</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_aer.primitives</span> <span class="kn">import</span> <span class="n">Sampler</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_ibm_runtime.fake_provider</span> <span class="kn">import</span> <span class="n">FakeManilaV2</span>
 
-<span class="c1"># Set up a Qiskit Aer Sampler primitive for each circuit partition</span>
-<span class="n">samplers</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">label</span><span class="p">:</span> <span class="n">Sampler</span><span class="p">(</span><span class="n">run_options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;shots&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="o">**</span><span class="mi">12</span><span class="p">})</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">subexperiments</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
-<span class="p">}</span>
-
-<span class="c1"># Retrieve results from each partition&#39;s subexperiments</span>
-<span class="n">results</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="n">label</span><span class="p">:</span> <span class="n">sampler</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">subexperiments</span><span class="p">[</span><span class="n">label</span><span class="p">])</span><span class="o">.</span><span class="n">result</span><span class="p">()</span>
-    <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">sampler</span> <span class="ow">in</span> <span class="n">samplers</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="p">}</span>
+<span class="n">backend</span> <span class="o">=</span> <span class="n">FakeManilaV2</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
-<p>To use the Qiskit Runtime Sampler, replace the code above with this commented block.</p>
+</section>
+<section id="Prepare-the-subexperiments-for-the-backend">
+<h2>Prepare the subexperiments for the backend<a class="headerlink" href="#Prepare-the-subexperiments-for-the-backend" title="Link to this heading">¶</a></h2>
+<p>We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># from qiskit_ibm_runtime import Session, Options, Sampler, QiskitRuntimeService</span>
-<span class="c1"># from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager</span>
-<span class="c1">#</span>
-<span class="c1"># service = QiskitRuntimeService()</span>
-<span class="c1"># backend = service.least_busy(operational=True, simulator=False)</span>
-<span class="c1">#</span>
-<span class="c1"># # Prepare transpiler for target backend</span>
-<span class="c1"># pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)</span>
-<span class="c1">#</span>
-<span class="c1"># with Session(backend=backend) as session:</span>
-<span class="c1">#     # Set up Qiskit Runtime Sampler primitives.</span>
-<span class="c1">#     samplers = {</span>
-<span class="c1">#         label: Sampler(Options(execution={&quot;shots&quot;: 2**12})) for label in subexperiments.keys()</span>
-<span class="c1">#     }</span>
-<span class="c1">#</span>
-<span class="c1">#     # Retrieve results from each subexperiment</span>
-<span class="c1">#     results = {</span>
-<span class="c1">#         label: sampler.run(pass_manager.run(subexperiments[label])).result()</span>
-<span class="c1">#         for label, sampler in samplers.items()</span>
-<span class="c1">#     }</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit.transpiler.preset_passmanagers</span> <span class="kn">import</span> <span class="n">generate_preset_pass_manager</span>
+
+<span class="c1"># Transpile the subexperiments to ISA circuits</span>
+<span class="n">pass_manager</span> <span class="o">=</span> <span class="n">generate_preset_pass_manager</span><span class="p">(</span><span class="n">optimization_level</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="n">backend</span><span class="p">)</span>
+<span class="n">isa_subexperiments</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="n">label</span><span class="p">:</span> <span class="n">pass_manager</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">partition_subexpts</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">partition_subexpts</span> <span class="ow">in</span> <span class="n">subexperiments</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+<span class="p">}</span>
 </pre></div>
 </div>
 </div>
 </section>
+<section id="Run-the-subexperiments-using-the-Qiskit-Runtime-Sampler-primitive">
+<h2>Run the subexperiments using the Qiskit Runtime Sampler primitive<a class="headerlink" href="#Run-the-subexperiments-using-the-Qiskit-Runtime-Sampler-primitive" title="Link to this heading">¶</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_ibm_runtime</span> <span class="kn">import</span> <span class="n">SamplerV2</span><span class="p">,</span> <span class="n">Batch</span>
+
+<span class="c1"># Set up a Qiskit Runtime Sampler primitive for each circuit partition</span>
+<span class="n">samplers</span> <span class="o">=</span> <span class="p">{</span><span class="n">label</span><span class="p">:</span> <span class="n">SamplerV2</span><span class="p">(</span><span class="n">backend</span><span class="o">=</span><span class="n">backend</span><span class="p">)</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">subexperiments</span><span class="o">.</span><span class="n">keys</span><span class="p">()}</span>
+
+<span class="c1"># Submit each partition&#39;s subexperiments as a single batch</span>
+<span class="k">with</span> <span class="n">Batch</span><span class="p">(</span><span class="n">backend</span><span class="o">=</span><span class="n">backend</span><span class="p">):</span>
+    <span class="n">jobs</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="n">label</span><span class="p">:</span> <span class="n">sampler</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">isa_subexperiments</span><span class="p">[</span><span class="n">label</span><span class="p">],</span> <span class="n">shots</span><span class="o">=</span><span class="mi">2</span><span class="o">**</span><span class="mi">12</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">sampler</span> <span class="ow">in</span> <span class="n">samplers</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+    <span class="p">}</span>
+
+<span class="c1"># Retrive results</span>
+<span class="n">results</span> <span class="o">=</span> <span class="p">{</span><span class="n">label</span><span class="p">:</span> <span class="n">job</span><span class="o">.</span><span class="n">result</span><span class="p">()</span> <span class="k">for</span> <span class="n">label</span><span class="p">,</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/home/runner/work/circuit-knitting-toolbox/circuit-knitting-toolbox/.tox/docs/lib/python3.9/site-packages/qiskit_ibm_runtime/session.py:157: UserWarning: Session is not supported in local testing mode or when using a simulator.
+  warnings.warn(
+</pre></div></div>
+</div>
+<p>To use the Qiskit Runtime Sampler, replace the code above with this commented block.</p>
+</section>
 <section id="Reconstruct-the-expectation-values">
 <h2>Reconstruct the expectation values<a class="headerlink" href="#Reconstruct-the-expectation-values" title="Link to this heading">¶</a></h2>
-<p>Use the subexperiment results, subobservables, and sampling coefficients to reconstruct the expectation value of the original circuit.</p>
+<p>Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">circuit_knitting.cutting</span> <span class="kn">import</span> <span class="n">reconstruct_expectation_values</span>
@@ -604,33 +624,26 @@ <h2>Reconstruct the expectation values<a class="headerlink" href="#Reconstruct-t
     <span class="n">coefficients</span><span class="p">,</span>
     <span class="n">subobservables</span><span class="p">,</span>
 <span class="p">)</span>
+<span class="n">final_expval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">reconstructed_expvals</span><span class="p">,</span> <span class="n">observable</span><span class="o">.</span><span class="n">coeffs</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 </section>
-<section id="Compare-the-reconstructed-expectation-values-with-the-exact-expectation-values-from-the-original-circuit">
-<h2>Compare the reconstructed expectation values with the exact expectation values from the original circuit<a class="headerlink" href="#Compare-the-reconstructed-expectation-values-with-the-exact-expectation-values-from-the-original-circuit" title="Link to this heading">¶</a></h2>
+<section id="Compare-the-reconstructed-expectation-values-with-the-exact-expectation-value-from-the-original-circuit-and-observable">
+<h2>Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable<a class="headerlink" href="#Compare-the-reconstructed-expectation-values-with-the-exact-expectation-value-from-the-original-circuit-and-observable" title="Link to this heading">¶</a></h2>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_aer.primitives</span> <span class="kn">import</span> <span class="n">Estimator</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">qiskit_aer.primitives</span> <span class="kn">import</span> <span class="n">EstimatorV2</span>
 
-<span class="n">estimator</span> <span class="o">=</span> <span class="n">Estimator</span><span class="p">(</span><span class="n">run_options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;shots&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">},</span> <span class="n">approximation</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">exact_expvals</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">estimator</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">qc_0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">observables_0</span><span class="p">),</span> <span class="nb">list</span><span class="p">(</span><span class="n">observables_0</span><span class="p">))</span><span class="o">.</span><span class="n">result</span><span class="p">()</span><span class="o">.</span><span class="n">values</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Reconstructed expectation values: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">reconstructed_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Exact expectation values: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Errors in estimation: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">reconstructed_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">-</span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
-<span class="p">)</span>
+<span class="n">estimator</span> <span class="o">=</span> <span class="n">EstimatorV2</span><span class="p">()</span>
+<span class="n">exact_expval</span> <span class="o">=</span> <span class="n">estimator</span><span class="o">.</span><span class="n">run</span><span class="p">([(</span><span class="n">qc_0</span><span class="p">,</span> <span class="n">observable</span><span class="p">)])</span><span class="o">.</span><span class="n">result</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">evs</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Reconstructed expectation value: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">final_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">))</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Exact expectation value: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">exact_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Error in estimation: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">final_expval</span><span class="o">-</span><span class="n">exact_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">))</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Relative errors in estimation: </span><span class="si">{</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">((</span><span class="n">reconstructed_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">-</span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">exact_expvals</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="w"> </span><span class="mi">8</span><span class="p">)</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">exact_expvals</span><span class="p">))]</span><span class="si">}</span><span class="s2">&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;Relative error in estimation: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">((</span><span class="n">final_expval</span><span class="o">-</span><span class="n">exact_expval</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">exact_expval</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">))</span><span class="si">}</span><span class="s2">&quot;</span>
 <span class="p">)</span>
 </pre></div>
 </div>
@@ -640,10 +653,10 @@ <h2>Compare the reconstructed expectation values with the exact expectation valu
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Reconstructed expectation values: [0.15630245, 0.68548346, 0.69142044]
-Exact expectation values: [0.1767767, 0.70710678, 0.70710678]
-Errors in estimation: [-0.02047424, -0.02162333, -0.01568635]
-Relative errors in estimation: [-0.11581981, -0.03058, -0.02218384]
+Reconstructed expectation value: 1.54284137
+Exact expectation value: 1.59099026
+Error in estimation: -0.04814888
+Relative error in estimation: -0.03026347
 </pre></div></div>
 </div>
 </section>
@@ -653,12 +666,12 @@ <h2>Compare the reconstructed expectation values with the exact expectation valu
       </div>
       <footer>
         <div class="related-pages">
-          <a class="next-page" href="../explanation/index.html">
+          <a class="next-page" href="04_automatic_cut_finding.html">
               <div class="page-info">
                 <div class="context">
                   <span>Next</span>
                 </div>
-                <div class="title">Explanatory material for the circuit cutting module</div>
+                <div class="title">Automatically find cuts using CKT</div>
               </div>
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
             </a>
@@ -712,16 +725,18 @@ <h2>Compare the reconstructed expectation values with the exact expectation valu
             <ul>
 <li><a class="reference internal" href="#">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a><ul>
 <li><a class="reference internal" href="#Create-a-circuit-to-cut">Create a circuit to cut</a></li>
-<li><a class="reference internal" href="#Specify-some-observables">Specify some observables</a></li>
+<li><a class="reference internal" href="#Specify-an-observable">Specify an observable</a></li>
 <li><a class="reference internal" href="#Create-a-new-circuit-where-Move-instructions-have-been-placed-at-the-desired-cut-locations">Create a new circuit where <code class="docutils literal notranslate"><span class="pre">Move</span></code> instructions have been placed at the desired cut locations</a></li>
-<li><a class="reference internal" href="#Create-observables-to-go-with-the-new-circuit">Create observables to go with the new circuit</a></li>
+<li><a class="reference internal" href="#Create-observable-to-go-with-the-new-circuit">Create observable to go with the new circuit</a></li>
 <li><a class="reference internal" href="#Separate-the-circuit-and-observables">Separate the circuit and observables</a></li>
 <li><a class="reference internal" href="#Visualize-the-decomposed-problem">Visualize the decomposed problem</a></li>
 <li><a class="reference internal" href="#Calculate-the-sampling-overhead-for-the-chosen-cuts">Calculate the sampling overhead for the chosen cuts</a></li>
 <li><a class="reference internal" href="#Generate-the-subexperiments-to-run-on-the-backend">Generate the subexperiments to run on the backend</a></li>
-<li><a class="reference internal" href="#Run-the-subexperiments-using-the-Qiskit-Sampler-primitive">Run the subexperiments using the Qiskit Sampler primitive</a></li>
+<li><a class="reference internal" href="#Choose-a-backend">Choose a backend</a></li>
+<li><a class="reference internal" href="#Prepare-the-subexperiments-for-the-backend">Prepare the subexperiments for the backend</a></li>
+<li><a class="reference internal" href="#Run-the-subexperiments-using-the-Qiskit-Runtime-Sampler-primitive">Run the subexperiments using the Qiskit Runtime Sampler primitive</a></li>
 <li><a class="reference internal" href="#Reconstruct-the-expectation-values">Reconstruct the expectation values</a></li>
-<li><a class="reference internal" href="#Compare-the-reconstructed-expectation-values-with-the-exact-expectation-values-from-the-original-circuit">Compare the reconstructed expectation values with the exact expectation values from the original circuit</a></li>
+<li><a class="reference internal" href="#Compare-the-reconstructed-expectation-values-with-the-exact-expectation-value-from-the-original-circuit-and-observable">Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable</a></li>
 </ul>
 </li>
 </ul>
@@ -735,7 +750,7 @@ <h2>Compare the reconstructed expectation values with the exact expectation valu
   </div>
 </div><script src="../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb b/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb
index dc25adb29..ce326d5e9 100644
--- a/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb
+++ b/circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb
@@ -30,12 +30,19 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "3bcae0ed-4308-4686-b85c-8595c6e916bc",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:05.442544Z",
+     "iopub.status.busy": "2024-04-19T17:42:05.442302Z",
+     "iopub.status.idle": "2024-04-19T17:42:05.948185Z",
+     "shell.execute_reply": "2024-04-19T17:42:05.947389Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x7fb6f8b361f0>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x7f8640f91340>"
       ]
      },
      "execution_count": 1,
@@ -65,11 +72,18 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "1dcaff2d-2d1b-4cc0-87d1-0f4f5de823ff",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:05.951993Z",
+     "iopub.status.busy": "2024-04-19T17:42:05.951230Z",
+     "iopub.status.idle": "2024-04-19T17:42:06.747817Z",
+     "shell.execute_reply": "2024-04-19T17:42:06.746915Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 956.385x618.722 with 1 Axes>"
       ]
@@ -88,21 +102,26 @@
    "id": "bd1617a1-e793-43a9-a24a-c81c18fd2a1e",
    "metadata": {},
    "source": [
-    "### Specify some observables\n",
-    "\n",
-    "Next, we specify a list of observables whose expectation values we would like to determine."
+    "### Specify an observable"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "id": "b791aa42-c485-453b-a110-3c790194adaf",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:06.750528Z",
+     "iopub.status.busy": "2024-04-19T17:42:06.750245Z",
+     "iopub.status.idle": "2024-04-19T17:42:06.754252Z",
+     "shell.execute_reply": "2024-04-19T17:42:06.753499Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit.quantum_info import PauliList\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
-    "observables_0 = PauliList([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])"
+    "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])"
    ]
   },
   {
@@ -123,11 +142,18 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "22f19d29-a182-4758-b5d0-6b66f9a946be",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:06.757184Z",
+     "iopub.status.busy": "2024-04-19T17:42:06.756691Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.216831Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.215960Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1290.83x702.333 with 1 Axes>"
       ]
@@ -163,19 +189,26 @@
    "id": "088431d9-de2f-4b5d-90d2-7e7a5947dcb5",
    "metadata": {},
    "source": [
-    "### Create observables to go with the new circuit\n",
+    "### Create observable to go with the new circuit\n",
     "\n",
-    "These observables correspond with `observables_0`, but we must account correctly for the extra qubit wire that has been added (i.e., we insert an \"I\" at index 4).  Note that in Qiskit, the string representation qubit-0 corresponds to the right-most Pauli character."
+    "This observable corresponds with `observable`, but we must account correctly for the extra qubit wire that has been added (i.e., we insert an \"I\" at index 4).  Note that in Qiskit, the string representation qubit-0 corresponds to the right-most Pauli character."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "id": "d33d5580-879f-466f-ac87-dcc6a19fbab6",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.220100Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.219501Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.224077Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.223355Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "observables_1 = PauliList([\"ZIIIIIII\", \"IIIIZIII\", \"IIIIIIIZ\"])"
+    "observable_expanded = SparsePauliOp([\"ZIIIIIII\", \"IIIIZIII\", \"IIIIIIIZ\"])"
    ]
   },
   {
@@ -192,13 +225,20 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "fc3738c7-2bb2-4d67-ae2b-7a3090b31e6a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.226529Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.226328Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.239655Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.238873Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.cutting import partition_problem\n",
     "\n",
     "partitioned_problem = partition_problem(\n",
-    "    circuit=qc_1, partition_labels=\"AAAABBBB\", observables=observables_1\n",
+    "    circuit=qc_1, partition_labels=\"AAAABBBB\", observables=observable_expanded.paulis\n",
     ")\n",
     "subcircuits = partitioned_problem.subcircuits\n",
     "subobservables = partitioned_problem.subobservables\n",
@@ -217,7 +257,14 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "9c53a862-f762-471a-bda7-b27e88292ac9",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.242366Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.241966Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.246915Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.246177Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -239,11 +286,18 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "69df912e-6709-45bb-8eb3-c66a70edefdc",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.249463Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.249031Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.535187Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.534423Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0IAAAEvCAYAAABoq3HZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABPCklEQVR4nO3deVxWdf738dcFyCa4IO4UJIsL7guilkZp2qKpU6Nli2ZWlumU6f3LnGruJndnHKsx2/WuKSetfpppVGZumTjuJiooJQoligiKrNf9xxlIEpQLr+s6F9f1fj4ePpRzneVzDsfrnPc53/M9FqvVakVERERERMSDeJldgIiIiIiIiLMpCImIiIiIiMdREBIREREREY+jICQiIiIiIh5HQUhERERERDyOgpCIiIiIiHgcBSEREREREfE4CkIiIiIiIuJxFIRERERERMTjKAiJiIiIiIjHURASERERERGPoyAkIiIiIiIeR0FIREREREQ8joKQiIiIiIh4HAUhERERERHxOApCIiIiIiLicRSERERERETE4ygIiYiIiIiIx1EQEhERERERj6MgJCIiIiIiHkdBSEREREREPI6CkIiIiIiIeBwFIRERERER8TgKQiIiIiIi4nEUhERERERExOMoCImIiIiIiMdREBIREREREY+jICQiIiIiIh5HQUhERERERDyOgpCIiIiIiHgcBSEREREREfE4CkIiIiIiIuJxFIRERERERMTjKAiJiIiIiIjHURASERERERGPoyAkIiIiIiIeR0FIREREREQ8jo/ZBYj9WK1QWmR2FbbxqgMWi9lViIiIiIinURByI6VF8O1Cs6uwTcJE8PY1uwoRERER8TRqGiciIiIiIh5HQUhERERERDyOgpCIiIiIiHgcBSEREREREfE4CkIiIiIiIuJxFIRERERERMTjKAiJiIiIiIjH0XuEhN2p63nm9YQKw/x96xLWOIb+Xe9naJ8n8fbWriIiIiIi7kNnt1IuofM9xLW5DStWsnMz+eo/S3l91dP8/OsBnrrrDbPLExERERGxGwUhKRfdsiv9u91X/vPg3o8zdk4b1mx7izGDXqZBUGMTqxMRERERsR89IyRVCvCtS5vweKxWKydOpZpdjoiIiIiI3SgIVSExMZFBgwbRqFEjAgMD6dChAzNnzqSwsNDs0pwq478BqF5giMmViIiIiIjYj4JQJebPn8/AgQP58ssvCQ4OpnXr1iQnJzNt2jQSEhLIz883u0SHuFB0npxzWZzJO8nRjL0s/OQJUo7vpM01cYQ1jjG7PBERERERu7FYrVar2UW4km3bthEfHw/Au+++y4MPPghAamoqgwYNIiUlhUmTJrFgwQITq6xcSSF8u9D26SrrNa7M9e2H8+Sw1wip1+wqq6tcwkTw9nXIrO2mpBQuFIGPN/h6g8VidkUiIiLOVVQChcXg52McD0XcgTpL+J2XXnoJq9XKQw89VB6CACIjI3n77bfp168fixYtYtq0aTRp0sTESu3v9p6P0Lfj3RSXFnE0Yy/L1s8mKycd3zr+5eMUFhfw+IKuJHS5l1E3P1c+fM5HozmT9wszHl5jRul2Z7VCyi+w6RDsTYfS/14uaBgIvaMhPgqC/S8/DxERkdqsoBh2pBnHwuPZvw2PbAI3xECHa8BbbYukFnP73TcrK4upU6cSFRWFv78/11xzDZMmTeLcuXOMHTsWi8XCq6++CkBubi6JiYkAjBs37pJ59e3bl5iYGAoLC1m5cqVT18MZWoZG0zWmP3FtbmVEwlReGrOKg+lJ/GPFY+Xj+Pr4MXXkUj76ZgapJ3YDsHnfZ2w9sIqn737brNLt6kIRvP4tvPYN7D72WwgCyD4Pq3fDXz41Dg4iIiLu6OdT8Nf/hWU/VAxBAKm/wnubYPZqOJVnTn0i9uDWQWjXrl106NCBuXPnkpmZSbt27SgqKmLhwoWMGDGCAwcOANC5c2cAdu7cSWFhIX5+fnTv3r3SeV5//fUAbN261SnrYKbYiN7073o/63cvY3/alvLhMWHduKvfM8z56AFOnklnwfJHeHLYa4TWb2FitfZRWAyvr4ODGZcfr7gUlm5WGBIREfdz7DS8+jXkXrj8eL+ehYWJcOa8c+oSsTe3DUJZWVkMHjyYzMxMJk+eTEZGBjt27CAzM5PZs2ezevVqkpKSsFgsdOzYEYBDhw4BEB4ejo9P5a0GIyMjK4zr7kb1/zNeXt4s+fL53w2fjreXD+MXdKFTVAIJnUeaVKF9Je6DtKzqj/+v7yHvCgcKERGR2qLUCks2GRcGqyMnHz5y/2vD4qbcNghNnDiR9PR0JkyYwLx58wgODi7/bOrUqXTq1Ini4mIiIiKoV68eANnZxr3fhg0bVjnfss/KxnV3LUOjSOg0kp0p37D3yMby4T7edWgX0Zucc1kM7D7GxArtp6gEvk+xbZriUtiqVyyJiIibOJgBWbm2TZNcg2lEXIFbBqEDBw6wbNkyQkNDmTlzZqXjdOvWDYBOnTqVD7twwbi07+tbdTdmfn5+AJd0oX306FGGDBlCcHAwDRs25IEHHuDUqVNXtR6u4p6bn8PL4sWSxN/uCu09spHE7e9xZ58J/HPlJAqKan+X4nuOwbkC26fbctj+tYiIiJihpse0LTZeSBRxBW7Za9yHH35IaWkpo0aNIigoqNJxAgICgIpByN/f6Absci9NLSgoqDA9GJ0sJCQkEBISwocffkh+fj5Tp07ljjvuYPPmzXh51Sxvdu/enczMzGqP7+sTwBsTbP8G6xR5I1/NrboX9fCmbflyTkn5z/kFecxdNpqxt85icK/xTH69H++smcb4IX+3ednRMdEUFrtGiIq9ZQptb55k83Snz8G1EVGUFquNnIiI1G4Dn9lAcONWNk/3/vKveHywe7QQkdqlWbNmbN++vUbTumUQWrduHQAJCZW/GwcgPT0dqBiEqtPsrbLmc2+88QbHjx9nw4YNXHvttQCEhYXRu3dvVq5cydChQ2u0HpmZmRw/frza4/vXCazRcmy1eNVkmoVcx5Dej2OxWJjyx/d4bEFn+rQfRsdWfW2aV8aJE1woco2nLK+7UHUAvpJfT56i4PwZ+xUjIiJiAqulZi8JKi7BpnMWEVfglkHop59+AoxODypTXFzM5s2bgYpBKCYmpnz64uLiSjtMSE1NrTAuwOeff871119fHoIAevXqRatWrVi1alWNg1CzZra9xNTXJ+DKI12lbclrWL97GW88vQfLf98s2iI0krG3zmLesjEsnryHAN+61Z5f8xYtXOaOkK+lqEbTlRYXEhoSBA2rv94iIiKuqORCTs0mLD5Hy5Yt7VuMSDXYer58MbcMQufOnQMufY6nzLJly8jKyiI4OJjrrruufHiXLl3w9fWloKCA7du3Ex8ff8m0mzZtAqBnz57lw3788UfuvvvuS8aNjY3lxx9/rPF62Hqbr6QQvl1Y48VVS1ybW/nspTOXDL+zzxPc2ecJm+d3+NBhvKt+JMupTp6Fl1fZPl23SF8WHjtm/4JERESc7Kt9xvvybPXCxKF0mjfU7vWIOJJbdpZQlgx37NhxyWcZGRlMmTIFgI4dO5bf1QAIDg5mwIABALz55puXTLthwwYOHTqEr68vd955Z/nw7OxsGjRocMn4ISEhnD59+qrWRZyncT1o09z26a6Ptn8tIiIiZoiPAm8bzw7rB0D7MMfUI+JIbhmE+vfvD8Ds2bMrvO8nKSmJhIQEsrKMF8WUvUj1YtOnT8disfDuu++yZMmS8uGpqamMHTsWgEcffZQmTZo4cA3ELIM62nYAaNMcWmlXEBERNxHsDwltbZvmtk62hycRV+CWu+3UqVNp1KgRx44dIzY2lg4dOhAdHU1cXBytWrXipptuAio+H1QmPj6eWbNmYbVaGT16NBEREXTp0oU2bdqQkpJCz549mTVrVoVpGjZsyJkzZy6Z1+nTpwkJCXHIOopjRITCg9dX7wu9VWMYfQNcdFNRRESk1rutE8RVs+O42zpBz0jH1iPiKG4ZhMLCwti4cSO33347/v7+pKWlERISwuLFi1m9enX5XaLKghAYQWrt2rUMGDCAnJwckpOTiYmJ4eWXX+a7774jMLBi72xt27at9FmgH3/8kbZtbbysIqbreA08OaDqZnJB/nBLexh/M/jXcW5tIiIijuZlgXvi4a4e0Di48nGubQRjbjCOhyK1lcVqtVb9Ahk3lJeXR7169bBYLOTm5l4Sampi3rx5TJs2jSNHjhAWZjSS/eGHH4iPj+eTTz5h2LBhV72M6nBGZwn2ljARl+ksoTJZucaLVr/cCwXFEFAHXvoD+NSsd1EREZFapdQKhzPhnQ3GcdDPB57obwQhkdrOLe8IXc7+/fuxWq1ER0fbJQQBPPLIIzRv3pw777yTzz//nOXLl3PPPfcQFxdXoVMFqX1Cg+Gmdr/d+fH1UQgSERHP4WWB1s1/Ow7611EIEvfhcUFo7969QNXN4mqiXr16rFu3jubNmzNy5Egefvhhevfuzeeff46Xl3ts4g17lvOPFeMrDFub9C4DpljYvO8zc4oSEREREakht3yP0OU4IggBREZG8vnnn9t1nq5k875P6d/tgfKfM0+nseaHN2l77aXvWhIRERERcXUKQgJAXv4Zxs1vT0FRPo3rX0NRSQGZp45wc7f7mTR8EfvTNjNlxHsAlJaW8rePH+aJoa+weNVkcwsXEREREakBjwtC69atM7sElxQU0ICbOt9LgF8w9w34M0kHv+TDdTOYfPdbbD+YSLvw3vh4Gw2EV2z4G7ERfYgJ62Zy1SIiIiIiNeMeD7CIXaSc2EVUyy4AHE7/D1EtjH9v2f8ZfdobPd8dzdzHxr0rGNV/uml1ioiIiIhcLY+7IyRVO/K7INQrdghWq5XtB79k3O1zANh3ZCO/ZKcxenY0AKdzM1mw/BFOn81gcO/xVc5bRERERMSVKAgJAFk5x8FiIbR+SwCOZO7h3pufI/nYNq5t2pYAvyAABvceXyHwTF50I8Nv+BN92g81o2wRERERkRpREBIAUo7vLG8KBxDk34CV3/+T+nVD6R071LzCREREREQcQEFIAIhvdwfx7e4o//m1SUkAPDwvlrmPfVvldPPHr3d0aSIiIiIidqcgJJf11jP7zS5BRERERMTu1GuciIiIiIh4HAUhERERERHxOApCIiIiIiLicfSMkBvxqgMJE82uwjZedcyuQEREREQ8kYKQG7FYwNvX7CpERERERFyfmsaJiIiIiIjHURASERERERGPoyAkIiIiIiIeR0FIREREREQ8joKQiIiIiIh4HAUhERERERHxOApCIiIiIiLicRSERERERETE4ygIiYiIiIiIx1EQEhERERERj6MgJCIiIiIiHkdBSEREREREPI6CkIiIiIiIeBwFIRERERER8TgKQiIiIiIi4nEUhERERERExOMoCImIiIiIiMdREBIREREREY/jY3YBYj9WK5QWmV2FbbzqgMVidhUiIiIi4mkUhNxIaRF8u9DsKmyTMBG8fc2uQkREREQ8jZrGiYiIiIiIx1EQEhERERERj6MgJCIiIiIiHkdBSEREREREPI6CkIiIiIiIeBwFIRERERER8TgKQiIiIiIi4nH0HiFhd+p6nnk9ocIwf9+6hDWOoX/X+xna50m8vbWriIiIiIj70NmtlEvofA9xbW7DipXs3Ey++s9SXl/1ND//eoCn7nrD7PJEREREROxGQUjKRbfsSv9u95X/PLj344yd04Y1295izKCXaRDU2MTqRERERETsR88IVSExMZFBgwbRqFEjAgMD6dChAzNnzqSwsNDs0pwmwLcubcLjsVqtnDiVanY5IiIiIiJ2oyBUifnz5zNw4EC+/PJLgoODad26NcnJyUybNo2EhATy8/PNLtFpMv4bgOoFhphciYiIiIiI/SgI/c62bduYMmUKFouF9957j7S0NHbu3ElycjJRUVFs2bKFZ5991uwyHeJC0XlyzmVxJu8kRzP2svCTJ0g5vpM218QR1jjG7PJEREREROxGzwj9zksvvYTVauWhhx7iwQcfLB8eGRnJ22+/Tb9+/Vi0aBHTpk2jSZMmJlZqf0sTX2Bp4gsVhl3ffjhPDnvNpIrEbCdzYWsKZOVCiRWC/aFrOEQ1BYvF7OpEREQcq9QKhzJh10+QVwDeXtAkGOKjoFGQ2dXJ1XL7O0JZWVlMnTqVqKgo/P39ueaaa5g0aRLnzp1j7NixWCwWXn31VQByc3NJTEwEYNy4cZfMq2/fvsTExFBYWMjKlSuduh7OcHvPR5g97iteHvsFD982m+DAELJy0vGt418+TmFxAQ/Pi+WDb16uMO2cj0Yz7a1bnV2yOMipPHh9Hby8Er75EXYfg33p8H0KvPYNzPocfjxudpUiIiKOs/tnmLnKOB5uTTWOg7t/hq/2w1//F95cD2fOm12lXA23DkK7du2iQ4cOzJ07l8zMTNq1a0dRURELFy5kxIgRHDhwAIDOnTsDsHPnTgoLC/Hz86N79+6VzvP6668HYOvWrU5ZB2dqGRpN15j+xLW5lREJU3lpzCoOpifxjxWPlY/j6+PH1JFL+eibGaSe2A3A5n2fsfXAKp6++22zShc7ysyBv38JyRlVj/PLWeMAsO2I08oSERFxmk2H4N2NRsuIyliB/cdhwZdGqwmpndw2CGVlZTF48GAyMzOZPHkyGRkZ7Nixg8zMTGbPns3q1atJSkrCYrHQsWNHAA4dOgRAeHg4Pj6VtxqMjIysMK47i43oTf+u97N+9zL2p20pHx4T1o27+j3DnI8e4OSZdBYsf4Qnh71GaP0WJlYr9nChCBZ/C3kXrjyuFfhoKxw96fCyREREnOZgBqxIqt64Z87DG+uhuMShJYmDuG0QmjhxIunp6UyYMIF58+YRHBxc/tnUqVPp1KkTxcXFREREUK9ePQCys7MBaNiwYZXzLfusbFx3N6r/n/Hy8mbJl8//bvh0vL18GL+gC52iEkjoPNKkCsWe/nMUss9Vf/xSq9F0TkRExF18vd+42Fddv56FXT87rBxxILcMQgcOHGDZsmWEhoYyc+bMSsfp1q0bAJ06dSofduGCcRnc19e3ynn7+fkBVOhCuyxwxcXF4efnh8WNniJvGRpFQqeR7Ez5hr1HNpYP9/GuQ7uI3uScy2Jg9zEmVij2YrXCpsO2T7f/OJzOs389IiIizpaZA4d/sX26ze7fUMgtuWUQ+vDDDyktLWXUqFEEBVXepUdAQABQMQj5+xudAlzupakFBQUVpgdISUlhxYoVNGvWjB49elx1/a7mnpufw8vixZLE3+4K7T2ykcTt73Fnnwn8c+UkCoo8591K7upsPmScsX06qxUOZtq9HBEREae73POxl3M0y2heLrWLW3afvW7dOgASEhKqHCc9PR2oGISq0+ytsuZzffv2JSPD+J/z4osvsnnz5hpWXlH37t3JzKz+GaavTwBvTLD9kn6nyBv5am7VN4HDm7blyzm/NX7NL8hj7rLRjL11FoN7jWfy6/14Z800xg/5u83Ljo6JprDY9UPUbdOSCKzfnIzMDMLC3C/sAgQ3iWLg5PU1mnban/8vhze+Ydd6RETEdXjCcRCgbf+niB0wuUbTdujcg/ycGiYpqbFmzZqxffv2Gk3rlkHop59+AoxODypTXFxcHlYuDkIxMTHl0xcXF1faYUJqamqFcQG8vBxzYy0zM5Pjx6vfR7F/nUCH1PF7i1dNplnIdQzp/TgWi4Upf3yPxxZ0pk/7YXRs1demeWWcOMGFItfve7KkpKT8b1t+J7VJ8AXvGk976tcTbrtdRETEM46DAE2zat7E4dhPqRSc84xnyN2FWwahc+eMp70vfo7nYsuWLSMrK4vg4GCuu+668uFdunTB19eXgoICtm/fTnx8/CXTbtq0CYCePXs6oPKKmjVrZtP4vj4BVx7pKm1LXsP63ct44+k95c9CtQiNZOyts5i3bAyLJ+8hwLdutefXvEWLWnFHyNvbu/zvli1bmlyNY1i8ID8nk4D61d/vrFYrFouF0tw0t90uIiLiGcdBgJKco8Bvx7fqys06SmjDutDAORel5Te2ni9fzC2DULNmzcjOzmbHjh306tWrwmcZGRlMmTIFgI4dO1bYyYODgxkwYACrV6/mzTffvCQIbdiwgUOHDuHr68udd97p8PWw9TZfSSF8u9BBxfxXXJtb+eylM5cMv7PPE9zZ5wmb53f40GG8q+6bwmW88Ank5EPzZs3Lm1W6ozV74Mu91R/fYrEQ3gj2bVvruKJERMR0nnIctFph9mrIzLGt46v7brmOtycdc1BV4ihu2VlC//79AZg9e3aF9/0kJSWRkJBAVlYW8NuLVC82ffp0LBYL7777LkuWLCkfnpqaytixYwF49NFHadKkiQPXQMQcvaLA18YWcv3aOKYWERERZ7NYoG9r26bxrwNxrRxTjziWWwahqVOn0qhRI44dO0ZsbCwdOnQgOjqauLg4WrVqxU033QRUfD6oTHx8PLNmzcJqtTJ69GgiIiLo0qULbdq0ISUlhZ49ezJr1ixnr5KIUzQIhAeuB69qXgjr2xq6VP4onoiISK3UK6r6wcbbCx7qC3X9HFuTOIZbBqGwsDA2btzI7bffjr+/P2lpaYSEhLB48WJWr15dfpeosiAERpBau3YtAwYMICcnh+TkZGJiYnj55Zf57rvvCAxU+09xX+3DYNyNEHSZL3VvLxjUAYZ1M66eiYiIuAuLBUbGw01tL39hsH4APH4TxNT8ERUxmVs+IwTQtm1bPv/880uG5+XlkZaWhpeXF+3bt69y+oEDBzJw4EBHlijistq2gBeHwe6f4ftUSP3VaDftZYHbOkHPSAj2N7tKERERx/CywJCu0K8tbE2BXT8ZL1u1Aj5ecF8f6BBmXBiU2sttg1BV9u/fj9VqJSYmxq53dpYvXw7Ajz/+WOHniIgIunfvbrfliDiLjzd0u874U/aQbLA/9I81uzIRERHnqB8AAzsYf8qOhXX9oPO1Zlcm9uBxOXbvXqNLrKqaxdXU3Xffzd13383HH39c4edXX33Vrssxy4Y9y/nHivEVhq1NepcBUyxs3veZOUWJiIiIiNSQx90RclQQslqtdp2fq9m871P6d3ug/OfM02ms+eFN2l576buWRERERERcnYKQAJCXf4Zx89tTUJRP4/rXUFRSQOapI9zc7X4mDV/E/rTNTBnxHgClpaX87eOHeWLoKyxeNdncwkVEREREasDjgtC6devMLsElBQU04KbO9xLgF8x9A/5M0sEv+XDdDCbf/RbbDybSLrw3Pt51AFix4W/ERvQhJqybyVWLiIiIiNSMxz0jJFVLObGLqJZdADic/h+iWhj/3rL/M/q0HwbA0cx9bNy7glH9p5tWp4iIiIjI1fK4O0JStSO/C0K9YodgtVrZfvBLxt0+B4B9RzbyS3Yao2dHA3A6N5MFyx/h9NkMBvceX+W8RURERERciYKQAJCVcxwsFkLrtwTgSOYe7r35OZKPbePapm0J8AsCYHDv8RUCz+RFNzL8hj/Rp/1QM8oWEREREakRBSEBIOX4zvKmcABB/g1Y+f0/qV83lN6xQ80rTERERETEARSEBID4dncQ3+6O8p9fm5QEwMPzYpn72LdVTjd//HpHlyYiIiIiYncKQnJZbz2z3+wSRERERETsTr3GiYiIiIiIx1EQEhERERERj6MgJCIiIiIiHkdBSEREREREPI46S3AjXnUgYaLZVdjGq47ZFYiIiIiIJ1IQciMWC3j7ml2FiIiIiIjrU9M4ERERERHxOApCIiIiIiLicRSERERERETE4ygIiYiIiIiIx1EQEhERERERj6MgJCIiIiIiHkdBSEREREREPI6CkIiIiIiIeBwFIRERERER8TgKQiIiIiIi4nEUhERERERExOMoCImIiIiIiMdREBIREREREY+jICQiIiIiIh5HQUhERERERDyOgpCIiIiIiHgcBSEREREREfE4PmYXIPZjtUJpkdlV2MarDlgsZlchIiIiIp5GQciNlBbBtwvNrsI2CRPB29fsKkRERETE06hpnIiIiIiIeBwFIRERERER8TgKQiIiIiIi4nEUhERERERExOMoCImIiIiIiMdREBIREREREY+jICQiIiIiIh5H7xESdqeu55nXEyoM8/etS1jjGPp3vZ+hfZ7E21u7ioiIiIi4D53dSrmEzvcQ1+Y2rFjJzs3kq/8s5fVVT/Pzrwd46q43zC5PRERERMRuFISkXHTLrvTvdl/5z4N7P87YOW1Ys+0txgx6mQZBjU2sTkRERETEfvSMUBUSExMZNGgQjRo1IjAwkA4dOjBz5kwKCwvNLs1pAnzr0iY8HqvVyolTqWaXIyIiIiJiNwpClZg/fz4DBw7kyy+/JDg4mNatW5OcnMy0adNISEggPz/f7BKdJuO/AaheYIjJlYiIiIiI2I+C0O9s27aNKVOmYLFYeO+990hLS2Pnzp0kJycTFRXFli1bePbZZ80u0yEuFJ0n51wWZ/JOcjRjLws/eYKU4ztpc00cYY1jzC5PRERERMRu9IzQ77z00ktYrVYeeughHnzwwfLhkZGRvP322/Tr149FixYxbdo0mjRpYmKl9rc08QWWJr5QYdj17Yfz5LDXTKpIxHz5hfDzKSgoBj8fuCYEAv3MrkpERMR5snLh17NQUgpB/hDeCLzc4HaK2wehrKws5syZwyeffEJ6ejqNGzdm+PDhzJgxg4kTJ/LOO+/wyiuvMGHCBHJzc0lMTARg3Lhxl8yrb9++xMTEcOjQIVauXMnDDz/s7NVxqNt7PkLfjndTXFrE0Yy9LFs/m6ycdHzr+JePU1hcwOMLupLQ5V5G3fxc+fA5H43mTN4vzHh4jRmli9hdZg5sSIbtaVBY/NvwOt7QNQL6toaWDc2qTkRExLGsVtibDpsOwaHMip81DITe0cafurX44qAbZLmq7dq1iw4dOjB37lwyMzNp164dRUVFLFy4kBEjRnDgwAEAOnfuDMDOnTspLCzEz8+P7t27VzrP66+/HoCtW7c6ZR2cqWVoNF1j+hPX5lZGJEzlpTGrOJiexD9WPFY+jq+PH1NHLuWjb2aQemI3AJv3fcbWA6t4+u63zSpdxK52/QzzvoAtKRVDEEBRCfyQCvPXQNIRc+oTERFxpJJS+HArvLPh0hAEkH0eVu+Gv62Fk2edX5+9uG0QysrKYvDgwWRmZjJ58mQyMjLYsWMHmZmZzJ49m9WrV5OUlITFYqFjx44AHDp0CIDw8HB8fCq/WRYZGVlhXHcWG9Gb/l3vZ/3uZexP21I+PCasG3f1e4Y5Hz3AyTPpLFj+CE8Oe43Q+i1MrFbEPpIzYOkmKC69/HilVvjX97DnmHPqEhERcZYV22FbNS72ncqDf66Ds7W0HzG3DUITJ04kPT2dCRMmMG/ePIKDg8s/mzp1Kp06daK4uJiIiAjq1asHQHZ2NgANG1bd3qXss7Jx3d2o/n/Gy8ubJV8+/7vh0/H28mH8gi50ikogofNIkyoUsZ9SK3y8zfi7OqzA8iTjypmIiIg7+PkUbDlc/fGzz0HiPsfV40huGYQOHDjAsmXLCA0NZebMmZWO061bNwA6depUPuzChQsA+Pr6VjlvPz+jIeTFXWgvX76cP/zhD4SHhxMYGEibNm147rnnyMvLu+p1MVvL0CgSOo1kZ8o37D2ysXy4j3cd2kX0JudcFgO7jzGxQhH7OZhhXN2yxdl8ow21iIiIO9hUg0ZPSUegoMj+tTiaWwahDz/8kNLSUkaNGkVQUFCl4wQEBAAVg5C/v9EpwOVemlpQUFBheoB58+bh7e3NjBkzWLNmDePHj2fRokUMGjSI0tLaf6n4npufw8vixZLE3+4K7T2ykcTt73Fnnwn8c+UkCopq6T1RkYtUpxlAZfSskIiIuIPiEtj5k+3TFRTXzqbibtlr3Lp16wBISEiocpz0dOMS7sVBqDrN3iprPrdq1SoaN25c/nO/fv1o3Lgxo0aNYtOmTfTt27cGawHdu3cnM7OSJ9Sq4OsTwBsTbLiX+V+dIm/kq7lVtwUKb9qWL+eUlP+cX5DH3GWjGXvrLAb3Gs/k1/vxzpppjB/yd5uXHR0TTWGx64eo26YlEVi/ORmZGYSF9TC7HKfzlPW/cfynhEbYvn6bk/bzl/sGOqAiERHX4CnHgcvxhG3gX68Zdzy3vUbTPvvCLJK/fdXOFV1Zs2bN2L69ZjW7ZRD66ScjyoaHh1f6eXFxMZs3bwYqBqGYmJjy6YuLiyvtMCE1NbXCuECFEFSmrNe548eP12QVAMjMzLRpev86gTVeli0Wr5pMs5DrGNL7cSwWC1P++B6PLehMn/bD6NjKttCXceIEF4rOO6hS+ykpKSn/+2p+p7WVp6x/4X/v+No8XWGhW28XERFPOQ5cjidsg7rnat6SKScnp9ZtF7cMQufOnQMqPsdzsWXLlpGVlUVwcDDXXXdd+fAuXbrg6+tLQUEB27dvJz4+/pJpN23aBEDPnj0vW8O3334LQNu2bWu0DmAkXFv4+gRceaSrtC15Det3L+ONp/dgsVgAaBEaydhbZzFv2RgWT95DgG/das+veYsWteKOkLe3d/nfLVu2NLka5/OU9S/Oz6rZdOdPuvV2ERHxlOPA5XjCNvDy9qW4MB8fX9vPKetYz5myXWw9X76YxWq1VrN/pNqjXbt2HDhwgFdffZUnnniiwmcZGRl069aNjIwM+vTpUx5sytxxxx2sXr2ahx56iLffrvhenA0bNtCvXz98fX05duwYTZo0qXT5x48fp0uXLnTr1o01a5z3gtGSQvh2odMWZxcJE8G76r4pXMYLn0BOPtQPgL8MN7sa5/OU9U/OgNfX2T7dmBug07X2r0dExFV4ynHgcjxlG3y0Fbam2jaNfx34yzDwq+OYmhzFLTtL6N+/PwCzZ8+u8L6fpKQkEhISyMoyrvqWvUj1YtOnT8disfDuu++yZMmS8uGpqamMHTsWgEcffbTKEJSXl8edd96Jr68v77zzjr1WSUScIKYZhAZfebyL1Q+A9mGOqUdERMTZ+sRceZzf69Gq9oUgcNMgNHXqVBo1asSxY8eIjY2lQ4cOREdHExcXR6tWrbjpppuAis8HlYmPj2fWrFlYrVZGjx5NREQEXbp0oU2bNqSkpNCzZ09mzZpV6XLz8/MZPHgwR48eJTExkebNmzt0PUXEvrws8Mc44+/qsGCM7+2W36QiIuKJrgmBG2wIQ42C4Jb2jqvHkdzy8B0WFsbGjRu5/fbb8ff3Jy0tjZCQEBYvXszq1avL7xJVFoTACFJr165lwIAB5OTkkJycTExMDC+//DLfffcdgYGXdkpQVFTEXXfdxfbt21mzZg3t2rVz6DqKiGPENDOauvl4X348by+4vw/E6m6QiIi4mWHdoFfUlcdrHAzjb4Jgf8fX5Ahu2VkCGJ0UfP7555cMz8vLIy0tDS8vL9q3rzq+Dhw4kIEDq9cdbtk7i7755hu++OIL4uLialy3iJivwzUw9TbYeNB4t1BBccXPe0fBDa2heQNTyhMREXEoLy+jxUP7lrDxkPEM7cUaBUGfaIiPgsBa8Kx3Vdw2CFVl//79WK1WYmJiKr2zUxNPPPEEH3/8Mf/zP/9DYGAgW7duLf8sMjKy0u61RcS1NakHf+gBd3SGY6fhnQ1wvhDq+cMfL99ppIiISK1nsRitHmLD4HQezF8D5wqhrh88N6T6zchdmVs2jbucvXv3AlU3i6uJsp7hZs2aRa9evSr8Wb16td2WY6YNe5bzjxXjKwxbm/QuA6ZY2LzvM3OKEnECvzoQ1RTq/LepnMUNvvhFRERsERL0W5NxHy/3CEHggXeEHBGE0tLS7DYvV7V536f07/ZA+c+Zp9NY88ObtL320nctiYiIiIi4OgUhASAv/wzj5renoCifxvWvoaikgMxTR7i52/1MGr6I/WmbmTLiPcB4JupvHz/ME0NfYfGqyeYWLiIiIiJSAx4XhNatq8HbEj1AUEADbup8LwF+wdw34M8kHfySD9fNYPLdb7H9YCLtwnvj4210EL9iw9+IjehDTFg3k6sWEREREakZj3tGSKqWcmIXUS27AHA4/T9EtTD+vWX/Z/RpPwyAo5n72Lh3BaP6TzetThERERGRq+Vxd4Skakd+F4R6xQ7BarWy/eCXjLt9DgD7jmzkl+w0Rs+OBuB0biYLlj/C6bMZDO49vsp5i4iIiIi4EgUhASAr5zhYLITWbwnAkcw93HvzcyQf28a1TdsS4BcEwODe4ysEnsmLbmT4DX+iT/uhZpQtIiIiIlIjCkICQMrxneVN4QCC/Buw8vt/Ur9uKL1jh5pXmIiIiIiIAygICQDx7e4gvt0d5T+/NikJgIfnxTL3sW+rnG7++PWOLk1ERERExO4UhOSy3npmv9kliIiIiIjYnXqNExERERERj6MgJCIiIiIiHkdBSEREREREPI6CkIiIiIiIeBx1luBGvOpAwkSzq7CNVx2zKxARERERT6Qg5EYsFvD2NbsKERERERHXp6ZxIiIiIiLicRSERERERETE4ygIiYiIiIiIx1EQEhERERERj6MgJCIiIiIiHkdBSEREREREPI6CkIiIiIiIeBwFIRERERER8TgKQiIiIiIi4nEUhERERERExOMoCImIiIiIiMdREBIREREREY+jICQiIiIiIh5HQUhERERERDyOgpCIiFTLmTNnePHFF1m/fr3ZpdQaOTk5PPnkk7Rs2RJ/f39iY2NZtGgRVqvV7NJqRPuAbTIyMnjuuecYNGgQjRs3xmKxMHr0aLPLEpH/UhASEZFqOXPmDH/5y190ElxNhYWFDBgwgNdff50RI0bwyiuv0Lp1ax5//HH+8pe/mF1ejWgfsM3BgweZMWMGP/74Iz169DC7HBH5HR+zCxAREXFHb731FklJSSxcuJAnn3wSgHHjxvGHP/yBGTNmMGbMGMLDw02uUhypW7du/PrrrzRu3JisrCwaN25sdkkichHdEXIjViuUFNauP7W0dYhIrVBYWMicOXPo3LkzgYGB1K9fn+7du/Pqq6+WjzN69GgsFkul01/cjGf9+vVcd911APzlL3/BYrFgsViIiIiwqaayea5bt45evXoRGBhIWFgYs2fPBiA7O5uxY8fSpEkTAgMDueOOOzhx4sQl80lLS+P++++nadOm+Pn5ERkZybRp0zh//nz5OIsWLcJisbBy5cpLpi8tLSUsLIzOnTtXGL59+3aGDRtGaGgofn5+tG7dmpdffpni4mKb1hPgX//6F4GBgYwbN67C8D/96U8UFRWxbNkym+dpK+0D5u4DwcHBCj8iLkx3hNxIaRF8u9DsKmyTMBG8fc2uQsT9FBYWMnDgQNavX88tt9zCfffdh7+/P3v37uWTTz5hwoQJNs2vbdu2/P3vf+epp55i2LBhDB8+HICgoCCba9u5cyerVq3ikUce4YEHHuDf//43//M//4O/vz9LliwhIiKCF198kZSUFBYuXMgDDzzA119/XT79Tz/9RFxcHDk5OTz++ONER0ezfv16Zs6cyebNm/nmm2/w8fFh5MiRPPXUUyxdupQhQ4ZUqOGbb77h+PHjTJ48uXzY6tWrGT58OFFRUUyePJmQkBC+//57nn/+eXbt2sXHH39c7XUsLS1lx44ddO3aFX9//wqfxcXFYbFYSEpKsnnb2UL7gLn7gIi4PgUhERE3tGDBAtavX8+zzz7LjBkzKnxWWlpq8/yaNm3K0KFDeeqpp+jYsSP33XdfjWvbu3cv33//PT179gRg7NixhIeH89RTTzFhwgQWLqx4Refvf/87Bw8epHXr1gBMmzaNkydPsnr1am677TYAHn/8caZMmcK8efNYsmQJY8eOpWHDhgwePJhVq1aRnZ1Nw4YNy+e5dOlSfHx8GDVqFAAXLlxg7Nix9OzZk3Xr1uHjYxweH330UTp16sTTTz/N+vXrufHGG6u1jtnZ2eTn59OyZctLPvPz8yM0NJTjx4/btuFspH3A3H1ARFyfmsaJiLihDz74gIYNG/L8889f8pmXl7lf/b169So/AQbw9fUlLi4Oq9XKxIkTK4x7ww03AHD48GHAOIFfuXIlXbp0KT8BLvPss8/i5eXFp59+Wj7swQcfpKCgoEIztLy8PD799FMGDRpEkyZNAPjqq6/45ZdfGDNmDGfOnCErK6v8T9lyEhMTq72OZc2z/Pz8Kv3c39+/QhMuR9A+YDBrHxAR16c7QiIibujw4cN07tz5kmZZrqBVq1aXDCu7Ul/2DMrvh586dQqAkydPkpeXR2xs7CXzCAkJoXnz5hw5cqR8WNmJ7tKlS3nssccAWLFiBefOneOBBx4oH+/AgQMAPPTQQ1XW/csvv1Rr/QACAwMBKCgoqPTzCxculI/jKNoHDGbtAyLi+hSEREQ8WFUPydfkwfDq8vb2tvmzmr53x8fHh3vvvZcFCxaQkpJCVFQUS5cupWHDhhWeGSmb/9y5cy95eL5MixYtqr3chg0bEhAQUGnzt4KCArKysujXr59tK+Mg2gcqzt9e+4CIuD4FIRERNxQTE0NycjIFBQVVNs8C4wo6wOnTp8v/DVS4ol6mqhNmZ2rcuDHBwcHs37//ks+ys7PJyMi45CT2wQcfZMGCBSxdupRx48axfv16HnnkkQrbJTo6GoC6devSv3//q67Ty8uLrl27snPnzkt+B9u2bcNqtdK9e/erXs7laB/4jRn7gIi4Pj0jJFKJ3Avw43FI3Av/+h7OFxrD8wvh+xRIPw3FJebW6EhFJfDzKdh8CD7e9tv6ny+Er/fDwQw4V3mLH7dgtUJWLuz6CVbthPe3XLQPFMGONDiZ69rdv48aNYrs7Gz++te/XvLZxVfWY2JiACr0yAUwf/78S6Yr6x3s9OnT9izVJl5eXgwePJidO3eydu3aCp/NmjWL0tJShg0bVmF4586d6dixI++//z7/7//9P0pLS3nwwQcrjDNw4ECaNGnCrFmzKl2//Px8cnNzbar1nnvu4fz587zxxhsVhi9YsAAfHx9GjBhh0/xspX3gN2btA7VZqRV+OQv/OQr/u6PicWD1Ltj9M5zKc+3vwauVdwEOnICv9l16LrDlMBw75d7nAsUlxjpuOQzLf3cu8NU+SHaDcwHdERL5r+IS2HMMNh2CIycrH6ewBJb9YPzbvw7EtYI+MdC0nvPqdKT007D5sHHgK6zky72oBD7f9dvPbZrD9THQrgWY/Oy1XZwvgG1HjG1wsorzncJiWLrZ+HejIOgdBT0jIcjFHsOYNGkSq1at4q9//StJSUnccsst+Pv7s3//fg4ePFh+0nvPPfcwbdo0HnnkEZKTkwkJCWHt2rVkZWVdMs9GjRoRFRXFRx99RGRkJE2bNqVu3boMHjzYqes2Y8YMvvrqK4YOHcrjjz9OVFQUGzZsYNmyZfTt2/eSE1ww7ghMnjyZ2bNnExMTQ3x8fIXP69aty9KlSxk6dCitW7fmoYceIioqijNnzpCcnMwnn3zCp59+alOPYePGjePdd9/l6aefJi0tjbZt2/LFF1/w6aefMn36dJvfv2Mr7QMVmbEPAOVBtKxzjD179pQP69u3L3379q3BFnCcs/mwNQW2pMCZSvrzKCqBry66Gde0nnEc6H4dBLjB6zBKSmFfunEucLiKR8IKS+Df24x/+/lAj1bQJxqaN3BamQ6Vcca4EJp0FAoqaSFbVAKrd//2c3RTYx9oHwbetexcQEFI2J26nmdeT6gwzN+3LmGNY+jf9X6G9nkSb2/33VWsVtj5E3z2Hzh7ofrTXSiCDQeNPx2vgbt6QL0Ax9XpSFm5xpf6oUzbpkvOMP40CjLWv20tbT5fXGLc6frmR+MLvrpO5cGqXbBmD/RrA4M6Qp2qH31wKl9fXxITE5k/fz7/+te/mDZtGv7+/kRHRzNmzJjy8erVq8cXX3zB008/zYwZMwgKCmL48OG8//77FboaLvPBBx/w1FNPlb+4Mjw83OknweHh4fzwww88//zzvP/++5w5c4awsDCeffZZpk+fXt7t8cVGjRrF//k//4ezZ88yderUSuc7cOBAkpKSmDVrFu+//z4nT56kYcOGREZG8vTTT9OxY0eb6vT19eXrr79m+vTpfPjhh5w6dYrIyEheeeUVnnjiiRqtu63L1z7wGzP2AYA///nPFX7euXMnO3fuBOCFF15wmSBUWGyc3G46ZISB6vrlLKzYbnwX3tIeEtrWvpPhMnuPGetSWQCsSkGxsc02HYLYlnB3HDRwbD8oDpN9zmgF8uOl7y++rMO/GH8aBBrnAu3DHFOfI1isNX36UFxOSWHNXqhaFoQSOt9DXJvbsGIlOzeTr/6zlLTMfdzWcxxP3fXGlWdUA2a/UDX3gnG7d/exq59XoC8M725cFastSq3GVZ9Vu4yD4NWKj4Q7u9auq4LHs+GD7+FE9tXPq2k9uLcXhIde/bxERJwl9Vf48HvIyrv6eV3byPgebFb/6uflLOcK4JPt8J+0q5+Xfx0Y2g16tgIXeKSuWqxW+CEVPtthXOS9Wt0ijPOhulU/mugyFITcyNUGoUdun8vdNz5TPjy/8Bxj57Qh6+xx/v38LzQIamzHag1mBqGTufDPb4wrIPZ0czu4o7PrfwGWlBrN/LZd+jz0VWleHx67GerXgrtjPx6HdzfadhfoSry94IE+0Ola+81TRMRRth2Bj7YaF8bsxc8HHu4H0c3sN09HOZ0Hi9ZV3Ry6pm6IgWHdwcvFzwVKrfBJEmw6bN/5Ng6Gx2+GhnXtO197c9/2TnLVAnzr0iY8no17lnPiVKpDgpBZTuXBq19BTr795/3Nj8YXy5AurhuGSq3GgS/pqP3nnZEDr30NEwe43nMzFztwAt7eYFsTkOooKYX3NsGYG4wmk57g5MmTlJRcPk0GBQWVP2hfm+Xl5ZGXd/nL5t7e3jRu7D7fl9WhfaCi2rIPJB0xOgGwt4JieGM9PJYAkU3tP397OXMeXv0aTtv5gijAxkPGsfauHq57LmC1Gq1itqTYf94nc41tO3EA1HfhpoK1tBWn4yUmJjJo0CAaNWpEYGAgHTp0YObMmRQWFppdmlNlnEoFoF5gyBXGrD0Ki+GNbx0Tgsp8ewC2pjpu/lfr632OCUFlfj1rhIxSO4cMe/nlLLzjgBBUxmqFJZuMZneeoEePHjRv3vyyf+bNm2d2mXYxb968K65rjx49zC7T6bQP1L594OhJ+HCr4+ZfVAJvfmf/Vhf2UlIKb613TAgqs/kwfHfQcfO/Wt8lOyYElTmVZ+wDjjrW2oPuCFVi/vz5PPOM0UQsPDyca6+9ln379jFt2jQ+//xzvv76awICakG7HxtdKDpPzrksrFbjGaFV379OyvGdtLkmjrDGMWaXZzdr9hgnwrZ4epDREcLZfPjb2iuPD0bnC22au95t4ePZsHavbdPUZP2PnjQOAAltba/RkUpLjbbwtjaHs3UblPx3OU8Nqr0PDlfXBx98QH7+5a8stGrV6pJhycnJ1V7Gr7/+yr///W/++Mc/0qRJk2pP16ZNm2qPWx0PPPAA119//WXHccfjw5XUZB+w5fcPNdsH7P37B/fYBwqLjTtBtjSHq8lx4EKR0frgsZtc767IV/sg3caLVTXZBqt3GT2rNnGx3mV/OVux57fqqMn6p582OiMa2MH2Gp1BQeh3tm3bxpQpU7BYLLz77rvlXXCmpqYyaNAgtmzZwrPPPsuCBQvMLdQBlia+wNLEFyoMu779cJ4c9ppJFdlfWhast+3YCxj/8W3tBaag2PUOACWlth/8oGbrD/DFbmjfEhq70AFgw0FjP7BVTbZBerZrHwDspU+fPg5fxsmTJ3nttddISEiwKQjZW6tWrSoNdZ5O+0DtsnaP7c/E1PQ4cDDTaCHRK8r2aR3lRDYk7rN9uppsg6IS46LYk7e4zvNCpdaaXRCs6T6QuA86hEGLSzuhNJ2bX6e03UsvvYTVamXMmDEV3kMQGRnJ22+/DcCiRYv49ddfzSrRYW7v+Qizx33Fy2O/4OHbZhMcGEJWTjq+dX570KOwuICH58XywTcvV5h2zkejmfbWrc4u2WZf7XPuy98OZtbspNtR9h93bnOtohJYd8B5y7uSsm6ynenbA5W/h0FExAznCoznV5wpca9rNZX+ar99O4e4kqNZcNjG11M40iEnn5uUlBrPT7sitw9CWVlZTJ06laioKPz9/bnmmmuYNGkS586dY+zYsVgsFl599VUAcnNzSUxMBIwX4f1e3759iYmJobCwkJUrVzp1PZyhZWg0XWP6E9fmVkYkTOWlMas4mJ7EP1Y8Vj6Or48fU0cu5aNvZpB6wrinunnfZ2w9sIqn737brNKr5VSe0UuYs2128gHncjaZUMt/jv72Nmqz7f4Z8pz8FuwLRbAjzbnLFBGpyrYj9u0pszqyz9v+bhpHOZtvHAucbbOde2W7GmacC+z62Xhliatx6yC0a9cuOnTowNy5c8nMzKRdu3YUFRWxcOFCRowYwYEDxqXqzp07A8ZLzgoLC/Hz86N79+6VzrOsXfDWrQ58wtBFxEb0pn/X+1m/exn707aUD48J68Zd/Z5hzkcPcPJMOguWP8KTw14jtL5rv01zawqY0Vf8zp+NK3BmO5lr+wtT7aGwBLY7sGMGW5h1IHKlMCwinstqNe97cIuLBIEfUp17N6jM3nTIseFFrY5y5rzROsTZSkqNbe9q3DYIZWVlMXjwYDIzM5k8eTIZGRns2LGDzMxMZs+ezerVq0lKSsJisZS/KfrQIeNsJTw8vNK3UoPRRO7icd3dqP5/xsvLmyVfPv+74dPx9vJh/IIudIpKIKHzSJMqrL7Dv5iz3JJS12gel2LS+pu97DJFJeb9HtKzId9F7orVVvXr12fw4MHUr1+L3tIodqV94Orl5EOWnd+XU12pv5oTQH7PrOOR1WpsA7Ol/urcRwQu5grnAr/ntkFo4sSJpKenM2HCBObNm0dwcHD5Z1OnTqVTp04UFxcTERFBvXrGk9zZ2cbDEw0bVv00V9lnZeO6u5ahUSR0GsnOlG/Ye2Rj+XAf7zq0i+hNzrksBnYfY2KF1VNSam5XxsdOm7dsV6jBFdb/RLa5B+F0F9gGtVlYWBhz5swhLCzM7FLEJNoHrt6xU+Ytu6AYTtrYY6u9Wa3mHo9c4ThgZg3HTpsXwqrilkHowIEDLFu2jNDQUGbOnFnpON26dQOgU6dO5cMuXDAaL/r6+lY5bz8/P4AK3YRu3LiR/v3707x5c/z8/AgLC6vQ9K62u+fm5/CyeLEk8be7QnuPbCRx+3vc2WcC/1w5iYIiB76Uxw5O5jq/TfTFPP3LL/uc+c0Dbe0m1d5cIQzWZgUFBfz0008UFLhAO1MxhfaBq+fp34PZ58x9ZtXs7Q/mngucKzCa5rkSt+w++8MPP6S0tJRRo0ZV+Rbrsj7+Lw5C/v5G72iXe2lq2Rfwxe8IyM7OpkOHDjz66KM0adKE9PR0Zs6cSa9evdi3b1+Nr151796dzMzqP9Th6xPAGxNsb4TbKfJGvppbdUQPb9qWL+f8liLyC/KYu2w0Y2+dxeBe45n8ej/eWTON8UP+bvOyo2OiKSx2fIgKvS6eGx9bXulnZf3iX049/9/+fnFY1eNV1bf++k1JvDjqMhM6waCpmwlqFF7pZ1faBtVdf6h6G3TreQN5WeY9LNTmpom0Hzi10s+csQ/M+dtrPLC28gsznmr06NHVHjcjI4N33nmHhx56iObNm1d7uvfee8/2wsQpbPn9Q832Af3+K+p851+J6j260s+ccRx45n9eIGWzeR0rNWzZkZsnflHpZ844DmzbsZ8ZDw6sZrWO0f9PX9GgeeUv+HPGPnBDwiDOnKhB3+WX0axZM7Zv316jad0yCK1btw6AhISEKsdJT08HKgah6jR7q6z53JAhQxgyZEiF8Xr06EHr1q1ZsWIFkyZNsnENDJmZmRw/Xv0n2vzr1KBz9xpYvGoyzUKuY0jvx7FYLEz543s8tqAzfdoPo2OrvjbNK+PECS4UOf7ygFeDM1V+Zku/+F5eNetDv6QUm36XjmC9zA3g6m6Dmq4/QNapbE6ZuA2uOVd14HbGPnA+/4Lp+4CrOX+++v/3y+7YX7hwwabptM1dly2/R6jZPqDff0WtL1R9odcZx4Hcc+dN/Z2UBFxT5WdOORewWkzfJy/XjblTzgVOZ/OLC/2/dMsg9NNPPwFGpweVKS4uZvPmzUDFIBQTE1M+fXFxcaUdJqSmplYYtyqNGjUCqLLThepo1qyZTeP7+jj+Tdbbktewfvcy3nh6D5b/viW0RWgkY2+dxbxlY1g8eQ8BvnWrPb/mLVo45Y5QSIOq3+h5thqLr+dv/McvLYWzl+n+sap5eVtKadmy5ZUX5EjWql9mc6VtUN31v9y8GoU0wN9q3jaoG+hX5WfO2AcC/X3N3wdcTGBg9Y+kZXfs/f39bZpO29x12fJ7hJrtA/r9VxTgV6fKz5xxHAgK9Df1d9KgYdUdbTjjOOBFien7pJel6iTklHOBhvXxsfM2sPV8+WIWq9XVHlu6eiEhIWRnZ7NlyxZ69ep1yecffPAB9913H8HBweTk5JSf0Ofm5hIaGkphYSHff/898fHxl0zbunVrDh06xBtvvHHJu4ZKSkooLS3lp59+4tlnn2XTpk3s2bOHxo0bO2ZFf6ekEL5d6JRF2U3CRPCu+pEsuzmdB//3f2s+/YvDjKsfZ87Di5/aPn23CLjf8S9ev6zXvq55z3lXu/4WC8z+I/iaeOll+1F4f8uVx6vK1W6Du+OgT3TNl++OkpOTqz3u/v37ueuuu1i+fDmxsbHVnq5NmzY1KU2cwJbfP9RsH9Dvv6J1P8LKnTWb9mq/AwEe7gftTezrIu8CTF9R8+mvdhu0DzO2gZneXF/z7rPtsQ+8fBfUrfq6pNO5ZWcJZclwx44dl3yWkZHBlClTAOjYsWN5CAIIDg5mwIABALz55puXTLthwwYOHTqEr68vd9555yWf9+vXD19fX6Kjo9m1axfr1q1zWgiSy2tY19z/eNeEmLdsV6ihWT1zQxBAmMm/A1fYB0TEs5n9PWj28oP8jfMBs7jCccDMGhoFuVYIAjcNQv379wdg9uzZFd73k5SUREJCAllZxstEyl6kerHp06djsVh49913WbJkSfnw1NRUxo4dC1DeKcLvvf3222zdupUPP/yQevXqccstt/Dzzya8vlguYbGY+wXsCl9+Zq5/WCPzll2mSbB5YczbC1o0MGfZ7iI2NpYDBw7YdDdI3Iv2gatn5nEg2B/qO74F/xWZeTx2hXMBM2swOwhXxi2D0NSpU2nUqBHHjh0jNjaWDh06EB0dTVxcHK1ateKmm24CKj4fVCY+Pp5Zs2ZhtVoZPXo0ERERdOnShTZt2pCSkkLPnj2ZNWtWpctt3bo1PXv2ZOTIkXzzzTfk5uYyZ84ch66rVF97k5rl1vWD8FBzln2x1s3Bx6T/8bEu0EzfywvatTBn2a2bgY+3OcsWESkT6AutTGqoEtvSuChpNrOOR34+EHnpNXSni2xq1GIGVzgX+D23DEJhYWFs3LiR22+/HX9/f9LS0ggJCWHx4sWsXr26/C5RZUEIjCC1du1aBgwYQE5ODsnJycTExPDyyy/z3XffVeshzQYNGhAVFUVKSopd101qrvt15twRiI90jZPgun7QJcL5y60fAB1c5P2H11++jxO3W647OXr0KCNHjuToUfO6YBdzaR+wD7O+j/q4yPdgl3AjEDpbXCu4TF8VTuNfB3pc5/zlBvoa297VuGWvcQBt27bl888/v2R4Xl4eaWlpeHl50b59+yqnHzhwIAMH1ryv919//ZWDBw/Ss2fPGs9D7CvAF7pHwBYnZlML0NuFHpC/PhqSjjh3mfFRRtMwVxDZBJrVh8wc5y2zURC0qf5rb6QK58+fZ/fu3TZ3uSzuQ/uAfXS8xmimlnuFXr/s6dpGrtEsDIwLonGtYL1tfXVcNVcJgmDUssn2105elZ6RUMcFLgr/noucnjjP/v37sVqtREdH29x1Z1Xuu+8+XnzxRT777DPWr1/Pm2++yY033oiPjw9PPfWUXZYh9nFLB+NqiLPc0No4EXYV4aHOvSLTIBBudKFOmywWuLOrc5c5pIvRLE9ExBX4eMMdnZ23PAsw1Mnfu1fSPxaCnPjQfnykcRHOVTRvYAQTZwnyh5vbOW95tvC4w/PevXuBqpvF1UR8fDxffPEFY8aM4dZbb2Xu3LnccMMN7Nq1i6ioKLstx0wb9iznHyvGVxi2NuldBkyxsHnfZ+YUVQMNAmF4d+csKzTYuQeb6vpDd+cdAEb0NO7EuZK2LYyDkjN0CYdO1zpnWSIi1RXXynnPTPZtA61c4NmYiwX5w11xzllWg0AY2s05y7LF0K41fymqrf4YZ2xzV6QgZAcTJkxg27ZtZGdnk5+fz6FDh1i8eHGVL3StjTbv+5Te7YeW/5x5Oo01P7xJ22svfdeSq+txndE0wBZn841+86vzwjUwmoKN6mV+l9GVCfKHkfHGVbrqsnX9wWiG19akzgmu5M6uEGrjnTpbt0GDQCN0ioi4GosF/tjTaCJXXTU5DjSvD7fb73TLrjpfa/uzMrZuAy8L3BPv3JYo1RXga9TmZcPJQE32gR6tbD/nciYXPE1zLEcEIXeQl3+GcfPbU1CUT+P611BUUkDmqSPc3O1+Jg1fxP60zUwZ8R4ApaWl/O3jh3li6CssXjXZ3MJrwGKB+3obLxWr7gtG/7a2+vP3ssCD18N1LvwKqfZhxkFw2Q/VG9+W9QfjAOOsO281EeAL42+GV74yvtSrw5ZtEOwPj9/sulfAaqOWLVsye/Zs09/KLubRPmBfDQJh/E3w6tdwvvDK49t6HAgNgsducs0LgmVG9IRzBfDjieqNb8s2sFjg3l5Gj62uqnVzo8YPtoC1GuPbug/EtoSRLv6ovMfdEVq3bh1Wq5Xbb7/d7FJcSlBAA27qfC/Dr/8Ti5/exfghC2gTHs/ku99iV8q3tAvvjY+3cUljxYa/ERvRh5gwF7zXW02+PjDuRvt35ejrDWP7uvbVjzK9oowvQFuuBlVHXCu4v4/rPxfTKAgmDoDGwfadb0hdY75N6tl3vp6uQYMGDBkyhAYNGphdiphE+4D9tWgITw6w//t9mjeAJ2+B+k5qelVTPt7wUF/j4p1d5+tlXBDtbkLvbLbqfp1Rq71fr9ElHMbc4DqdJVXFxcsTZ0o5sYuoll0AOJz+H6JaGP/esv8z+rQfBsDRzH1s3LuCUf2nm1anvfj6wNh+cFcP+1yximwCU2+HWBfpKro64lrB5FuhZcOrn1egrxGA7ol3/S++MiFB8MxtRqcW9tArytgHGisE2d3p06f54IMPOH36tNmliEm0DzhG8wbG91b3iKufl8UCN7WDpwe5xstTq8PH2wgCI+3UhC08FKbcZv9w5Uidw41j4bV2ePm5fx3jPOCBPq7x6pArceEbluJsR34XhHrFDsFqtbL94JeMu914Mey+Ixv5JTuN0bONPqFP52ayYPkjnD6bweDe46uct6vyshjvVGjbAlbthD3HoLQ694cv0rCu0RtK72j7311xhpYNjYPWtwfgu2Tbu1T18YKuETC4MwTXkgPfxfx8jGd5Ol0Dq3fD0ZO2z+PaRnBbJ3WT7UgZGRn89a9/pXPnzoSEuEg/vOJU2gccp64f3NfHOCFesweOZ9s+j8gmMLgLRLjAC8RtZbEYnei0aW6cC+z6GUpKbZtH/QBIaAt9W7t+i4jKNKsPk26BjQeN84EcG54DAuMCaOdrjX3AWZ0w2IOCkACQlXMcLBZC6xttxY5k7uHem58j+dg2rm3algA/48nywb3HVwg8kxfdyPAb/kSfizpSqI0aBcHoGyDnPHyfArt/hsyzYK0iFAX6Gs8A9Yoyet6pjV96F/P2MroTvbEN7E2HrSmQlgUFxZWP72UxmlR0DTfuKrnDszBRTY2DwPFs2HwIkjPg9Lmqx28QaLSv7hNtn6toIiJmax9mNBn/6ZTxPXgo8/InxI2CjAuJ18e4VvfQNdUg0GjZMLQrbE2FnT9D5pmqL5D61/ntXCC2Ze1pDVEVby+4sa3RSmJfurENjp6EC0WVj+9lgWYNoMu1RpCsjRdDFYQEgJTjO8ubwgEE+Tdg5ff/pH7dUHrHDjWvMCerHwiDOhp/CouNk+KsXCgqMcJOQB0ICzGeA7HUwrs/V+LjbbTr7RJufPFn5cKJbLhQbITCOt7Gsy/NG7jmi9HsoWVDoyMJMB6iPXba6CGnuMQ4SNQLMF4M6A7hT0Tk9ywW465O2Z2ds/mQftpoLVBSahwnGgRCWEMIdOK7eJwpOAAGtDf+FBbDiTNw8iwUlxrbJ6COcaxoFOSe5wLeXsarHzpdaxz7s/KMc4H8IuNnHy+jCXiLBq7dGUZ11PLyxV7i291BfLs7yn9+bVISAA/Pi2XuY99WOd388esdXZppfH2MKz2u3PubI3lZjNDjyQ/91/VTczcR8Wz1AqCdB3fU5+tTMRh6GovF6FTI3h0LuQoFIbmst57Zb3YJIiLUrVuXPn36ULduXbNLEZNoHxARe1MQEhERlxcREcFbb71ldhliIu0DImJvtfyxLhER8QQlJSXk5eVRUlJidiliEu0DImJvCkIiIuLykpOT6dGjB8nJyWaXIibRPiAi9qamcW7Eqw4kTDS7Ctt42eHlZSIiIiIitlIQciMWC3j7ml2FiIiIiIjrU9M4ERERERHxOApCIiIiIiLicdQ0TkREXF5MTAybN28mONhN3+onV6R9QETsTUFIRERcXp06dQgJCTG7DDGR9gERsTc1jRMREZf3888/8/jjj/Pzzz+bXYqYRPuAiNibgpCIiLi83Nxcvv32W3Jzc80uRUyifUBE7E1BSEREREREPI6CkIiIiIiIeBwFIRERERER8TgWq9VqNbsIERGRy/nll1/44IMPGDVqFE2bNjW7HDGB9gERsTcFIRERERER8ThqGiciIiIiIh5HQUhERERERDyOgpCIiIiIiHgcBSEREREREfE4CkIiIiIiIuJxFIRERERERMTjKAiJiIiIiIjHURASERERERGPoyAkIiIiIiIeR0FIREREREQ8joKQiIiIiIh4HAUhERERERHxOApCIiIiIiLicRSERERERETE4/x/gkV0yd8npkAAAAAASUVORK5CYII=",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1058.03x367.889 with 1 Axes>"
       ]
@@ -261,11 +315,18 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "d851adcb-e524-48c8-8adc-0d1606613c8d",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.537882Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.537648Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.711037Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.710365Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 723.783x367.889 with 1 Axes>"
       ]
@@ -295,7 +356,14 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "7af74c54-3e68-4d58-a2e6-02bc212d911d",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.713923Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.713702Z",
+     "iopub.status.idle": "2024-04-19T17:42:07.717904Z",
+     "shell.execute_reply": "2024-04-19T17:42:07.716959Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -325,7 +393,14 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "d28a8b82-8405-47b2-8142-62d56266409a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:07.721136Z",
+     "iopub.status.busy": "2024-04-19T17:42:07.720389Z",
+     "iopub.status.idle": "2024-04-19T17:42:08.196614Z",
+     "shell.execute_reply": "2024-04-19T17:42:08.195634Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.cutting import generate_cutting_experiments\n",
@@ -337,68 +412,120 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5327bee0-39eb-4da9-8316-a617e5a6ff01",
+   "id": "9f66e3eb-7cbc-45a9-8f40-9f92b61b6e65",
    "metadata": {},
    "source": [
-    "### Run the subexperiments using the Qiskit Sampler primitive"
+    "### Choose a backend\n",
+    "\n",
+    "Here we are using a fake backend, which will result in Qiskit Runtime running in local mode (i.e., on a local simulator)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "1bce51e7-0ae6-4626-b204-573a7c014955",
-   "metadata": {},
+   "id": "6944546f-e3e0-4863-9049-9feed1086e68",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:08.200171Z",
+     "iopub.status.busy": "2024-04-19T17:42:08.199914Z",
+     "iopub.status.idle": "2024-04-19T17:42:08.756774Z",
+     "shell.execute_reply": "2024-04-19T17:42:08.756066Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit_aer.primitives import Sampler\n",
+    "from qiskit_ibm_runtime.fake_provider import FakeManilaV2\n",
+    "\n",
+    "backend = FakeManilaV2()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdb47142-690c-4b1a-ad0b-85e1260a2cea",
+   "metadata": {},
+   "source": [
+    "### Prepare the subexperiments for the backend\n",
     "\n",
-    "# Set up a Qiskit Aer Sampler primitive for each circuit partition\n",
-    "samplers = {\n",
-    "    label: Sampler(run_options={\"shots\": 2**12}) for label in subexperiments.keys()\n",
-    "}\n",
+    "We must transpile the circuits with our backend as the target before submitting them to Qiskit Runtime."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0229e6f2-a637-481d-ac76-84dfaadc264f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:08.761659Z",
+     "iopub.status.busy": "2024-04-19T17:42:08.760351Z",
+     "iopub.status.idle": "2024-04-19T17:42:11.493961Z",
+     "shell.execute_reply": "2024-04-19T17:42:11.493042Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
     "\n",
-    "# Retrieve results from each partition's subexperiments\n",
-    "results = {\n",
-    "    label: sampler.run(subexperiments[label]).result()\n",
-    "    for label, sampler in samplers.items()\n",
+    "# Transpile the subexperiments to ISA circuits\n",
+    "pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
+    "isa_subexperiments = {\n",
+    "    label: pass_manager.run(partition_subexpts)\n",
+    "    for label, partition_subexpts in subexperiments.items()\n",
     "}"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "03bf7ba4-14c7-46de-901a-855456372d7b",
+   "id": "9578c64a-8087-4696-bb2b-ca53d45442ba",
    "metadata": {},
    "source": [
-    "To use the Qiskit Runtime Sampler, replace the code above with this commented block."
+    "### Run the subexperiments using the Qiskit Runtime Sampler primitive"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "d30a278d-0c8a-4c96-b7ca-718fa6f59991",
+   "execution_count": 14,
+   "id": "49cfcf6f-7ac3-4c1e-9dc7-ea79f011d37c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:11.497416Z",
+     "iopub.status.busy": "2024-04-19T17:42:11.496951Z",
+     "iopub.status.idle": "2024-04-19T17:42:21.708906Z",
+     "shell.execute_reply": "2024-04-19T17:42:21.708185Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/runner/work/circuit-knitting-toolbox/circuit-knitting-toolbox/.tox/docs/lib/python3.9/site-packages/qiskit_ibm_runtime/session.py:157: UserWarning: Session is not supported in local testing mode or when using a simulator.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit_ibm_runtime import SamplerV2, Batch\n",
+    "\n",
+    "# Set up a Qiskit Runtime Sampler primitive for each circuit partition\n",
+    "samplers = {label: SamplerV2(backend=backend) for label in subexperiments.keys()}\n",
+    "\n",
+    "# Submit each partition's subexperiments as a single batch\n",
+    "with Batch(backend=backend):\n",
+    "    jobs = {\n",
+    "        label: sampler.run(isa_subexperiments[label], shots=2**12)\n",
+    "        for label, sampler in samplers.items()\n",
+    "    }\n",
+    "\n",
+    "# Retrive results\n",
+    "results = {label: job.result() for label, job in jobs.items()}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03bf7ba4-14c7-46de-901a-855456372d7b",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# from qiskit_ibm_runtime import Session, Options, Sampler, QiskitRuntimeService\n",
-    "# from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
-    "#\n",
-    "# service = QiskitRuntimeService()\n",
-    "# backend = service.least_busy(operational=True, simulator=False)\n",
-    "#\n",
-    "# # Prepare transpiler for target backend\n",
-    "# pass_manager = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
-    "#\n",
-    "# with Session(backend=backend) as session:\n",
-    "#     # Set up Qiskit Runtime Sampler primitives.\n",
-    "#     samplers = {\n",
-    "#         label: Sampler(Options(execution={\"shots\": 2**12})) for label in subexperiments.keys()\n",
-    "#     }\n",
-    "#\n",
-    "#     # Retrieve results from each subexperiment\n",
-    "#     results = {\n",
-    "#         label: sampler.run(pass_manager.run(subexperiments[label])).result()\n",
-    "#         for label, sampler in samplers.items()\n",
-    "#     }"
+    "To use the Qiskit Runtime Sampler, replace the code above with this commented block."
    ]
   },
   {
@@ -408,14 +535,21 @@
    "source": [
     "### Reconstruct the expectation values\n",
     "\n",
-    "Use the subexperiment results, subobservables, and sampling coefficients to reconstruct the expectation value of the original circuit."
+    "Reconstruct expectation values for each observable term and combine them to reconstruct the expectation value for the original observable."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "a1904c54-27fa-45e0-a9dd-727b4542db71",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:21.712202Z",
+     "iopub.status.busy": "2024-04-19T17:42:21.711788Z",
+     "iopub.status.idle": "2024-04-19T17:42:24.261549Z",
+     "shell.execute_reply": "2024-04-19T17:42:24.260798Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from circuit_knitting.cutting import reconstruct_expectation_values\n",
@@ -424,7 +558,8 @@
     "    results,\n",
     "    coefficients,\n",
     "    subobservables,\n",
-    ")"
+    ")\n",
+    "final_expval = np.dot(reconstructed_expvals, observable.coeffs)"
    ]
   },
   {
@@ -432,44 +567,43 @@
    "id": "cdc793f2-3e2b-417b-b863-7b9d57b86a8f",
    "metadata": {},
    "source": [
-    "### Compare the reconstructed expectation values with the exact expectation values from the original circuit"
+    "### Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "6e3d63e4-a510-4712-bc43-48df6e2f7ded",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:24.265047Z",
+     "iopub.status.busy": "2024-04-19T17:42:24.264536Z",
+     "iopub.status.idle": "2024-04-19T17:42:24.279626Z",
+     "shell.execute_reply": "2024-04-19T17:42:24.278941Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reconstructed expectation values: [0.15630245, 0.68548346, 0.69142044]\n",
-      "Exact expectation values: [0.1767767, 0.70710678, 0.70710678]\n",
-      "Errors in estimation: [-0.02047424, -0.02162333, -0.01568635]\n",
-      "Relative errors in estimation: [-0.11581981, -0.03058, -0.02218384]\n"
+      "Reconstructed expectation value: 1.54284137\n",
+      "Exact expectation value: 1.59099026\n",
+      "Error in estimation: -0.04814888\n",
+      "Relative error in estimation: -0.03026347\n"
      ]
     }
    ],
    "source": [
-    "from qiskit_aer.primitives import Estimator\n",
+    "from qiskit_aer.primitives import EstimatorV2\n",
     "\n",
-    "estimator = Estimator(run_options={\"shots\": None}, approximation=True)\n",
-    "exact_expvals = (\n",
-    "    estimator.run([qc_0] * len(observables_0), list(observables_0)).result().values\n",
-    ")\n",
-    "print(\n",
-    "    f\"Reconstructed expectation values: {[np.round(reconstructed_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Exact expectation values: {[np.round(exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Errors in estimation: {[np.round(reconstructed_expvals[i]-exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
-    ")\n",
+    "estimator = EstimatorV2()\n",
+    "exact_expval = estimator.run([(qc_0, observable)]).result()[0].data.evs\n",
+    "print(f\"Reconstructed expectation value: {np.real(np.round(final_expval, 8))}\")\n",
+    "print(f\"Exact expectation value: {np.round(exact_expval, 8)}\")\n",
+    "print(f\"Error in estimation: {np.real(np.round(final_expval-exact_expval, 8))}\")\n",
     "print(\n",
-    "    f\"Relative errors in estimation: {[np.round((reconstructed_expvals[i]-exact_expvals[i]) / exact_expvals[i], 8) for i in range(len(exact_expvals))]}\"\n",
+    "    f\"Relative error in estimation: {np.real(np.round((final_expval-exact_expval) / exact_expval, 8))}\"\n",
     ")"
    ]
   }
@@ -490,7 +624,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/circuit_cutting/tutorials/04_automatic_cut_finding.html b/circuit_cutting/tutorials/04_automatic_cut_finding.html
new file mode 100644
index 000000000..887d54014
--- /dev/null
+++ b/circuit_cutting/tutorials/04_automatic_cut_finding.html
@@ -0,0 +1,609 @@
+<!doctype html>
+<html class="no-js" lang="en">
+  <head><meta charset="utf-8"/>
+    <meta name="viewport" content="width=device-width,initial-scale=1"/>
+    <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
+<link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="Explanatory material for the circuit cutting module" href="../explanation/index.html" /><link rel="prev" title="Wire Cutting Phrased as a Two-Qubit Move Instruction" href="03_wire_cutting_via_move_instruction.html" />
+
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>Automatically find cuts using CKT - Circuit Knitting Toolbox 0.7.0</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=82a976d7" />
+    <link rel="stylesheet" type="text/css" href="../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
+    <link rel="stylesheet" type="text/css" href="../../_static/styles/qiskit-ecosystem.css?v=745c5aa7" />
+    <link rel="stylesheet" type="text/css" href="../../_static/plot_directive.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/jupyter-sphinx.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../../_static/nbsphinx-code-cells.css" />
+    <link rel="stylesheet" type="text/css" href="../../_static/styles/furo-extensions.css?v=36a5483c" />
+    
+    
+
+
+<style>
+  body {
+    --color-code-background: #ffffff;
+  --color-code-foreground: black;
+  
+  }
+  @media not print {
+    body[data-theme="dark"] {
+      --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+    }
+    @media (prefers-color-scheme: dark) {
+      body:not([data-theme="light"]) {
+        --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+      }
+    }
+  }
+</style><link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin><link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&family=IBM+Plex+Sans:ital,wght@0,400;0,600;1,400;1,600&display=swap" rel="stylesheet">
+<script src="../../_static/js/web-components/top-nav-bar.js"></script></head>
+  <body>
+    
+    <script>document.body.dataset.theme = "light";</script>
+    
+<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
+  <symbol id="svg-toc" viewBox="0 0 24 24">
+    <title>Contents</title>
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32">
+      <defs><style>.cls-1{fill:none;}</style></defs>
+      <path d="M28,4H4A2,2,0,0,0,2,6V26a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V6A2,2,0,0,0,28,4ZM4,6H20V26H4ZM28,26H22V6h6Z"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-menu" viewBox="0 0 24 24">
+    <title>Menu</title>
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32">
+      <defs><style>.cls-1{fill:none;}</style></defs>
+      <rect x="4" y="6" width="24" height="2"/>
+      <rect x="4" y="24" width="24" height="2"/>
+      <rect x="4" y="12" width="24" height="2"/>
+      <rect x="4" y="18" width="24" height="2"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
+    <title>Expand</title>
+    <svg version="1.1" id="icon" xmlns="http://www.w3.org/2000/svg" x="0px" y="0px"
+         viewBox="0 0 32 32" style="enable-background:new 0 0 32 32;" xml:space="preserve"><polygon points="22,16 12,26 10.6,24.6 19.2,16 10.6,7.4 12,6 " stroke="currentColor"/>
+      <rect id="_x3C_Transparent_Rectangle_x3E_" fill="none" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-new-tab" viewBox="0 0 32 32">
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 32 32">
+      <defs>
+        <style>
+          .cls-1 {
+            fill: none;
+          }
+        </style>
+      </defs>
+      <path fill="#6929C4" d="M26,28H6a2.0027,2.0027,0,0,1-2-2V6A2.0027,2.0027,0,0,1,6,4H16V6H6V26H26V16h2V26A2.0027,2.0027,0,0,1,26,28Z"/>
+      <polygon fill="#6929C4" points="20 2 20 4 26.586 4 18 12.586 19.414 14 28 5.414 28 12 30 12 30 2 20 2"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-sun" viewBox="0 0 24 24">
+    <title>Light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      <circle cx="12" cy="12" r="5"></circle>
+      <line x1="12" y1="1" x2="12" y2="3"></line>
+      <line x1="12" y1="21" x2="12" y2="23"></line>
+      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
+      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
+      <line x1="1" y1="12" x2="3" y2="12"></line>
+      <line x1="21" y1="12" x2="23" y2="12"></line>
+      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
+      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon" viewBox="0 0 24 24">
+    <title>Dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
+    </svg>
+  </symbol>
+  <symbol id="svg-sun-half" viewBox="0 0 24 24">
+    <title>Auto light/dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+      <circle cx="12" cy="12" r="9" />
+      <path d="M13 12h5" />
+      <path d="M13 15h4" />
+      <path d="M13 18h1" />
+      <path d="M13 9h4" />
+      <path d="M13 6h1" />
+    </svg>
+  </symbol>
+</svg>
+
+<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
+<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
+<label class="overlay sidebar-overlay" for="__navigation">
+  <div class="visually-hidden">Hide navigation sidebar</div>
+</label>
+<label class="overlay toc-overlay" for="__toc">
+  <div class="visually-hidden">Hide table of contents sidebar</div>
+</label>
+
+
+
+<div class="page">
+  <header class="mobile-header">
+    <div class="header-left">
+      <label class="nav-overlay-icon" for="__navigation">
+        <div class="visually-hidden">Toggle site navigation sidebar</div>
+        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
+      </label>
+    </div>
+    <div class="header-center">
+      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
+    </div>
+    <div class="header-right">
+      <div class="theme-toggle-container theme-toggle-header">
+        <button class="theme-toggle">
+          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+        </button>
+      </div>
+      <label class="toc-overlay-icon toc-header-icon" for="__toc">
+        <div class="visually-hidden">Toggle table of contents sidebar</div>
+        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+      </label>
+    </div>
+  </header>
+  <aside class="sidebar-drawer">
+    <div class="sidebar-container">
+      
+      <div class="sidebar-sticky"><div class="sidebar-brand">
+  <a href="https://www.qiskit.org/ecosystem">
+    <div class="sidebar-logo-container">
+      <img class="sidebar-logo" src="../../_static/images/ecosystem-logo.svg" alt="Qiskit Ecosystem logo"/>
+    </div>
+  </a>
+  
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
+</div><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
+  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
+  <input type="hidden" name="check_keywords" value="yes">
+  <input type="hidden" name="area" value="default">
+</form>
+<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
+  <ul>
+<li class="toctree-l1"><a class="reference internal" href="../../index.html">About Circuit Knitting Toolbox</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../install.html">Installation Instructions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Circuit Cutting</span></p>
+<ul class="current">
+<li class="toctree-l1 current has-children"><a class="reference internal" href="index.html">Cutting Tutorials</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of Cutting Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
+<li class="toctree-l2"><a class="reference internal" href="02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
+<li class="toctree-l2"><a class="reference internal" href="03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2 current current-page"><a class="current reference internal" href="#">Automatically find cuts using CKT</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../explanation/index.html">Cutting Explanatory Material</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../how-tos/index.html">Cutting How-To Guides</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Cutting How-To Guides</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../how-tos/how_to_generate_exact_quasi_dists_from_sampler.html">How to generate exact quasiprobability distributions from Sampler</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../how-tos/how_to_generate_exact_sampling_coefficients.html">How to generate exact sampling coefficients</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../how-tos/how_to_specify_cut_wires.html">How to place wire cuts using a single-qubit <code class="docutils literal notranslate"><span class="pre">CutWire</span></code> instruction</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../cutqc/index.html">CutQC (legacy circuit cutting implementation)</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of CutQC (legacy circuit cutting implementation)</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../cutqc/tutorials/tutorial_1_automatic_cut_finding.html">CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../cutqc/tutorials/tutorial_2_manual_cutting.html">CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">References</span></p>
+<ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../../apidocs/index.html">API References</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of API References</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../apidocs/circuit_cutting.html">Circuit Cutting</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Circuit Cutting</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.cut_wires.html">circuit_knitting.cutting.cut_wires</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.expand_observables.html">circuit_knitting.cutting.expand_observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.partition_circuit_qubits.html">circuit_knitting.cutting.partition_circuit_qubits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.partition_problem.html">circuit_knitting.cutting.partition_problem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.cut_gates.html">circuit_knitting.cutting.cut_gates</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.generate_cutting_experiments.html">circuit_knitting.cutting.generate_cutting_experiments</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.reconstruct_expectation_values.html">circuit_knitting.cutting.reconstruct_expectation_values</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.TwoQubitQPDGate.html">circuit_knitting.cutting.qpd.TwoQubitQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.WeightType.html">circuit_knitting.cutting.qpd.WeightType</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.html">circuit_knitting.cutting.qpd.generate_qpd_weights</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.html">circuit_knitting.cutting.qpd.decompose_qpd_instructions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction.html">circuit_knitting.cutting.qpd.qpdbasis_from_instruction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances.html">circuit_knitting.cutting.cutqc.run_subcircuit_instances</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.cutqc.generate_summation_terms.html">circuit_knitting.cutting.cutqc.generate_summation_terms</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.cutqc.build.html">circuit_knitting.cutting.cutqc.build</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.cutqc.verify.html">circuit_knitting.cutting.cutqc.verify</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires.html">circuit_knitting.cutting.cutqc.cut_circuit_wires</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits.html">circuit_knitting.cutting.cutqc.evaluate_subcircuits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.html">circuit_knitting.cutting.cutqc.reconstruct_full_distribution</a></li>
+</ul>
+</li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../../apidocs/utils.html">Utilities</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of Utilities</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.bitwise.bit_count.html">circuit_knitting.utils.bitwise.bit_count</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.conversion.quasi_to_real.html">circuit_knitting.utils.conversion.quasi_to_real</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.conversion.nearest_probability_distribution.html">circuit_knitting.utils.conversion.nearest_probability_distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.conversion.naive_probability_distribution.html">circuit_knitting.utils.conversion.naive_probability_distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.conversion.dict_to_array.html">circuit_knitting.utils.conversion.dict_to_array</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.iteration.unique_by_id.html">circuit_knitting.utils.iteration.unique_by_id</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.iteration.unique_by_eq.html">circuit_knitting.utils.iteration.unique_by_eq</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.metrics.chi2_distance.html">circuit_knitting.utils.metrics.chi2_distance</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.metrics.MSE.html">circuit_knitting.utils.metrics.MSE</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.metrics.MAPE.html">circuit_knitting.utils.metrics.MAPE</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.metrics.cross_entropy.html">circuit_knitting.utils.metrics.cross_entropy</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.metrics.HOP.html">circuit_knitting.utils.metrics.HOP</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.html">circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.observable_grouping.CommutingObservableGroup.html">circuit_knitting.utils.observable_grouping.CommutingObservableGroup</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.observable_grouping.ObservableCollection.html">circuit_knitting.utils.observable_grouping.ObservableCollection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.simulation.simulate_statevector_outcomes.html">circuit_knitting.utils.simulation.simulate_statevector_outcomes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.simulation.ExactSampler.html">circuit_knitting.utils.simulation.ExactSampler</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.transforms.separate_circuit.html">circuit_knitting.utils.transforms.separate_circuit</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.transforms.SeparatedCircuits.html">circuit_knitting.utils.transforms.SeparatedCircuits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.transpiler_passes.RemoveFinalReset.html">circuit_knitting.utils.transpiler_passes.RemoveFinalReset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.utils.transpiler_passes.ConsolidateResets.html">circuit_knitting.utils.transpiler_passes.ConsolidateResets</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../release-notes.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference external" href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox">GitHub</a></li>
+</ul>
+
+</div></div>
+      </div>
+      
+    </div>
+  </aside>
+  <div class="main">
+    <div class="content">
+      <div class="article-container">
+        <a href="#" class="back-to-top muted-link">
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
+            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
+          </svg>
+          <span>Back to top</span>
+        </a>
+        <div class="content-icon-container">
+          <div class="edit-this-page">
+  <a class="muted-link" href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/edit/main/docs/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb" title="Edit this page">
+    <svg aria-hidden="true" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+      <path d="M4 20h4l10.5 -10.5a1.5 1.5 0 0 0 -4 -4l-10.5 10.5v4" />
+      <line x1="13.5" y1="6.5" x2="17.5" y2="10.5" />
+    </svg>
+    <span class="visually-hidden">Edit this page</span>
+  </a>
+</div><div class="theme-toggle-container theme-toggle-content">
+            <button class="theme-toggle">
+              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+            </button>
+          </div>
+          <label class="toc-overlay-icon toc-content-icon" for="__toc">
+            <div class="visually-hidden">Toggle table of contents sidebar</div>
+            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+          </label>
+        </div>
+        <article role="main">
+          <section id="Automatically-find-cuts-using-CKT">
+<h1>Automatically find cuts using CKT<a class="headerlink" href="#Automatically-find-cuts-using-CKT" title="Link to this heading">¶</a></h1>
+<section id="Create-a-circuit-and-observables">
+<h2>Create a circuit and observables<a class="headerlink" href="#Create-a-circuit-and-observables" title="Link to this heading">¶</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">qiskit.circuit.random</span> <span class="kn">import</span> <span class="n">random_circuit</span>
+<span class="kn">from</span> <span class="nn">qiskit.quantum_info</span> <span class="kn">import</span> <span class="n">SparsePauliOp</span>
+
+<span class="n">circuit</span> <span class="o">=</span> <span class="n">random_circuit</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">max_operands</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1242</span><span class="p">)</span>
+<span class="n">observable</span> <span class="o">=</span> <span class="n">SparsePauliOp</span><span class="p">([</span><span class="s2">&quot;ZIIIIII&quot;</span><span class="p">,</span> <span class="s2">&quot;IIIZIII&quot;</span><span class="p">,</span> <span class="s2">&quot;IIIIIIZ&quot;</span><span class="p">])</span>
+
+
+<span class="n">circuit</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="s2">&quot;mpl&quot;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/circuit_cutting_tutorials_04_automatic_cut_finding_2_0.png" src="../../_images/circuit_cutting_tutorials_04_automatic_cut_finding_2_0.png" />
+</div>
+</div>
+</section>
+<section id="Find-cut-locations,-given-a-maximum-of-4-qubits-per-subcircuit.-This-circuit-can-be-separated-in-two-by-making-a-single-wire-cut-and-cutting-one-CRZGate">
+<h2>Find cut locations, given a maximum of 4 qubits per subcircuit. This circuit can be separated in two by making a single wire cut and cutting one <code class="docutils literal notranslate"><span class="pre">CRZGate</span></code><a class="headerlink" href="#Find-cut-locations,-given-a-maximum-of-4-qubits-per-subcircuit.-This-circuit-can-be-separated-in-two-by-making-a-single-wire-cut-and-cutting-one-CRZGate" title="Link to this heading">¶</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">circuit_knitting.cutting.automated_cut_finding</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">find_cuts</span><span class="p">,</span>
+    <span class="n">OptimizationParameters</span><span class="p">,</span>
+    <span class="n">DeviceConstraints</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Specify settings for the cut-finding optimizer</span>
+<span class="n">optimization_settings</span> <span class="o">=</span> <span class="n">OptimizationParameters</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">111</span><span class="p">)</span>
+
+<span class="c1"># Specify the size of the QPUs available</span>
+<span class="n">device_constraints</span> <span class="o">=</span> <span class="n">DeviceConstraints</span><span class="p">(</span><span class="n">qubits_per_subcircuit</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+
+<span class="n">cut_circuit</span><span class="p">,</span> <span class="n">metadata</span> <span class="o">=</span> <span class="n">find_cuts</span><span class="p">(</span><span class="n">circuit</span><span class="p">,</span> <span class="n">optimization_settings</span><span class="p">,</span> <span class="n">device_constraints</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s1">&#39;Found solution using </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">metadata</span><span class="p">[</span><span class="s2">&quot;cuts&quot;</span><span class="p">])</span><span class="si">}</span><span class="s1"> cuts with a sampling &#39;</span>
+    <span class="sa">f</span><span class="s1">&#39;overhead of </span><span class="si">{</span><span class="n">metadata</span><span class="p">[</span><span class="s2">&quot;sampling_overhead&quot;</span><span class="p">]</span><span class="si">}</span><span class="s1">.&#39;</span>
+<span class="p">)</span>
+<span class="k">for</span> <span class="n">cut</span> <span class="ow">in</span> <span class="n">metadata</span><span class="p">[</span><span class="s2">&quot;cuts&quot;</span><span class="p">]:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2"> at circuit instruction index </span><span class="si">{</span><span class="n">cut</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">cut_circuit</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="s2">&quot;mpl&quot;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">fold</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Found solution using 2 cuts with a sampling overhead of 127.06026169907257.
+Wire Cut at circuit instruction index 19
+Gate Cut at circuit instruction index 28
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/circuit_cutting_tutorials_04_automatic_cut_finding_4_1.png" src="../../_images/circuit_cutting_tutorials_04_automatic_cut_finding_4_1.png" />
+</div>
+</div>
+</section>
+<section id="Add-ancillas-for-wire-cuts-and-expand-the-observables-to-account-for-ancilla-qubits">
+<h2>Add ancillas for wire cuts and expand the observables to account for ancilla qubits<a class="headerlink" href="#Add-ancillas-for-wire-cuts-and-expand-the-observables-to-account-for-ancilla-qubits" title="Link to this heading">¶</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">circuit_knitting.cutting</span> <span class="kn">import</span> <span class="n">cut_wires</span><span class="p">,</span> <span class="n">expand_observables</span>
+
+<span class="n">qc_w_ancilla</span> <span class="o">=</span> <span class="n">cut_wires</span><span class="p">(</span><span class="n">cut_circuit</span><span class="p">)</span>
+<span class="n">observables_expanded</span> <span class="o">=</span> <span class="n">expand_observables</span><span class="p">(</span><span class="n">observable</span><span class="o">.</span><span class="n">paulis</span><span class="p">,</span> <span class="n">circuit</span><span class="p">,</span> <span class="n">qc_w_ancilla</span><span class="p">)</span>
+<span class="n">qc_w_ancilla</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="s2">&quot;mpl&quot;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">fold</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/circuit_cutting_tutorials_04_automatic_cut_finding_6_0.png" src="../../_images/circuit_cutting_tutorials_04_automatic_cut_finding_6_0.png" />
+</div>
+</div>
+</section>
+<section id="Partition-the-circuit-and-observables-into-subcircuits-and-subobservables.-Calculate-the-sampling-overhead-incurred-from-cutting-these-gates-and-wires.">
+<h2>Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires.<a class="headerlink" href="#Partition-the-circuit-and-observables-into-subcircuits-and-subobservables.-Calculate-the-sampling-overhead-incurred-from-cutting-these-gates-and-wires." title="Link to this heading">¶</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">circuit_knitting.cutting</span> <span class="kn">import</span> <span class="n">partition_problem</span>
+
+<span class="n">partitioned_problem</span> <span class="o">=</span> <span class="n">partition_problem</span><span class="p">(</span>
+    <span class="n">circuit</span><span class="o">=</span><span class="n">qc_w_ancilla</span><span class="p">,</span> <span class="n">observables</span><span class="o">=</span><span class="n">observables_expanded</span>
+<span class="p">)</span>
+<span class="n">subcircuits</span> <span class="o">=</span> <span class="n">partitioned_problem</span><span class="o">.</span><span class="n">subcircuits</span>
+<span class="n">subobservables</span> <span class="o">=</span> <span class="n">partitioned_problem</span><span class="o">.</span><span class="n">subobservables</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;Sampling overhead: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">([</span><span class="n">basis</span><span class="o">.</span><span class="n">overhead</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">basis</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="n">partitioned_problem</span><span class="o">.</span><span class="n">bases</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Sampling overhead: 127.06026169907257
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">subobservables</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{0: PauliList([&#39;IIII&#39;, &#39;IZII&#39;, &#39;IIIZ&#39;]),
+ 1: PauliList([&#39;ZIII&#39;, &#39;IIII&#39;, &#39;IIII&#39;])}
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">subcircuits</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="s2">&quot;mpl&quot;</span><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="s2">&quot;iqp&quot;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/circuit_cutting_tutorials_04_automatic_cut_finding_10_0.png" src="../../_images/circuit_cutting_tutorials_04_automatic_cut_finding_10_0.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">subcircuits</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="s2">&quot;mpl&quot;</span><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="s2">&quot;iqp&quot;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/circuit_cutting_tutorials_04_automatic_cut_finding_11_0.png" src="../../_images/circuit_cutting_tutorials_04_automatic_cut_finding_11_0.png" />
+</div>
+</div>
+</section>
+<section id="Generate-the-experiments-to-run-on-the-backend.">
+<h2>Generate the experiments to run on the backend.<a class="headerlink" href="#Generate-the-experiments-to-run-on-the-backend." title="Link to this heading">¶</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">circuit_knitting.cutting</span> <span class="kn">import</span> <span class="n">generate_cutting_experiments</span>
+
+<span class="n">subexperiments</span><span class="p">,</span> <span class="n">coefficients</span> <span class="o">=</span> <span class="n">generate_cutting_experiments</span><span class="p">(</span>
+    <span class="n">circuits</span><span class="o">=</span><span class="n">subcircuits</span><span class="p">,</span> <span class="n">observables</span><span class="o">=</span><span class="n">subobservables</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">=</span><span class="mi">1_000</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">subexperiments</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="nb">len</span><span class="p">(</span><span class="n">subexperiments</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="si">}</span><span class="s2"> total subexperiments to run on backend.&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+96 total subexperiments to run on backend.
+</pre></div></div>
+</div>
+</section>
+</section>
+
+        </article>
+      </div>
+      <footer>
+        <div class="related-pages">
+          <a class="next-page" href="../explanation/index.html">
+              <div class="page-info">
+                <div class="context">
+                  <span>Next</span>
+                </div>
+                <div class="title">Explanatory material for the circuit cutting module</div>
+              </div>
+              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
+            </a>
+          <a class="prev-page" href="03_wire_cutting_via_move_instruction.html">
+              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
+              <div class="page-info">
+                <div class="context">
+                  <span>Previous</span>
+                </div>
+                
+                <div class="title">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</div>
+                
+              </div>
+            </a>
+        </div>
+        <div class="bottom-of-page">
+          <div class="left-details">
+            <div class="copyright">
+                Copyright &#169; 2023
+            </div>
+            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
+            
+            <a href="https://github.com/pradyunsg/furo">Furo</a>
+            
+          </div>
+          <div class="right-details">
+            <div class="icons">
+              <a class="muted-link " href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox" aria-label="GitHub">
+                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
+                </svg>
+            </a>
+              
+            </div>
+          </div>
+        </div>
+        
+      </footer>
+    </div>
+    <aside class="toc-drawer">
+      
+      
+      <div class="toc-sticky toc-scroll">
+        <div class="toc-title-container">
+          <span class="toc-title">
+            On this page
+          </span>
+        </div>
+        <div class="toc-tree-container">
+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">Automatically find cuts using CKT</a><ul>
+<li><a class="reference internal" href="#Create-a-circuit-and-observables">Create a circuit and observables</a></li>
+<li><a class="reference internal" href="#Find-cut-locations,-given-a-maximum-of-4-qubits-per-subcircuit.-This-circuit-can-be-separated-in-two-by-making-a-single-wire-cut-and-cutting-one-CRZGate">Find cut locations, given a maximum of 4 qubits per subcircuit. This circuit can be separated in two by making a single wire cut and cutting one <code class="docutils literal notranslate"><span class="pre">CRZGate</span></code></a></li>
+<li><a class="reference internal" href="#Add-ancillas-for-wire-cuts-and-expand-the-observables-to-account-for-ancilla-qubits">Add ancillas for wire cuts and expand the observables to account for ancilla qubits</a></li>
+<li><a class="reference internal" href="#Partition-the-circuit-and-observables-into-subcircuits-and-subobservables.-Calculate-the-sampling-overhead-incurred-from-cutting-these-gates-and-wires.">Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires.</a></li>
+<li><a class="reference internal" href="#Generate-the-experiments-to-run-on-the-backend.">Generate the experiments to run on the backend.</a></li>
+</ul>
+</li>
+</ul>
+
+          </div>
+        </div>
+      </div>
+      
+      
+    </aside>
+  </div>
+</div><script src="../../_static/jquery.js?v=5d32c60e"></script>
+    <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+    <script src="../../_static/documentation_options.js?v=fe7df9b0"></script>
+    <script src="../../_static/doctools.js?v=9a2dae69"></script>
+    <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
+    <script src="../../_static/copybutton.js?v=f281be69"></script>
+    <script src="../../_static/scripts/qiskit-sphinx-theme.js?v=4d77b8ca"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/@jupyter-widgets/html-manager@^1.0.1/dist/embed-amd.js"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    </body>
+</html>
\ No newline at end of file
diff --git a/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb b/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb
new file mode 100644
index 000000000..47094b4a1
--- /dev/null
+++ b/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb
@@ -0,0 +1,339 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Automatically find cuts using CKT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create a circuit and observables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:27.266446Z",
+     "iopub.status.busy": "2024-04-19T17:42:27.266196Z",
+     "iopub.status.idle": "2024-04-19T17:42:28.358961Z",
+     "shell.execute_reply": "2024-04-19T17:42:28.358112Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1300.22x494.978 with 1 Axes>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from qiskit.circuit.random import random_circuit\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "\n",
+    "circuit = random_circuit(7, 6, max_operands=2, seed=1242)\n",
+    "observable = SparsePauliOp([\"ZIIIIII\", \"IIIZIII\", \"IIIIIIZ\"])\n",
+    "\n",
+    "\n",
+    "circuit.draw(\"mpl\", scale=0.8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Find cut locations, given a maximum of 4 qubits per subcircuit. This circuit can be separated in two by making a single wire cut and cutting one `CRZGate`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:28.361713Z",
+     "iopub.status.busy": "2024-04-19T17:42:28.361425Z",
+     "iopub.status.idle": "2024-04-19T17:42:28.779585Z",
+     "shell.execute_reply": "2024-04-19T17:42:28.778903Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found solution using 2 cuts with a sampling overhead of 127.06026169907257.\n",
+      "Wire Cut at circuit instruction index 19\n",
+      "Gate Cut at circuit instruction index 28\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1367.11x494.978 with 1 Axes>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from circuit_knitting.cutting.automated_cut_finding import (\n",
+    "    find_cuts,\n",
+    "    OptimizationParameters,\n",
+    "    DeviceConstraints,\n",
+    ")\n",
+    "\n",
+    "# Specify settings for the cut-finding optimizer\n",
+    "optimization_settings = OptimizationParameters(seed=111)\n",
+    "\n",
+    "# Specify the size of the QPUs available\n",
+    "device_constraints = DeviceConstraints(qubits_per_subcircuit=4)\n",
+    "\n",
+    "cut_circuit, metadata = find_cuts(circuit, optimization_settings, device_constraints)\n",
+    "print(\n",
+    "    f'Found solution using {len(metadata[\"cuts\"])} cuts with a sampling '\n",
+    "    f'overhead of {metadata[\"sampling_overhead\"]}.'\n",
+    ")\n",
+    "for cut in metadata[\"cuts\"]:\n",
+    "    print(f\"{cut[0]} at circuit instruction index {cut[1]}\")\n",
+    "cut_circuit.draw(\"mpl\", scale=0.8, fold=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Add ancillas for wire cuts and expand the observables to account for ancilla qubits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:28.782250Z",
+     "iopub.status.busy": "2024-04-19T17:42:28.781889Z",
+     "iopub.status.idle": "2024-04-19T17:42:29.150532Z",
+     "shell.execute_reply": "2024-04-19T17:42:29.149699Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1367.11x561.867 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from circuit_knitting.cutting import cut_wires, expand_observables\n",
+    "\n",
+    "qc_w_ancilla = cut_wires(cut_circuit)\n",
+    "observables_expanded = expand_observables(observable.paulis, circuit, qc_w_ancilla)\n",
+    "qc_w_ancilla.draw(\"mpl\", scale=0.8, fold=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:29.153658Z",
+     "iopub.status.busy": "2024-04-19T17:42:29.153186Z",
+     "iopub.status.idle": "2024-04-19T17:42:29.169241Z",
+     "shell.execute_reply": "2024-04-19T17:42:29.168559Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sampling overhead: 127.06026169907257\n"
+     ]
+    }
+   ],
+   "source": [
+    "from circuit_knitting.cutting import partition_problem\n",
+    "\n",
+    "partitioned_problem = partition_problem(\n",
+    "    circuit=qc_w_ancilla, observables=observables_expanded\n",
+    ")\n",
+    "subcircuits = partitioned_problem.subcircuits\n",
+    "subobservables = partitioned_problem.subobservables\n",
+    "print(\n",
+    "    f\"Sampling overhead: {np.prod([basis.overhead for basis in partitioned_problem.bases])}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:29.172554Z",
+     "iopub.status.busy": "2024-04-19T17:42:29.172099Z",
+     "iopub.status.idle": "2024-04-19T17:42:29.177041Z",
+     "shell.execute_reply": "2024-04-19T17:42:29.176349Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: PauliList(['IIII', 'IZII', 'IIIZ']),\n",
+       " 1: PauliList(['ZIII', 'IIII', 'IIII'])}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subobservables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:29.179712Z",
+     "iopub.status.busy": "2024-04-19T17:42:29.179108Z",
+     "iopub.status.idle": "2024-04-19T17:42:29.400997Z",
+     "shell.execute_reply": "2024-04-19T17:42:29.400316Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 980.198x294.311 with 1 Axes>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subcircuits[0].draw(\"mpl\", style=\"iqp\", scale=0.8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:29.403930Z",
+     "iopub.status.busy": "2024-04-19T17:42:29.403727Z",
+     "iopub.status.idle": "2024-04-19T17:42:29.616812Z",
+     "shell.execute_reply": "2024-04-19T17:42:29.615970Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1114.14x294.311 with 1 Axes>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subcircuits[1].draw(\"mpl\", style=\"iqp\", scale=0.8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Generate the experiments to run on the backend."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-04-19T17:42:29.619427Z",
+     "iopub.status.busy": "2024-04-19T17:42:29.619202Z",
+     "iopub.status.idle": "2024-04-19T17:42:30.174075Z",
+     "shell.execute_reply": "2024-04-19T17:42:30.173155Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "96 total subexperiments to run on backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from circuit_knitting.cutting import generate_cutting_experiments\n",
+    "\n",
+    "subexperiments, coefficients = generate_cutting_experiments(\n",
+    "    circuits=subcircuits, observables=subobservables, num_samples=1_000\n",
+    ")\n",
+    "print(\n",
+    "    f\"{len(subexperiments[0]) + len(subexperiments[1])} total subexperiments to run on backend.\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/circuit_cutting/tutorials/index.html b/circuit_cutting/tutorials/index.html
index 2a36870ef..368c2c3e7 100644
--- a/circuit_cutting/tutorials/index.html
+++ b/circuit_cutting/tutorials/index.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../../genindex.html" /><link rel="search" title="Search" href="../../search.html" /><link rel="next" title="Gate Cutting to Reduce Circuit Width" href="01_gate_cutting_to_reduce_circuit_width.html" /><link rel="prev" title="Installation Instructions" href="../../install.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>Circuit Cutting Tutorials - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>Circuit Cutting Tutorials - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -146,7 +146,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -173,7 +173,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -190,6 +190,7 @@
 <li class="toctree-l2"><a class="reference internal" href="01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../explanation/index.html">Cutting Explanatory Material</a></li>
@@ -219,6 +220,9 @@
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="../../stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -321,13 +325,17 @@
   <div>Gate Cutting to Reduce Circuit Width</div>
 </a>
 <a class="reference internal" href="02_gate_cutting_to_reduce_circuit_depth.html">
-  <div><img alt="" src="../../_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_19_1.png"></div>
+  <div><img alt="" src="../../_images/circuit_cutting_tutorials_02_gate_cutting_to_reduce_circuit_depth_17_1.png"></div>
   <div>Gate Cutting to Reduce Circuit Depth</div>
 </a>
 <a class="reference internal" href="03_wire_cutting_via_move_instruction.html">
   <div><img alt="" src="../../_images/circuit_cutting_tutorials_03_wire_cutting_via_move_instruction_15_0.png"></div>
   <div>Wire Cutting Phrased as a Two-Qubit Move Instruction</div>
 </a>
+<a class="reference internal" href="04_automatic_cut_finding.html">
+  <div><img alt="" src="../../_images/circuit_cutting_tutorials_04_automatic_cut_finding_11_0.png"></div>
+  <div>Automatically find cuts using CKT</div>
+</a>
 </div>
 </div>
 </section>
@@ -389,7 +397,7 @@
   </div>
 </div><script src="../../_static/jquery.js?v=5d32c60e"></script>
     <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../../_static/doctools.js?v=9a2dae69"></script>
     <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/genindex.html b/genindex.html
index a2597e024..6f9e8cc12 100644
--- a/genindex.html
+++ b/genindex.html
@@ -4,7 +4,7 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="#" /><link rel="search" title="Search" href="search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 --><title>Index - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 --><title>Index - Circuit Knitting Toolbox 0.7.0</title>
 <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -144,7 +144,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -171,7 +171,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -188,6 +188,7 @@
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -217,6 +218,9 @@
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -296,7 +300,7 @@
           
 <section class="genindex-section">
   <h1 id="index">Index</h1>
-  <div class="genindex-jumpbox"><a href="#_"><strong>_</strong></a> | <a href="#B"><strong>B</strong></a> | <a href="#C"><strong>C</strong></a> | <a href="#D"><strong>D</strong></a> | <a href="#E"><strong>E</strong></a> | <a href="#G"><strong>G</strong></a> | <a href="#H"><strong>H</strong></a> | <a href="#M"><strong>M</strong></a> | <a href="#N"><strong>N</strong></a> | <a href="#O"><strong>O</strong></a> | <a href="#P"><strong>P</strong></a> | <a href="#Q"><strong>Q</strong></a> | <a href="#R"><strong>R</strong></a> | <a href="#S"><strong>S</strong></a> | <a href="#T"><strong>T</strong></a> | <a href="#U"><strong>U</strong></a> | <a href="#V"><strong>V</strong></a> | <a href="#W"><strong>W</strong></a></div>
+  <div class="genindex-jumpbox"><a href="#_"><strong>_</strong></a> | <a href="#B"><strong>B</strong></a> | <a href="#C"><strong>C</strong></a> | <a href="#D"><strong>D</strong></a> | <a href="#E"><strong>E</strong></a> | <a href="#F"><strong>F</strong></a> | <a href="#G"><strong>G</strong></a> | <a href="#H"><strong>H</strong></a> | <a href="#M"><strong>M</strong></a> | <a href="#N"><strong>N</strong></a> | <a href="#O"><strong>O</strong></a> | <a href="#P"><strong>P</strong></a> | <a href="#Q"><strong>Q</strong></a> | <a href="#R"><strong>R</strong></a> | <a href="#S"><strong>S</strong></a> | <a href="#T"><strong>T</strong></a> | <a href="#U"><strong>U</strong></a> | <a href="#V"><strong>V</strong></a> | <a href="#W"><strong>W</strong></a></div>
 </section>
 <section id="_" class="genindex-section">
   <h2>_</h2>
@@ -445,6 +449,8 @@ <h2>D</h2>
 </li>
     </ul></td>
     <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="stubs/circuit_knitting.cutting.DeviceConstraints.html#circuit_knitting.cutting.DeviceConstraints">DeviceConstraints (class in circuit_knitting.cutting)</a>
+</li>
         <li><a href="stubs/circuit_knitting.utils.conversion.dict_to_array.html#circuit_knitting.utils.conversion.dict_to_array">dict_to_array() (in module circuit_knitting.utils.conversion)</a>
 </li>
     </ul></td>
@@ -467,6 +473,16 @@ <h2>E</h2>
   </tr></table>
 </section>
 
+<section id="F" class="genindex-section">
+  <h2>F</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="stubs/circuit_knitting.cutting.find_cuts.html#circuit_knitting.cutting.find_cuts">find_cuts() (in module circuit_knitting.cutting)</a>
+</li>
+    </ul></td>
+  </tr></table>
+</section>
+
 <section id="G" class="genindex-section">
   <h2>G</h2>
   <table style="width: 100%" class="indextable genindextable"><tr>
@@ -559,6 +575,8 @@ <h2>O</h2>
     </ul></td>
     <td style="width: 33%; vertical-align: top;"><ul>
         <li><a href="stubs/circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.html#circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem">observables_restricted_to_subsystem() (in module circuit_knitting.utils.observable_grouping)</a>
+</li>
+        <li><a href="stubs/circuit_knitting.cutting.OptimizationParameters.html#circuit_knitting.cutting.OptimizationParameters">OptimizationParameters (class in circuit_knitting.cutting)</a>
 </li>
     </ul></td>
   </tr></table>
@@ -716,7 +734,7 @@ <h2>W</h2>
   </div>
 </div><script src="_static/jquery.js?v=5d32c60e"></script>
     <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="_static/thebelab-helper.js"></script>
diff --git a/index.html b/index.html
index a7eb29784..4034857a4 100644
--- a/index.html
+++ b/index.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Installation Instructions" href="install.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="#"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="#"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -353,6 +357,7 @@ <h2>Contents<a class="headerlink" href="#contents" title="Link to this heading">
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="circuit_cutting/explanation/index.html">Cutting Explanatory Material</a><ul>
@@ -389,6 +394,7 @@ <h2>Contents<a class="headerlink" href="#contents" title="Link to this heading">
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="release-notes.html">Release Notes</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="release-notes.html#release-notes-0-7-0">0.7.0</a></li>
 <li class="toctree-l2"><a class="reference internal" href="release-notes.html#release-notes-0-6-0">0.6.0</a></li>
 <li class="toctree-l2"><a class="reference internal" href="release-notes.html#release-notes-0-5-0">0.5.0</a></li>
 <li class="toctree-l2"><a class="reference internal" href="release-notes.html#release-notes-0-4-0">0.4.0</a></li>
@@ -473,7 +479,7 @@ <h2>Contents<a class="headerlink" href="#contents" title="Link to this heading">
   </div>
 </div><script src="_static/jquery.js?v=5d32c60e"></script>
     <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/install.html b/install.html
index fc8023b34..979df358e 100644
--- a/install.html
+++ b/install.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Circuit Cutting Tutorials" href="circuit_cutting/tutorials/index.html" /><link rel="prev" title="Circuit Knitting Toolbox" href="index.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>Installation Instructions - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>Installation Instructions - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -435,7 +439,8 @@ <h2>Pre-Installation<a class="headerlink" href="#pre-installation" title="Link t
 <blockquote>
 <div><ul class="simple">
 <li><p>The automatic wire cut search in the <code class="docutils literal notranslate"><span class="pre">cutqc</span></code> package depends
-on CPLEX, which is only available on Intel chips.</p></li>
+on CPLEX, which is only available on Intel chips and is not yet available
+for Python 3.12.</p></li>
 </ul>
 </div></blockquote>
 <p>In each case, one method that is guaranteed to work is to <a class="reference internal" href="#option-3"><span class="std std-ref">use
@@ -533,7 +538,7 @@ <h2>Pre-Installation<a class="headerlink" href="#pre-installation" title="Link t
   </div>
 </div><script src="_static/jquery.js?v=5d32c60e"></script>
     <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/objects.inv b/objects.inv
index 1b30b7765..e1f1cbdd8 100644
Binary files a/objects.inv and b/objects.inv differ
diff --git a/plot_directive/circuit_cutting/explanation/index-1.svg b/plot_directive/circuit_cutting/explanation/index-1.svg
index 258c0f17c..58fd40ebb 100644
--- a/plot_directive/circuit_cutting/explanation/index-1.svg
+++ b/plot_directive/circuit_cutting/explanation/index-1.svg
@@ -6,7 +6,7 @@
   <rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
    <cc:Work>
     <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
-    <dc:date>2024-04-17T21:19:29.289216</dc:date>
+    <dc:date>2024-04-19T17:42:25.566851</dc:date>
     <dc:format>image/svg+xml</dc:format>
     <dc:creator>
      <cc:Agent>
@@ -243,7 +243,7 @@ L 64.7424 233.736932
 L 61.1712 233.682238 
 L 57.6 233.664 
 z
-" clip-path="url(#p2864c6c48a)" style="fill: #57ffff"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: #57ffff"/>
    </g>
    <g id="PolyCollection_2">
     <path d="M 57.6 233.664 
@@ -451,7 +451,7 @@ L 64.7424 233.664
 L 61.1712 233.664 
 L 57.6 233.664 
 z
-" clip-path="url(#p2864c6c48a)" style="fill: #2b568c"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: #2b568c"/>
    </g>
    <g id="PolyCollection_3">
     <path d="M 57.6 233.664 
@@ -659,7 +659,7 @@ L 64.7424 231.343262
 L 61.1712 232.503058 
 L 57.6 233.664 
 z
-" clip-path="url(#p2864c6c48a)" style="fill: #007da3"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: #007da3"/>
    </g>
    <g id="PolyCollection_4">
     <path d="M 57.6 233.664 
@@ -867,7 +867,7 @@ L 64.7424 229.022525
 L 61.1712 231.342117 
 L 57.6 233.664 
 z
-" clip-path="url(#p2864c6c48a)" style="fill: #ffa502"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: #ffa502"/>
    </g>
    <g id="PolyCollection_5">
     <path d="M 57.6 233.664 
@@ -1075,7 +1075,7 @@ L 64.7424 226.701787
 L 61.1712 230.181175 
 L 57.6 233.664 
 z
-" clip-path="url(#p2864c6c48a)" style="fill: #7abaff"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: #7abaff"/>
    </g>
    <g id="PolyCollection_6">
     <path d="M 57.6 233.664 
@@ -1283,18 +1283,18 @@ L 64.7424 224.38105
 L 61.1712 229.020233 
 L 57.6 233.664 
 z
-" clip-path="url(#p2864c6c48a)" style="fill: #f2cc86"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: #f2cc86"/>
    </g>
    <g id="matplotlib.axis_1">
     <g id="xtick_1">
      <g id="line2d_1">
       <defs>
-       <path id="mf5119957ef" d="M 0 0 
+       <path id="mb2d40fea38" d="M 0 0 
 L 0 3.5 
 " style="stroke: #000000; stroke-width: 0.8"/>
       </defs>
       <g>
-       <use xlink:href="#mf5119957ef" x="57.6" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#mb2d40fea38" x="57.6" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_1">
@@ -1330,7 +1330,7 @@ z
     <g id="xtick_2">
      <g id="line2d_2">
       <g>
-       <use xlink:href="#mf5119957ef" x="146.88" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#mb2d40fea38" x="146.88" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_2">
@@ -1396,7 +1396,7 @@ z
     <g id="xtick_3">
      <g id="line2d_3">
       <g>
-       <use xlink:href="#mf5119957ef" x="236.16" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#mb2d40fea38" x="236.16" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_3">
@@ -1437,7 +1437,7 @@ z
     <g id="xtick_4">
      <g id="line2d_4">
       <g>
-       <use xlink:href="#mf5119957ef" x="325.44" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#mb2d40fea38" x="325.44" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_4">
@@ -1487,7 +1487,7 @@ z
     <g id="xtick_5">
      <g id="line2d_5">
       <g>
-       <use xlink:href="#mf5119957ef" x="414.72" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#mb2d40fea38" x="414.72" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_5">
@@ -1815,12 +1815,12 @@ z
     <g id="ytick_1">
      <g id="line2d_6">
       <defs>
-       <path id="mf02425d1ee" d="M 0 0 
+       <path id="m18cd9b8d97" d="M 0 0 
 L -3.5 0 
 " style="stroke: #000000; stroke-width: 0.8"/>
       </defs>
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="307.584" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_7">
@@ -1844,7 +1844,7 @@ z
     <g id="ytick_2">
      <g id="line2d_7">
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="270.624" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="270.624" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_8">
@@ -1886,7 +1886,7 @@ z
     <g id="ytick_3">
      <g id="line2d_8">
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="233.664" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="233.664" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_9">
@@ -1917,7 +1917,7 @@ z
     <g id="ytick_4">
      <g id="line2d_9">
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="196.704" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="196.704" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_10">
@@ -1932,7 +1932,7 @@ z
     <g id="ytick_5">
      <g id="line2d_10">
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="159.744" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="159.744" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_11">
@@ -1947,7 +1947,7 @@ z
     <g id="ytick_6">
      <g id="line2d_11">
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="122.784" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="122.784" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_12">
@@ -1962,7 +1962,7 @@ z
     <g id="ytick_7">
      <g id="line2d_12">
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="85.824" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="85.824" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_13">
@@ -1977,7 +1977,7 @@ z
     <g id="ytick_8">
      <g id="line2d_13">
       <g>
-       <use xlink:href="#mf02425d1ee" x="57.6" y="48.864" style="stroke: #000000; stroke-width: 0.8"/>
+       <use xlink:href="#m18cd9b8d97" x="57.6" y="48.864" style="stroke: #000000; stroke-width: 0.8"/>
       </g>
      </g>
      <g id="text_14">
@@ -2311,17 +2311,17 @@ z
    <g id="line2d_14">
     <path d="M 236.16 307.584 
 L 236.16 41.472 
-" clip-path="url(#p2864c6c48a)" style="fill: none; stroke-dasharray: 5.55,2.4; stroke-dashoffset: 0; stroke: #aaaaaa; stroke-width: 1.5"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: none; stroke-dasharray: 5.55,2.4; stroke-dashoffset: 0; stroke: #aaaaaa; stroke-width: 1.5"/>
    </g>
    <g id="line2d_15">
     <path d="M 146.88 307.584 
 L 146.88 41.472 
-" clip-path="url(#p2864c6c48a)" style="fill: none; stroke-dasharray: 1.5,2.475; stroke-dashoffset: 0; stroke: #aaaaaa; stroke-width: 1.5"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: none; stroke-dasharray: 1.5,2.475; stroke-dashoffset: 0; stroke: #aaaaaa; stroke-width: 1.5"/>
    </g>
    <g id="line2d_16">
     <path d="M 57.6 233.664 
 L 414.72 233.664 
-" clip-path="url(#p2864c6c48a)" style="fill: none; stroke: #aaaaaa; stroke-width: 1.5; stroke-linecap: square"/>
+" clip-path="url(#pa23f73a4d8)" style="fill: none; stroke: #aaaaaa; stroke-width: 1.5; stroke-linecap: square"/>
    </g>
    <g id="patch_3">
     <path d="M 57.6 307.584 
@@ -2858,7 +2858,7 @@ z
   </g>
  </g>
  <defs>
-  <clipPath id="p2864c6c48a">
+  <clipPath id="pa23f73a4d8">
    <rect x="57.6" y="41.472" width="357.12" height="266.112"/>
   </clipPath>
  </defs>
diff --git a/plot_directive/stubs/circuit_knitting-cutting-instructions-Move-1.svg b/plot_directive/stubs/circuit_knitting-cutting-instructions-Move-1.svg
index c7c398eee..7efc7db40 100644
--- a/plot_directive/stubs/circuit_knitting-cutting-instructions-Move-1.svg
+++ b/plot_directive/stubs/circuit_knitting-cutting-instructions-Move-1.svg
@@ -6,7 +6,7 @@
   <rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
    <cc:Work>
     <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
-    <dc:date>2024-04-17T21:19:25.611736</dc:date>
+    <dc:date>2024-04-19T17:42:05.219728</dc:date>
     <dc:format>image/svg+xml</dc:format>
     <dc:creator>
      <cc:Agent>
@@ -32,22 +32,22 @@ z
    <g id="line2d_1">
     <path d="M 122.178864 68.868799 
 L 451.29226 68.868799 
-" clip-path="url(#p4e778baf8d)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
    </g>
    <g id="line2d_2">
     <path d="M 122.178864 115.222798 
 L 451.29226 115.222798 
-" clip-path="url(#p4e778baf8d)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
    </g>
    <g id="line2d_3">
     <path d="M 122.178864 161.576798 
 L 451.29226 161.576798 
-" clip-path="url(#p4e778baf8d)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
    </g>
    <g id="line2d_4">
     <path d="M 122.178864 207.930797 
 L 451.29226 207.930797 
-" clip-path="url(#p4e778baf8d)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: none; stroke: #000000; stroke-width: 2; stroke-linecap: square"/>
    </g>
    <g id="patch_2">
     <path d="M 117.543464 45.691799 
@@ -55,7 +55,7 @@ L -1041.306521 45.691799
 L -1041.306521 277.461796 
 L 117.543464 277.461796 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #ffffff; stroke: #ffffff; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #ffffff; stroke: #ffffff; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_3">
     <path d="M 129.018397 130.287848 
@@ -63,7 +63,7 @@ L 165.401651 130.287848
 L 165.401651 53.803749 
 L 129.018397 53.803749 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_4">
     <path d="M 132.144974 222.995847 
@@ -71,7 +71,7 @@ L 162.275073 222.995847
 L 162.275073 192.865747 
 L 132.144974 192.865747 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_5">
     <path d="M 193.237227 176.641848 
@@ -79,7 +79,7 @@ L 240.244818 176.641848
 L 240.244818 100.157749 
 L 193.237227 100.157749 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_6">
     <path d="M 201.675973 83.933849 
@@ -87,7 +87,7 @@ L 231.806073 83.933849
 L 231.806073 53.803749 
 L 201.675973 53.803749 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #33b1ff; stroke: #33b1ff; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #33b1ff; stroke: #33b1ff; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_7">
     <path d="M 268.080395 222.995847 
@@ -95,7 +95,7 @@ L 304.463649 222.995847
 L 304.463649 146.511748 
 L 268.080395 146.511748 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_8">
     <path d="M 332.299225 176.641848 
@@ -103,7 +103,7 @@ L 379.306816 176.641848
 L 379.306816 100.157749 
 L 332.299225 100.157749 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_9">
     <path d="M 340.737971 222.995847 
@@ -111,7 +111,7 @@ L 370.868071 222.995847
 L 370.868071 192.865747 
 L 340.737971 192.865747 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="patch_10">
     <path d="M 407.142393 130.287848 
@@ -119,10 +119,10 @@ L 443.525647 130.287848
 L 443.525647 53.803749 
 L 407.142393 53.803749 
 z
-" clip-path="url(#p4e778baf8d)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
+" clip-path="url(#p581eb3dbb7)" style="fill: #9f1853; stroke: #9f1853; stroke-width: 1.5; stroke-linejoin: miter"/>
    </g>
    <g id="text_1">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- ${q}_{0}$ -->
      <g transform="translate(94.870564 73.319776) scale(0.1625 -0.1625)">
       <defs>
@@ -184,7 +184,7 @@ z
     </g>
    </g>
    <g id="text_2">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- ${q}_{1}$ -->
      <g transform="translate(94.870564 119.673775) scale(0.1625 -0.1625)">
       <defs>
@@ -209,7 +209,7 @@ z
     </g>
    </g>
    <g id="text_3">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- ${q}_{2}$ -->
      <g transform="translate(94.870564 166.027774) scale(0.1625 -0.1625)">
       <defs>
@@ -244,7 +244,7 @@ z
     </g>
    </g>
    <g id="text_4">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- ${q}_{3}$ -->
      <g transform="translate(94.870564 212.381774) scale(0.1625 -0.1625)">
       <defs>
@@ -287,7 +287,7 @@ z
     </g>
    </g>
    <g id="text_5">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 0 -->
      <g style="fill: #ffffff" transform="translate(132.263177 72.455987) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-30"/>
@@ -295,7 +295,7 @@ z
     </g>
    </g>
    <g id="text_6">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 1 -->
      <g style="fill: #ffffff" transform="translate(132.263177 118.809986) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-31"/>
@@ -303,7 +303,7 @@ z
     </g>
    </g>
    <g id="text_7">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\pi$/4 -->
      <g style="fill: #ffffff" transform="translate(145.988964 102.133479) scale(0.08 -0.08)">
       <defs>
@@ -364,7 +364,7 @@ z
     </g>
    </g>
    <g id="text_8">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\mathrm{R_{YY}}$ -->
      <g style="fill: #ffffff" transform="translate(141.973964 88.216346) scale(0.13 -0.13)">
       <defs>
@@ -416,7 +416,7 @@ z
     </g>
    </g>
    <g id="text_9">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\pi$/4 -->
      <g style="fill: #ffffff" transform="translate(140.890024 219.177327) scale(0.08 -0.08)">
       <use xlink:href="#DejaVuSans-Oblique-3c0" transform="translate(0 0.09375)"/>
@@ -426,7 +426,7 @@ z
     </g>
    </g>
    <g id="text_10">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\mathrm{R_X}$ -->
      <g style="fill: #ffffff" transform="translate(139.280024 206.99847) scale(0.13 -0.13)">
       <defs>
@@ -452,7 +452,7 @@ z
     </g>
    </g>
    <g id="text_11">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 0 -->
      <g style="fill: #ffffff" transform="translate(196.482007 118.809986) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-30"/>
@@ -460,7 +460,7 @@ z
     </g>
    </g>
    <g id="text_12">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 1 -->
      <g style="fill: #ffffff" transform="translate(196.482007 165.163985) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-31"/>
@@ -468,7 +468,7 @@ z
     </g>
    </g>
    <g id="text_13">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- Move -->
      <g style="fill: #ffffff" transform="translate(204.407775 141.986986) scale(0.13 -0.13)">
       <defs>
@@ -553,7 +553,7 @@ z
     </g>
    </g>
    <g id="text_14">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\pi$/4 -->
      <g transform="translate(210.421023 80.115329) scale(0.08 -0.08)">
       <use xlink:href="#DejaVuSans-Oblique-3c0" transform="translate(0 0.09375)"/>
@@ -563,7 +563,7 @@ z
     </g>
    </g>
    <g id="text_15">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\mathrm{R_Z}$ -->
      <g transform="translate(208.811023 67.936472) scale(0.13 -0.13)">
       <defs>
@@ -587,7 +587,7 @@ z
     </g>
    </g>
    <g id="text_16">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 0 -->
      <g style="fill: #ffffff" transform="translate(271.325175 165.163985) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-30"/>
@@ -595,7 +595,7 @@ z
     </g>
    </g>
    <g id="text_17">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 1 -->
      <g style="fill: #ffffff" transform="translate(271.325175 211.517985) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-31"/>
@@ -603,7 +603,7 @@ z
     </g>
    </g>
    <g id="text_18">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\pi$/4 -->
      <g style="fill: #ffffff" transform="translate(285.050962 194.841477) scale(0.08 -0.08)">
       <use xlink:href="#DejaVuSans-Oblique-3c0" transform="translate(0 0.09375)"/>
@@ -613,7 +613,7 @@ z
     </g>
    </g>
    <g id="text_19">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\mathrm{R_{YY}}$ -->
      <g style="fill: #ffffff" transform="translate(281.035962 180.924345) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-52" transform="translate(0 0.09375)"/>
@@ -623,7 +623,7 @@ z
     </g>
    </g>
    <g id="text_20">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 0 -->
      <g style="fill: #ffffff" transform="translate(335.544005 165.163985) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-30"/>
@@ -631,7 +631,7 @@ z
     </g>
    </g>
    <g id="text_21">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 1 -->
      <g style="fill: #ffffff" transform="translate(335.544005 118.809986) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-31"/>
@@ -639,7 +639,7 @@ z
     </g>
    </g>
    <g id="text_22">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- Move -->
      <g style="fill: #ffffff" transform="translate(343.469773 141.986986) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-4d"/>
@@ -650,7 +650,7 @@ z
     </g>
    </g>
    <g id="text_23">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\pi$/4 -->
      <g style="fill: #ffffff" transform="translate(349.483021 219.177327) scale(0.08 -0.08)">
       <use xlink:href="#DejaVuSans-Oblique-3c0" transform="translate(0 0.09375)"/>
@@ -660,7 +660,7 @@ z
     </g>
    </g>
    <g id="text_24">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\mathrm{R_X}$ -->
      <g style="fill: #ffffff" transform="translate(347.873021 206.99847) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-52" transform="translate(0 0.09375)"/>
@@ -669,7 +669,7 @@ z
     </g>
    </g>
    <g id="text_25">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 0 -->
      <g style="fill: #ffffff" transform="translate(410.387173 72.455987) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-30"/>
@@ -677,7 +677,7 @@ z
     </g>
    </g>
    <g id="text_26">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- 1 -->
      <g style="fill: #ffffff" transform="translate(410.387173 118.809986) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-31"/>
@@ -685,7 +685,7 @@ z
     </g>
    </g>
    <g id="text_27">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\pi$/4 -->
      <g style="fill: #ffffff" transform="translate(424.11296 102.133479) scale(0.08 -0.08)">
       <use xlink:href="#DejaVuSans-Oblique-3c0" transform="translate(0 0.09375)"/>
@@ -695,7 +695,7 @@ z
     </g>
    </g>
    <g id="text_28">
-    <g clip-path="url(#p4e778baf8d)">
+    <g clip-path="url(#p581eb3dbb7)">
      <!-- $\mathrm{R_{YY}}$ -->
      <g style="fill: #ffffff" transform="translate(420.09796 88.216346) scale(0.13 -0.13)">
       <use xlink:href="#DejaVuSans-52" transform="translate(0 0.09375)"/>
@@ -707,7 +707,7 @@ z
   </g>
  </g>
  <defs>
-  <clipPath id="p4e778baf8d">
+  <clipPath id="p581eb3dbb7">
    <rect x="64.769668" y="31.7856" width="391.157992" height="203.957597"/>
   </clipPath>
  </defs>
diff --git a/py-modindex.html b/py-modindex.html
index 89387a7bb..7764fa99a 100644
--- a/py-modindex.html
+++ b/py-modindex.html
@@ -4,7 +4,7 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 --><title>Python Module Index - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 --><title>Python Module Index - Circuit Knitting Toolbox 0.7.0</title>
 <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -144,7 +144,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -171,7 +171,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -188,6 +188,7 @@
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -217,6 +218,9 @@
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -419,7 +423,7 @@ <h1>Python Module Index</h1>
   </div>
 </div><script src="_static/jquery.js?v=5d32c60e"></script>
     <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="_static/thebelab-helper.js"></script>
diff --git a/release-notes.html b/release-notes.html
index d41ee9dfa..1225544dc 100644
--- a/release-notes.html
+++ b/release-notes.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="prev" title="circuit_knitting.utils.transpiler_passes.ConsolidateResets" href="stubs/circuit_knitting.utils.transpiler_passes.ConsolidateResets.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>Release Notes - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>Release Notes - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -305,10 +309,81 @@
         <article role="main">
           <section id="release-notes">
 <span id="id1"></span><h1>Release Notes<a class="headerlink" href="#release-notes" title="Link to this heading">¶</a></h1>
-<section id="release-notes-0-6-0">
-<span id="id2"></span><h2>0.6.0<a class="headerlink" href="#release-notes-0-6-0" title="Link to this heading">¶</a></h2>
+<section id="release-notes-0-7-0">
+<span id="id2"></span><h2>0.7.0<a class="headerlink" href="#release-notes-0-7-0" title="Link to this heading">¶</a></h2>
+<section id="prelude">
+<span id="release-notes-0-7-0-prelude"></span><h3>Prelude<a class="headerlink" href="#prelude" title="Link to this heading">¶</a></h3>
+<p>The 0.7 release introduces an automated cut finding code for the new circuit cutting workflow.  With this milestone, the older cutting workflow (CutQC) is now deprecated.  Additionally, this is the first CKT release to support version 2 of the Qiskit Runtime primitives. User are encouraged to <a class="reference external" href="https://docs.quantum.ibm.com/api/migration-guides/v2-primitives">migrate to v2 primitives</a> as soon as possible.</p>
+</section>
+<section id="new-features">
+<span id="release-notes-0-7-0-new-features"></span><h3>New Features<a class="headerlink" href="#new-features" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Added a cut-finder function, <a class="reference internal" href="stubs/circuit_knitting.cutting.find_cuts.html#circuit_knitting.cutting.find_cuts" title="circuit_knitting.cutting.find_cuts"><code class="xref py py-func docutils literal notranslate"><span class="pre">circuit_knitting.cutting.find_cuts()</span></code></a>, for automatically
+identifying locations to place LO gate and wire cuts such that the circuit is
+separable and runnable, given the maximum number of qubits per subcircuit as a parameter. 
+The cut-finder will search for cut schemes which minimize the sampling overhead. 
+Note, however, that for larger circuits, the number of cuts needed to separate the
+circuit will naturally grow larger, leading to an exponentially increasing sampling overhead.
+For instances of wire cuts, the cut-finder assumes no qubit reuse. Therefore, for each wire 
+cut, a new wire is added to the circuit. In addition, the cut-finder requires that every gate
+in an input circuit be at most a two qubit gate. The search algorithm used by the cut-finder
+to identify cut locations is Dijkstra’s best first search algorithm which is guaranteed to find
+solutions with the lowest sampling overhead, provided any user-specified value for the maximum number
+of allowed backjumps or for the maximum sampling overhead does not prematurely stop the search. If 
+the user wishes to time-restrict the search when running the cut-finder on large circuits, they can 
+specify a maximum sampling overhead and/or a maximum number of allowed backjumps, in which case the
+cut-finder will return a valid albeit suboptimal cut scheme.</p></li>
+</ul>
+<ul class="simple">
+<li><p>Circuit cutting reconstruction can now interpret the
+<a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.PrimitiveResult" title="(in Qiskit v1.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">PrimitiveResult</span></code></a> object, which is
+returned by version 2 of the sampler primitive
+(<a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.BaseSamplerV2" title="(in Qiskit v1.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseSamplerV2</span></code></a>).  See the <a class="reference external" href="https://docs.quantum.ibm.com/api/migration-guides/qiskit-1.0-features#qiskitprimitives">migration guide</a>
+for details on upgrading to version 2 of the Qiskit primitives.</p></li>
+</ul>
+</section>
 <section id="upgrade-notes">
-<span id="release-notes-0-6-0-upgrade-notes"></span><h3>Upgrade Notes<a class="headerlink" href="#upgrade-notes" title="Link to this heading">¶</a></h3>
+<span id="release-notes-0-7-0-upgrade-notes"></span><h3>Upgrade Notes<a class="headerlink" href="#upgrade-notes" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>CKT now requires updated versions of some dependencies: <code class="docutils literal notranslate"><span class="pre">qiskit</span></code>
+1.0 or later, <code class="docutils literal notranslate"><span class="pre">qiskit-aer</span></code> 0.14.0 or later, and
+<code class="docutils literal notranslate"><span class="pre">qiskit-ibm-runtime</span></code> 0.23.0 or later.</p></li>
+</ul>
+<ul class="simple">
+<li><p>The code in the <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting.qpd.qpd</span></code> submodule has
+been split into three separate files.  If you were importing
+directly from this submodule, you will now need to import from
+<code class="xref py py-mod docutils literal notranslate"><span class="pre">circuit_knitting.cutting.qpd</span></code> instead.</p></li>
+</ul>
+</section>
+<section id="deprecation-notes">
+<span id="release-notes-0-7-0-deprecation-notes"></span><h3>Deprecation Notes<a class="headerlink" href="#deprecation-notes" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>The <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting.cutqc</span></code> package is deprecated and will be removed no sooner than Circuit Knitting Toolbox 0.8.0. 
+The wire cutting functionality in the <a class="reference internal" href="apidocs/circuit_cutting.html#module-circuit_knitting.cutting" title="circuit_knitting.cutting"><code class="xref py py-mod docutils literal notranslate"><span class="pre">circuit_knitting.cutting</span></code></a> package is what will be maintained going forward. 
+Additionally, there is a new automated gate and wire cut-finding functionality in the <code class="xref py py-mod docutils literal notranslate"><span class="pre">circuit_knitting.cutting.automated_cut_finding</span></code> module.
+A <a class="reference external" href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/main/docs/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb">tutorial</a> has been added to demonstrate automated cut-finding.</p></li>
+</ul>
+</section>
+<section id="other-notes">
+<span id="release-notes-0-7-0-other-notes"></span><h3>Other Notes<a class="headerlink" href="#other-notes" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>The cutting tutorials have been rephrased with the goal of
+reconstructing the expectation value of a single
+<a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.quantum_info.SparsePauliOp" title="(in Qiskit v1.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">SparsePauliOp</span></code></a> with many terms,
+rather than multiple independent
+<a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.quantum_info.Pauli" title="(in Qiskit v1.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pauli</span></code></a> observables.</p></li>
+</ul>
+<ul class="simple">
+<li><p>The <a class="reference internal" href="circuit_cutting/explanation/index.html"><span class="doc">circuit cutting explanation</span></a> document has been
+expanded significantly.</p></li>
+</ul>
+</section>
+</section>
+<section id="release-notes-0-6-0">
+<span id="id3"></span><h2>0.6.0<a class="headerlink" href="#release-notes-0-6-0" title="Link to this heading">¶</a></h2>
+<section id="release-notes-0-6-0-upgrade-notes">
+<span id="id4"></span><h3>Upgrade Notes<a class="headerlink" href="#release-notes-0-6-0-upgrade-notes" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>The minimum supported version of <code class="docutils literal notranslate"><span class="pre">qiskit</span></code> is now 0.45.0, and the
 minimum supported version of <code class="docutils literal notranslate"><span class="pre">qiskit-ibm-runtime</span></code> is now 0.12.2.
@@ -356,17 +431,17 @@
 for details.</p></li>
 </ul>
 </section>
-<section id="other-notes">
-<span id="release-notes-0-6-0-other-notes"></span><h3>Other Notes<a class="headerlink" href="#other-notes" title="Link to this heading">¶</a></h3>
+<section id="release-notes-0-6-0-other-notes">
+<span id="id5"></span><h3>Other Notes<a class="headerlink" href="#release-notes-0-6-0-other-notes" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>Removed the entanglement forging tool, as the Qiskit application modules are no longer supported, and packages in <code class="docutils literal notranslate"><span class="pre">Qiskit-Extensions</span></code> may not have dependency on those modules.  With this change, CKT no longer depends on <code class="docutils literal notranslate"><span class="pre">qiskit-algorithms</span></code> or Qiskit Nature.</p></li>
 </ul>
 </section>
 </section>
 <section id="release-notes-0-5-0">
-<span id="id3"></span><h2>0.5.0<a class="headerlink" href="#release-notes-0-5-0" title="Link to this heading">¶</a></h2>
-<section id="prelude">
-<span id="release-notes-0-5-0-prelude"></span><h3>Prelude<a class="headerlink" href="#prelude" title="Link to this heading">¶</a></h3>
+<span id="id6"></span><h2>0.5.0<a class="headerlink" href="#release-notes-0-5-0" title="Link to this heading">¶</a></h2>
+<section id="release-notes-0-5-0-prelude">
+<span id="id7"></span><h3>Prelude<a class="headerlink" href="#release-notes-0-5-0-prelude" title="Link to this heading">¶</a></h3>
 <p>The primary purpose of this release is to swap the order of the
 classical registers in the circuits generated the
 <a class="reference internal" href="apidocs/circuit_cutting.html#module-circuit_knitting.cutting" title="circuit_knitting.cutting"><code class="xref py py-mod docutils literal notranslate"><span class="pre">circuit_knitting.cutting</span></code></a> module.  With this change,
@@ -375,7 +450,7 @@
 <code class="docutils literal notranslate"><span class="pre">num_qpd_bits</span></code> into the result metadata by hand.</p>
 </section>
 <section id="release-notes-0-5-0-upgrade-notes">
-<span id="id4"></span><h3>Upgrade Notes<a class="headerlink" href="#release-notes-0-5-0-upgrade-notes" title="Link to this heading">¶</a></h3>
+<span id="id8"></span><h3>Upgrade Notes<a class="headerlink" href="#release-notes-0-5-0-upgrade-notes" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>CKT now depends on the <a class="reference external" href="https://github.com/qiskit-community/qiskit-algorithms">qiskit-algorithms</a>
 package.  This new package replaces the <code class="docutils literal notranslate"><span class="pre">qiskit.algorithms</span></code>
@@ -394,7 +469,7 @@
 </ul>
 </section>
 <section id="release-notes-0-5-0-other-notes">
-<span id="id5"></span><h3>Other Notes<a class="headerlink" href="#release-notes-0-5-0-other-notes" title="Link to this heading">¶</a></h3>
+<span id="id9"></span><h3>Other Notes<a class="headerlink" href="#release-notes-0-5-0-other-notes" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">qiskit-nature</span></code> is now pinned to version 0.6.X, as the
 entanglement forging code has not yet been updated to work with
@@ -404,17 +479,17 @@
 </section>
 </section>
 <section id="release-notes-0-4-0">
-<span id="id6"></span><h2>0.4.0<a class="headerlink" href="#release-notes-0-4-0" title="Link to this heading">¶</a></h2>
+<span id="id10"></span><h2>0.4.0<a class="headerlink" href="#release-notes-0-4-0" title="Link to this heading">¶</a></h2>
 <section id="release-notes-0-4-0-prelude">
-<span id="id7"></span><h3>Prelude<a class="headerlink" href="#release-notes-0-4-0-prelude" title="Link to this heading">¶</a></h3>
+<span id="id11"></span><h3>Prelude<a class="headerlink" href="#release-notes-0-4-0-prelude" title="Link to this heading">¶</a></h3>
 <p>The primary goal of this release is to modify the circuit cutting
 workflow to enable direct use of the Sampler primitive.  Previously,
 the Sampler was called in <code class="xref py py-func docutils literal notranslate"><span class="pre">execute_experiments()</span></code>, a function
 which is now deprecated in favor of
 <a class="reference internal" href="stubs/circuit_knitting.cutting.generate_cutting_experiments.html#circuit_knitting.cutting.generate_cutting_experiments" title="circuit_knitting.cutting.generate_cutting_experiments"><code class="xref py py-func docutils literal notranslate"><span class="pre">generate_cutting_experiments()</span></code></a>.</p>
 </section>
-<section id="new-features">
-<span id="release-notes-0-4-0-new-features"></span><h3>New Features<a class="headerlink" href="#new-features" title="Link to this heading">¶</a></h3>
+<section id="release-notes-0-4-0-new-features">
+<span id="id12"></span><h3>New Features<a class="headerlink" href="#release-notes-0-4-0-new-features" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>Added a module, <code class="xref py py-mod docutils literal notranslate"><span class="pre">circuit_knitting.cutting.cutting_experiments</span></code>, which is intended to hold
 functions used for generating the quantum experiments needed for circuit cutting. This module
@@ -425,7 +500,7 @@
 </ul>
 </section>
 <section id="release-notes-0-4-0-upgrade-notes">
-<span id="id8"></span><h3>Upgrade Notes<a class="headerlink" href="#release-notes-0-4-0-upgrade-notes" title="Link to this heading">¶</a></h3>
+<span id="id13"></span><h3>Upgrade Notes<a class="headerlink" href="#release-notes-0-4-0-upgrade-notes" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>The <code class="docutils literal notranslate"><span class="pre">circuit-knitting-toolbox</span></code> Python package now depends on
 <code class="docutils literal notranslate"><span class="pre">qiskit</span></code> rather than <code class="docutils literal notranslate"><span class="pre">qiskit-terra</span></code>.  This should have no
@@ -439,8 +514,8 @@
 <li><p><a class="reference internal" href="stubs/circuit_knitting.cutting.reconstruct_expectation_values.html#circuit_knitting.cutting.reconstruct_expectation_values" title="circuit_knitting.cutting.reconstruct_expectation_values"><code class="xref py py-func docutils literal notranslate"><span class="pre">reconstruct_expectation_values()</span></code></a> now takes, as its first argument, a <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.SamplerResult" title="(in Qiskit v1.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">SamplerResult</span></code></a> instance or a dictionary mapping partition labels to <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.SamplerResult" title="(in Qiskit v1.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">SamplerResult</span></code></a> instances.  This new <code class="docutils literal notranslate"><span class="pre">results</span></code> argument replaces the old <code class="docutils literal notranslate"><span class="pre">quasi_dists</span></code> argument. The <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.SamplerResult" title="(in Qiskit v1.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">SamplerResult</span></code></a> instances are expected to contain the number of QPD bits used in each circuit input to the Sampler. This should be specified in the <code class="docutils literal notranslate"><span class="pre">num_qpd_bits</span></code> field of the experiment result metadata.</p></li>
 </ul>
 </section>
-<section id="deprecation-notes">
-<span id="release-notes-0-4-0-deprecation-notes"></span><h3>Deprecation Notes<a class="headerlink" href="#deprecation-notes" title="Link to this heading">¶</a></h3>
+<section id="release-notes-0-4-0-deprecation-notes">
+<span id="id14"></span><h3>Deprecation Notes<a class="headerlink" href="#release-notes-0-4-0-deprecation-notes" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>The <code class="xref py py-func docutils literal notranslate"><span class="pre">execute_experiments()</span></code> function has been deprecated.
 Going forward, users should first call
@@ -451,9 +526,9 @@
 </section>
 </section>
 <section id="release-notes-0-3-0">
-<span id="id9"></span><h2>0.3.0<a class="headerlink" href="#release-notes-0-3-0" title="Link to this heading">¶</a></h2>
+<span id="id15"></span><h2>0.3.0<a class="headerlink" href="#release-notes-0-3-0" title="Link to this heading">¶</a></h2>
 <section id="release-notes-0-3-0-prelude">
-<span id="id10"></span><h3>Prelude<a class="headerlink" href="#release-notes-0-3-0-prelude" title="Link to this heading">¶</a></h3>
+<span id="id16"></span><h3>Prelude<a class="headerlink" href="#release-notes-0-3-0-prelude" title="Link to this heading">¶</a></h3>
 <p>The 0.3.0 release introduces significant new features while maintaining
 backwards compatibility with the 0.2.0 release.  The most striking
 change in this release is the shortened module names:
@@ -465,7 +540,7 @@
 of arbitrary two-qubit gates.</p>
 </section>
 <section id="release-notes-0-3-0-new-features">
-<span id="id11"></span><h3>New Features<a class="headerlink" href="#release-notes-0-3-0-new-features" title="Link to this heading">¶</a></h3>
+<span id="id17"></span><h3>New Features<a class="headerlink" href="#release-notes-0-3-0-new-features" title="Link to this heading">¶</a></h3>
 <ul>
 <li><p>The circuit cutting module now supports the cutting of arbitrary
 two-qubit gates.  This is supported via a KAK decomposition, using
@@ -532,7 +607,7 @@
 </ul>
 </section>
 <section id="release-notes-0-3-0-upgrade-notes">
-<span id="id12"></span><h3>Upgrade Notes<a class="headerlink" href="#release-notes-0-3-0-upgrade-notes" title="Link to this heading">¶</a></h3>
+<span id="id18"></span><h3>Upgrade Notes<a class="headerlink" href="#release-notes-0-3-0-upgrade-notes" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p><code class="xref py py-func docutils literal notranslate"><span class="pre">execute_experiments()</span></code> no longer creates separate jobs for each subcircuit by default.
 Now, separate jobs are only created if separate <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseSampler</span></code> instances are provided for each circuit partition.</p></li>
@@ -557,7 +632,7 @@
 </ul>
 </section>
 <section id="release-notes-0-3-0-deprecation-notes">
-<span id="id13"></span><h3>Deprecation Notes<a class="headerlink" href="#release-notes-0-3-0-deprecation-notes" title="Link to this heading">¶</a></h3>
+<span id="id19"></span><h3>Deprecation Notes<a class="headerlink" href="#release-notes-0-3-0-deprecation-notes" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p><code class="xref py py-func docutils literal notranslate"><span class="pre">decompose_gates()</span></code> is deprecated and will be removed no sooner than v0.4.0. Users should migrate to the identical <a class="reference internal" href="stubs/circuit_knitting.cutting.cut_gates.html#circuit_knitting.cutting.cut_gates" title="circuit_knitting.cutting.cut_gates"><code class="xref py py-func docutils literal notranslate"><span class="pre">cut_gates()</span></code></a> function.</p></li>
 </ul>
@@ -589,7 +664,7 @@
 </ul>
 </section>
 <section id="release-notes-0-3-0-bug-fixes">
-<span id="id14"></span><h3>Bug Fixes<a class="headerlink" href="#release-notes-0-3-0-bug-fixes" title="Link to this heading">¶</a></h3>
+<span id="id20"></span><h3>Bug Fixes<a class="headerlink" href="#release-notes-0-3-0-bug-fixes" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>Fixed a bug in <a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.html#circuit_knitting.cutting.qpd.decompose_qpd_instructions" title="circuit_knitting.cutting.qpd.decompose_qpd_instructions"><code class="xref py py-func docutils literal notranslate"><span class="pre">decompose_qpd_instructions()</span></code></a> which would
 cause index errors if <a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.TwoQubitQPDGate.html#circuit_knitting.cutting.qpd.TwoQubitQPDGate" title="circuit_knitting.cutting.qpd.TwoQubitQPDGate"><code class="xref py py-class docutils literal notranslate"><span class="pre">TwoQubitQPDGate</span></code></a> indices were
@@ -603,9 +678,9 @@
 </section>
 </section>
 <section id="release-notes-0-2-0">
-<span id="id15"></span><h2>0.2.0<a class="headerlink" href="#release-notes-0-2-0" title="Link to this heading">¶</a></h2>
+<span id="id21"></span><h2>0.2.0<a class="headerlink" href="#release-notes-0-2-0" title="Link to this heading">¶</a></h2>
 <section id="release-notes-0-2-0-prelude">
-<span id="id16"></span><h3>Prelude<a class="headerlink" href="#release-notes-0-2-0-prelude" title="Link to this heading">¶</a></h3>
+<span id="id22"></span><h3>Prelude<a class="headerlink" href="#release-notes-0-2-0-prelude" title="Link to this heading">¶</a></h3>
 <p>0.2.0 is centered around the addition of functions which allow for the easy
 implementation of a circuit cutting technique called gate cutting. For more details on circuit cutting, check out our
 <a class="reference internal" href="circuit_cutting/explanation/index.html#circuit-cutting-explanation"><span class="std std-ref">explanation guide</span></a>.</p>
@@ -621,7 +696,7 @@
 Check out the <a class="reference internal" href="circuit_cutting/tutorials/index.html#circuit-cutting-tutorials"><span class="std std-ref">tutorials</span></a> for a look at a couple of example circuit cutting workflows.</p>
 </section>
 <section id="release-notes-0-2-0-new-features">
-<span id="id17"></span><h3>New Features<a class="headerlink" href="#release-notes-0-2-0-new-features" title="Link to this heading">¶</a></h3>
+<span id="id23"></span><h3>New Features<a class="headerlink" href="#release-notes-0-2-0-new-features" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>Addition of a <code class="xref py py-mod docutils literal notranslate"><span class="pre">qpd</span></code> package which allows for easy
 transformation of <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.circuit.QuantumCircuit" title="(in Qiskit v1.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuantumCircuit</span></code></a> gates and wires into elements which may be decomposed to a
@@ -649,7 +724,7 @@
 </ul>
 </section>
 <section id="release-notes-0-2-0-upgrade-notes">
-<span id="id18"></span><h3>Upgrade Notes<a class="headerlink" href="#release-notes-0-2-0-upgrade-notes" title="Link to this heading">¶</a></h3>
+<span id="id24"></span><h3>Upgrade Notes<a class="headerlink" href="#release-notes-0-2-0-upgrade-notes" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>Support for running with Python 3.7 has been removed. To run the
 Circuit Knitting Toolbox, you now need Python version 3.8 or
@@ -657,7 +732,7 @@
 </ul>
 </section>
 <section id="release-notes-0-2-0-deprecation-notes">
-<span id="id19"></span><h3>Deprecation Notes<a class="headerlink" href="#release-notes-0-2-0-deprecation-notes" title="Link to this heading">¶</a></h3>
+<span id="id25"></span><h3>Deprecation Notes<a class="headerlink" href="#release-notes-0-2-0-deprecation-notes" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>The <code class="docutils literal notranslate"><span class="pre">circuit_knitting_toolbox.circuit_cutting.wire_cutting</span></code> namespace
 is now deprecated. It has been renamed to
@@ -671,9 +746,9 @@
 </section>
 </section>
 <section id="release-notes-0-1-0">
-<span id="id20"></span><h2>0.1.0<a class="headerlink" href="#release-notes-0-1-0" title="Link to this heading">¶</a></h2>
+<span id="id26"></span><h2>0.1.0<a class="headerlink" href="#release-notes-0-1-0" title="Link to this heading">¶</a></h2>
 <section id="release-notes-0-1-0-new-features">
-<span id="id21"></span><h3>New Features<a class="headerlink" href="#release-notes-0-1-0-new-features" title="Link to this heading">¶</a></h3>
+<span id="id27"></span><h3>New Features<a class="headerlink" href="#release-notes-0-1-0-new-features" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>Support for Python 3.11.</p></li>
 </ul>
@@ -682,7 +757,7 @@
 </ul>
 </section>
 <section id="release-notes-0-1-0-upgrade-notes">
-<span id="id22"></span><h3>Upgrade Notes<a class="headerlink" href="#release-notes-0-1-0-upgrade-notes" title="Link to this heading">¶</a></h3>
+<span id="id28"></span><h3>Upgrade Notes<a class="headerlink" href="#release-notes-0-1-0-upgrade-notes" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>The minimum supported version of each Qiskit dependency has been
 updated.  This release depends on <code class="docutils literal notranslate"><span class="pre">qiskit-terra&gt;=0.23.3</span></code>,
@@ -712,7 +787,7 @@
 </ul>
 </section>
 <section id="release-notes-0-1-0-deprecation-notes">
-<span id="id23"></span><h3>Deprecation Notes<a class="headerlink" href="#release-notes-0-1-0-deprecation-notes" title="Link to this heading">¶</a></h3>
+<span id="id29"></span><h3>Deprecation Notes<a class="headerlink" href="#release-notes-0-1-0-deprecation-notes" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>Support for running with Python 3.7 has been deprecated.  Future
 versions of the Circuit Knitting Toolbox will require Python 3.8
@@ -776,23 +851,31 @@
           <div class="toc-tree">
             <ul>
 <li><a class="reference internal" href="#">Release Notes</a><ul>
-<li><a class="reference internal" href="#release-notes-0-6-0">0.6.0</a><ul>
+<li><a class="reference internal" href="#release-notes-0-7-0">0.7.0</a><ul>
+<li><a class="reference internal" href="#prelude">Prelude</a></li>
+<li><a class="reference internal" href="#new-features">New Features</a></li>
 <li><a class="reference internal" href="#upgrade-notes">Upgrade Notes</a></li>
-<li><a class="reference internal" href="#bug-fixes">Bug Fixes</a></li>
+<li><a class="reference internal" href="#deprecation-notes">Deprecation Notes</a></li>
 <li><a class="reference internal" href="#other-notes">Other Notes</a></li>
 </ul>
 </li>
+<li><a class="reference internal" href="#release-notes-0-6-0">0.6.0</a><ul>
+<li><a class="reference internal" href="#release-notes-0-6-0-upgrade-notes">Upgrade Notes</a></li>
+<li><a class="reference internal" href="#bug-fixes">Bug Fixes</a></li>
+<li><a class="reference internal" href="#release-notes-0-6-0-other-notes">Other Notes</a></li>
+</ul>
+</li>
 <li><a class="reference internal" href="#release-notes-0-5-0">0.5.0</a><ul>
-<li><a class="reference internal" href="#prelude">Prelude</a></li>
+<li><a class="reference internal" href="#release-notes-0-5-0-prelude">Prelude</a></li>
 <li><a class="reference internal" href="#release-notes-0-5-0-upgrade-notes">Upgrade Notes</a></li>
 <li><a class="reference internal" href="#release-notes-0-5-0-other-notes">Other Notes</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#release-notes-0-4-0">0.4.0</a><ul>
 <li><a class="reference internal" href="#release-notes-0-4-0-prelude">Prelude</a></li>
-<li><a class="reference internal" href="#new-features">New Features</a></li>
+<li><a class="reference internal" href="#release-notes-0-4-0-new-features">New Features</a></li>
 <li><a class="reference internal" href="#release-notes-0-4-0-upgrade-notes">Upgrade Notes</a></li>
-<li><a class="reference internal" href="#deprecation-notes">Deprecation Notes</a></li>
+<li><a class="reference internal" href="#release-notes-0-4-0-deprecation-notes">Deprecation Notes</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#release-notes-0-3-0">0.3.0</a><ul>
@@ -830,7 +913,7 @@
   </div>
 </div><script src="_static/jquery.js?v=5d32c60e"></script>
     <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/search.html b/search.html
index 7d0a81a10..31540cf9e 100644
--- a/search.html
+++ b/search.html
@@ -4,7 +4,7 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="#" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 --><title>Search - Circuit Knitting Toolbox 0.6.0</title><link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 --><title>Search - Circuit Knitting Toolbox 0.7.0</title><link rel="stylesheet" type="text/css" href="_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
     <link rel="stylesheet" type="text/css" href="_static/styles/qiskit-ecosystem.css?v=745c5aa7" />
@@ -143,7 +143,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -170,7 +170,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="#" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -187,6 +187,7 @@
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -216,6 +217,9 @@
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -343,7 +347,7 @@
   </div>
 </div><script src="_static/jquery.js?v=5d32c60e"></script>
     <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="_static/doctools.js?v=9a2dae69"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="_static/thebelab-helper.js"></script>
diff --git a/searchindex.js b/searchindex.js
index c6ce8e539..3933a006d 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"0.1.0": [[17, "release-notes-0-1-0"]], "0.2.0": [[17, "release-notes-0-2-0"]], "0.3.0": [[17, "release-notes-0-3-0"]], "0.4.0": [[17, "release-notes-0-4-0"]], "0.5.0": [[17, "release-notes-0-5-0"]], "0.6.0": [[17, "release-notes-0-6-0"]], "An example: cutting a RZZGate": [[6, "an-example-cutting-a-rzzgate"]], "Bitwise utilities (circuit_knitting.utils.bitwise)": [[2, "module-circuit_knitting.utils.bitwise"]], "Bug Fixes": [[17, "bug-fixes"], [17, "release-notes-0-3-0-bug-fixes"]], "Calculate the sampling overhead for the chosen cuts": [[11, "Calculate-the-sampling-overhead-for-the-chosen-cuts"], [12, "Calculate-the-sampling-overhead-for-the-chosen-cuts"], [13, "Calculate-the-sampling-overhead-for-the-chosen-cuts"]], "Circuit Cutting": [[0, "module-circuit_knitting.cutting"], [0, "id2"], [15, null]], "Circuit Cutting How-To Guides": [[10, "circuit-cutting-how-to-guides"]], "Circuit Cutting Tutorials": [[14, "circuit-cutting-tutorials"]], "Circuit Knitting Toolbox": [[15, "circuit-knitting-toolbox"]], "Circuit Knitting Toolbox API References": [[1, "circuit-knitting-toolbox-api-references"]], "Circuit cutting as a quasiprobability decomposition (QPD)": [[6, "circuit-cutting-as-a-quasiprobability-decomposition-qpd"]], "Citing this project": [[15, "citing-this-project"]], "Compare reconstructed expectation values to exact expectation values from the original circuit": [[12, "Compare-reconstructed-expectation-values-to-exact-expectation-values-from-the-original-circuit"]], "Compare the reconstructed expectation values with the exact expectation values from the original circuit": [[11, "Compare-the-reconstructed-expectation-values-with-the-exact-expectation-values-from-the-original-circuit"], [13, "Compare-the-reconstructed-expectation-values-with-the-exact-expectation-values-from-the-original-circuit"]], "Contents": [[15, "contents"]], "Conversion (circuit_knitting.utils.conversion)": [[2, "module-circuit_knitting.utils.conversion"]], "Create a circuit to cut": [[11, "Create-a-circuit-to-cut"], [13, "Create-a-circuit-to-cut"]], "Create a circuit to run on the backend": [[12, "Create-a-circuit-to-run-on-the-backend"]], "Create a new circuit where Move instructions have been placed at the desired cut locations": [[13, "Create-a-new-circuit-where-Move-instructions-have-been-placed-at-the-desired-cut-locations"]], "Create a quantum circuit with Qiskit": [[4, "Create-a-quantum-circuit-with-Qiskit"], [5, "Create-a-quantum-circuit-with-Qiskit"]], "Create observables to go with the new circuit": [[13, "Create-observables-to-go-with-the-new-circuit"]], "Current limitations": [[6, "current-limitations"]], "CutQC": [[0, "cutqc"]], "CutQC (legacy circuit cutting implementation)": [[3, "cutqc-legacy-circuit-cutting-implementation"]], "CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding": [[4, "CutQC-Tutorial-1:-Circuit-Cutting-with-Automatic-Cut-Finding"]], "CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting": [[5, "CutQC-Tutorial-2:-Circuit-Cutting-with-Manual-Wire-Cutting"]], "CutQC Tutorials": [[3, "cutqc-tutorials"]], "Decompose the circuit with wire cutting": [[4, "Decompose-the-circuit-with-wire-cutting"], [5, "Decompose-the-circuit-with-wire-cutting"]], "Demonstrate how to find the minimum num_samples needed to retrieve all exact weights for 2 CNOT cuts": [[8, "Demonstrate-how-to-find-the-minimum-num_samples-needed-to-retrieve-all-exact-weights-for-2-CNOT-cuts"]], "Demonstrate how to obtain all weights exactly": [[8, "Demonstrate-how-to-obtain-all-weights-exactly"]], "Demonstrate that the QPD subexperiments will be shallower after cutting distant gates": [[12, "Demonstrate-that-the-QPD-subexperiments-will-be-shallower-after-cutting-distant-gates"]], "Deprecation Notes": [[17, "deprecation-notes"], [17, "release-notes-0-3-0-deprecation-notes"], [17, "release-notes-0-2-0-deprecation-notes"], [17, "release-notes-0-1-0-deprecation-notes"]], "Developer guide": [[15, "developer-guide"]], "Evaluate the subcircuits": [[4, "Evaluate-the-subcircuits"], [5, "Evaluate-the-subcircuits"]], "Explanatory material for the circuit cutting module": [[6, "explanatory-material-for-the-circuit-cutting-module"]], "Gate Cutting to Reduce Circuit Depth": [[12, "Gate-Cutting-to-Reduce-Circuit-Depth"]], "Gate Cutting to Reduce Circuit Width": [[11, "Gate-Cutting-to-Reduce-Circuit-Width"]], "Generate and run the cutting experiments; reconstruct and compare against uncut expectation values": [[9, "Generate-and-run-the-cutting-experiments;-reconstruct-and-compare-against-uncut-expectation-values"]], "Generate the subexperiments to run on the backend": [[11, "Generate-the-subexperiments-to-run-on-the-backend"], [12, "Generate-the-subexperiments-to-run-on-the-backend"], [13, "Generate-the-subexperiments-to-run-on-the-backend"]], "How to generate exact quasiprobability distributions from Sampler": [[7, "How-to-generate-exact-quasiprobability-distributions-from-Sampler"]], "How to generate exact sampling coefficients": [[8, "How-to-generate-exact-sampling-coefficients"]], "How to place wire cuts using a single-qubit CutWire instruction": [[9, "How-to-place-wire-cuts-using-a-single-qubit-CutWire-instruction"]], "Installation Instructions": [[16, "installation-instructions"]], "Iteration utilities (circuit_knitting.utils.iteration)": [[2, "module-circuit_knitting.utils.iteration"]], "Key terms": [[6, "key-terms"]], "Known Issues": [[17, "known-issues"]], "Metrics (circuit_knitting.utils.metrics)": [[2, "module-circuit_knitting.utils.metrics"]], "More general cut two-qubit gates via the KAK decomposition": [[6, "more-general-cut-two-qubit-gates-via-the-kak-decomposition"]], "New Features": [[17, "new-features"], [17, "release-notes-0-3-0-new-features"], [17, "release-notes-0-2-0-new-features"], [17, "release-notes-0-1-0-new-features"]], "Observable grouping (circuit_knitting.utils.observable_grouping)": [[2, "module-circuit_knitting.utils.observable_grouping"]], "Observe the coefficient weights returned from generate_cutting_experiments are WeightType.EXACT": [[8, "Observe-the-coefficient-weights-returned-from-generate_cutting_experiments-are-WeightType.EXACT"]], "Option 1: Pip Installation": [[16, "option-1-pip-installation"]], "Option 2: Install from Source": [[16, "option-2-install-from-source"]], "Option 3: Use within Docker": [[16, "option-3-use-within-docker"]], "Other Notes": [[17, "other-notes"], [17, "release-notes-0-5-0-other-notes"]], "Overview of circuit cutting": [[6, "overview-of-circuit-cutting"]], "Platform Support": [[16, "platform-support"]], "Pre-Installation": [[16, "pre-installation"]], "Prelude": [[17, "prelude"], [17, "release-notes-0-4-0-prelude"], [17, "release-notes-0-3-0-prelude"], [17, "release-notes-0-2-0-prelude"]], "Prepare a circuit for cutting": [[9, "Prepare-a-circuit-for-cutting"]], "Quasi-Probability Decomposition (QPD)": [[0, "quasi-probability-decomposition-qpd"]], "Reconstruct the expectation values": [[11, "Reconstruct-the-expectation-values"], [12, "Reconstruct-the-expectation-values"], [13, "Reconstruct-the-expectation-values"]], "Reconstruct the full circuit output": [[4, "Reconstruct-the-full-circuit-output"], [5, "Reconstruct-the-full-circuit-output"]], "Recover the uncut circuit": [[9, "Recover-the-uncut-circuit"]], "References": [[6, "references"], [15, null]], "Release Notes": [[17, "release-notes"]], "Replace distant gates with TwoQubitQPDGates by specifying their indices": [[12, "Replace-distant-gates-with-TwoQubitQPDGates-by-specifying-their-indices"]], "Run the subexperiments using the Qiskit Sampler primitive": [[11, "Run-the-subexperiments-using-the-Qiskit-Sampler-primitive"], [12, "Run-the-subexperiments-using-the-Qiskit-Sampler-primitive"], [13, "Run-the-subexperiments-using-the-Qiskit-Sampler-primitive"]], "Sample weights in CKT": [[6, "sample-weights-in-ckt"]], "Sampling overhead reference table": [[6, "sampling-overhead-reference-table"]], "Separate the circuit and observables": [[9, "Separate-the-circuit-and-observables"], [13, "Separate-the-circuit-and-observables"]], "Separate the circuit and observables according to a specified qubit partitioning": [[11, "Separate-the-circuit-and-observables-according-to-a-specified-qubit-partitioning"]], "Set up the Qiskit Runtime Service": [[5, "Set-up-the-Qiskit-Runtime-Service"]], "Simulation (circuit_knitting.utils.simulation)": [[2, "module-circuit_knitting.utils.simulation"]], "Specify a backend": [[12, "Specify-a-backend"]], "Specify some observables": [[9, "Specify-some-observables"], [11, "Specify-some-observables"], [12, "Specify-some-observables"], [13, "Specify-some-observables"]], "Transform cuts to moves": [[9, "Transform-cuts-to-moves"]], "Transforms (circuit_knitting.utils.transforms)": [[2, "module-circuit_knitting.utils.transforms"]], "Transpile the circuit, visualize the swaps, and note the depth": [[12, "Transpile-the-circuit,-visualize-the-swaps,-and-note-the-depth"]], "Transpiler passes (circuit_knitting.utils.transpiler_passes)": [[2, "module-circuit_knitting.utils.transpiler_passes"]], "Update the observables": [[9, "Update-the-observables"]], "Upgrade Notes": [[17, "upgrade-notes"], [17, "release-notes-0-5-0-upgrade-notes"], [17, "release-notes-0-4-0-upgrade-notes"], [17, "release-notes-0-3-0-upgrade-notes"], [17, "release-notes-0-2-0-upgrade-notes"], [17, "release-notes-0-1-0-upgrade-notes"]], "Utilities": [[2, "module-circuit_knitting.utils"]], "Verify the results": [[4, "Verify-the-results"], [5, "Verify-the-results"]], "Visualize the decomposed problem": [[9, "Visualize-the-decomposed-problem"], [11, "Visualize-the-decomposed-problem"], [13, "Visualize-the-decomposed-problem"]], "Wire Cutting Phrased as a Two-Qubit Move Instruction": [[13, "Wire-Cutting-Phrased-as-a-Two-Qubit-Move-Instruction"]], "Wire cutting phrased as a two-qubit Move operation": [[6, "wire-cutting-phrased-as-a-two-qubit-move-operation"]], "circuit_knitting.cutting.PartitionedCuttingProblem": [[18, "circuit-knitting-cutting-partitionedcuttingproblem"]], "circuit_knitting.cutting.cut_gates": [[19, "circuit-knitting-cutting-cut-gates"]], "circuit_knitting.cutting.cut_wires": [[20, "circuit-knitting-cutting-cut-wires"]], "circuit_knitting.cutting.cutqc.build": [[21, "circuit-knitting-cutting-cutqc-build"]], "circuit_knitting.cutting.cutqc.cut_circuit_wires": [[22, "circuit-knitting-cutting-cutqc-cut-circuit-wires"]], "circuit_knitting.cutting.cutqc.evaluate_subcircuits": [[23, "circuit-knitting-cutting-cutqc-evaluate-subcircuits"]], "circuit_knitting.cutting.cutqc.generate_summation_terms": [[24, "circuit-knitting-cutting-cutqc-generate-summation-terms"]], "circuit_knitting.cutting.cutqc.reconstruct_full_distribution": [[25, "circuit-knitting-cutting-cutqc-reconstruct-full-distribution"]], "circuit_knitting.cutting.cutqc.run_subcircuit_instances": [[26, "circuit-knitting-cutting-cutqc-run-subcircuit-instances"]], "circuit_knitting.cutting.cutqc.verify": [[27, "circuit-knitting-cutting-cutqc-verify"]], "circuit_knitting.cutting.expand_observables": [[28, "circuit-knitting-cutting-expand-observables"]], "circuit_knitting.cutting.generate_cutting_experiments": [[29, "circuit-knitting-cutting-generate-cutting-experiments"]], "circuit_knitting.cutting.instructions.CutWire": [[30, "circuit-knitting-cutting-instructions-cutwire"]], "circuit_knitting.cutting.instructions.Move": [[31, "circuit-knitting-cutting-instructions-move"]], "circuit_knitting.cutting.partition_circuit_qubits": [[32, "circuit-knitting-cutting-partition-circuit-qubits"]], "circuit_knitting.cutting.partition_problem": [[33, "circuit-knitting-cutting-partition-problem"]], "circuit_knitting.cutting.qpd.BaseQPDGate": [[34, "circuit-knitting-cutting-qpd-baseqpdgate"]], "circuit_knitting.cutting.qpd.QPDBasis": [[35, "circuit-knitting-cutting-qpd-qpdbasis"]], "circuit_knitting.cutting.qpd.SingleQubitQPDGate": [[36, "circuit-knitting-cutting-qpd-singlequbitqpdgate"]], "circuit_knitting.cutting.qpd.TwoQubitQPDGate": [[37, "circuit-knitting-cutting-qpd-twoqubitqpdgate"]], "circuit_knitting.cutting.qpd.WeightType": [[38, "circuit-knitting-cutting-qpd-weighttype"]], "circuit_knitting.cutting.qpd.decompose_qpd_instructions": [[39, "circuit-knitting-cutting-qpd-decompose-qpd-instructions"]], "circuit_knitting.cutting.qpd.generate_qpd_weights": [[40, "circuit-knitting-cutting-qpd-generate-qpd-weights"]], "circuit_knitting.cutting.qpd.qpdbasis_from_instruction": [[41, "circuit-knitting-cutting-qpd-qpdbasis-from-instruction"]], "circuit_knitting.cutting.reconstruct_expectation_values": [[42, "circuit-knitting-cutting-reconstruct-expectation-values"]], "circuit_knitting.utils.bitwise.bit_count": [[43, "circuit-knitting-utils-bitwise-bit-count"]], "circuit_knitting.utils.conversion.dict_to_array": [[44, "circuit-knitting-utils-conversion-dict-to-array"]], "circuit_knitting.utils.conversion.naive_probability_distribution": [[45, "circuit-knitting-utils-conversion-naive-probability-distribution"]], "circuit_knitting.utils.conversion.nearest_probability_distribution": [[46, "circuit-knitting-utils-conversion-nearest-probability-distribution"]], "circuit_knitting.utils.conversion.quasi_to_real": [[47, "circuit-knitting-utils-conversion-quasi-to-real"]], "circuit_knitting.utils.iteration.unique_by_eq": [[48, "circuit-knitting-utils-iteration-unique-by-eq"]], "circuit_knitting.utils.iteration.unique_by_id": [[49, "circuit-knitting-utils-iteration-unique-by-id"]], "circuit_knitting.utils.metrics.HOP": [[50, "circuit-knitting-utils-metrics-hop"]], "circuit_knitting.utils.metrics.MAPE": [[51, "circuit-knitting-utils-metrics-mape"]], "circuit_knitting.utils.metrics.MSE": [[52, "circuit-knitting-utils-metrics-mse"]], "circuit_knitting.utils.metrics.chi2_distance": [[53, "circuit-knitting-utils-metrics-chi2-distance"]], "circuit_knitting.utils.metrics.cross_entropy": [[54, "circuit-knitting-utils-metrics-cross-entropy"]], "circuit_knitting.utils.observable_grouping.CommutingObservableGroup": [[55, "circuit-knitting-utils-observable-grouping-commutingobservablegroup"]], "circuit_knitting.utils.observable_grouping.ObservableCollection": [[56, "circuit-knitting-utils-observable-grouping-observablecollection"]], "circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem": [[57, "circuit-knitting-utils-observable-grouping-observables-restricted-to-subsystem"]], "circuit_knitting.utils.simulation.ExactSampler": [[58, "circuit-knitting-utils-simulation-exactsampler"]], "circuit_knitting.utils.simulation.simulate_statevector_outcomes": [[59, "circuit-knitting-utils-simulation-simulate-statevector-outcomes"]], "circuit_knitting.utils.transforms.SeparatedCircuits": [[60, "circuit-knitting-utils-transforms-separatedcircuits"]], "circuit_knitting.utils.transforms.separate_circuit": [[61, "circuit-knitting-utils-transforms-separate-circuit"]], "circuit_knitting.utils.transpiler_passes.ConsolidateResets": [[62, "circuit-knitting-utils-transpiler-passes-consolidateresets"]], "circuit_knitting.utils.transpiler_passes.RemoveFinalReset": [[63, "circuit-knitting-utils-transpiler-passes-removefinalreset"]]}, "docnames": ["apidocs/circuit_cutting", "apidocs/index", "apidocs/utils", "circuit_cutting/cutqc/index", "circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding", "circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting", "circuit_cutting/explanation/index", "circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler", "circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients", "circuit_cutting/how-tos/how_to_specify_cut_wires", "circuit_cutting/how-tos/index", "circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width", "circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth", "circuit_cutting/tutorials/03_wire_cutting_via_move_instruction", "circuit_cutting/tutorials/index", "index", "install", "release-notes", "stubs/circuit_knitting.cutting.PartitionedCuttingProblem", "stubs/circuit_knitting.cutting.cut_gates", "stubs/circuit_knitting.cutting.cut_wires", "stubs/circuit_knitting.cutting.cutqc.build", "stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires", "stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits", "stubs/circuit_knitting.cutting.cutqc.generate_summation_terms", "stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution", "stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances", "stubs/circuit_knitting.cutting.cutqc.verify", "stubs/circuit_knitting.cutting.expand_observables", "stubs/circuit_knitting.cutting.generate_cutting_experiments", "stubs/circuit_knitting.cutting.instructions.CutWire", "stubs/circuit_knitting.cutting.instructions.Move", "stubs/circuit_knitting.cutting.partition_circuit_qubits", "stubs/circuit_knitting.cutting.partition_problem", "stubs/circuit_knitting.cutting.qpd.BaseQPDGate", "stubs/circuit_knitting.cutting.qpd.QPDBasis", "stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate", "stubs/circuit_knitting.cutting.qpd.TwoQubitQPDGate", "stubs/circuit_knitting.cutting.qpd.WeightType", "stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions", "stubs/circuit_knitting.cutting.qpd.generate_qpd_weights", "stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction", "stubs/circuit_knitting.cutting.reconstruct_expectation_values", "stubs/circuit_knitting.utils.bitwise.bit_count", "stubs/circuit_knitting.utils.conversion.dict_to_array", "stubs/circuit_knitting.utils.conversion.naive_probability_distribution", "stubs/circuit_knitting.utils.conversion.nearest_probability_distribution", "stubs/circuit_knitting.utils.conversion.quasi_to_real", "stubs/circuit_knitting.utils.iteration.unique_by_eq", "stubs/circuit_knitting.utils.iteration.unique_by_id", "stubs/circuit_knitting.utils.metrics.HOP", "stubs/circuit_knitting.utils.metrics.MAPE", "stubs/circuit_knitting.utils.metrics.MSE", "stubs/circuit_knitting.utils.metrics.chi2_distance", "stubs/circuit_knitting.utils.metrics.cross_entropy", "stubs/circuit_knitting.utils.observable_grouping.CommutingObservableGroup", "stubs/circuit_knitting.utils.observable_grouping.ObservableCollection", "stubs/circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem", "stubs/circuit_knitting.utils.simulation.ExactSampler", "stubs/circuit_knitting.utils.simulation.simulate_statevector_outcomes", "stubs/circuit_knitting.utils.transforms.SeparatedCircuits", "stubs/circuit_knitting.utils.transforms.separate_circuit", "stubs/circuit_knitting.utils.transpiler_passes.ConsolidateResets", "stubs/circuit_knitting.utils.transpiler_passes.RemoveFinalReset"], "envversion": {"nbsphinx": 4, "sphinx": 61, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1, "sphinx.ext.viewcode": 1}, "filenames": ["apidocs/circuit_cutting.rst", "apidocs/index.rst", "apidocs/utils.rst", "circuit_cutting/cutqc/index.rst", "circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb", "circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb", "circuit_cutting/explanation/index.rst", "circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb", "circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb", "circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb", "circuit_cutting/how-tos/index.rst", "circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb", "circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb", "circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb", "circuit_cutting/tutorials/index.rst", "index.rst", "install.rst", "release-notes.rst", "stubs/circuit_knitting.cutting.PartitionedCuttingProblem.rst", "stubs/circuit_knitting.cutting.cut_gates.rst", "stubs/circuit_knitting.cutting.cut_wires.rst", "stubs/circuit_knitting.cutting.cutqc.build.rst", "stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires.rst", "stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits.rst", "stubs/circuit_knitting.cutting.cutqc.generate_summation_terms.rst", "stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.rst", "stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances.rst", "stubs/circuit_knitting.cutting.cutqc.verify.rst", "stubs/circuit_knitting.cutting.expand_observables.rst", "stubs/circuit_knitting.cutting.generate_cutting_experiments.rst", "stubs/circuit_knitting.cutting.instructions.CutWire.rst", "stubs/circuit_knitting.cutting.instructions.Move.rst", "stubs/circuit_knitting.cutting.partition_circuit_qubits.rst", "stubs/circuit_knitting.cutting.partition_problem.rst", "stubs/circuit_knitting.cutting.qpd.BaseQPDGate.rst", "stubs/circuit_knitting.cutting.qpd.QPDBasis.rst", "stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.rst", "stubs/circuit_knitting.cutting.qpd.TwoQubitQPDGate.rst", "stubs/circuit_knitting.cutting.qpd.WeightType.rst", "stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.rst", "stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.rst", "stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction.rst", "stubs/circuit_knitting.cutting.reconstruct_expectation_values.rst", "stubs/circuit_knitting.utils.bitwise.bit_count.rst", "stubs/circuit_knitting.utils.conversion.dict_to_array.rst", "stubs/circuit_knitting.utils.conversion.naive_probability_distribution.rst", "stubs/circuit_knitting.utils.conversion.nearest_probability_distribution.rst", "stubs/circuit_knitting.utils.conversion.quasi_to_real.rst", "stubs/circuit_knitting.utils.iteration.unique_by_eq.rst", "stubs/circuit_knitting.utils.iteration.unique_by_id.rst", "stubs/circuit_knitting.utils.metrics.HOP.rst", "stubs/circuit_knitting.utils.metrics.MAPE.rst", "stubs/circuit_knitting.utils.metrics.MSE.rst", "stubs/circuit_knitting.utils.metrics.chi2_distance.rst", "stubs/circuit_knitting.utils.metrics.cross_entropy.rst", "stubs/circuit_knitting.utils.observable_grouping.CommutingObservableGroup.rst", "stubs/circuit_knitting.utils.observable_grouping.ObservableCollection.rst", "stubs/circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.rst", "stubs/circuit_knitting.utils.simulation.ExactSampler.rst", "stubs/circuit_knitting.utils.simulation.simulate_statevector_outcomes.rst", "stubs/circuit_knitting.utils.transforms.SeparatedCircuits.rst", "stubs/circuit_knitting.utils.transforms.separate_circuit.rst", "stubs/circuit_knitting.utils.transpiler_passes.ConsolidateResets.rst", "stubs/circuit_knitting.utils.transpiler_passes.RemoveFinalReset.rst"], "indexentries": {"__init__() (commutingobservablegroup method)": [[55, "circuit_knitting.utils.observable_grouping.CommutingObservableGroup.__init__", false]], "__init__() (consolidateresets method)": [[62, "circuit_knitting.utils.transpiler_passes.ConsolidateResets.__init__", false]], "__init__() (exactsampler method)": [[58, "circuit_knitting.utils.simulation.ExactSampler.__init__", false]], "__init__() (observablecollection method)": [[56, "circuit_knitting.utils.observable_grouping.ObservableCollection.__init__", false]], "__init__() (removefinalreset method)": [[63, "circuit_knitting.utils.transpiler_passes.RemoveFinalReset.__init__", false]], "baseqpdgate (class in circuit_knitting.cutting.qpd)": [[34, "circuit_knitting.cutting.qpd.BaseQPDGate", false]], "bit_count() (in module circuit_knitting.utils.bitwise)": [[43, "circuit_knitting.utils.bitwise.bit_count", false]], "build() (in module circuit_knitting.cutting.cutqc)": [[21, "circuit_knitting.cutting.cutqc.build", false]], "chi2_distance() (in module circuit_knitting.utils.metrics)": [[53, "circuit_knitting.utils.metrics.chi2_distance", false]], "circuit_knitting": [[1, "module-circuit_knitting", false]], "circuit_knitting.cutting": [[0, "module-circuit_knitting.cutting", false]], "circuit_knitting.utils": [[2, "module-circuit_knitting.utils", false]], "circuit_knitting.utils.bitwise": [[2, "module-circuit_knitting.utils.bitwise", false]], "circuit_knitting.utils.conversion": [[2, "module-circuit_knitting.utils.conversion", false]], "circuit_knitting.utils.iteration": [[2, "module-circuit_knitting.utils.iteration", false]], "circuit_knitting.utils.metrics": [[2, "module-circuit_knitting.utils.metrics", false]], "circuit_knitting.utils.observable_grouping": [[2, "module-circuit_knitting.utils.observable_grouping", false]], "circuit_knitting.utils.simulation": [[2, "module-circuit_knitting.utils.simulation", false]], "circuit_knitting.utils.transforms": [[2, "module-circuit_knitting.utils.transforms", false]], "circuit_knitting.utils.transpiler_passes": [[2, "module-circuit_knitting.utils.transpiler_passes", false]], "commutingobservablegroup (class in circuit_knitting.utils.observable_grouping)": [[55, "circuit_knitting.utils.observable_grouping.CommutingObservableGroup", false]], "consolidateresets (class in circuit_knitting.utils.transpiler_passes)": [[62, "circuit_knitting.utils.transpiler_passes.ConsolidateResets", false]], "cross_entropy() (in module circuit_knitting.utils.metrics)": [[54, "circuit_knitting.utils.metrics.cross_entropy", false]], "cut_circuit_wires() (in module circuit_knitting.cutting.cutqc)": [[22, "circuit_knitting.cutting.cutqc.cut_circuit_wires", false]], "cut_gates() (in module circuit_knitting.cutting)": [[19, "circuit_knitting.cutting.cut_gates", false]], "cut_wires() (in module circuit_knitting.cutting)": [[20, "circuit_knitting.cutting.cut_wires", false]], "cutwire (class in circuit_knitting.cutting.instructions)": [[30, "circuit_knitting.cutting.instructions.CutWire", false]], "decompose_qpd_instructions() (in module circuit_knitting.cutting.qpd)": [[39, "circuit_knitting.cutting.qpd.decompose_qpd_instructions", false]], "dict_to_array() (in module circuit_knitting.utils.conversion)": [[44, "circuit_knitting.utils.conversion.dict_to_array", false]], "evaluate_subcircuits() (in module circuit_knitting.cutting.cutqc)": [[23, "circuit_knitting.cutting.cutqc.evaluate_subcircuits", false]], "exactsampler (class in circuit_knitting.utils.simulation)": [[58, "circuit_knitting.utils.simulation.ExactSampler", false]], "expand_observables() (in module circuit_knitting.cutting)": [[28, "circuit_knitting.cutting.expand_observables", false]], "generate_cutting_experiments() (in module circuit_knitting.cutting)": [[29, "circuit_knitting.cutting.generate_cutting_experiments", false]], "generate_qpd_weights() (in module circuit_knitting.cutting.qpd)": [[40, "circuit_knitting.cutting.qpd.generate_qpd_weights", false]], "generate_summation_terms() (in module circuit_knitting.cutting.cutqc)": [[24, "circuit_knitting.cutting.cutqc.generate_summation_terms", false]], "hop() (in module circuit_knitting.utils.metrics)": [[50, "circuit_knitting.utils.metrics.HOP", false]], "mape() (in module circuit_knitting.utils.metrics)": [[51, "circuit_knitting.utils.metrics.MAPE", false]], "module": [[0, "module-circuit_knitting.cutting", false], [1, "module-circuit_knitting", false], [2, "module-circuit_knitting.utils", false], [2, "module-circuit_knitting.utils.bitwise", false], [2, "module-circuit_knitting.utils.conversion", false], [2, "module-circuit_knitting.utils.iteration", false], [2, "module-circuit_knitting.utils.metrics", false], [2, "module-circuit_knitting.utils.observable_grouping", false], [2, "module-circuit_knitting.utils.simulation", false], [2, "module-circuit_knitting.utils.transforms", false], [2, "module-circuit_knitting.utils.transpiler_passes", false]], "move (class in circuit_knitting.cutting.instructions)": [[31, "circuit_knitting.cutting.instructions.Move", false]], "mse() (in module circuit_knitting.utils.metrics)": [[52, "circuit_knitting.utils.metrics.MSE", false]], "naive_probability_distribution() (in module circuit_knitting.utils.conversion)": [[45, "circuit_knitting.utils.conversion.naive_probability_distribution", false]], "nearest_probability_distribution() (in module circuit_knitting.utils.conversion)": [[46, "circuit_knitting.utils.conversion.nearest_probability_distribution", false]], "observablecollection (class in circuit_knitting.utils.observable_grouping)": [[56, "circuit_knitting.utils.observable_grouping.ObservableCollection", false]], "observables_restricted_to_subsystem() (in module circuit_knitting.utils.observable_grouping)": [[57, "circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem", false]], "partition_circuit_qubits() (in module circuit_knitting.cutting)": [[32, "circuit_knitting.cutting.partition_circuit_qubits", false]], "partition_problem() (in module circuit_knitting.cutting)": [[33, "circuit_knitting.cutting.partition_problem", false]], "partitionedcuttingproblem (class in circuit_knitting.cutting)": [[18, "circuit_knitting.cutting.PartitionedCuttingProblem", false]], "qpdbasis (class in circuit_knitting.cutting.qpd)": [[35, "circuit_knitting.cutting.qpd.QPDBasis", false]], "qpdbasis_from_instruction() (in module circuit_knitting.cutting.qpd)": [[41, "circuit_knitting.cutting.qpd.qpdbasis_from_instruction", false]], "quasi_to_real() (in module circuit_knitting.utils.conversion)": [[47, "circuit_knitting.utils.conversion.quasi_to_real", false]], "reconstruct_expectation_values() (in module circuit_knitting.cutting)": [[42, "circuit_knitting.cutting.reconstruct_expectation_values", false]], "reconstruct_full_distribution() (in module circuit_knitting.cutting.cutqc)": [[25, "circuit_knitting.cutting.cutqc.reconstruct_full_distribution", false]], "removefinalreset (class in circuit_knitting.utils.transpiler_passes)": [[63, "circuit_knitting.utils.transpiler_passes.RemoveFinalReset", false]], "run_subcircuit_instances() (in module circuit_knitting.cutting.cutqc)": [[26, "circuit_knitting.cutting.cutqc.run_subcircuit_instances", false]], "separate_circuit() (in module circuit_knitting.utils.transforms)": [[61, "circuit_knitting.utils.transforms.separate_circuit", false]], "separatedcircuits (class in circuit_knitting.utils.transforms)": [[60, "circuit_knitting.utils.transforms.SeparatedCircuits", false]], "simulate_statevector_outcomes() (in module circuit_knitting.utils.simulation)": [[59, "circuit_knitting.utils.simulation.simulate_statevector_outcomes", false]], "singlequbitqpdgate (class in circuit_knitting.cutting.qpd)": [[36, "circuit_knitting.cutting.qpd.SingleQubitQPDGate", false]], "twoqubitqpdgate (class in circuit_knitting.cutting.qpd)": [[37, "circuit_knitting.cutting.qpd.TwoQubitQPDGate", false]], "unique_by_eq() (in module circuit_knitting.utils.iteration)": [[48, "circuit_knitting.utils.iteration.unique_by_eq", false]], "unique_by_id() (in module circuit_knitting.utils.iteration)": [[49, "circuit_knitting.utils.iteration.unique_by_id", false]], "verify() (in module circuit_knitting.cutting.cutqc)": [[27, "circuit_knitting.cutting.cutqc.verify", false]], "weighttype (class in circuit_knitting.cutting.qpd)": [[38, "circuit_knitting.cutting.qpd.WeightType", false]]}, "objects": {"": [[1, 0, 0, "-", "circuit_knitting"]], "circuit_knitting": [[0, 0, 0, "-", "cutting"], [2, 0, 0, "-", "utils"]], "circuit_knitting.cutting": [[18, 1, 1, "", "PartitionedCuttingProblem"], [19, 2, 1, "", "cut_gates"], [20, 2, 1, "", "cut_wires"], [28, 2, 1, "", "expand_observables"], [29, 2, 1, "", "generate_cutting_experiments"], [32, 2, 1, "", "partition_circuit_qubits"], [33, 2, 1, "", "partition_problem"], [42, 2, 1, "", "reconstruct_expectation_values"]], "circuit_knitting.cutting.cutqc": [[21, 2, 1, "", "build"], [22, 2, 1, "", "cut_circuit_wires"], [23, 2, 1, "", "evaluate_subcircuits"], [24, 2, 1, "", "generate_summation_terms"], [25, 2, 1, "", "reconstruct_full_distribution"], [26, 2, 1, "", "run_subcircuit_instances"], [27, 2, 1, "", "verify"]], "circuit_knitting.cutting.instructions": [[30, 1, 1, "", "CutWire"], [31, 1, 1, "", "Move"]], "circuit_knitting.cutting.qpd": [[34, 1, 1, "", "BaseQPDGate"], [35, 1, 1, "", "QPDBasis"], [36, 1, 1, "", "SingleQubitQPDGate"], [37, 1, 1, "", "TwoQubitQPDGate"], [38, 1, 1, "", "WeightType"], [39, 2, 1, "", "decompose_qpd_instructions"], [40, 2, 1, "", "generate_qpd_weights"], [41, 2, 1, "", "qpdbasis_from_instruction"]], "circuit_knitting.utils": [[2, 0, 0, "-", "bitwise"], [2, 0, 0, "-", "conversion"], [2, 0, 0, "-", "iteration"], [2, 0, 0, "-", "metrics"], [2, 0, 0, "-", "observable_grouping"], [2, 0, 0, "-", "simulation"], [2, 0, 0, "-", "transforms"], [2, 0, 0, "-", "transpiler_passes"]], "circuit_knitting.utils.bitwise": [[43, 2, 1, "", "bit_count"]], "circuit_knitting.utils.conversion": [[44, 2, 1, "", "dict_to_array"], [45, 2, 1, "", "naive_probability_distribution"], [46, 2, 1, "", "nearest_probability_distribution"], [47, 2, 1, "", "quasi_to_real"]], "circuit_knitting.utils.iteration": [[48, 2, 1, "", "unique_by_eq"], [49, 2, 1, "", "unique_by_id"]], "circuit_knitting.utils.metrics": [[50, 2, 1, "", "HOP"], [51, 2, 1, "", "MAPE"], [52, 2, 1, "", "MSE"], [53, 2, 1, "", "chi2_distance"], [54, 2, 1, "", "cross_entropy"]], "circuit_knitting.utils.observable_grouping": [[55, 1, 1, "", "CommutingObservableGroup"], [56, 1, 1, "", "ObservableCollection"], [57, 2, 1, "", "observables_restricted_to_subsystem"]], "circuit_knitting.utils.observable_grouping.CommutingObservableGroup": [[55, 3, 1, "", "__init__"]], "circuit_knitting.utils.observable_grouping.ObservableCollection": [[56, 3, 1, "", "__init__"]], "circuit_knitting.utils.simulation": [[58, 1, 1, "", "ExactSampler"], [59, 2, 1, "", "simulate_statevector_outcomes"]], "circuit_knitting.utils.simulation.ExactSampler": [[58, 3, 1, "", "__init__"]], "circuit_knitting.utils.transforms": [[60, 1, 1, "", "SeparatedCircuits"], [61, 2, 1, "", "separate_circuit"]], "circuit_knitting.utils.transpiler_passes": [[62, 1, 1, "", "ConsolidateResets"], [63, 1, 1, "", "RemoveFinalReset"]], "circuit_knitting.utils.transpiler_passes.ConsolidateResets": [[62, 3, 1, "", "__init__"]], "circuit_knitting.utils.transpiler_passes.RemoveFinalReset": [[63, 3, 1, "", "__init__"]]}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "class", "Python class"], "2": ["py", "function", "Python function"], "3": ["py", "method", "Python method"]}, "objtypes": {"0": "py:module", "1": "py:class", "2": "py:function", "3": "py:method"}, "terms": {"": [4, 5, 6, 11, 12, 13, 15, 16, 17, 18, 19, 20, 23, 26, 28, 29, 31, 32, 33, 36, 42], "0": [4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 24, 29, 31, 33, 36, 42, 45, 48, 49, 50, 51, 52, 53, 54, 61], "00016": 6, "00223614": 9, "00260745": 9, "00348059": 11, "00368749": 9, "00420255": 11, "00509918": 11, "00634799": 11, "0066282": 11, "00710346": 11, "00799979": 12, "00ed70b5342f79f0a970ee9821c271eeffaf760a7dcd36ec": 16, "01264952": 9, "01302933": 11, "01375639": 12, "01425288": 12, "01568635": 13, "01612639": 11, "01644866": 12, "01719568": 11, "01825501": 9, "01989373": 12, "02047424": 13, "02058": 6, "0209120": 6, "02162333": 13, "02218384": 13, "02333": 3, "02581648": 9, "02657423": 12, "0275": 52, "03058": 13, "03366": 6, "03366v1": [9, 13, 20], "03656231": 12, "03803565": 12, "03902028": 12, "0394048": 11, "05759096": 11, "07024487": 12, "070502": 46, "07534": 6, "0x7fb6f8b361f0": 13, "1": [3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 24, 25, 29, 31, 33, 39, 40, 42, 45, 50, 51, 52, 53, 54, 57, 61], "10": [4, 5, 9, 11, 12, 13, 15], "100": [12, 51], "1000": [7, 40], "101": 12, "10396": 41, "108": [17, 46], "10927476": 11, "11": [4, 5, 9, 11, 12, 13, 17], "11174": [6, 17], "11550725": 12, "11581981": 13, "11638": 6, "11923456": 11, "12": [4, 5, 9, 11, 12, 13, 17], "12230408": 11, "1243059": 11, "127": 16, "12940509": 11, "13": [4, 5, 9, 12, 13, 24], "13178352181795e": 4, "14": [4, 5, 9, 12, 13], "15": [4, 5, 13], "15630245": 13, "16": [4, 5, 6], "1612": 6, "16666667": 8, "17": 4, "17531269": 12, "1767767": [9, 13], "17901284": 9, "18": 4, "1810": 58, "1811": 58, "19": 4, "1909": 6, "1d": 29, "1st": 4, "2": [3, 4, 6, 7, 9, 11, 12, 13, 14, 15, 24, 29, 31, 37, 39, 45, 48, 52, 53, 60, 61], "20": 4, "2006": [6, 17], "2012": [3, 46], "2022": [4, 5], "2023": 15, "20349121": 12, "20855487": 12, "21645021645021645": 53, "2205": 6, "23": 17, "23006544": 12, "2302": [6, 9, 13, 20], "2312": 6, "23416169": 12, "24511719": 12, "24999999999999992": 8, "25": [8, 50, 51, 52, 53, 54], "25061035": 12, "256": [4, 13], "259": 6, "26": 12, "260720927719812e": 5, "262": 6, "264": 6, "28001606": 11, "285": 17, "29614246": 11, "2d": 39, "2nd": 4, "3": [4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 20, 31, 50, 51, 52, 53, 54, 57, 61], "32": [4, 5], "34": 5, "35": 4, "36": 8, "36167086": 12, "36916216": 11, "37542725": 12, "37551016": 11, "3862943611198906": 54, "39": [4, 5, 9, 11, 13], "3d": 17, "4": [4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 24, 31, 61], "4000": [4, 5], "406": 17, "44": 17, "45": 17, "455": 17, "4880309854901524e": 4, "4880563544629694e": 4, "49": 6, "5": [4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 50, 51, 52, 53, 54], "50983039": 12, "52511814": 11, "5281": 15, "52859873": 11, "52972412": 12, "531": 6, "532": 6, "554441697306333e": 5, "56127511": 12, "564551116068219": 4, "5692749": 12, "5760386594463524e": 5, "58741736": 11, "59161991": 11, "5996810883678423": 5, "599681088367844": 5, "6": [4, 5, 6, 8, 9, 11, 12, 13, 15], "64": 16, "66666666666659": 51, "68548346": 13, "68885177": 9, "69142044": 13, "7": [4, 5, 6, 8, 9, 11, 12, 13, 16, 17, 24], "70710678": [9, 13], "70971423": 9, "729": 12, "748059826691265e": 5, "75": 5, "7794080473092745e": 4, "7987997": 15, "8": [4, 5, 6, 8, 9, 11, 12, 13, 17], "81": 11, "828": 6, "8888": 16, "9": [4, 5, 6, 9, 11, 12, 13, 17], "9004283905932716": 5, "9004283905932736": 5, "91": 51, "9657": 58, "9945381353717198": 4, "99453813537172": 4, "A": [4, 5, 6, 8, 9, 11, 13, 19, 20, 21, 22, 23, 24, 27, 29, 31, 32, 33, 34, 35, 39, 40, 42, 57, 61], "And": 4, "As": [6, 9, 13, 17], "By": 6, "For": [4, 5, 6, 9, 11, 12, 13, 16, 17, 31], "If": [4, 5, 6, 7, 8, 15, 16, 29, 31, 33, 39, 42, 61], "In": [4, 5, 6, 7, 8, 9, 11, 12, 13, 16, 17, 29], "It": [6, 17], "Its": 12, "No": [6, 16], "One": [6, 13], "Or": 16, "The": [3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 37, 39, 40, 41, 42, 44, 45, 46, 47, 50, 51, 52, 53, 54, 56, 57, 58, 61], "There": [6, 16, 17], "These": [3, 6, 9, 13, 17], "To": [11, 12, 13, 15, 16, 17], "With": [6, 17], "_": 61, "__init__": [55, 56, 58, 62, 63], "_i": 6, "_min_nonzero": 8, "_run_subcircuit": 25, "a_1": 6, "a_2": 6, "a_3": 6, "a_4": 6, "a_5": 6, "a_6": 6, "a_i": 6, "aaaabbbb": 13, "aabb": [8, 11], "ab": [6, 7], "abba": 61, "abc": 49, "abl": [9, 13], "about": [4, 5, 15, 20, 26], "abov": [4, 5, 6, 8, 11, 12, 13, 40, 50, 58], "absolut": [4, 5, 6, 8, 40, 51], "abstract": 6, "abstractli": 6, "accept": [11, 12, 13], "access": [4, 5, 16, 17, 39], "accord": [15, 28, 39, 61], "account": [4, 13], "achiev": 8, "acquir": 16, "across": 16, "act": [6, 19, 32, 33, 34, 37], "activ": 16, "active_account": [4, 5], "actual": [6, 9, 50, 51, 52, 53, 54], "ad": [6, 9, 13, 17, 29], "adapt": 17, "add": [17, 24], "addit": [6, 9, 13, 17], "addition": 17, "adjust": 16, "aer": [11, 12, 13, 17, 58], "affect": 32, "aforement": 31, "after": [6, 28], "again": 6, "against": [4, 5], "agata": 15, "al": [4, 5, 46], "algorithm": 17, "all": [4, 6, 10, 16, 17, 20, 24, 26, 32, 33, 39, 41, 45, 48, 58, 59], "alloc": [9, 13, 20], "allow": [4, 5, 6, 13, 16, 17, 22, 29], "almudena": 15, "along": [9, 33, 59], "alreadi": 17, "also": [4, 6, 12, 17, 29, 41], "alter": [4, 5], "altern": [4, 5, 13], "alwai": 29, "ambigu": 6, "amend": [4, 5], "amount": 6, "an": [4, 9, 12, 13, 15, 16, 17, 30, 33, 34, 35, 39, 42, 50], "ancillari": 4, "andrew": 15, "angl": 6, "ani": [4, 5, 6, 8, 9, 11, 12, 13, 22, 23, 24, 26, 33, 35], "anoth": [12, 13, 31], "anywher": 58, "apach": [4, 5], "api": [6, 15, 17], "appear": [4, 5], "append": [9, 13, 29, 31], "appendix": [6, 20], "appl": 16, "appli": 39, "applic": [6, 17], "appropri": [16, 17, 28, 48], "approx": 6, "approxim": [9, 11, 12, 13], "ar": [3, 4, 5, 6, 9, 11, 12, 13, 16, 17, 22, 28, 29, 33, 39, 40, 41, 42, 48, 51, 52, 54, 56, 59], "arbitrari": 17, "arbitrarili": 12, "areeq": 15, "arg": [11, 13, 16, 62, 63], "argument": [4, 5, 6, 17, 22, 42], "arm": 16, "around": 17, "arrai": [2, 29, 44, 47, 50, 51, 52, 53, 54], "arxiv": [3, 6, 9, 13, 17], "asc": [4, 5], "ascend": 17, "assign": [34, 35, 36, 56], "assign_paramet": [4, 8, 11, 12], "associ": [6, 17, 29, 38, 42], "attribut": [18, 30, 31, 34, 35, 36, 37, 38, 55, 56, 58, 60, 62, 63], "attributeerror": 17, "author": 15, "autom": 3, "automat": [3, 5, 9, 13, 16, 17, 22, 33, 56], "avail": [6, 15, 16, 17, 31], "avoid": 6, "awai": 17, "b": [8, 11, 13, 61], "baac": 61, "backend": [4, 5, 6, 17, 23, 26], "backend_nam": [4, 5, 23, 26], "backward": 17, "base": [4, 5, 6, 7, 8, 9, 11, 12, 13, 18, 30, 31, 33, 34, 35, 36, 37, 38, 39, 60], "baseqpdg": [6, 17, 36, 37, 39], "basesampl": [7, 17], "basi": [6, 11, 12, 13, 17, 34, 35, 36, 37, 40], "basic": [4, 5, 6], "basis_id": [34, 36, 37], "batch": 6, "becaus": [4, 6, 9], "becom": 13, "been": [4, 5, 6, 9, 12, 17, 31], "befor": [16, 17, 19, 32, 42], "begin": 13, "behavior": 17, "being": 6, "bello": 15, "belong": [29, 32, 36, 39], "below": [4, 5, 6, 16, 17, 42], "benefit": 6, "beta": 6, "better": 48, "between": [6, 8, 12, 27, 28, 42, 48, 51, 52, 53, 54], "beyond": 6, "bi": 6, "bib": 15, "bin": 16, "binary_prob": [4, 5], "birgitta": 6, "bit": [17, 19, 33, 43, 58], "bitstr": [4, 5, 17, 50], "block": [11, 12, 13], "bool": [19, 22, 32], "boost": 6, "both": [4, 5, 6, 17, 29, 48], "boundari": 33, "bra": 15, "bravyi": [6, 15], "break": 15, "brenner": 6, "brief": 16, "browser": 16, "bryce": 15, "build": [13, 16, 17], "built": 17, "bypass": 17, "c": [15, 16, 61], "calcul": [4, 5, 7, 8, 17, 42], "caleb": 15, "call": [6, 7, 9, 17, 28], "can": [4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17, 28, 35, 41, 48, 58, 59], "cannot": [28, 41], "capabl": 6, "captur": [4, 5], "care": [16, 42], "carrera": 15, "carri": [4, 5], "case": [4, 5, 6, 8, 9, 16, 17, 29, 31, 33, 41, 48], "caus": [13, 17], "cd": 16, "center": 17, "certain": [6, 12, 16, 17], "chamber": 6, "chang": [15, 17], "channel": [6, 31], "charact": 13, "check": [4, 5, 17], "chgate": [6, 17], "chi": [4, 5, 53], "chi2": [4, 5], "chip": 16, "choic": [6, 48], "choos": [12, 13, 16], "chosen": 6, "christoph": 6, "circuit": [2, 7, 8, 16, 17, 18, 19, 20, 21, 22, 24, 25, 27, 28, 29, 31, 32, 33, 35, 39, 42, 44, 58, 59, 60, 61], "circuit_cut": 17, "circuit_knit": [0, 4, 5, 7, 8, 9, 11, 12, 13, 17], "circuit_knitting_toolbox": 17, "circular": 12, "citaton": 15, "ckt": [11, 12, 15, 16, 17], "class": [7, 17, 18, 30, 31, 34, 35, 36, 37, 38, 48, 49, 55, 56, 58, 60, 62, 63], "classic": [4, 5, 6, 17, 19, 33, 39, 58, 59], "classical_cost": 4, "clone": 16, "closest": 46, "cnot": [6, 11, 12], "co": [6, 15], "code": [2, 3, 4, 5, 6, 11, 12, 13, 15, 17, 31], "coeff": 35, "coeffici": [6, 7, 9, 10, 11, 12, 13, 15, 24, 29, 35, 42], "coeffient": 6, "collaps": [13, 31], "collect": [22, 49, 56], "com": [15, 16, 58], "combin": [4, 6, 8, 11, 12, 13], "come": 17, "command": 16, "comment": [11, 12, 13], "common": [11, 13, 16, 39, 52], "commun": 6, "commut": [9, 55, 56], "commuting_observ": 55, "commutingobservablegroup": 56, "compar": [2, 27], "comparison": [4, 5, 48], "compat": [17, 28], "compil": 16, "complete_path_map": [4, 24], "compon": [16, 61], "compos": 16, "compris": [4, 5, 39], "comput": [4, 5, 6, 21, 47, 50, 51, 52, 53, 54], "conceptu": 13, "conda": 16, "conda_subdir": 16, "condit": 17, "conduct": 2, "configur": [4, 5, 16], "connect": [9, 17, 23, 33], "consecut": [11, 12, 13, 17, 33, 61], "consid": [3, 6], "consist": 6, "consolid": 62, "consolidatereset": [17, 29], "constraint": 6, "construct": [6, 48], "consult": 16, "contain": [5, 6, 9, 10, 12, 15, 16, 17, 19, 21, 22, 23, 24, 26, 27, 29, 31, 32, 33, 39, 42, 44, 59, 61], "container": 16, "continu": 6, "contrast": [13, 48], "contribut": [6, 15, 40, 42], "control": [6, 11, 12, 13, 40], "conveni": 9, "convent": 6, "convers": 41, "convert": [2, 6, 44, 45, 46, 47], "copi": [4, 5, 16, 19, 32, 48, 49], "copyright": [4, 5], "core": [4, 5], "correctli": 13, "correl": 53, "correspond": [4, 6, 11, 13, 28, 29, 31, 33, 39, 40, 42, 60], "cost": [4, 6, 16], "could": [4, 5, 36], "count": [6, 43], "counter": 4, "coupl": 17, "coupling_map": 12, "cphaseg": [6, 17], "cplex": [16, 17], "creat": [16, 17, 18, 30, 31, 60], "creg": 39, "criteria": 4, "cross": [4, 5, 54], "crxgate": [6, 17], "crygat": [6, 17], "crzgate": [6, 17], "csdggate": [6, 17], "csgate": [6, 17], "csxgate": [6, 17], "current": [3, 11, 12, 15, 17, 58], "cut": [1, 7, 16, 17], "cut_circuit_wir": [4, 5], "cut_gat": [12, 17], "cut_indic": 12, "cut_wir": [6, 9, 13], "cutqc": [15, 16, 17], "cutter": [4, 5], "cutting_decomposit": 17, "cutting_evalu": 17, "cutting_execut": 17, "cutting_experi": 17, "cutting_reconstruct": 17, "cutwir": [6, 10, 13, 15, 20], "cx": [5, 7, 9, 13, 61], "cxgate": [6, 8], "cygat": [6, 17], "czgate": 6, "czyk": 15, "d": [15, 16], "dag": [22, 24], "dagger": 6, "daniel": 15, "data": [4, 5, 12], "david": 6, "dcxgate": [6, 17], "decompos": [6, 8, 12, 15, 17, 18, 19, 22, 33, 34, 35, 36, 37, 39, 41], "decompose_g": 17, "decompose_qpd_instruct": 17, "decomposit": [8, 12, 15, 17, 29, 34, 36, 37, 39, 40], "decreas": 14, "deep": 52, "deeper": [12, 17], "def": 8, "default": [4, 5, 17, 58], "defin": [6, 35, 46, 50, 51, 53, 54], "definit": 17, "demonstr": [9, 31], "denot": [6, 19, 30, 32, 33, 42], "depend": [6, 16, 17], "deprec": 15, "depth": [6, 14, 15], "deriv": [4, 5], "describ": [3, 6, 11, 12, 13, 17, 35, 36], "deseri": 17, "design": 28, "desin": 31, "desir": [6, 17, 31], "destin": [13, 31, 33], "detail": [4, 6, 13, 17], "determin": [8, 9, 13, 16, 17, 33], "detin": 13, "dev": 16, "develop": 16, "devic": [4, 5], "dharmashankar": 15, "dict": [4, 5, 18, 21, 22, 23, 24, 25, 26, 27, 29, 40, 42, 48, 51, 52, 54, 58, 59, 60], "dict_kei": [4, 61], "dictionari": [4, 11, 13, 17, 22, 23, 26, 29, 33, 42, 44, 53], "did": 28, "differ": [6, 16, 20, 27, 28, 33, 39, 51, 54], "direct": [6, 17], "directli": [13, 17], "directori": [4, 5, 16], "discard": [6, 13], "disconnect": [6, 61], "disregard": 6, "dist": 45, "distanc": [2, 52, 53], "distant": 6, "distribut": [2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 15, 17, 21, 27, 29, 39, 40, 44, 45, 46, 47, 54], "distribution_dict": 44, "do": [4, 6, 7, 13, 16, 17, 42, 58], "doc": 16, "docker": 15, "dockerfil": 16, "docplex": 17, "document": [4, 5, 6, 15], "doe": [9, 13, 15, 17, 22, 32, 33, 39, 45, 61], "doi": 15, "done": 9, "draw": [4, 5, 8, 9, 11, 12, 13, 29, 31], "driver": 17, "due": [6, 16], "duplic": 62, "dure": 6, "dynam": 17, "e": [4, 6, 13, 16, 17, 24, 31, 41], "e4a04564eb39": 16, "each": [4, 5, 6, 8, 9, 11, 12, 13, 15, 16, 17, 19, 22, 23, 25, 26, 29, 31, 32, 33, 35, 39, 40, 42, 56, 57, 58, 59, 60, 61], "earlier": 17, "easi": 17, "ecrgat": [6, 17], "eddin": 15, "effect": [6, 13, 17, 31], "effici": [4, 6, 56], "efficientsu2": [4, 8, 11, 12], "egger": 15, "eigenst": [4, 5], "either": [5, 6, 12, 16, 40, 47], "electronicstructureproblem": 17, "element": [6, 17, 24, 39, 40, 42, 48, 60], "emul": 16, "enabl": [6, 17], "encourag": 16, "end": [6, 61], "engin": 6, "enhanc": 17, "enough": [4, 5], "ensur": [6, 31], "entangl": [4, 8, 11, 12, 13, 17, 31], "entanglement_forg": 17, "entanglementforginggroundstatesolv": 17, "entanglementforgingresult": 17, "enter": 16, "entir": 6, "entri": 24, "entropi": [4, 5, 54], "enum": 38, "enumer": [4, 5, 12], "environ": 16, "epsilon": 6, "eq": 6, "equal": [6, 11, 12, 17, 32, 33, 37, 39, 42, 48, 60, 61], "equat": 6, "equival": [6, 8, 9, 13], "error": [4, 5, 6, 9, 11, 12, 13, 17, 51, 52], "essenti": 6, "estim": [6, 9, 11, 12, 13], "et": [4, 5, 46], "evalu": [6, 8, 12, 17, 23, 25, 27, 29, 40], "evaluate_subcircuit": [4, 5], "even": 17, "everi": [20, 29, 42], "everyth": 16, "evolv": 15, "exact": [4, 5, 9, 10, 15, 17, 27, 40, 58], "exact_dict_bitstr": [4, 5], "exact_distribut": [4, 5], "exact_expv": [9, 11, 12, 13], "exact_prob": [4, 5], "exact_sampl": 7, "exactli": [4, 5, 6, 17, 40], "exactsampl": [7, 10], "exampl": [4, 5, 8, 12, 13, 15, 17, 50, 51, 52, 53, 54], "except": [51, 52, 53, 54], "execut": [4, 5, 6, 11, 12, 13, 15, 17, 21, 26, 27, 44, 48], "execute_experi": 17, "exist": [6, 13, 15, 28], "exp": 6, "expand": [6, 28], "expand_observ": 9, "expect": [3, 4, 5, 6, 8, 17, 29, 31, 42], "experi": [4, 5, 6, 11, 15, 17, 29, 42, 50], "explain": [6, 17], "explan": 17, "explanatori": [11, 12, 13, 15, 17], "explicit": [6, 17], "explicitli": [6, 9, 17, 33], "exponenti": 6, "extens": [15, 16, 17], "extra": 13, "f": [6, 8, 9, 11, 12, 13], "f401": [4, 5], "factor": 6, "fail": 41, "fake": 12, "fake_provid": 12, "fals": [11, 12, 13, 19, 32, 39], "famili": 6, "favor": 17, "feasibl": 6, "featur": [3, 6, 50, 51, 52, 53, 54], "few": [16, 17], "fewer": 6, "field": [17, 52], "fig": [4, 5, 9, 13], "figsiz": [4, 5], "figur": 5, "file": [4, 5, 15, 16], "final": [4, 6, 9, 24, 28, 39, 63], "final_circuit": 28, "find": [3, 12, 16, 17], "find_bit": 12, "finit": 8, "first": [4, 5, 6, 8, 9, 13, 16, 17, 31, 40], "fix": 6, "flag": [19, 22, 32], "flexibl": 17, "flip": 6, "float": [4, 27, 29, 35, 40, 42, 59], "fock": 17, "fold": [4, 5, 12], "follow": [4, 5, 6, 8, 9, 12, 13, 15, 16, 17, 29, 31, 42, 45], "forc": 44, "force_prob": 44, "forg": 17, "form": 6, "formal": 6, "forward": [3, 9, 17], "found": [4, 5, 22, 28], "foundat": 17, "four": [4, 5, 11, 13], "frac": [51, 53], "framework": 6, "free": [6, 16, 35], "fresh": 31, "from": [4, 5, 6, 9, 10, 15, 17, 21, 23, 25, 26, 27, 28, 29, 31, 33, 40, 42, 44, 46], "from_gat": 17, "from_instruct": [8, 17], "fujii": 6, "full": [11, 12, 13, 16, 17, 21, 25, 31], "full_circuit": 27, "fuller": 15, "fulli": 61, "function": [2, 4, 5, 6, 9, 13, 16, 17, 25, 28, 29, 48, 58], "fundament": 6, "furthermor": 17, "futur": [6, 17], "g": [4, 17, 24, 41], "gacon": 15, "gambetta": 6, "gamma": 6, "garrison": 15, "gate": [5, 8, 13, 14, 15, 17, 19, 22, 29, 30, 32, 33, 34, 36, 37, 39, 41, 58, 59], "gate_id": 19, "gener": [4, 5, 10, 15, 17, 21, 24, 26, 29, 40, 41, 42], "general_observ": 55, "generate_cutting_experi": [7, 9, 11, 12, 13, 17, 42], "generate_preset_pass_manag": [11, 12, 13], "generate_qpd_sampl": 17, "generate_qpd_weight": 17, "generate_summation_term": 21, "genericbackendv2": 12, "geometr": 6, "geq": 6, "get": [4, 6, 8, 16], "git": 16, "github": [15, 16, 58], "given": [6, 8, 11, 12, 13, 17, 28, 40, 41, 57], "glick": 15, "global": 6, "global_observ": 57, "go": [3, 6, 16, 17], "goal": [6, 17], "goe": 6, "greater": 17, "ground": [4, 5, 27], "group": [11, 13, 33], "gt": [8, 13], "guarante": [16, 17, 48], "guid": [6, 7, 8, 9, 13, 17], "gujarati": 15, "h": [5, 7, 61], "ha": [4, 6, 12, 13, 17, 31, 33, 39, 42], "had": [6, 39], "half": [9, 12], "hamamura": 15, "hand": 17, "hardwar": [4, 6, 12, 17], "hartre": 17, "hasan": 15, "hashabl": [18, 29, 32, 33, 42, 48, 60, 61], "have": [4, 5, 6, 8, 9, 16, 17, 20, 22, 28, 29, 36], "heavi": 50, "heaviest": 6, "heavili": 17, "here": [4, 5, 8, 9, 11, 12, 13], "heurist": 9, "hf_energi": 17, "higher": 17, "histogram": [4, 5], "histor": 3, "hold": [17, 39], "home": 16, "honor": 6, "hop": [4, 5], "how": [4, 5, 6, 11, 12, 13, 15, 16, 17, 42, 47], "howev": [6, 13, 17], "howpublish": 15, "html": 16, "http": [4, 5, 6, 15, 16, 58], "i": [3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17, 28, 29, 31, 33, 34, 36, 39, 40, 41, 42, 48, 50, 51, 52, 53, 54, 57, 60, 61, 63], "ibm": [4, 5, 17], "ibm_cairo": 12, "ibmq": 17, "ibmq_qasm_simul": [4, 5], "id": [29, 39], "idea": 6, "ideal": 35, "ident": [6, 9, 17, 28, 49], "idle_wir": 12, "ieva": 15, "ignor": [4, 5, 8, 17, 33], "ii": [11, 48, 57], "iiii": [9, 13], "iiiii": 9, "iiiiiiiiz": 9, "iiiiiiiz": 13, "iiiiiiz": [9, 13], "iiiiz": 9, "iiiiziii": 13, "iiiz": 13, "iiiziii": [9, 13], "iiiziiiii": 9, "iizz": [11, 12], "ikko": 15, "imag": 16, "imamichi": 15, "immedi": 31, "implement": [4, 5, 6, 15, 17, 31, 41], "import": [4, 5, 7, 8, 9, 11, 12, 13, 17, 31, 50], "imposs": 6, "improv": [15, 17], "includ": [3, 4, 5, 6, 15, 16, 17, 22, 41, 60], "incom": [13, 31], "incompat": [17, 29, 42], "increas": 6, "incur": [6, 11, 12, 13], "independ": 6, "index": [11, 13, 17, 33, 34, 36, 39, 40, 60], "indic": [4, 5, 6, 17, 19, 22, 39, 40], "individu": 6, "induc": 4, "inf": [8, 9, 11, 12, 13], "infin": [6, 8, 29], "info": [4, 5], "inform": [4, 5, 6, 11, 12, 13, 17, 20, 22, 26, 44], "ing": 45, "init": 24, "init_label": 24, "initi": [4, 12, 17, 31, 34, 36, 37], "initial_layout": 12, "inner": 39, "inplac": [4, 8, 11, 12, 19, 32, 39], "input": [7, 9, 17, 19, 22, 29, 32, 33, 40, 42, 45, 46, 61], "insert": [6, 13, 17], "inspect": 4, "inspir": [9, 13], "instal": [15, 17], "instanc": [6, 9, 12, 16, 17, 18, 19, 24, 25, 26, 29, 32, 39, 42, 60], "instanti": [6, 17, 41], "instead": [3, 4, 5, 6, 16, 17], "instruct": [6, 10, 12, 15, 17, 20, 33, 34, 35, 39, 41, 63], "instruction_id": 39, "instructionset": 13, "int": [4, 5, 19, 21, 22, 23, 24, 25, 26, 29, 34, 36, 39, 40, 57, 59, 60], "integ": [3, 4, 33, 61], "integraldriv": 17, "intel": 16, "intend": [7, 8, 9, 13, 16, 17], "interact": 6, "interest": 56, "interfac": [3, 7, 10, 15, 16, 17], "introduc": [6, 17], "introductori": 31, "involv": 4, "isclos": 8, "iskandar": 15, "issu": [6, 16, 41, 58], "iswapg": [6, 17], "item": [7, 9, 11, 13], "iter": 56, "its": [4, 6, 17, 39, 40, 42, 57, 59, 61], "ix": 11, "ixix": [11, 12], "ixyz": 57, "iz": 11, "izzi": [11, 12], "j": [6, 15], "jame": 15, "jennif": 15, "jiri": 6, "job": 17, "johnson": 15, "joint": [8, 11, 12, 13, 17, 39, 40], "jovyan": 16, "jpserver": 16, "julien": 15, "jun": 6, "jupyt": 16, "just": [6, 9, 16], "k": 6, "kak": [15, 17], "keep": [15, 17], "kei": [4, 11, 13, 15, 22, 29, 40, 60, 61], "keisuk": 6, "knit": [2, 3, 4, 5, 6, 11, 12, 13, 16, 17], "known": 6, "kosuk": 6, "kron": [17, 24], "kronecker_term": 24, "kwarg": [62, 63], "l": 15, "l2": 46, "lab": 16, "label": [7, 9, 11, 13, 17, 29, 30, 31, 32, 33, 34, 36, 37, 42, 60, 61], "languag": 6, "larg": [6, 16], "larger": [6, 36], "last": 16, "later": 17, "layer": 4, "layout": 12, "ldot": [29, 42], "learn": 52, "least": [8, 29], "least_busi": [11, 12, 13], "left": 6, "legaci": 15, "legend": [4, 5], "len": [4, 8, 9, 11, 12, 13], "length": [29, 32, 39, 60, 61], "less": [4, 6, 17], "let": [6, 16], "lett": 46, "level": 17, "librari": [4, 8, 11, 12], "licens": [4, 5, 16], "liepuoniut": 15, "like": [6, 9, 11, 12, 13, 16, 17], "likewis": 6, "limit": 15, "line": [4, 5, 13], "linear": [4, 6, 8, 11, 39], "linux": 16, "list": [4, 5, 6, 9, 11, 12, 13, 17, 18, 19, 21, 22, 24, 26, 29, 33, 39, 42, 48, 49, 57, 60], "literatur": 6, "ll": [4, 5], "lo": 6, "local": [4, 5, 6, 11, 12, 13, 16, 33, 36, 39], "locat": [3, 9, 15, 16, 17, 30], "locc": 6, "lockwood": 15, "log": 54, "long": 15, "longer": 17, "look": 17, "loos": 6, "loss": [4, 5], "low": 6, "lt": [8, 13], "luciano": 15, "luka": 6, "lvert": 6, "m": [6, 15, 16, 29, 42], "m1": 16, "m2": 16, "m_z": 6, "mac": 16, "machin": 16, "maco": 16, "magnitud": [6, 8], "mai": [4, 5, 6, 9, 13, 15, 16, 17, 29, 48], "maintain": 17, "major": 41, "make": [15, 48], "mani": [6, 11, 12, 13, 14, 17, 36], "manipul": 2, "manual": [3, 4, 9, 13, 17, 22], "map": [4, 8, 11, 13, 17, 28, 29, 33, 34, 35, 39, 40, 42, 46], "map_id": 39, "mario": 15, "mark": [9, 13, 15, 17, 20], "match": [4, 5, 22, 33], "materi": [11, 12, 13, 15, 17], "mathcal": 6, "mathrm": 6, "matter": [6, 8, 16], "max": [15, 22], "max_cut": [4, 22], "max_subcircuit_cut": 22, "max_subcircuit_s": 22, "max_subcircuit_width": [4, 22], "maximum": 6, "md": 15, "mea": 24, "mean": [4, 5, 6, 51, 52], "meant": 15, "meas_label": 24, "measur": [2, 4, 5, 6, 7, 10, 17, 26, 27, 39, 50, 52, 53, 54, 56, 58, 59], "median": 50, "member": [34, 35, 36, 56], "mention": 6, "messag": 16, "metadata": 17, "method": [3, 4, 5, 6, 9, 13, 16, 17, 18, 22, 30, 31, 34, 35, 36, 37, 41, 46, 55, 56, 58, 60, 62, 63], "metric": [4, 5, 27], "mid": [7, 10, 17, 59], "middl": 13, "might": [6, 9], "migrat": 17, "min": 8, "mind": 17, "minim": 16, "minimum": 17, "mip": 4, "misc": 15, "mitarai": 6, "mitig": 6, "mix": [3, 4], "mixtur": [17, 35], "mode": [7, 16, 17, 47], "model": [3, 4], "modif": [4, 5], "modifi": [4, 5, 17], "modul": [2, 3, 17], "more": [4, 5, 9, 11, 12, 13, 15, 17, 20, 32, 33, 58, 61], "most": [6, 8, 13, 17, 40], "motta": 15, "move": [10, 14, 15, 16, 17, 20, 41], "mpl": [4, 5, 8, 9, 11, 12, 13, 31], "much": 6, "multipl": [4, 5, 6], "multipli": 42, "multithread": 21, "must": [4, 5, 6, 9, 12, 13, 17, 28, 29, 31], "mutual": [55, 56], "n": [6, 15, 16, 28, 29, 42, 48], "naiv": [4, 5, 9, 47], "name": [16, 17, 23, 29, 34, 60, 61], "namedtupl": [18, 60], "namespac": 17, "nativ": 16, "natur": 17, "navig": 16, "ndarrai": [21, 23, 25, 26, 27], "nearest": [4, 5, 46, 47], "necessari": [6, 17], "need": [3, 4, 5, 7, 9, 16, 17], "neg": [4, 6, 45], "new": [3, 6, 9, 15, 16, 18, 20, 28, 39, 47, 60], "newer": 6, "newli": [13, 20, 41], "next": [4, 5, 9, 13, 16], "node": 22, "nois": [6, 35], "noisi": [6, 35], "non": [6, 11, 12, 13, 17, 33], "none": [4, 5, 9, 11, 12, 13, 18, 22, 23, 26, 30, 31, 33, 34, 35, 36, 37, 39, 58, 61], "nonloc": [14, 32], "noqa": [4, 5], "norm": 46, "normal": [44, 45], "note": [4, 6, 13, 15, 16, 22, 29], "notebook": 16, "notebook_1": 16, "noth": [6, 9], "notic": [4, 5, 9], "notion": 6, "now": [6, 17], "np": [4, 5, 8, 9, 11, 12, 13, 23, 26, 31, 50, 51, 52, 53, 54], "num_bit": [4, 5], "num_cut": [4, 21, 24], "num_cx_cut": 8, "num_qpd_bit": 17, "num_qubit": [4, 5, 12, 28, 34, 61], "num_sampl": [6, 7, 9, 11, 12, 13, 17, 29, 40], "num_subcircuit": [4, 22], "num_thread": [21, 25], "number": [4, 6, 8, 11, 12, 13, 17, 21, 22, 24, 25, 28, 29, 32, 33, 34, 35, 37, 39, 40, 43, 48, 60, 61], "number_to_keep": [4, 5], "numpi": [4, 5, 8, 9, 11, 12, 13, 17, 31, 51, 52, 53, 54], "o": [6, 48], "ob": [50, 51, 52, 53, 54], "object": [4, 35, 40, 41, 48, 49], "observ": [6, 7, 17, 18, 28, 29, 33, 42, 50, 51, 52, 53, 54, 55, 56, 57], "observable_": [29, 42], "observable_measur": 17, "observables_0": [9, 13], "observables_1": [9, 13], "obtain": [4, 5, 6, 42], "occasion": [6, 15], "occur": 17, "old": [15, 17], "onc": [9, 16, 58], "one": [4, 6, 9, 11, 12, 13, 16, 17, 19, 20, 29, 32, 33, 39, 40, 42, 50, 61], "ones": [15, 17], "onli": [4, 6, 11, 12, 16, 17, 48], "open": 16, "oper": [9, 10, 11, 12, 13, 14, 15, 28, 31, 35, 39, 41, 58, 59, 61], "opflow": 17, "optim": [4, 6, 16, 17, 22], "optimization_level": [11, 12, 13], "option": [4, 5, 11, 12, 13, 15, 17, 23, 26, 33, 34, 58], "orang": 6, "orbit": 17, "order": [7, 8, 12, 15, 16, 17, 21, 29, 42], "org": [4, 5, 6], "organ": 56, "origin": [3, 4, 5, 6, 8, 9, 15, 17, 20, 25, 27, 28, 60], "original_circuit": 28, "osx": 16, "other": [6, 9, 16, 22, 29, 31], "otherwis": [6, 13, 17, 42, 48, 57], "otim": [4, 6], "our": [4, 6, 17], "out": [9, 17, 36, 45], "outcom": [2, 6, 15, 39, 58, 59], "output": [6, 17, 22, 25, 29, 32, 42, 50], "over": 17, "overal": 6, "overhead": [15, 21], "overview": 15, "owen": 15, "p": 15, "p_i": 4, "p_j": 4, "packag": [16, 17], "pair": 48, "paper": [3, 6], "parallel": [4, 5, 25], "param": 4, "paramet": [4, 6, 8, 11, 12, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 32, 33, 34, 35, 36, 39, 40, 41, 42, 44, 45, 46, 47, 50, 51, 52, 53, 54, 56, 57, 58, 61], "parametr": 6, "part": 36, "partial": [13, 31], "particular": 17, "particularli": 6, "partit": [6, 8, 13, 17, 29, 32, 33, 42, 60, 61], "partition_circuit_qubit": 17, "partition_label": [7, 8, 9, 11, 13, 17, 32, 33, 61], "partition_problem": [7, 8, 9, 11, 13, 17], "partitioned_problem": [7, 8, 9, 11, 13], "partitionedcuttingproblem": 33, "pass": [4, 5, 6, 8, 9, 17, 29], "pass_manag": [11, 12, 13], "past": [9, 16], "path": [16, 17, 24], "pauli": [2, 4, 5, 11, 12, 13, 33, 56, 57], "paulilist": [7, 8, 9, 11, 12, 13, 17, 18, 28, 29, 33, 42, 56, 57], "pedro": 15, "pemmaraju": 15, "per": [6, 40], "percentag": [4, 5, 51], "perform": [3, 6, 10, 16, 17, 31], "permiss": 16, "persist": 16, "ph": 6, "phase": [6, 11, 12, 33, 42], "phrase": 15, "phy": 46, "physic": 6, "pi": [4, 5, 6, 9, 13, 17, 31], "piec": [6, 42], "pin": 17, "pip": 15, "piveteau": 6, "place": [6, 15, 16, 28], "plan": [6, 16], "platform": 15, "pleas": [6, 15], "plot": [4, 5, 6], "plot_histogram": [4, 5], "podman": 16, "point": [4, 6], "polici": 15, "portion": 6, "posit": [6, 12, 42], "possibl": [15, 17, 58], "possibli": [11, 12, 13], "post": [6, 21], "postprocess": [4, 5], "potenti": 48, "powershel": 16, "practic": 6, "pre": 15, "preced": 31, "precis": [2, 59], "prefer": 6, "prefix": 17, "prepar": [4, 5, 7, 11, 13, 16], "present": [4, 5, 6], "preset_passmanag": [11, 12, 13], "previou": [4, 13], "previous": 17, "primari": [16, 17], "primarili": 6, "primit": [4, 5, 6, 7, 9, 17, 42, 58], "print": [4, 8, 9, 11, 12, 13, 22], "prior": 31, "prob": [4, 5], "probabilist": [12, 17, 34, 35], "probabl": [2, 3, 4, 5, 6, 8, 17, 21, 25, 26, 27, 29, 39, 44, 45, 46, 47, 50, 54, 58, 59], "probdistribut": [4, 5], "problem": [6, 8, 17], "process": [4, 6, 15, 21], "processor": 15, "prod": [11, 12, 13], "produc": [4, 5], "program": [3, 4], "project": [4, 5, 6, 58, 59], "proport": 6, "provabl": [6, 22], "provid": [4, 5, 6, 12, 16, 17, 22, 26, 27, 33, 39, 57, 58, 61], "ps1": 16, "pull": [16, 17], "purpos": 17, "py": [48, 49], "python": [16, 17], "python3": 16, "q": 12, "q_0": 31, "q_1": 31, "qasm": [4, 5], "qc": [11, 31, 59, 61], "qc_0": [9, 13], "qc_1": [9, 13], "qiskit": [6, 7, 8, 9, 15, 16, 17, 23, 31, 39, 41, 42, 58], "qiskit_a": [9, 11, 12, 13, 58], "qiskit_ibm_runtim": [4, 5, 11, 12, 13], "qiskit_natur": 17, "qiskitruntimeservic": [4, 5, 11, 12, 13, 23, 26], "qpd": [8, 15, 17, 33], "qpd_base": 40, "qpd_basis_cx": 8, "qpd_circuit": 12, "qpd_measur": 17, "qpdbasi": [8, 12, 17, 18, 19, 34, 37, 40, 41], "qpdbasis_from_g": 17, "qpdbasis_from_instruct": 17, "qpdgate": 36, "qpy": 17, "quant": 6, "quantiti": [4, 6], "quantum": [2, 6, 15, 17, 27, 31, 35, 50], "quantum_info": [7, 8, 9, 11, 12, 13], "quantumcircuit": [4, 5, 6, 7, 9, 13, 17, 18, 19, 20, 24, 25, 26, 27, 28, 29, 31, 32, 33, 39, 60, 61], "quasi": [8, 10, 17, 29, 47], "quasi_dist": 17, "quasiprob": [11, 12, 13, 15, 34, 35, 36, 37, 40, 45, 46, 47], "quasiprobabilist": 35, "qubit": [4, 5, 8, 10, 12, 14, 15, 17, 20, 22, 24, 28, 31, 32, 33, 34, 36, 37, 41, 42, 55, 56, 57, 60, 61, 63], "qubit_id": 36, "qubit_map": [60, 61], "r": 15, "rais": [19, 22, 28, 29, 32, 33, 36, 37, 39, 41, 42, 51, 52, 53, 54, 61], "randomli": 39, "rang": [4, 5, 9, 11, 12, 13, 36, 51], "rangl": [4, 6], "rapidli": 15, "rather": [3, 6, 10, 17, 29], "raw": 4, "re": [4, 5, 9, 13, 16, 17], "reach": 6, "real": [6, 35], "realiz": 6, "recombin": 6, "recommend": 16, "recompos": 25, "reconstruct": [3, 6, 8, 15, 17, 21, 24, 25, 27, 42], "reconstruct_expectation_valu": [9, 11, 12, 13, 17], "reconstruct_full_distribut": [4, 5], "reconstructed_dict_bitstr": [4, 5], "reconstructed_distribut": [4, 5], "reconstructed_expv": [9, 11, 12, 13], "reconstructed_output": 27, "reconstructed_prob": [4, 5], "reduc": [6, 15, 17], "redund": 6, "ref": 6, "refer": [11, 12, 13, 17], "regist": [17, 19, 33, 39], "rel": [9, 11, 12, 13, 36], "relat": 58, "releas": 15, "reli": 17, "remain": [6, 8, 9, 17, 40, 58], "remaind": [13, 31], "remov": [17, 29, 63], "removefinalreset": [17, 29], "removeresetinzerost": [17, 29], "renam": 17, "rep": [4, 8, 11], "repeat": 6, "replac": [6, 11, 13, 17, 19, 20, 32, 33, 39], "repositori": [15, 16], "repres": [4, 5, 6, 29, 31, 56], "represent": [6, 13, 20, 35], "request": 17, "requir": [6, 12, 14, 17, 28], "research": [3, 15], "reset": [6, 13, 17, 29, 31, 58, 59, 62, 63], "resolv": 17, "respect": [6, 11, 13], "restrict": [6, 57], "result": [6, 7, 8, 9, 11, 12, 13, 15, 17, 18, 23, 24, 26, 27, 31, 33, 40, 42, 44, 60], "retain": [4, 5], "retriev": [11, 12, 13], "return": [4, 7, 12, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 32, 33, 35, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 57, 58, 59, 61], "retval": [28, 39], "reus": [9, 13], "rev": 46, "right": [6, 13], "rigor": [6, 29], "rivero": 15, "root": [4, 5, 6, 15], "rossmannek": 15, "rotat": 6, "round": [9, 11, 12, 13], "rtype": [48, 49], "run": [4, 5, 6, 7, 10, 16, 17, 23, 29, 42, 62], "run_opt": [9, 11, 12, 13], "runtim": [4, 6, 11, 12, 13, 16, 17, 23, 26], "rvert": 6, "rx": [5, 9, 13, 31], "rxxgate": [6, 17], "ry": [4, 5], "ryi": 31, "ryygat": [6, 17], "rz": 31, "rzxgate": 6, "rzzgate": [15, 17], "s_i": 4, "s_j": 4, "sake": 17, "same": [6, 8, 9, 11, 12, 29, 33, 39, 42, 61], "sampl": [10, 15, 17, 29, 38, 39, 40, 42], "sample_": [29, 42], "sampler": [4, 5, 9, 15, 17, 42, 58], "samplerresult": [17, 42], "sastri": 6, "save": 16, "scale": [4, 5, 8, 11, 12, 51], "schmitt": 6, "scholten": 15, "script": 16, "sdggate": 6, "search": 16, "sec": [6, 20], "second": [4, 5, 6, 9, 13, 17, 31, 40], "second_q": 17, "second_quant": 17, "section": [4, 16], "see": [4, 5, 6, 13, 16, 17, 20, 28, 41], "seelam": 15, "seetharami": 15, "send": 6, "sent": 6, "separ": [6, 12, 14, 17, 18, 29, 33, 42, 61], "separate_circuit": 60, "separatedcircuit": 61, "seq": 8, "sequenc": [8, 19, 21, 22, 23, 24, 26, 27, 29, 32, 33, 35, 39, 40, 42, 57, 61], "sergei": 15, "seri": 16, "serial": 17, "server": 16, "serverless": 17, "servic": [4, 11, 12, 13, 23, 26], "service_arg": [4, 5], "session": [11, 12, 13], "set": [4, 6, 8, 11, 12, 13, 14, 16, 17, 29, 43, 48, 55, 56], "seven": 13, "sever": [4, 5, 17], "sgate": 6, "shankar": 6, "share": [11, 13, 16, 31], "short": [6, 16], "shorten": 17, "shot": [4, 5, 6, 9, 11, 12, 13, 44], "should": [3, 6, 16, 17, 19, 28, 33, 34, 36, 39, 41, 42], "show": [5, 6, 7, 8], "shown": [5, 6, 42], "side": 5, "sign": 6, "signific": 17, "simpl": [8, 16], "simplest": 31, "simul": [4, 5, 6, 7, 8, 11, 12, 13, 17, 35], "simultan": 6, "sin": 6, "sinc": [4, 5, 17, 31], "singl": [6, 8, 10, 13, 15, 20, 32, 36, 42, 62], "singlequbitqpdg": [6, 17, 29, 33], "sitdikov": 15, "six": [6, 8], "size": [4, 6, 11, 12, 13], "skip": 16, "small": [4, 5, 8, 48], "smaller": [4, 5, 6, 15], "smolin": 46, "so": [4, 6, 8, 9, 13, 16, 17, 29, 42, 48], "solv": 17, "some": [4, 6, 17, 29, 41, 58], "someth": [6, 17], "sooner": 17, "sort": [4, 5], "sourc": [4, 5, 6, 13, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63], "space": 6, "span": [11, 13, 32, 33, 61], "sparsepauliop": [11, 12, 17], "special": [6, 41], "specif": [3, 17, 39, 40], "specifi": [3, 4, 5, 6, 10, 14, 17, 19, 22, 29, 33, 41], "spell": 9, "sphinx_autodoc_typehints_typ": [48, 49], "split": 13, "sqrt": 6, "squar": [4, 5, 6, 52, 53], "stage": 17, "standalon": 36, "standard_g": 8, "start": [16, 33, 61], "state": [4, 5, 6, 13, 31], "statevector": [4, 5, 7, 17], "statist": 53, "stefan": 15, "step": [4, 5, 9, 11, 12, 13, 16, 17, 45], "still": 17, "str": [21, 22, 23, 24, 26, 27, 34], "straight": 16, "strike": 17, "string": 13, "su2_gat": 4, "sub": [17, 42], "subcircuit": [6, 7, 8, 9, 11, 12, 13, 17, 18, 21, 22, 23, 24, 25, 26, 27, 33, 60, 61], "subcircuit_0_entry_0": 24, "subcircuit_1_entry_13": 24, "subcircuit_2_entry_7": 24, "subcircuit_entri": 24, "subcircuit_entry_idx": 24, "subcircuit_entry_prob": 21, "subcircuit_idx": [24, 26], "subcircuit_inst": [24, 26], "subcircuit_instance_idx": [24, 26], "subcircuit_instance_prob": [4, 5, 25, 26], "subcircuit_vertic": [5, 22], "subdirectori": 16, "subexperi": [6, 7, 8, 9, 17, 29, 42], "submit": [17, 42], "subobserv": [7, 8, 9, 11, 12, 13, 18], "subramanian": 15, "subset": 6, "substanti": 15, "subsystem": 57, "sudo": 16, "suffici": 6, "suit": 16, "sum": [4, 6, 24, 42], "sum_i": [6, 51, 52, 53, 54], "summat": [21, 24], "summation_term": [21, 24], "summation_term_0": 24, "summation_term_1": 24, "superset": 28, "support": [3, 4, 5, 6, 7, 11, 12, 15, 17, 41, 57, 58, 59], "sure": 12, "sutter": 6, "swap": [6, 13, 14, 17, 31], "swapgat": [6, 17], "symbol": 31, "system": [13, 16, 31], "t": 5, "tabl": 15, "tail": [6, 8, 40], "takashi": 15, "take": [6, 8, 16, 17, 42, 45, 46, 56], "taken": [6, 11, 12, 13], "tang": [4, 5, 15], "tanvi": 15, "target": [11, 13, 50, 51, 52, 53, 54], "techniqu": [3, 4, 5, 6, 11, 12, 13, 17], "temm": 6, "term": [15, 21, 24, 42], "termin": 16, "terra": [17, 58], "test": 8, "than": [3, 4, 6, 9, 10, 11, 12, 13, 17, 29, 32, 33, 58, 61], "thei": [3, 4, 5, 7, 8, 9, 15, 16, 17, 29, 36], "them": [9, 15, 16, 17, 42], "themselv": 22, "theori": 6, "therefor": 8, "theta": [6, 17], "theta_i": 6, "theta_x": 6, "theta_z": 6, "thi": [3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 16, 17, 26, 27, 28, 29, 31, 33, 34, 35, 36, 41, 42, 48, 58], "thing": 16, "think": 6, "third": 6, "those": [4, 5, 6, 8, 15, 17, 28, 31, 40, 61], "thread": [4, 5, 21, 25], "three": [6, 11, 12, 13, 16, 17], "threshold": [6, 8, 17, 40], "through": [6, 7, 24], "thu": 6, "time": [4, 6, 9, 17, 48], "titl": 15, "to_matrix": 41, "todai": 6, "togeth": [4, 5, 11, 13, 33, 39, 42, 60], "token": 16, "too": 16, "tool": [4, 5, 15, 16, 17], "toolbox": [3, 4, 5, 16, 17], "top": 17, "tos": 17, "total": [22, 39], "tox": 16, "track": [6, 17], "tradeoff": 6, "transfer": [6, 13], "transform": [6, 13, 17, 19, 20], "transform_cuts_to_mov": 28, "transpil": [11, 13, 29], "transpiled_qc": 12, "transpiled_qpd_circuit": 12, "travi": 15, "treat": 6, "tree": [4, 5], "trigger": 8, "true": [4, 8, 9, 11, 12, 13, 22], "truth": [4, 5, 27], "try": [4, 6, 22], "tupl": [19, 21, 24, 26, 27, 29, 33, 35, 40, 42, 60, 61], "turn": 31, "tutori": [8, 9, 11, 12, 13, 15, 16, 17, 31], "two": [4, 5, 8, 9, 10, 11, 12, 14, 15, 17, 20, 31, 32, 37, 41, 45, 51, 52, 53, 54], "twoqubitqpdg": [9, 17, 19, 20, 32, 33], "twoqubitweyldecomposit": 17, "txt": [4, 5], "type": [2, 6, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 32, 33, 38, 39, 40, 41, 42, 57, 59, 61], "typic": 31, "u": 6, "unbound": 41, "uncom": [4, 5], "under": [4, 5], "unifi": 6, "uniform": 17, "uniqu": [6, 8, 10, 11, 12, 13, 29, 40, 42, 48, 49], "unitari": [6, 8, 58], "uniti": 4, "unless": 3, "unlik": 9, "unnecessari": 29, "unsepar": 29, "unsupport": 32, "until": 17, "unus": 58, "up": [4, 6, 8, 11, 12, 13, 16, 17, 61], "updat": [17, 45], "upfront": 6, "upgrad": 16, "upon": 17, "upstream": 58, "url": [15, 16], "us": [2, 3, 4, 5, 6, 7, 8, 10, 15, 17, 21, 22, 23, 25, 26, 28, 31, 34, 35, 36, 37, 41, 42, 52], "user": [3, 7, 8, 9, 15, 16, 17, 42], "usual": 9, "util": [1, 4, 5, 7, 15, 17], "v": 20, "v0": [11, 12, 17], "v_1": 6, "v_2": 6, "v_3": 6, "v_4": 6, "vala": 6, "valid": [17, 31, 47], "valu": [3, 4, 6, 8, 17, 38, 40, 42, 45], "valueerror": [19, 22, 28, 29, 32, 33, 36, 37, 39, 41, 42, 61], "valuesview": 49, "variabl": [34, 35, 36, 56], "variant": 4, "varieti": [6, 17, 27], "vast": 41, "vazquez": 15, "vec": 6, "vector": [21, 25, 26, 50, 51, 52, 53, 54], "venv": 16, "verbos": 22, "version": [4, 5, 6, 16, 17], "vertic": 22, "via": [15, 17, 51, 52, 54], "virtual": [6, 16], "visibl": 17, "visual": [4, 5, 16], "volum": [16, 50], "wa": [3, 4, 5, 17, 27, 31, 33, 42], "wai": [6, 13, 16, 22, 28, 31, 42], "want": 16, "we": [4, 5, 6, 8, 9, 11, 12, 13, 15, 16], "web": 16, "wei": 15, "weight": [15, 17, 29, 38, 40], "weighttyp": [29, 40, 42], "well": 16, "were": [9, 17, 42], "weyl": 6, "whalei": 6, "when": [6, 8, 12, 15, 16, 17, 63], "where": [4, 6, 9, 29], "whether": [19, 22, 32, 44], "which": [6, 9, 16, 17, 28, 29, 31, 33, 34, 35, 36, 39, 40, 41, 42, 48, 58, 60, 61], "while": [6, 13, 17], "who": [16, 42], "whose": [4, 5, 8, 13, 28, 31, 40], "width": 15, "window": 16, "wire": [3, 10, 14, 15, 16, 17, 20, 29, 30, 31, 33, 36, 41, 63], "wire_cut": 17, "wise": [55, 56], "wish": [8, 9, 16, 17], "within": [6, 15, 29, 39], "without": [6, 17], "woerner": 15, "work": [4, 5, 13, 16, 17, 31], "workflow": [6, 9, 17], "worst": 48, "worth": 6, "would": [6, 8, 13, 17], "wrap": [9, 17, 20], "written": [6, 58], "www": [4, 5], "x": [4, 6, 8, 17, 31, 43, 61], "x86": 16, "x86_venv": 16, "x_i": [51, 52, 53, 54], "xi": 11, "xixi": [11, 12], "xxminusyyg": 6, "xxplusyyg": 6, "y": [4, 6], "y_i": [51, 52, 53, 54], "yaml": 16, "year": 15, "yet": [3, 6, 17], "you": [4, 5, 6, 8, 12, 15, 16, 17], "your": [15, 16, 17], "z": [4, 6], "zenodo": 15, "zero": [8, 45], "zgate": 6, "zhang": 6, "zi": 11, "ziii": [9, 13], "ziiii": 9, "ziiiiii": [9, 13], "ziiiiiii": 13, "ziiiiiiii": 9, "zizz": [11, 12], "zx": 57, "zz": [6, 7, 11], "zzii": [11, 12], "zzxx": 57, "zzzz": 8}, "titles": ["Circuit Cutting", "Circuit Knitting Toolbox API References", "Utilities", "CutQC (legacy circuit cutting implementation)", "CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding", "CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting", "Explanatory material for the circuit cutting module", "How to generate exact quasiprobability distributions from Sampler", "How to generate exact sampling coefficients", "How to place wire cuts using a single-qubit <code class=\"docutils literal notranslate\"><span class=\"pre\">CutWire</span></code> instruction", "Circuit Cutting How-To Guides", "Gate Cutting to Reduce Circuit Width", "Gate Cutting to Reduce Circuit Depth", "Wire Cutting Phrased as a Two-Qubit <code class=\"docutils literal notranslate\"><span class=\"pre\">Move</span></code> Instruction", "Circuit Cutting Tutorials", "Circuit Knitting Toolbox", "Installation Instructions", "Release Notes", "circuit_knitting.cutting.PartitionedCuttingProblem", "circuit_knitting.cutting.cut_gates", "circuit_knitting.cutting.cut_wires", "circuit_knitting.cutting.cutqc.build", "circuit_knitting.cutting.cutqc.cut_circuit_wires", "circuit_knitting.cutting.cutqc.evaluate_subcircuits", "circuit_knitting.cutting.cutqc.generate_summation_terms", "circuit_knitting.cutting.cutqc.reconstruct_full_distribution", "circuit_knitting.cutting.cutqc.run_subcircuit_instances", "circuit_knitting.cutting.cutqc.verify", "circuit_knitting.cutting.expand_observables", "circuit_knitting.cutting.generate_cutting_experiments", "circuit_knitting.cutting.instructions.CutWire", "circuit_knitting.cutting.instructions.Move", "circuit_knitting.cutting.partition_circuit_qubits", "circuit_knitting.cutting.partition_problem", "circuit_knitting.cutting.qpd.BaseQPDGate", "circuit_knitting.cutting.qpd.QPDBasis", "circuit_knitting.cutting.qpd.SingleQubitQPDGate", "circuit_knitting.cutting.qpd.TwoQubitQPDGate", "circuit_knitting.cutting.qpd.WeightType", "circuit_knitting.cutting.qpd.decompose_qpd_instructions", "circuit_knitting.cutting.qpd.generate_qpd_weights", "circuit_knitting.cutting.qpd.qpdbasis_from_instruction", "circuit_knitting.cutting.reconstruct_expectation_values", "circuit_knitting.utils.bitwise.bit_count", "circuit_knitting.utils.conversion.dict_to_array", "circuit_knitting.utils.conversion.naive_probability_distribution", "circuit_knitting.utils.conversion.nearest_probability_distribution", "circuit_knitting.utils.conversion.quasi_to_real", "circuit_knitting.utils.iteration.unique_by_eq", "circuit_knitting.utils.iteration.unique_by_id", "circuit_knitting.utils.metrics.HOP", "circuit_knitting.utils.metrics.MAPE", "circuit_knitting.utils.metrics.MSE", "circuit_knitting.utils.metrics.chi2_distance", "circuit_knitting.utils.metrics.cross_entropy", "circuit_knitting.utils.observable_grouping.CommutingObservableGroup", "circuit_knitting.utils.observable_grouping.ObservableCollection", "circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem", "circuit_knitting.utils.simulation.ExactSampler", "circuit_knitting.utils.simulation.simulate_statevector_outcomes", "circuit_knitting.utils.transforms.SeparatedCircuits", "circuit_knitting.utils.transforms.separate_circuit", "circuit_knitting.utils.transpiler_passes.ConsolidateResets", "circuit_knitting.utils.transpiler_passes.RemoveFinalReset"], "titleterms": {"0": 17, "1": [4, 16, 17], "2": [5, 8, 16, 17], "3": [16, 17], "4": 17, "5": 17, "6": 17, "To": 10, "accord": 11, "after": 12, "against": 9, "all": 8, "an": 6, "api": 1, "ar": 8, "automat": 4, "backend": [11, 12, 13], "baseqpdg": 34, "been": 13, "bit_count": 43, "bitwis": [2, 43], "bug": 17, "build": 21, "calcul": [11, 12, 13], "chi2_dist": 53, "chosen": [11, 12, 13], "circuit": [0, 1, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15], "circuit_knit": [2, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63], "cite": 15, "ckt": 6, "cnot": 8, "coeffici": 8, "commutingobservablegroup": 55, "compar": [9, 11, 12, 13], "consolidatereset": 62, "content": 15, "convers": [2, 44, 45, 46, 47], "creat": [4, 5, 11, 12, 13], "cross_entropi": 54, "current": 6, "cut": [0, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42], "cut_circuit_wir": 22, "cut_gat": 19, "cut_wir": 20, "cutqc": [0, 3, 4, 5, 21, 22, 23, 24, 25, 26, 27], "cutwir": [9, 30], "decompos": [4, 5, 9, 11, 13], "decompose_qpd_instruct": 39, "decomposit": [0, 6], "demonstr": [8, 12], "deprec": 17, "depth": 12, "desir": 13, "develop": 15, "dict_to_arrai": 44, "distant": 12, "distribut": 7, "docker": 16, "evalu": [4, 5], "evaluate_subcircuit": 23, "exact": [7, 8, 11, 12, 13], "exactli": 8, "exactsampl": 58, "exampl": 6, "expand_observ": 28, "expect": [9, 11, 12, 13], "experi": 9, "explanatori": 6, "featur": 17, "find": [4, 8], "fix": 17, "from": [7, 8, 11, 12, 13, 16], "full": [4, 5], "gate": [6, 11, 12], "gener": [6, 7, 8, 9, 11, 12, 13], "generate_cutting_experi": [8, 29], "generate_qpd_weight": 40, "generate_summation_term": 24, "go": 13, "group": 2, "guid": [10, 15], "have": 13, "hop": 50, "how": [7, 8, 9, 10], "implement": 3, "indic": 12, "instal": 16, "instruct": [9, 13, 16, 30, 31], "issu": 17, "iter": [2, 48, 49], "kak": 6, "kei": 6, "knit": [1, 15], "known": 17, "legaci": 3, "limit": 6, "locat": 13, "manual": 5, "mape": 51, "materi": 6, "metric": [2, 50, 51, 52, 53, 54], "minimum": 8, "modul": 6, "more": 6, "move": [6, 9, 13, 31], "mse": 52, "naive_probability_distribut": 45, "nearest_probability_distribut": 46, "need": 8, "new": [13, 17], "note": [12, 17], "num_sampl": 8, "observ": [2, 8, 9, 11, 12, 13], "observable_group": [2, 55, 56, 57], "observablecollect": 56, "observables_restricted_to_subsystem": 57, "obtain": 8, "oper": 6, "option": 16, "origin": [11, 12, 13], "other": 17, "output": [4, 5], "overhead": [6, 11, 12, 13], "overview": 6, "partit": 11, "partition_circuit_qubit": 32, "partition_problem": 33, "partitionedcuttingproblem": 18, "pass": 2, "phrase": [6, 13], "pip": 16, "place": [9, 13], "platform": 16, "pre": 16, "prelud": 17, "prepar": 9, "primit": [11, 12, 13], "probabl": 0, "problem": [9, 11, 13], "project": 15, "qiskit": [4, 5, 11, 12, 13], "qpd": [0, 6, 12, 34, 35, 36, 37, 38, 39, 40, 41], "qpdbasi": 35, "qpdbasis_from_instruct": 41, "quantum": [4, 5], "quasi": 0, "quasi_to_r": 47, "quasiprob": [6, 7], "qubit": [6, 9, 11, 13], "reconstruct": [4, 5, 9, 11, 12, 13], "reconstruct_expectation_valu": 42, "reconstruct_full_distribut": 25, "recov": 9, "reduc": [11, 12], "refer": [1, 6, 15], "releas": 17, "removefinalreset": 63, "replac": 12, "result": [4, 5], "retriev": 8, "return": 8, "run": [9, 11, 12, 13], "run_subcircuit_inst": 26, "runtim": 5, "rzzgate": 6, "sampl": [6, 8, 11, 12, 13], "sampler": [7, 11, 12, 13], "separ": [9, 11, 13], "separate_circuit": 61, "separatedcircuit": 60, "servic": 5, "set": 5, "shallow": 12, "simul": [2, 58, 59], "simulate_statevector_outcom": 59, "singl": 9, "singlequbitqpdg": 36, "some": [9, 11, 12, 13], "sourc": 16, "specifi": [9, 11, 12, 13], "subcircuit": [4, 5], "subexperi": [11, 12, 13], "support": 16, "swap": 12, "tabl": 6, "term": 6, "thi": 15, "toolbox": [1, 15], "transform": [2, 9, 60, 61], "transpil": [2, 12], "transpiler_pass": [2, 62, 63], "tutori": [3, 4, 5, 14], "two": [6, 13], "twoqubitqpdg": [12, 37], "uncut": 9, "unique_by_eq": 48, "unique_by_id": 49, "up": 5, "updat": 9, "upgrad": 17, "us": [9, 11, 12, 13, 16], "util": [2, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63], "valu": [9, 11, 12, 13], "verifi": [4, 5, 27], "via": 6, "visual": [9, 11, 12, 13], "weight": [6, 8], "weighttyp": [8, 38], "where": 13, "width": 11, "wire": [4, 5, 6, 9, 13], "within": 16}})
\ No newline at end of file
+Search.setIndex({"alltitles": {"0.1.0": [[18, "release-notes-0-1-0"]], "0.2.0": [[18, "release-notes-0-2-0"]], "0.3.0": [[18, "release-notes-0-3-0"]], "0.4.0": [[18, "release-notes-0-4-0"]], "0.5.0": [[18, "release-notes-0-5-0"]], "0.6.0": [[18, "release-notes-0-6-0"]], "0.7.0": [[18, "release-notes-0-7-0"]], "Add ancillas for wire cuts and expand the observables to account for ancilla qubits": [[14, "Add-ancillas-for-wire-cuts-and-expand-the-observables-to-account-for-ancilla-qubits"]], "An example: cutting a RZZGate": [[6, "an-example-cutting-a-rzzgate"]], "Automatic Cut Finding": [[0, "automatic-cut-finding"]], "Automatically find cuts using CKT": [[14, "Automatically-find-cuts-using-CKT"]], "Bitwise utilities (circuit_knitting.utils.bitwise)": [[2, "module-circuit_knitting.utils.bitwise"]], "Bug Fixes": [[18, "bug-fixes"], [18, "release-notes-0-3-0-bug-fixes"]], "Calculate the sampling overhead for the chosen cuts": [[11, "Calculate-the-sampling-overhead-for-the-chosen-cuts"], [12, "Calculate-the-sampling-overhead-for-the-chosen-cuts"], [13, "Calculate-the-sampling-overhead-for-the-chosen-cuts"]], "Choose a backend": [[11, "Choose-a-backend"], [13, "Choose-a-backend"]], "Circuit Cutting": [[0, "module-circuit_knitting.cutting"], [0, "id2"], [16, null]], "Circuit Cutting How-To Guides": [[10, "circuit-cutting-how-to-guides"]], "Circuit Cutting Tutorials": [[15, "circuit-cutting-tutorials"]], "Circuit Knitting Toolbox": [[16, "circuit-knitting-toolbox"]], "Circuit Knitting Toolbox API References": [[1, "circuit-knitting-toolbox-api-references"]], "Circuit cutting as a quasiprobability decomposition (QPD)": [[6, "circuit-cutting-as-a-quasiprobability-decomposition-qpd"]], "Citing this project": [[16, "citing-this-project"]], "Compare the reconstructed expectation values with the exact expectation value from the original circuit and observable": [[11, "Compare-the-reconstructed-expectation-values-with-the-exact-expectation-value-from-the-original-circuit-and-observable"], [12, "Compare-the-reconstructed-expectation-values-with-the-exact-expectation-value-from-the-original-circuit-and-observable"], [13, "Compare-the-reconstructed-expectation-values-with-the-exact-expectation-value-from-the-original-circuit-and-observable"]], "Contents": [[16, "contents"]], "Conversion (circuit_knitting.utils.conversion)": [[2, "module-circuit_knitting.utils.conversion"]], "Create a circuit and observables": [[14, "Create-a-circuit-and-observables"]], "Create a circuit to cut": [[11, "Create-a-circuit-to-cut"], [13, "Create-a-circuit-to-cut"]], "Create a circuit to run on the backend": [[12, "Create-a-circuit-to-run-on-the-backend"]], "Create a new circuit where Move instructions have been placed at the desired cut locations": [[13, "Create-a-new-circuit-where-Move-instructions-have-been-placed-at-the-desired-cut-locations"]], "Create a quantum circuit with Qiskit": [[4, "Create-a-quantum-circuit-with-Qiskit"], [5, "Create-a-quantum-circuit-with-Qiskit"]], "Create observable to go with the new circuit": [[13, "Create-observable-to-go-with-the-new-circuit"]], "Current limitations": [[6, "current-limitations"]], "CutQC": [[0, "cutqc"]], "CutQC (legacy circuit cutting implementation)": [[3, "cutqc-legacy-circuit-cutting-implementation"]], "CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding": [[4, "CutQC-Tutorial-1:-Circuit-Cutting-with-Automatic-Cut-Finding"]], "CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting": [[5, "CutQC-Tutorial-2:-Circuit-Cutting-with-Manual-Wire-Cutting"]], "CutQC Tutorials": [[3, "cutqc-tutorials"]], "Decompose the circuit with wire cutting": [[4, "Decompose-the-circuit-with-wire-cutting"], [5, "Decompose-the-circuit-with-wire-cutting"]], "Demonstrate how to find the minimum num_samples needed to retrieve all exact weights for 2 CNOT cuts": [[8, "Demonstrate-how-to-find-the-minimum-num_samples-needed-to-retrieve-all-exact-weights-for-2-CNOT-cuts"]], "Demonstrate how to obtain all weights exactly": [[8, "Demonstrate-how-to-obtain-all-weights-exactly"]], "Demonstrate that the QPD subexperiments will be shallower after cutting distant gates": [[12, "Demonstrate-that-the-QPD-subexperiments-will-be-shallower-after-cutting-distant-gates"]], "Deprecation Notes": [[18, "deprecation-notes"], [18, "release-notes-0-4-0-deprecation-notes"], [18, "release-notes-0-3-0-deprecation-notes"], [18, "release-notes-0-2-0-deprecation-notes"], [18, "release-notes-0-1-0-deprecation-notes"]], "Developer guide": [[16, "developer-guide"]], "Evaluate the subcircuits": [[4, "Evaluate-the-subcircuits"], [5, "Evaluate-the-subcircuits"]], "Explanatory material for the circuit cutting module": [[6, "explanatory-material-for-the-circuit-cutting-module"]], "Find cut locations, given a maximum of 4 qubits per subcircuit. This circuit can be separated in two by making a single wire cut and cutting one CRZGate": [[14, "Find-cut-locations,-given-a-maximum-of-4-qubits-per-subcircuit.-This-circuit-can-be-separated-in-two-by-making-a-single-wire-cut-and-cutting-one-CRZGate"]], "Gate Cutting to Reduce Circuit Depth": [[12, "Gate-Cutting-to-Reduce-Circuit-Depth"]], "Gate Cutting to Reduce Circuit Width": [[11, "Gate-Cutting-to-Reduce-Circuit-Width"]], "Generate and run the cutting experiments; reconstruct and compare against uncut expectation values": [[9, "Generate-and-run-the-cutting-experiments;-reconstruct-and-compare-against-uncut-expectation-values"]], "Generate the experiments to run on the backend.": [[14, "Generate-the-experiments-to-run-on-the-backend."]], "Generate the subexperiments to run on the backend": [[11, "Generate-the-subexperiments-to-run-on-the-backend"], [12, "Generate-the-subexperiments-to-run-on-the-backend"], [13, "Generate-the-subexperiments-to-run-on-the-backend"]], "How to generate exact quasiprobability distributions from Sampler": [[7, "How-to-generate-exact-quasiprobability-distributions-from-Sampler"]], "How to generate exact sampling coefficients": [[8, "How-to-generate-exact-sampling-coefficients"]], "How to place wire cuts using a single-qubit CutWire instruction": [[9, "How-to-place-wire-cuts-using-a-single-qubit-CutWire-instruction"]], "Installation Instructions": [[17, "installation-instructions"]], "Iteration utilities (circuit_knitting.utils.iteration)": [[2, "module-circuit_knitting.utils.iteration"]], "Key terms": [[6, "key-terms"]], "Known Issues": [[18, "known-issues"]], "Metrics (circuit_knitting.utils.metrics)": [[2, "module-circuit_knitting.utils.metrics"]], "More general cut two-qubit gates via the KAK decomposition": [[6, "more-general-cut-two-qubit-gates-via-the-kak-decomposition"]], "New Features": [[18, "new-features"], [18, "release-notes-0-4-0-new-features"], [18, "release-notes-0-3-0-new-features"], [18, "release-notes-0-2-0-new-features"], [18, "release-notes-0-1-0-new-features"]], "Observable grouping (circuit_knitting.utils.observable_grouping)": [[2, "module-circuit_knitting.utils.observable_grouping"]], "Observe the coefficient weights returned from generate_cutting_experiments are WeightType.EXACT": [[8, "Observe-the-coefficient-weights-returned-from-generate_cutting_experiments-are-WeightType.EXACT"]], "Option 1: Pip Installation": [[17, "option-1-pip-installation"]], "Option 2: Install from Source": [[17, "option-2-install-from-source"]], "Option 3: Use within Docker": [[17, "option-3-use-within-docker"]], "Other Notes": [[18, "other-notes"], [18, "release-notes-0-6-0-other-notes"], [18, "release-notes-0-5-0-other-notes"]], "Overview of circuit cutting": [[6, "overview-of-circuit-cutting"]], "Partition the circuit and observables into subcircuits and subobservables. Calculate the sampling overhead incurred from cutting these gates and wires.": [[14, "Partition-the-circuit-and-observables-into-subcircuits-and-subobservables.-Calculate-the-sampling-overhead-incurred-from-cutting-these-gates-and-wires."]], "Platform Support": [[17, "platform-support"]], "Pre-Installation": [[17, "pre-installation"]], "Prelude": [[18, "prelude"], [18, "release-notes-0-5-0-prelude"], [18, "release-notes-0-4-0-prelude"], [18, "release-notes-0-3-0-prelude"], [18, "release-notes-0-2-0-prelude"]], "Prepare a circuit for cutting": [[9, "Prepare-a-circuit-for-cutting"]], "Prepare subexperiments for the backend and run them using the Qiskit Runtime Sampler primitive": [[12, "Prepare-subexperiments-for-the-backend-and-run-them-using-the-Qiskit-Runtime-Sampler-primitive"]], "Prepare the subexperiments for the backend": [[11, "Prepare-the-subexperiments-for-the-backend"], [13, "Prepare-the-subexperiments-for-the-backend"]], "Quasi-Probability Decomposition (QPD)": [[0, "quasi-probability-decomposition-qpd"]], "Reconstruct the expectation values": [[11, "Reconstruct-the-expectation-values"], [12, "Reconstruct-the-expectation-values"], [13, "Reconstruct-the-expectation-values"]], "Reconstruct the full circuit output": [[4, "Reconstruct-the-full-circuit-output"], [5, "Reconstruct-the-full-circuit-output"]], "Recover the uncut circuit": [[9, "Recover-the-uncut-circuit"]], "References": [[6, "references"], [16, null]], "Release Notes": [[18, "release-notes"]], "Replace distant gates with TwoQubitQPDGates by specifying their indices": [[12, "Replace-distant-gates-with-TwoQubitQPDGates-by-specifying-their-indices"]], "Run the subexperiments using the Qiskit Runtime Sampler primitive": [[11, "Run-the-subexperiments-using-the-Qiskit-Runtime-Sampler-primitive"], [13, "Run-the-subexperiments-using-the-Qiskit-Runtime-Sampler-primitive"]], "Sample weights in CKT": [[6, "sample-weights-in-ckt"]], "Sampling overhead reference table": [[6, "sampling-overhead-reference-table"]], "Separate the circuit and observable according to a specified qubit partitioning": [[11, "Separate-the-circuit-and-observable-according-to-a-specified-qubit-partitioning"]], "Separate the circuit and observables": [[9, "Separate-the-circuit-and-observables"], [13, "Separate-the-circuit-and-observables"]], "Set up the Qiskit Runtime Service": [[5, "Set-up-the-Qiskit-Runtime-Service"]], "Simulation (circuit_knitting.utils.simulation)": [[2, "module-circuit_knitting.utils.simulation"]], "Specify a backend": [[12, "Specify-a-backend"]], "Specify an observable": [[9, "Specify-an-observable"], [11, "Specify-an-observable"], [12, "Specify-an-observable"], [13, "Specify-an-observable"]], "Transform cuts to moves": [[9, "Transform-cuts-to-moves"]], "Transforms (circuit_knitting.utils.transforms)": [[2, "module-circuit_knitting.utils.transforms"]], "Transpile the circuit, visualize the swaps, and note the depth": [[12, "Transpile-the-circuit,-visualize-the-swaps,-and-note-the-depth"]], "Transpiler passes (circuit_knitting.utils.transpiler_passes)": [[2, "module-circuit_knitting.utils.transpiler_passes"]], "Update the observable terms to account for the extra qubit": [[9, "Update-the-observable-terms-to-account-for-the-extra-qubit"]], "Upgrade Notes": [[18, "upgrade-notes"], [18, "release-notes-0-6-0-upgrade-notes"], [18, "release-notes-0-5-0-upgrade-notes"], [18, "release-notes-0-4-0-upgrade-notes"], [18, "release-notes-0-3-0-upgrade-notes"], [18, "release-notes-0-2-0-upgrade-notes"], [18, "release-notes-0-1-0-upgrade-notes"]], "Utilities": [[2, "module-circuit_knitting.utils"]], "Verify the results": [[4, "Verify-the-results"], [5, "Verify-the-results"]], "Visualize the decomposed problem": [[9, "Visualize-the-decomposed-problem"], [11, "Visualize-the-decomposed-problem"], [13, "Visualize-the-decomposed-problem"]], "Wire Cutting Phrased as a Two-Qubit Move Instruction": [[13, "Wire-Cutting-Phrased-as-a-Two-Qubit-Move-Instruction"]], "Wire cutting phrased as a two-qubit Move operation": [[6, "wire-cutting-phrased-as-a-two-qubit-move-operation"]], "circuit_knitting.cutting.DeviceConstraints": [[19, "circuit-knitting-cutting-deviceconstraints"]], "circuit_knitting.cutting.OptimizationParameters": [[20, "circuit-knitting-cutting-optimizationparameters"]], "circuit_knitting.cutting.PartitionedCuttingProblem": [[21, "circuit-knitting-cutting-partitionedcuttingproblem"]], "circuit_knitting.cutting.cut_gates": [[22, "circuit-knitting-cutting-cut-gates"]], "circuit_knitting.cutting.cut_wires": [[23, "circuit-knitting-cutting-cut-wires"]], "circuit_knitting.cutting.cutqc.build": [[24, "circuit-knitting-cutting-cutqc-build"]], "circuit_knitting.cutting.cutqc.cut_circuit_wires": [[25, "circuit-knitting-cutting-cutqc-cut-circuit-wires"]], "circuit_knitting.cutting.cutqc.evaluate_subcircuits": [[26, "circuit-knitting-cutting-cutqc-evaluate-subcircuits"]], "circuit_knitting.cutting.cutqc.generate_summation_terms": [[27, "circuit-knitting-cutting-cutqc-generate-summation-terms"]], "circuit_knitting.cutting.cutqc.reconstruct_full_distribution": [[28, "circuit-knitting-cutting-cutqc-reconstruct-full-distribution"]], "circuit_knitting.cutting.cutqc.run_subcircuit_instances": [[29, "circuit-knitting-cutting-cutqc-run-subcircuit-instances"]], "circuit_knitting.cutting.cutqc.verify": [[30, "circuit-knitting-cutting-cutqc-verify"]], "circuit_knitting.cutting.expand_observables": [[31, "circuit-knitting-cutting-expand-observables"]], "circuit_knitting.cutting.find_cuts": [[32, "circuit-knitting-cutting-find-cuts"]], "circuit_knitting.cutting.generate_cutting_experiments": [[33, "circuit-knitting-cutting-generate-cutting-experiments"]], "circuit_knitting.cutting.instructions.CutWire": [[34, "circuit-knitting-cutting-instructions-cutwire"]], "circuit_knitting.cutting.instructions.Move": [[35, "circuit-knitting-cutting-instructions-move"]], "circuit_knitting.cutting.partition_circuit_qubits": [[36, "circuit-knitting-cutting-partition-circuit-qubits"]], "circuit_knitting.cutting.partition_problem": [[37, "circuit-knitting-cutting-partition-problem"]], "circuit_knitting.cutting.qpd.BaseQPDGate": [[38, "circuit-knitting-cutting-qpd-baseqpdgate"]], "circuit_knitting.cutting.qpd.QPDBasis": [[39, "circuit-knitting-cutting-qpd-qpdbasis"]], "circuit_knitting.cutting.qpd.SingleQubitQPDGate": [[40, "circuit-knitting-cutting-qpd-singlequbitqpdgate"]], "circuit_knitting.cutting.qpd.TwoQubitQPDGate": [[41, "circuit-knitting-cutting-qpd-twoqubitqpdgate"]], "circuit_knitting.cutting.qpd.WeightType": [[42, "circuit-knitting-cutting-qpd-weighttype"]], "circuit_knitting.cutting.qpd.decompose_qpd_instructions": [[43, "circuit-knitting-cutting-qpd-decompose-qpd-instructions"]], "circuit_knitting.cutting.qpd.generate_qpd_weights": [[44, "circuit-knitting-cutting-qpd-generate-qpd-weights"]], "circuit_knitting.cutting.qpd.qpdbasis_from_instruction": [[45, "circuit-knitting-cutting-qpd-qpdbasis-from-instruction"]], "circuit_knitting.cutting.reconstruct_expectation_values": [[46, "circuit-knitting-cutting-reconstruct-expectation-values"]], "circuit_knitting.utils.bitwise.bit_count": [[47, "circuit-knitting-utils-bitwise-bit-count"]], "circuit_knitting.utils.conversion.dict_to_array": [[48, "circuit-knitting-utils-conversion-dict-to-array"]], "circuit_knitting.utils.conversion.naive_probability_distribution": [[49, "circuit-knitting-utils-conversion-naive-probability-distribution"]], "circuit_knitting.utils.conversion.nearest_probability_distribution": [[50, "circuit-knitting-utils-conversion-nearest-probability-distribution"]], "circuit_knitting.utils.conversion.quasi_to_real": [[51, "circuit-knitting-utils-conversion-quasi-to-real"]], "circuit_knitting.utils.iteration.unique_by_eq": [[52, "circuit-knitting-utils-iteration-unique-by-eq"]], "circuit_knitting.utils.iteration.unique_by_id": [[53, "circuit-knitting-utils-iteration-unique-by-id"]], "circuit_knitting.utils.metrics.HOP": [[54, "circuit-knitting-utils-metrics-hop"]], "circuit_knitting.utils.metrics.MAPE": [[55, "circuit-knitting-utils-metrics-mape"]], "circuit_knitting.utils.metrics.MSE": [[56, "circuit-knitting-utils-metrics-mse"]], "circuit_knitting.utils.metrics.chi2_distance": [[57, "circuit-knitting-utils-metrics-chi2-distance"]], "circuit_knitting.utils.metrics.cross_entropy": [[58, "circuit-knitting-utils-metrics-cross-entropy"]], "circuit_knitting.utils.observable_grouping.CommutingObservableGroup": [[59, "circuit-knitting-utils-observable-grouping-commutingobservablegroup"]], "circuit_knitting.utils.observable_grouping.ObservableCollection": [[60, "circuit-knitting-utils-observable-grouping-observablecollection"]], "circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem": [[61, "circuit-knitting-utils-observable-grouping-observables-restricted-to-subsystem"]], "circuit_knitting.utils.simulation.ExactSampler": [[62, "circuit-knitting-utils-simulation-exactsampler"]], "circuit_knitting.utils.simulation.simulate_statevector_outcomes": [[63, "circuit-knitting-utils-simulation-simulate-statevector-outcomes"]], "circuit_knitting.utils.transforms.SeparatedCircuits": [[64, "circuit-knitting-utils-transforms-separatedcircuits"]], "circuit_knitting.utils.transforms.separate_circuit": [[65, "circuit-knitting-utils-transforms-separate-circuit"]], "circuit_knitting.utils.transpiler_passes.ConsolidateResets": [[66, "circuit-knitting-utils-transpiler-passes-consolidateresets"]], "circuit_knitting.utils.transpiler_passes.RemoveFinalReset": [[67, "circuit-knitting-utils-transpiler-passes-removefinalreset"]]}, "docnames": ["apidocs/circuit_cutting", "apidocs/index", "apidocs/utils", "circuit_cutting/cutqc/index", "circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding", "circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting", "circuit_cutting/explanation/index", "circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler", "circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients", "circuit_cutting/how-tos/how_to_specify_cut_wires", "circuit_cutting/how-tos/index", "circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width", "circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth", "circuit_cutting/tutorials/03_wire_cutting_via_move_instruction", "circuit_cutting/tutorials/04_automatic_cut_finding", "circuit_cutting/tutorials/index", "index", "install", "release-notes", "stubs/circuit_knitting.cutting.DeviceConstraints", "stubs/circuit_knitting.cutting.OptimizationParameters", "stubs/circuit_knitting.cutting.PartitionedCuttingProblem", "stubs/circuit_knitting.cutting.cut_gates", "stubs/circuit_knitting.cutting.cut_wires", "stubs/circuit_knitting.cutting.cutqc.build", "stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires", "stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits", "stubs/circuit_knitting.cutting.cutqc.generate_summation_terms", "stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution", "stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances", "stubs/circuit_knitting.cutting.cutqc.verify", "stubs/circuit_knitting.cutting.expand_observables", "stubs/circuit_knitting.cutting.find_cuts", "stubs/circuit_knitting.cutting.generate_cutting_experiments", "stubs/circuit_knitting.cutting.instructions.CutWire", "stubs/circuit_knitting.cutting.instructions.Move", "stubs/circuit_knitting.cutting.partition_circuit_qubits", "stubs/circuit_knitting.cutting.partition_problem", "stubs/circuit_knitting.cutting.qpd.BaseQPDGate", "stubs/circuit_knitting.cutting.qpd.QPDBasis", "stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate", "stubs/circuit_knitting.cutting.qpd.TwoQubitQPDGate", "stubs/circuit_knitting.cutting.qpd.WeightType", "stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions", "stubs/circuit_knitting.cutting.qpd.generate_qpd_weights", "stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction", "stubs/circuit_knitting.cutting.reconstruct_expectation_values", "stubs/circuit_knitting.utils.bitwise.bit_count", "stubs/circuit_knitting.utils.conversion.dict_to_array", "stubs/circuit_knitting.utils.conversion.naive_probability_distribution", "stubs/circuit_knitting.utils.conversion.nearest_probability_distribution", "stubs/circuit_knitting.utils.conversion.quasi_to_real", "stubs/circuit_knitting.utils.iteration.unique_by_eq", "stubs/circuit_knitting.utils.iteration.unique_by_id", "stubs/circuit_knitting.utils.metrics.HOP", "stubs/circuit_knitting.utils.metrics.MAPE", "stubs/circuit_knitting.utils.metrics.MSE", "stubs/circuit_knitting.utils.metrics.chi2_distance", "stubs/circuit_knitting.utils.metrics.cross_entropy", "stubs/circuit_knitting.utils.observable_grouping.CommutingObservableGroup", "stubs/circuit_knitting.utils.observable_grouping.ObservableCollection", "stubs/circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem", "stubs/circuit_knitting.utils.simulation.ExactSampler", "stubs/circuit_knitting.utils.simulation.simulate_statevector_outcomes", "stubs/circuit_knitting.utils.transforms.SeparatedCircuits", "stubs/circuit_knitting.utils.transforms.separate_circuit", "stubs/circuit_knitting.utils.transpiler_passes.ConsolidateResets", "stubs/circuit_knitting.utils.transpiler_passes.RemoveFinalReset"], "envversion": {"nbsphinx": 4, "sphinx": 61, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1, "sphinx.ext.viewcode": 1}, "filenames": ["apidocs/circuit_cutting.rst", "apidocs/index.rst", "apidocs/utils.rst", "circuit_cutting/cutqc/index.rst", "circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.ipynb", "circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.ipynb", "circuit_cutting/explanation/index.rst", "circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.ipynb", "circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.ipynb", "circuit_cutting/how-tos/how_to_specify_cut_wires.ipynb", "circuit_cutting/how-tos/index.rst", "circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb", "circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.ipynb", "circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.ipynb", "circuit_cutting/tutorials/04_automatic_cut_finding.ipynb", "circuit_cutting/tutorials/index.rst", "index.rst", "install.rst", "release-notes.rst", "stubs/circuit_knitting.cutting.DeviceConstraints.rst", "stubs/circuit_knitting.cutting.OptimizationParameters.rst", "stubs/circuit_knitting.cutting.PartitionedCuttingProblem.rst", "stubs/circuit_knitting.cutting.cut_gates.rst", "stubs/circuit_knitting.cutting.cut_wires.rst", "stubs/circuit_knitting.cutting.cutqc.build.rst", "stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires.rst", "stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits.rst", "stubs/circuit_knitting.cutting.cutqc.generate_summation_terms.rst", "stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.rst", "stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances.rst", "stubs/circuit_knitting.cutting.cutqc.verify.rst", "stubs/circuit_knitting.cutting.expand_observables.rst", "stubs/circuit_knitting.cutting.find_cuts.rst", "stubs/circuit_knitting.cutting.generate_cutting_experiments.rst", "stubs/circuit_knitting.cutting.instructions.CutWire.rst", "stubs/circuit_knitting.cutting.instructions.Move.rst", "stubs/circuit_knitting.cutting.partition_circuit_qubits.rst", "stubs/circuit_knitting.cutting.partition_problem.rst", "stubs/circuit_knitting.cutting.qpd.BaseQPDGate.rst", "stubs/circuit_knitting.cutting.qpd.QPDBasis.rst", "stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.rst", "stubs/circuit_knitting.cutting.qpd.TwoQubitQPDGate.rst", "stubs/circuit_knitting.cutting.qpd.WeightType.rst", "stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.rst", "stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.rst", "stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction.rst", "stubs/circuit_knitting.cutting.reconstruct_expectation_values.rst", "stubs/circuit_knitting.utils.bitwise.bit_count.rst", "stubs/circuit_knitting.utils.conversion.dict_to_array.rst", "stubs/circuit_knitting.utils.conversion.naive_probability_distribution.rst", "stubs/circuit_knitting.utils.conversion.nearest_probability_distribution.rst", "stubs/circuit_knitting.utils.conversion.quasi_to_real.rst", "stubs/circuit_knitting.utils.iteration.unique_by_eq.rst", "stubs/circuit_knitting.utils.iteration.unique_by_id.rst", "stubs/circuit_knitting.utils.metrics.HOP.rst", "stubs/circuit_knitting.utils.metrics.MAPE.rst", "stubs/circuit_knitting.utils.metrics.MSE.rst", "stubs/circuit_knitting.utils.metrics.chi2_distance.rst", "stubs/circuit_knitting.utils.metrics.cross_entropy.rst", "stubs/circuit_knitting.utils.observable_grouping.CommutingObservableGroup.rst", "stubs/circuit_knitting.utils.observable_grouping.ObservableCollection.rst", "stubs/circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.rst", "stubs/circuit_knitting.utils.simulation.ExactSampler.rst", "stubs/circuit_knitting.utils.simulation.simulate_statevector_outcomes.rst", "stubs/circuit_knitting.utils.transforms.SeparatedCircuits.rst", "stubs/circuit_knitting.utils.transforms.separate_circuit.rst", "stubs/circuit_knitting.utils.transpiler_passes.ConsolidateResets.rst", "stubs/circuit_knitting.utils.transpiler_passes.RemoveFinalReset.rst"], "indexentries": {"__init__() (commutingobservablegroup method)": [[59, "circuit_knitting.utils.observable_grouping.CommutingObservableGroup.__init__", false]], "__init__() (consolidateresets method)": [[66, "circuit_knitting.utils.transpiler_passes.ConsolidateResets.__init__", false]], "__init__() (exactsampler method)": [[62, "circuit_knitting.utils.simulation.ExactSampler.__init__", false]], "__init__() (observablecollection method)": [[60, "circuit_knitting.utils.observable_grouping.ObservableCollection.__init__", false]], "__init__() (removefinalreset method)": [[67, "circuit_knitting.utils.transpiler_passes.RemoveFinalReset.__init__", false]], "baseqpdgate (class in circuit_knitting.cutting.qpd)": [[38, "circuit_knitting.cutting.qpd.BaseQPDGate", false]], "bit_count() (in module circuit_knitting.utils.bitwise)": [[47, "circuit_knitting.utils.bitwise.bit_count", false]], "build() (in module circuit_knitting.cutting.cutqc)": [[24, "circuit_knitting.cutting.cutqc.build", false]], "chi2_distance() (in module circuit_knitting.utils.metrics)": [[57, "circuit_knitting.utils.metrics.chi2_distance", false]], "circuit_knitting": [[1, "module-circuit_knitting", false]], "circuit_knitting.cutting": [[0, "module-circuit_knitting.cutting", false]], "circuit_knitting.utils": [[2, "module-circuit_knitting.utils", false]], "circuit_knitting.utils.bitwise": [[2, "module-circuit_knitting.utils.bitwise", false]], "circuit_knitting.utils.conversion": [[2, "module-circuit_knitting.utils.conversion", false]], "circuit_knitting.utils.iteration": [[2, "module-circuit_knitting.utils.iteration", false]], "circuit_knitting.utils.metrics": [[2, "module-circuit_knitting.utils.metrics", false]], "circuit_knitting.utils.observable_grouping": [[2, "module-circuit_knitting.utils.observable_grouping", false]], "circuit_knitting.utils.simulation": [[2, "module-circuit_knitting.utils.simulation", false]], "circuit_knitting.utils.transforms": [[2, "module-circuit_knitting.utils.transforms", false]], "circuit_knitting.utils.transpiler_passes": [[2, "module-circuit_knitting.utils.transpiler_passes", false]], "commutingobservablegroup (class in circuit_knitting.utils.observable_grouping)": [[59, "circuit_knitting.utils.observable_grouping.CommutingObservableGroup", false]], "consolidateresets (class in circuit_knitting.utils.transpiler_passes)": [[66, "circuit_knitting.utils.transpiler_passes.ConsolidateResets", false]], "cross_entropy() (in module circuit_knitting.utils.metrics)": [[58, "circuit_knitting.utils.metrics.cross_entropy", false]], "cut_circuit_wires() (in module circuit_knitting.cutting.cutqc)": [[25, "circuit_knitting.cutting.cutqc.cut_circuit_wires", false]], "cut_gates() (in module circuit_knitting.cutting)": [[22, "circuit_knitting.cutting.cut_gates", false]], "cut_wires() (in module circuit_knitting.cutting)": [[23, "circuit_knitting.cutting.cut_wires", false]], "cutwire (class in circuit_knitting.cutting.instructions)": [[34, "circuit_knitting.cutting.instructions.CutWire", false]], "decompose_qpd_instructions() (in module circuit_knitting.cutting.qpd)": [[43, "circuit_knitting.cutting.qpd.decompose_qpd_instructions", false]], "deviceconstraints (class in circuit_knitting.cutting)": [[19, "circuit_knitting.cutting.DeviceConstraints", false]], "dict_to_array() (in module circuit_knitting.utils.conversion)": [[48, "circuit_knitting.utils.conversion.dict_to_array", false]], "evaluate_subcircuits() (in module circuit_knitting.cutting.cutqc)": [[26, "circuit_knitting.cutting.cutqc.evaluate_subcircuits", false]], "exactsampler (class in circuit_knitting.utils.simulation)": [[62, "circuit_knitting.utils.simulation.ExactSampler", false]], "expand_observables() (in module circuit_knitting.cutting)": [[31, "circuit_knitting.cutting.expand_observables", false]], "find_cuts() (in module circuit_knitting.cutting)": [[32, "circuit_knitting.cutting.find_cuts", false]], "generate_cutting_experiments() (in module circuit_knitting.cutting)": [[33, "circuit_knitting.cutting.generate_cutting_experiments", false]], "generate_qpd_weights() (in module circuit_knitting.cutting.qpd)": [[44, "circuit_knitting.cutting.qpd.generate_qpd_weights", false]], "generate_summation_terms() (in module circuit_knitting.cutting.cutqc)": [[27, "circuit_knitting.cutting.cutqc.generate_summation_terms", false]], "hop() (in module circuit_knitting.utils.metrics)": [[54, "circuit_knitting.utils.metrics.HOP", false]], "mape() (in module circuit_knitting.utils.metrics)": [[55, "circuit_knitting.utils.metrics.MAPE", false]], "module": [[0, "module-circuit_knitting.cutting", false], [1, "module-circuit_knitting", false], [2, "module-circuit_knitting.utils", false], [2, "module-circuit_knitting.utils.bitwise", false], [2, "module-circuit_knitting.utils.conversion", false], [2, "module-circuit_knitting.utils.iteration", false], [2, "module-circuit_knitting.utils.metrics", false], [2, "module-circuit_knitting.utils.observable_grouping", false], [2, "module-circuit_knitting.utils.simulation", false], [2, "module-circuit_knitting.utils.transforms", false], [2, "module-circuit_knitting.utils.transpiler_passes", false]], "move (class in circuit_knitting.cutting.instructions)": [[35, "circuit_knitting.cutting.instructions.Move", false]], "mse() (in module circuit_knitting.utils.metrics)": [[56, "circuit_knitting.utils.metrics.MSE", false]], "naive_probability_distribution() (in module circuit_knitting.utils.conversion)": [[49, "circuit_knitting.utils.conversion.naive_probability_distribution", false]], "nearest_probability_distribution() (in module circuit_knitting.utils.conversion)": [[50, "circuit_knitting.utils.conversion.nearest_probability_distribution", false]], "observablecollection (class in circuit_knitting.utils.observable_grouping)": [[60, "circuit_knitting.utils.observable_grouping.ObservableCollection", false]], "observables_restricted_to_subsystem() (in module circuit_knitting.utils.observable_grouping)": [[61, "circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem", false]], "optimizationparameters (class in circuit_knitting.cutting)": [[20, "circuit_knitting.cutting.OptimizationParameters", false]], "partition_circuit_qubits() (in module circuit_knitting.cutting)": [[36, "circuit_knitting.cutting.partition_circuit_qubits", false]], "partition_problem() (in module circuit_knitting.cutting)": [[37, "circuit_knitting.cutting.partition_problem", false]], "partitionedcuttingproblem (class in circuit_knitting.cutting)": [[21, "circuit_knitting.cutting.PartitionedCuttingProblem", false]], "qpdbasis (class in circuit_knitting.cutting.qpd)": [[39, "circuit_knitting.cutting.qpd.QPDBasis", false]], "qpdbasis_from_instruction() (in module circuit_knitting.cutting.qpd)": [[45, "circuit_knitting.cutting.qpd.qpdbasis_from_instruction", false]], "quasi_to_real() (in module circuit_knitting.utils.conversion)": [[51, "circuit_knitting.utils.conversion.quasi_to_real", false]], "reconstruct_expectation_values() (in module circuit_knitting.cutting)": [[46, "circuit_knitting.cutting.reconstruct_expectation_values", false]], "reconstruct_full_distribution() (in module circuit_knitting.cutting.cutqc)": [[28, "circuit_knitting.cutting.cutqc.reconstruct_full_distribution", false]], "removefinalreset (class in circuit_knitting.utils.transpiler_passes)": [[67, "circuit_knitting.utils.transpiler_passes.RemoveFinalReset", false]], "run_subcircuit_instances() (in module circuit_knitting.cutting.cutqc)": [[29, "circuit_knitting.cutting.cutqc.run_subcircuit_instances", false]], "separate_circuit() (in module circuit_knitting.utils.transforms)": [[65, "circuit_knitting.utils.transforms.separate_circuit", false]], "separatedcircuits (class in circuit_knitting.utils.transforms)": [[64, "circuit_knitting.utils.transforms.SeparatedCircuits", false]], "simulate_statevector_outcomes() (in module circuit_knitting.utils.simulation)": [[63, "circuit_knitting.utils.simulation.simulate_statevector_outcomes", false]], "singlequbitqpdgate (class in circuit_knitting.cutting.qpd)": [[40, "circuit_knitting.cutting.qpd.SingleQubitQPDGate", false]], "twoqubitqpdgate (class in circuit_knitting.cutting.qpd)": [[41, "circuit_knitting.cutting.qpd.TwoQubitQPDGate", false]], "unique_by_eq() (in module circuit_knitting.utils.iteration)": [[52, "circuit_knitting.utils.iteration.unique_by_eq", false]], "unique_by_id() (in module circuit_knitting.utils.iteration)": [[53, "circuit_knitting.utils.iteration.unique_by_id", false]], "verify() (in module circuit_knitting.cutting.cutqc)": [[30, "circuit_knitting.cutting.cutqc.verify", false]], "weighttype (class in circuit_knitting.cutting.qpd)": [[42, "circuit_knitting.cutting.qpd.WeightType", false]]}, "objects": {"": [[1, 0, 0, "-", "circuit_knitting"]], "circuit_knitting": [[0, 0, 0, "-", "cutting"], [2, 0, 0, "-", "utils"]], "circuit_knitting.cutting": [[19, 1, 1, "", "DeviceConstraints"], [20, 1, 1, "", "OptimizationParameters"], [21, 1, 1, "", "PartitionedCuttingProblem"], [22, 2, 1, "", "cut_gates"], [23, 2, 1, "", "cut_wires"], [31, 2, 1, "", "expand_observables"], [32, 2, 1, "", "find_cuts"], [33, 2, 1, "", "generate_cutting_experiments"], [36, 2, 1, "", "partition_circuit_qubits"], [37, 2, 1, "", "partition_problem"], [46, 2, 1, "", "reconstruct_expectation_values"]], "circuit_knitting.cutting.cutqc": [[24, 2, 1, "", "build"], [25, 2, 1, "", "cut_circuit_wires"], [26, 2, 1, "", "evaluate_subcircuits"], [27, 2, 1, "", "generate_summation_terms"], [28, 2, 1, "", "reconstruct_full_distribution"], [29, 2, 1, "", "run_subcircuit_instances"], [30, 2, 1, "", "verify"]], "circuit_knitting.cutting.instructions": [[34, 1, 1, "", "CutWire"], [35, 1, 1, "", "Move"]], "circuit_knitting.cutting.qpd": [[38, 1, 1, "", "BaseQPDGate"], [39, 1, 1, "", "QPDBasis"], [40, 1, 1, "", "SingleQubitQPDGate"], [41, 1, 1, "", "TwoQubitQPDGate"], [42, 1, 1, "", "WeightType"], [43, 2, 1, "", "decompose_qpd_instructions"], [44, 2, 1, "", "generate_qpd_weights"], [45, 2, 1, "", "qpdbasis_from_instruction"]], "circuit_knitting.utils": [[2, 0, 0, "-", "bitwise"], [2, 0, 0, "-", "conversion"], [2, 0, 0, "-", "iteration"], [2, 0, 0, "-", "metrics"], [2, 0, 0, "-", "observable_grouping"], [2, 0, 0, "-", "simulation"], [2, 0, 0, "-", "transforms"], [2, 0, 0, "-", "transpiler_passes"]], "circuit_knitting.utils.bitwise": [[47, 2, 1, "", "bit_count"]], "circuit_knitting.utils.conversion": [[48, 2, 1, "", "dict_to_array"], [49, 2, 1, "", "naive_probability_distribution"], [50, 2, 1, "", "nearest_probability_distribution"], [51, 2, 1, "", "quasi_to_real"]], "circuit_knitting.utils.iteration": [[52, 2, 1, "", "unique_by_eq"], [53, 2, 1, "", "unique_by_id"]], "circuit_knitting.utils.metrics": [[54, 2, 1, "", "HOP"], [55, 2, 1, "", "MAPE"], [56, 2, 1, "", "MSE"], [57, 2, 1, "", "chi2_distance"], [58, 2, 1, "", "cross_entropy"]], "circuit_knitting.utils.observable_grouping": [[59, 1, 1, "", "CommutingObservableGroup"], [60, 1, 1, "", "ObservableCollection"], [61, 2, 1, "", "observables_restricted_to_subsystem"]], "circuit_knitting.utils.observable_grouping.CommutingObservableGroup": [[59, 3, 1, "", "__init__"]], "circuit_knitting.utils.observable_grouping.ObservableCollection": [[60, 3, 1, "", "__init__"]], "circuit_knitting.utils.simulation": [[62, 1, 1, "", "ExactSampler"], [63, 2, 1, "", "simulate_statevector_outcomes"]], "circuit_knitting.utils.simulation.ExactSampler": [[62, 3, 1, "", "__init__"]], "circuit_knitting.utils.transforms": [[64, 1, 1, "", "SeparatedCircuits"], [65, 2, 1, "", "separate_circuit"]], "circuit_knitting.utils.transpiler_passes": [[66, 1, 1, "", "ConsolidateResets"], [67, 1, 1, "", "RemoveFinalReset"]], "circuit_knitting.utils.transpiler_passes.ConsolidateResets": [[66, 3, 1, "", "__init__"]], "circuit_knitting.utils.transpiler_passes.RemoveFinalReset": [[67, 3, 1, "", "__init__"]]}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "class", "Python class"], "2": ["py", "function", "Python function"], "3": ["py", "method", "Python method"]}, "objtypes": {"0": "py:module", "1": "py:class", "2": "py:function", "3": "py:method"}, "terms": {"": [4, 5, 6, 9, 11, 12, 13, 16, 17, 18, 21, 22, 23, 26, 29, 31, 32, 33, 35, 36, 37, 40, 46], "0": [4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 24, 25, 26, 27, 28, 29, 30, 33, 35, 37, 40, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 65], "00016": 6, "00ed70b5342f79f0a970ee9821c271eeffaf760a7dcd36ec": 17, "01662904": 9, "02058": 6, "0209120": 6, "02333": 3, "02645664": 9, "0275": 56, "03026347": 13, "03366": 6, "03366v1": [9, 13, 23], "033924079765218e": 5, "04814888": 13, "06026169907257": 14, "070502": 50, "07534": 6, "0x7f8640f91340": 13, "1": [3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 27, 28, 33, 35, 37, 43, 44, 46, 49, 54, 55, 56, 57, 58, 61, 65], "10": [4, 5, 9, 11, 12, 13, 16], "100": [12, 55], "1000": [7, 44], "10000": 20, "1024": 20, "10396": 45, "108": [18, 50], "11": [4, 5, 9, 11, 12, 13, 18], "111": 14, "11174": [6, 18], "11638": 6, "12": [4, 5, 9, 11, 12, 13, 17, 18], "1242": 14, "127": [14, 17], "13": [4, 5, 9, 11, 13, 27], "14": [4, 9, 13, 18], "14649261": 11, "15": [4, 13], "157": [11, 13], "16": [4, 5, 6, 13], "1612": 6, "16421342": 12, "16666667": 8, "1722866042107947e": 5, "1810": 62, "1811": 62, "19": 14, "1909": 6, "1_000": 14, "1d": 33, "1st": 4, "2": [3, 4, 6, 7, 9, 11, 12, 13, 14, 15, 16, 27, 32, 33, 35, 41, 43, 49, 52, 56, 57, 64, 65], "2006": [6, 18], "2012": [3, 50], "2022": [4, 5], "2023": 16, "212151701573308e": 5, "21645021645021645": 57, "2205": 6, "23": 18, "2302": [6, 9, 13, 23], "2312": 6, "2429332936235563e": 5, "24999999999999992": 8, "25": [8, 54, 55, 56, 57, 58], "256": [4, 13], "259": 6, "26040996": 11, "262": 6, "264": 6, "28": 14, "285": 18, "2d": 43, "2nd": 4, "3": [4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 23, 35, 54, 55, 56, 57, 58, 61, 65], "30": 12, "32": [4, 5], "32519053": 12, "33": 5, "34": 4, "3419935587": [4, 5], "36": 8, "3862943611198906": 58, "39": [4, 5, 9, 11, 13, 14], "3d": 18, "4": [4, 5, 6, 7, 8, 9, 11, 12, 13, 16, 27, 35, 65], "4000": [4, 5], "406": 18, "4196057829": [4, 5], "44": 18, "45": 18, "455": 18, "49": 6, "5": [4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 54, 55, 56, 57, 58], "50497603": 12, "5281": 16, "531": 6, "532": 6, "54284137": 13, "550336449277775e": 4, "550351082551326e": 4, "56254612": 11, "5645511160682197": 4, "5645511160682206": 4, "59099026": [9, 13], "5996810883678423": 5, "599681088367844": 5, "6": [4, 5, 6, 8, 9, 11, 12, 13, 14, 16], "6174469": 9, "64": 17, "66666666666659": 55, "66918945": 12, "7": [4, 5, 6, 8, 9, 11, 12, 13, 14, 16, 17, 24, 25, 26, 27, 28, 29, 30, 48, 49, 50, 51, 54, 55, 56, 57, 58], "70903873": 11, "729": 12, "75": 5, "7987997": 16, "8": [4, 5, 6, 8, 9, 11, 12, 13, 14, 18, 24, 25, 26, 27, 28, 29, 30, 48, 49, 50, 51, 54, 55, 56, 57, 58], "81": 11, "8155290178685046e": 4, "828": 6, "8888": 17, "9": [4, 5, 6, 9, 11, 12, 13, 18], "9004283905932716": 5, "9004283905932735": 5, "91": 55, "96": 14, "9657": 62, "9945381353717199": 4, "9945381353717202": 4, "9965472816547853e": 4, "A": [4, 5, 6, 8, 9, 11, 13, 18, 22, 23, 24, 25, 26, 27, 30, 32, 33, 35, 36, 37, 38, 39, 43, 44, 46, 61, 65], "And": 4, "As": [6, 9, 13, 18], "By": 6, "For": [4, 5, 6, 9, 11, 12, 13, 17, 18, 35], "If": [4, 5, 6, 7, 8, 16, 17, 18, 33, 35, 37, 43, 46, 65], "In": [4, 5, 6, 7, 8, 9, 11, 12, 13, 17, 18, 33], "It": [4, 5, 6, 18, 24, 25, 26, 27, 28, 29, 30, 48, 49, 50, 51, 54, 55, 56, 57, 58], "Its": 12, "No": [6, 17], "One": [6, 13], "Or": 17, "The": [3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 41, 43, 44, 45, 46, 48, 49, 50, 51, 54, 55, 56, 57, 58, 60, 61, 62, 65], "There": [6, 17, 18], "These": [6, 18], "To": [11, 12, 13, 16, 17, 18], "With": [6, 18], "_": 65, "__init__": [59, 60, 62, 66, 67], "_i": 6, "_min_nonzero": 8, "_run_subcircuit": 28, "a_1": 6, "a_2": 6, "a_3": 6, "a_4": 6, "a_5": 6, "a_6": 6, "a_i": 6, "aaaabbbb": 13, "aabb": [8, 11], "ab": [6, 7], "abba": 65, "abc": 53, "abl": [9, 13], "about": [4, 5, 16, 23, 29], "abov": [4, 5, 6, 8, 13, 44, 54, 62], "absolut": [4, 5, 6, 8, 44, 55], "abstract": 6, "abstractli": 6, "accept": [11, 12, 13], "access": [4, 5, 17, 18, 43], "accord": [16, 31, 43, 65], "account": [4, 13], "achiev": 8, "acquir": 17, "across": 17, "act": [6, 22, 32, 36, 37, 38, 41], "activ": 17, "actual": [6, 9, 54, 55, 56, 57, 58], "ad": [6, 9, 13, 18, 33], "adapt": 18, "add": [18, 27], "addit": [6, 9, 13, 18], "addition": 18, "adjust": 17, "aer": [18, 62], "affect": 36, "aforement": 35, "after": [6, 31], "again": 6, "against": [4, 5], "agata": 16, "al": [4, 5, 50], "albeit": 18, "algorithm": [18, 32], "all": [4, 6, 10, 17, 18, 23, 27, 29, 36, 37, 43, 45, 49, 52, 62, 63], "alloc": [9, 13, 23], "allow": [4, 5, 6, 13, 17, 18, 25, 33], "almudena": 16, "along": [9, 37, 63], "alreadi": 18, "also": [4, 6, 12, 18, 33, 45], "alter": [4, 5], "altern": [4, 5, 13], "alwai": 33, "ambigu": 6, "amend": [4, 5], "amount": 6, "an": [4, 16, 17, 18, 34, 37, 38, 39, 43, 46, 54], "ancillari": 4, "andrew": 16, "angl": 6, "ani": [4, 5, 6, 8, 9, 11, 12, 13, 18, 25, 26, 27, 29, 37, 39], "anoth": [12, 13, 35], "anywher": 62, "apach": [4, 5], "api": [6, 16, 18], "appear": [4, 5], "append": [9, 13, 33, 35], "appendix": [6, 23], "appl": 17, "appli": [11, 12, 43], "applic": [6, 18], "appropri": [17, 18, 31, 52], "approx": 6, "ar": [4, 5, 6, 9, 11, 12, 13, 17, 18, 25, 31, 32, 33, 37, 43, 44, 45, 46, 52, 55, 56, 58, 60, 63], "arbitrari": 18, "arbitrarili": 12, "architectur": 12, "areeq": 16, "arg": [11, 13, 17, 66, 67], "argument": [4, 5, 6, 18, 25, 46], "arm": 17, "around": 18, "arrai": [2, 33, 48, 51, 54, 55, 56, 57, 58], "arxiv": [3, 6, 9, 13, 18], "asc": [4, 5], "ascend": 18, "assign": [38, 39, 40, 60], "assign_paramet": [4, 8, 11, 12], "associ": [6, 18, 33, 42, 46], "assum": 18, "attribut": [19, 20, 21, 34, 35, 38, 39, 40, 41, 42, 59, 60, 62, 64, 66, 67], "attributeerror": 18, "author": 16, "autom": [3, 4, 5, 18, 24, 25, 26, 27, 28, 29, 30], "automat": [3, 5, 9, 13, 16, 17, 18, 25, 37, 60], "automated_cut_find": [14, 18], "avail": [6, 14, 16, 17, 18, 35], "avoid": 6, "awai": 18, "b": [8, 11, 13, 65], "baac": 65, "backend": [4, 5, 6, 18, 26, 29], "backend_nam": [4, 5, 26, 29], "backjump": 18, "backward": 18, "base": [4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 19, 20, 21, 34, 35, 37, 38, 39, 40, 41, 42, 43, 64], "baseqpdg": [6, 18, 32, 40, 41, 43], "basesampl": [7, 18], "basesamplerv2": 18, "basi": [6, 11, 12, 13, 14, 18, 38, 39, 40, 41, 44], "basic": [4, 5, 6], "basis_id": [38, 40, 41], "batch": [6, 11, 13], "becaus": [4, 6, 9], "becom": 13, "been": [4, 5, 6, 9, 12, 18, 35], "befor": [11, 13, 17, 18, 22, 36, 46], "begin": 13, "behavior": [18, 32], "being": 6, "bello": 16, "belong": [33, 36, 40, 43], "below": [4, 5, 6, 17, 18, 46], "benefit": 6, "best": [18, 32], "beta": 6, "better": 52, "between": [6, 8, 12, 30, 31, 46, 52, 55, 56, 57, 58], "beyond": 6, "bi": 6, "bib": 16, "bin": 17, "binary_prob": [4, 5], "birgitta": 6, "bit": [18, 22, 37, 47, 62], "bitstr": [4, 5, 18, 54], "block": 13, "bool": [22, 25, 36], "boost": 6, "both": [4, 5, 6, 18, 33, 52], "boundari": 37, "bra": 16, "bravyi": [6, 16], "break": 16, "brenner": 6, "brief": 17, "browser": 17, "bryce": 16, "build": [13, 17, 18], "built": 18, "bypass": 18, "c": [16, 17, 65], "calcul": [4, 5, 7, 8, 18, 46], "caleb": 16, "call": [6, 7, 9, 18, 31], "can": [4, 5, 6, 7, 8, 9, 11, 12, 13, 16, 17, 18, 31, 39, 45, 52, 62, 63], "cannot": [31, 45], "capabl": 6, "captur": [4, 5], "care": [17, 46], "carrera": 16, "carri": [4, 5], "case": [4, 5, 6, 8, 9, 17, 18, 33, 35, 37, 45, 52], "caus": [13, 18], "cd": 17, "center": 18, "certain": [6, 12, 17, 18], "chamber": 6, "chang": [16, 18], "channel": [6, 35], "charact": 13, "check": [4, 5, 18], "chgate": [6, 18], "chi": [4, 5, 57], "chi2": [4, 5], "chip": 17, "choic": [6, 52], "choos": [12, 17], "chosen": [6, 32], "christoph": 6, "circuit": [2, 7, 8, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 39, 43, 46, 48, 49, 50, 51, 54, 55, 56, 57, 58, 62, 63, 64, 65], "circuit_cut": 18, "circuit_knit": [0, 4, 5, 7, 8, 9, 11, 12, 13, 14, 18], "circuit_knitting_toolbox": 18, "circular": 12, "citaton": 16, "ckt": [4, 5, 16, 17, 18, 24, 25, 26, 27, 28, 29, 30, 48, 49, 50, 51, 54, 55, 56, 57, 58], "class": [7, 18, 19, 20, 21, 34, 35, 38, 39, 40, 41, 42, 52, 53, 59, 60, 62, 64, 66, 67], "classic": [4, 5, 6, 18, 22, 37, 43, 62, 63], "classical_cost": 4, "clone": 17, "closest": 50, "cnot": [6, 11, 12], "co": [6, 16], "code": [2, 4, 5, 6, 13, 16, 18, 35], "coeff": [9, 11, 12, 13, 39], "coeffici": [6, 7, 9, 10, 11, 12, 13, 14, 16, 27, 33, 39, 46], "coeffient": 6, "collaps": [13, 35], "collect": [25, 53, 60], "com": [16, 17, 62], "combin": [4, 6, 8, 11, 12, 13], "come": 18, "command": 17, "comment": 13, "common": [11, 13, 17, 43, 56], "commun": 6, "commut": [9, 59, 60], "commuting_observ": 59, "commutingobservablegroup": 60, "compar": [2, 30], "comparison": [4, 5, 52], "compat": [18, 31], "compil": 17, "complete_path_map": [4, 27], "compon": [17, 65], "compos": 17, "compris": [4, 5, 43], "comput": [4, 5, 6, 24, 51, 54, 55, 56, 57, 58], "conceptu": 13, "conda": 17, "conda_subdir": 17, "condit": 18, "conduct": 2, "configur": [4, 5, 17], "connect": [9, 18, 26, 37], "consecut": [11, 12, 13, 18, 37, 65], "consid": 6, "consist": 6, "consolid": 66, "consolidatereset": [18, 33], "constraint": [6, 19, 32], "construct": [6, 52], "consult": 17, "contain": [5, 6, 9, 10, 12, 16, 17, 18, 22, 24, 25, 26, 27, 29, 30, 32, 33, 35, 36, 37, 43, 46, 48, 63, 65], "container": 17, "continu": 6, "contrast": [13, 52], "contribut": [6, 16, 44, 46], "control": [6, 11, 12, 13, 20, 32, 44], "conveni": 9, "convent": 6, "convers": 45, "convert": [2, 6, 48, 49, 50, 51], "copi": [4, 5, 17, 22, 36, 52, 53], "copyright": [4, 5], "core": [4, 5], "correctli": 13, "correl": 57, "correspond": [4, 6, 11, 13, 31, 33, 35, 37, 43, 44, 46, 64], "cost": [4, 6, 17], "could": [4, 5, 40], "count": [6, 47], "counter": 4, "coupl": 18, "cphaseg": [6, 18], "cplex": [17, 18], "creat": [17, 18, 21, 34, 35, 64], "creg": 43, "criteria": 4, "cross": [4, 5, 58], "crxgate": [6, 18], "crygat": [6, 18], "crzgate": [6, 18], "csdggate": [6, 18], "csgate": [6, 18], "csxgate": [6, 18], "current": [16, 18, 32, 62], "cut": [1, 7, 17, 18], "cut_circuit": 14, "cut_circuit_wir": [4, 5], "cut_gat": [12, 18], "cut_id": 32, "cut_indic": 12, "cut_typ": 32, "cut_wir": [6, 9, 13, 14], "cutqc": [16, 17, 18], "cutter": [4, 5], "cutting_decomposit": 18, "cutting_evalu": 18, "cutting_execut": 18, "cutting_experi": 18, "cutting_reconstruct": 18, "cutwir": [6, 10, 13, 16, 23], "cx": [5, 7, 9, 13, 65], "cxgate": [6, 8], "cygat": [6, 18], "czgate": 6, "czyk": 16, "d": [16, 17], "dag": [25, 27], "dagger": 6, "daniel": 16, "data": [4, 5, 9, 11, 12, 13, 32], "david": 6, "dcxgate": [6, 18], "decompos": [6, 8, 12, 16, 18, 21, 22, 25, 37, 38, 39, 40, 41, 43, 45], "decompose_g": 18, "decompose_qpd_instruct": 18, "decomposit": [8, 11, 12, 16, 18, 33, 38, 40, 41, 43, 44], "decreas": 15, "deep": 56, "deeper": [12, 18], "def": 8, "default": [4, 5, 18, 62], "defin": [6, 39, 50, 54, 55, 57, 58], "definit": 18, "demonstr": [9, 18, 35], "denot": [6, 22, 34, 36, 37, 46], "depend": [6, 17, 18], "deprec": [3, 4, 5, 16, 24, 25, 26, 27, 28, 29, 30, 48, 49, 50, 51, 54, 55, 56, 57, 58], "deprecationwarn": [4, 5], "depth": [6, 15, 16], "deriv": [4, 5], "describ": [3, 6, 11, 12, 13, 18, 32, 39, 40], "deseri": 18, "design": 31, "desin": 35, "desir": [6, 18, 35], "destin": [13, 35, 37], "detail": [4, 6, 13, 18], "determin": [8, 9, 17, 18, 37], "detin": 13, "dev": 17, "develop": 17, "devic": [4, 5, 32], "device_constraint": 14, "deviceconstraint": [14, 32], "dharmashankar": 16, "dict": [4, 5, 21, 24, 25, 26, 27, 28, 29, 30, 32, 33, 44, 46, 52, 55, 56, 58, 62, 63, 64], "dict_kei": [4, 65], "dictionari": [4, 11, 13, 18, 25, 26, 29, 32, 33, 37, 46, 48, 57], "did": 31, "differ": [6, 17, 23, 30, 31, 37, 43, 55, 58], "dijkstra": [18, 32], "direct": [6, 18], "directli": [13, 18], "directori": [4, 5, 17], "discard": [6, 13], "disconnect": [6, 65], "disregard": 6, "dist": 49, "distanc": [2, 56, 57], "distant": 6, "distribut": [2, 4, 5, 6, 8, 10, 11, 12, 13, 16, 18, 24, 30, 33, 43, 44, 48, 49, 50, 51, 58], "distribution_dict": 48, "do": [4, 6, 7, 13, 17, 18, 46, 62], "doc": [11, 13, 17], "docker": 16, "dockerfil": 17, "docplex": 18, "document": [4, 5, 6, 16, 18], "doe": [9, 13, 16, 18, 25, 36, 37, 43, 49, 65], "doi": 16, "done": 9, "dot": [9, 11, 12, 13], "draw": [4, 5, 8, 9, 11, 12, 13, 14, 33, 35], "driver": 18, "due": [6, 17], "duplic": 66, "dure": [6, 11, 12], "dynam": 18, "e": [4, 6, 11, 13, 17, 18, 27, 35, 45], "e4a04564eb39": 17, "each": [4, 5, 6, 8, 9, 11, 12, 13, 16, 17, 18, 22, 25, 26, 28, 29, 32, 33, 35, 36, 37, 39, 43, 44, 46, 60, 61, 62, 63, 64, 65], "earlier": 18, "easi": 18, "ecrgat": [6, 18], "eddin": 16, "effect": [6, 13, 18, 35], "effici": [4, 6, 60], "efficientsu2": [4, 8, 11, 12], "egger": 16, "eigenst": [4, 5], "either": [5, 6, 12, 17, 44, 51], "electronicstructureproblem": 18, "element": [6, 18, 27, 43, 44, 46, 52, 64], "emul": 17, "enabl": [6, 18], "encourag": [17, 18], "end": [6, 65], "engin": 6, "enhanc": 18, "enough": [4, 5], "ensur": [6, 35], "entangl": [4, 8, 11, 12, 13, 18, 35], "entanglement_forg": 18, "entanglementforginggroundstatesolv": 18, "entanglementforgingresult": 18, "enter": 17, "entir": 6, "entri": 27, "entropi": [4, 5, 58], "enum": 42, "enumer": [4, 5, 12], "environ": 17, "epsilon": 6, "eq": 6, "equal": [6, 18, 36, 37, 41, 43, 46, 52, 64, 65], "equat": 6, "equival": [6, 8, 9, 13], "error": [4, 5, 6, 9, 11, 12, 13, 18, 55, 56], "essenti": 6, "estim": [6, 9, 11, 12, 13], "estimatorv2": [9, 11, 12, 13], "et": [4, 5, 50], "ev": [9, 11, 12, 13], "evalu": [6, 8, 12, 18, 26, 28, 30, 33, 44], "evaluate_subcircuit": [4, 5], "even": 18, "everi": [18, 23, 33, 46], "everyth": 17, "evolv": 16, "exact": [4, 5, 9, 10, 16, 18, 30, 44, 62], "exact_dict_bitstr": [4, 5], "exact_distribut": [4, 5], "exact_expv": [9, 11, 12, 13], "exact_prob": [4, 5], "exact_sampl": 7, "exactli": [4, 5, 6, 18, 44], "exactsampl": [7, 10], "exampl": [4, 5, 8, 12, 13, 16, 18, 54, 55, 56, 57, 58], "except": [55, 56, 57, 58], "execut": [4, 5, 6, 11, 12, 13, 16, 18, 24, 29, 30, 48, 52], "execute_experi": 18, "exist": [6, 13, 16, 31], "exp": 6, "expand": [6, 18, 31], "expand_observ": [9, 14], "expect": [4, 5, 6, 8, 18, 33, 35, 46], "experi": [4, 5, 6, 11, 16, 18, 33, 46, 54], "explain": [6, 18], "explan": 18, "explanatori": [11, 12, 13, 16, 18], "explicit": [6, 18], "explicitli": [6, 9, 18, 37], "exponenti": [6, 18], "extens": [16, 17, 18], "extra": 13, "f": [6, 8, 9, 11, 12, 13, 14], "f401": [4, 5], "factor": 6, "fail": 45, "fake": [11, 12, 13], "fake_provid": [11, 12, 13], "fakemanilav2": [11, 12, 13], "fals": [12, 22, 36, 43], "famili": 6, "favor": 18, "feasibl": 6, "featur": [6, 54, 55, 56, 57, 58], "few": [17, 18], "fewer": 6, "field": [18, 32, 56], "fig": [4, 5, 9, 13], "figsiz": [4, 5], "figur": 5, "file": [4, 5, 16, 17, 18], "final": [4, 6, 9, 11, 12, 27, 31, 43, 67], "final_circuit": 31, "final_expv": [9, 11, 12, 13], "find": [3, 5, 12, 16, 17, 18, 24, 25, 26, 27, 28, 29, 30, 32], "find_bit": 12, "find_cut": [14, 18], "finder": 18, "finit": 8, "first": [4, 5, 6, 8, 9, 13, 17, 18, 32, 35, 44], "fix": 6, "flag": [22, 25, 36], "flexibl": 18, "flip": 6, "float": [4, 30, 32, 33, 39, 44, 46, 63], "fock": 18, "fold": [4, 5, 12, 14], "follow": [4, 5, 6, 8, 9, 13, 16, 17, 18, 33, 35, 46, 49], "forc": 48, "force_prob": 48, "forg": 18, "form": 6, "formal": 6, "format": 32, "forward": [3, 4, 5, 9, 18], "found": [4, 5, 14, 25, 31], "foundat": 18, "four": [4, 5, 11, 13], "frac": [55, 57], "framework": 6, "free": [6, 17, 39], "fresh": 35, "from": [4, 5, 6, 9, 10, 16, 18, 24, 26, 28, 29, 30, 31, 32, 33, 35, 37, 44, 46, 48, 50], "from_gat": 18, "from_instruct": [8, 18], "fujii": 6, "full": [11, 12, 13, 17, 18, 24, 28, 35], "full_circuit": 30, "fuller": 16, "fulli": 65, "function": [2, 4, 5, 6, 9, 13, 17, 18, 24, 25, 26, 27, 28, 29, 30, 31, 33, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 62], "fundament": 6, "furthermor": 18, "futur": [6, 18], "g": [4, 18, 27, 45], "gacon": 16, "gambetta": 6, "gamma": 6, "garrison": 16, "gate": [5, 8, 13, 15, 16, 18, 22, 25, 32, 33, 34, 36, 37, 38, 40, 41, 43, 45, 62, 63], "gate_id": 22, "gener": [4, 5, 10, 16, 18, 24, 27, 29, 33, 44, 45, 46], "general_observ": 59, "generate_cutting_experi": [7, 9, 11, 12, 13, 14, 18, 46], "generate_preset_pass_manag": [11, 12, 13], "generate_qpd_sampl": 18, "generate_qpd_weight": 18, "generate_summation_term": 24, "geometr": 6, "geq": 6, "get": [4, 6, 8, 11, 17], "git": 17, "github": [16, 17, 62], "given": [6, 8, 11, 12, 13, 18, 31, 32, 44, 45, 61], "glick": 16, "global": 6, "global_observ": 61, "go": [3, 4, 5, 6, 17, 18], "goal": [6, 18], "goe": 6, "greater": 18, "ground": [4, 5, 30], "group": [11, 13, 37], "grow": 18, "gt": [8, 13], "guarante": [17, 18, 52], "guid": [6, 7, 8, 9, 13, 18], "gujarati": 16, "h": [5, 7, 65], "ha": [4, 6, 9, 12, 13, 18, 35, 37, 43, 46], "had": [6, 43], "half": [9, 12], "hamamura": 16, "hand": 18, "hardwar": [4, 6, 12, 18], "hartre": 18, "hasan": 16, "hashabl": [21, 33, 36, 37, 46, 52, 64, 65], "have": [4, 5, 6, 8, 9, 17, 18, 23, 25, 31, 33, 40], "heavi": 54, "heaviest": 6, "heavili": 18, "here": [4, 5, 8, 9, 11, 12, 13], "heurist": 9, "hf_energi": 18, "higher": 18, "histogram": [4, 5], "histor": 3, "hold": [18, 43], "home": [11, 13, 17], "honor": 6, "hop": [4, 5], "how": [4, 5, 6, 11, 12, 13, 16, 17, 18, 32, 46, 51], "howev": [6, 13, 18], "howpublish": 16, "html": 17, "http": [4, 5, 6, 16, 17, 62], "i": [3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 16, 17, 18, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 37, 38, 40, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 61, 64, 65, 67], "ibm": [4, 5, 18], "ibmq": 18, "ibmq_qasm_simul": [4, 5], "id": [32, 33, 43], "idea": 6, "ideal": 39, "ident": [6, 9, 18, 31, 53], "identifi": 18, "idle_wir": 12, "ieva": 16, "ignor": [4, 5, 8, 9, 11, 12, 18, 37], "ii": [11, 52, 61], "iiii": [9, 13, 14], "iiiii": 9, "iiiiiiiiz": 9, "iiiiiiiz": 13, "iiiiiiz": [9, 13, 14], "iiiiz": 9, "iiiiziii": 13, "iiiz": [13, 14], "iiiziii": [9, 13, 14], "iiiziiiii": 9, "iizz": [11, 12], "ikko": 16, "imag": 17, "imamichi": 16, "immedi": 35, "implement": [4, 5, 6, 16, 18, 35, 45], "import": [4, 5, 7, 8, 9, 11, 12, 13, 14, 18, 35, 54], "imposs": 6, "improv": [4, 5, 16, 18], "includ": [4, 5, 6, 16, 17, 18, 25, 45, 64], "incom": [13, 35], "incompat": [18, 33, 46], "increas": [6, 18], "incur": [6, 11, 12, 13, 32], "independ": [6, 18], "index": [11, 13, 14, 18, 32, 37, 38, 40, 43, 44, 64], "indic": [4, 5, 6, 18, 22, 25, 43, 44], "individu": 6, "induc": 4, "inf": [8, 9, 11, 12, 13], "infin": [6, 8, 33], "info": [4, 5], "inform": [4, 5, 6, 11, 12, 13, 18, 23, 25, 29, 48], "ing": 49, "init": 27, "init_label": 27, "initi": [4, 12, 18, 35, 38, 40, 41], "initial_layout": 12, "inner": 43, "inplac": [4, 8, 11, 12, 22, 36, 43], "input": [7, 9, 18, 22, 25, 32, 33, 36, 37, 44, 46, 49, 50, 65], "insert": [6, 13, 18], "inspect": 4, "inspir": [9, 13], "instal": [16, 18], "instanc": [6, 9, 12, 17, 18, 21, 22, 27, 28, 29, 32, 33, 36, 43, 46, 64], "instanti": [6, 18, 45], "instead": [3, 4, 5, 6, 17, 18], "instruct": [6, 10, 12, 14, 16, 18, 23, 37, 38, 39, 43, 45, 67], "instruction_id": 43, "instructionset": 13, "int": [4, 5, 22, 24, 25, 26, 27, 28, 29, 32, 33, 38, 40, 43, 44, 61, 63, 64], "integ": [3, 4, 37, 65], "integraldriv": 18, "intel": 17, "intend": [7, 8, 9, 13, 17, 18], "interact": 6, "interest": 60, "interfac": [3, 7, 10, 16, 17, 18], "interpret": 18, "introduc": [6, 18], "introductori": 35, "involv": 4, "ipykernel_2967": 4, "ipykernel_3259": 5, "iqp": 14, "isa": [11, 12, 13], "isa_subexperi": [11, 12, 13], "isclos": 8, "iskandar": 16, "issu": [6, 17, 45, 62], "iswapg": [6, 18], "item": [7, 9, 11, 13], "iter": 60, "its": [4, 6, 18, 43, 44, 46, 61, 63, 65], "ix": 11, "ixix": [11, 12], "ixyz": 61, "iz": 11, "izii": 14, "izzi": [11, 12], "j": [6, 16], "jame": 16, "jennif": 16, "jiri": 6, "job": [11, 12, 13, 18], "johnson": 16, "joint": [8, 11, 12, 13, 18, 43, 44], "jovyan": 17, "jpserver": 17, "julien": 16, "jun": 6, "jupyt": 17, "just": [6, 9, 17], "k": 6, "kak": [16, 18], "keep": [16, 18], "kei": [4, 11, 13, 16, 25, 33, 44, 64, 65], "keisuk": 6, "knit": [2, 3, 4, 5, 6, 11, 12, 13, 17, 18, 24, 25, 26, 27, 28, 29, 30, 48, 49, 50, 51, 54, 55, 56, 57, 58], "known": 6, "kosuk": 6, "kron": [18, 27], "kronecker_term": 27, "kwarg": [11, 12, 66, 67], "l": 16, "l2": 50, "lab": 17, "label": [7, 9, 11, 13, 18, 33, 34, 35, 36, 37, 38, 40, 41, 46, 64, 65], "lambda": 12, "languag": 6, "larg": [6, 17, 18], "larger": [6, 18, 40], "last": 17, "later": 18, "layer": 4, "layout": 12, "ldot": [33, 46], "lead": 18, "learn": 56, "least": [8, 33], "left": 6, "legaci": 16, "legend": [4, 5], "len": [4, 8, 11, 12, 14], "length": [32, 33, 36, 43, 64, 65], "less": [4, 6, 18], "let": [6, 17], "lett": 50, "level": 18, "lib": [11, 13], "librari": [4, 8, 11, 12], "licens": [4, 5, 17], "liepuoniut": 16, "like": [6, 9, 11, 12, 13, 17, 18], "likewis": 6, "limit": 16, "line": [4, 5, 13], "linear": [4, 6, 8, 11, 43], "linux": 17, "list": [4, 5, 6, 12, 18, 21, 22, 24, 25, 27, 29, 32, 33, 37, 43, 46, 52, 53, 61, 64], "literatur": 6, "ll": [4, 5], "lo": [6, 18], "local": [4, 5, 6, 11, 12, 13, 17, 37, 40, 43], "locat": [3, 9, 16, 17, 18, 32, 34], "locc": 6, "lockwood": 16, "log": 58, "long": 16, "longer": 18, "look": 18, "loos": 6, "loss": [4, 5], "low": 6, "lowest": 18, "lt": [8, 13], "luciano": 16, "luka": 6, "lvert": 6, "m": [6, 16, 17, 33, 46], "m1": 17, "m2": 17, "m_z": 6, "mac": 17, "machin": 17, "maco": 17, "magnitud": [6, 8], "mai": [4, 5, 6, 9, 11, 12, 13, 16, 17, 18, 32, 33, 52], "maintain": [4, 5, 18], "major": 45, "make": [16, 52], "mani": [6, 11, 12, 13, 15, 18, 40], "manipul": 2, "manual": [3, 4, 9, 13, 18, 25], "map": [4, 8, 11, 13, 18, 31, 33, 37, 38, 39, 43, 44, 46, 50], "map_id": 43, "mario": 16, "mark": [9, 13, 16, 18, 23], "match": [4, 5, 25, 37], "materi": [11, 12, 13, 16, 18], "mathcal": 6, "mathrm": 6, "matter": [6, 8, 17], "max": [16, 25], "max_backjump": 20, "max_cut": [4, 25], "max_gamma": 20, "max_operand": 14, "max_subcircuit_cut": 25, "max_subcircuit_s": 25, "max_subcircuit_width": [4, 25], "maximum": [6, 18], "md": 16, "mea": 27, "mean": [4, 5, 6, 55, 56], "meant": 16, "meas_label": 27, "measur": [2, 4, 5, 6, 7, 10, 18, 29, 30, 43, 54, 56, 57, 58, 60, 62, 63], "median": 54, "meet": 32, "member": [38, 39, 40, 60], "mention": 6, "messag": 17, "metadata": [14, 18, 32], "method": [3, 4, 5, 6, 9, 13, 17, 18, 19, 20, 21, 25, 34, 35, 38, 39, 40, 41, 45, 50, 59, 60, 62, 64, 66, 67], "metric": [4, 5, 30], "mid": [7, 10, 18, 63], "middl": 13, "might": [6, 9], "migrat": 18, "mileston": 18, "min": 8, "mind": 18, "minim": [17, 18], "minimum": 18, "mip": 4, "misc": 16, "mitarai": 6, "mitig": 6, "mix": [3, 4], "mixtur": [18, 39], "mode": [7, 11, 13, 17, 18, 51], "model": [3, 4], "modif": [4, 5], "modifi": [4, 5, 18], "modul": [2, 3, 18], "more": [4, 5, 9, 11, 12, 13, 16, 18, 23, 32, 36, 37, 62, 65], "most": [6, 8, 13, 18, 44], "motta": 16, "move": [10, 15, 16, 17, 18, 23, 45], "mpl": [4, 5, 8, 9, 11, 12, 13, 14, 35], "much": 6, "multipl": [4, 5, 6, 18], "multipli": 46, "multithread": 24, "must": [4, 5, 6, 9, 11, 12, 13, 18, 19, 31, 33, 35], "mutual": [59, 60], "n": [6, 16, 17, 31, 33, 46, 52], "naiv": [4, 5, 9, 51], "name": [17, 18, 26, 33, 38, 64, 65], "namedtupl": [21, 64], "namespac": 18, "nativ": 17, "natur": 18, "navig": 17, "ndarrai": [24, 26, 28, 29, 30], "nearest": [4, 5, 50, 51], "necessari": [6, 18], "need": [4, 5, 7, 9, 17, 18], "neg": [4, 6, 49], "new": [3, 6, 9, 16, 17, 21, 23, 31, 43, 51, 64], "newer": 6, "newli": [13, 23, 45], "next": [4, 5, 9, 17], "node": 25, "nois": [6, 39], "noisi": [6, 39], "non": [6, 11, 12, 13, 18, 37], "none": [4, 5, 20, 21, 25, 26, 29, 34, 35, 37, 38, 39, 40, 41, 43, 62, 65], "nonloc": [15, 36], "noqa": [4, 5], "norm": 50, "normal": [48, 49], "note": [4, 5, 6, 11, 13, 16, 17, 25, 33], "notebook": 17, "notebook_1": 17, "noth": [6, 9], "notic": [4, 5, 9], "notion": 6, "now": [4, 5, 6, 18], "np": [4, 5, 8, 9, 11, 12, 13, 14, 26, 29, 35, 54, 55, 56, 57, 58], "num_bit": [4, 5], "num_cut": [4, 24, 27], "num_cx_cut": 8, "num_qpd_bit": 18, "num_qubit": [4, 5, 12, 31, 38, 65], "num_sampl": [6, 7, 9, 11, 12, 13, 14, 18, 33, 44], "num_subcircuit": [4, 25], "num_thread": [24, 28], "number": [4, 6, 8, 11, 12, 13, 18, 24, 25, 27, 28, 31, 33, 36, 37, 38, 39, 41, 43, 44, 47, 52, 64, 65], "number_to_keep": [4, 5], "numpi": [4, 5, 8, 9, 11, 12, 13, 14, 18, 35, 55, 56, 57, 58], "o": [6, 52], "ob": [54, 55, 56, 57, 58], "object": [4, 18, 19, 20, 39, 44, 45, 52, 53], "observ": [6, 7, 18, 21, 31, 33, 37, 46, 54, 55, 56, 57, 58, 59, 60, 61], "observable_": [33, 46], "observable_expand": 13, "observable_expanded_pauli": 9, "observable_measur": 18, "observables_expand": 14, "obtain": [4, 5, 6, 46], "occasion": [6, 16], "occur": 18, "old": [16, 18], "older": 18, "onc": [9, 17, 62], "one": [4, 6, 9, 11, 12, 13, 17, 18, 22, 23, 33, 36, 37, 43, 44, 46, 54, 65], "ones": [16, 18], "onli": [4, 6, 11, 17, 18, 52], "open": 17, "oper": [9, 10, 13, 15, 16, 31, 35, 39, 43, 45, 62, 63, 65], "opflow": 18, "optim": [4, 6, 14, 17, 18, 20, 25, 32], "optimization_level": [11, 12, 13], "optimization_set": 14, "optimizationparamet": [14, 32], "option": [4, 5, 16, 18, 26, 29, 32, 37, 38, 62], "orang": 6, "orbit": 18, "order": [7, 8, 12, 16, 17, 18, 24, 33, 46], "org": [4, 5, 6], "organ": 60, "origin": [3, 4, 5, 6, 8, 9, 16, 18, 23, 28, 30, 31, 64], "original_circuit": 31, "osx": 17, "other": [6, 9, 17, 25, 33, 35], "otherwis": [6, 13, 18, 46, 52, 61], "otim": [4, 6], "our": [4, 6, 11, 13, 18], "out": [9, 18, 40, 49], "outcom": [2, 6, 16, 43, 62, 63], "output": [6, 9, 18, 25, 28, 32, 33, 36, 46, 54], "over": 18, "overal": 6, "overhead": [16, 18, 24, 32], "overview": 16, "owen": 16, "p": 16, "p_i": 4, "p_j": 4, "packag": [4, 5, 11, 13, 17, 18, 24, 25, 26, 27, 28, 29, 30], "pair": 52, "paper": [3, 6], "parallel": [4, 5, 28], "param": 4, "paramet": [4, 6, 8, 11, 12, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 39, 40, 43, 44, 45, 46, 48, 49, 50, 51, 54, 55, 56, 57, 58, 60, 61, 62, 65], "parametr": 6, "part": 40, "partial": [13, 35], "particular": 18, "particularli": 6, "partit": [6, 8, 13, 18, 32, 33, 36, 37, 46, 64, 65], "partition_circuit_qubit": 18, "partition_label": [7, 8, 9, 11, 13, 18, 36, 37, 65], "partition_problem": [7, 8, 9, 11, 13, 14, 18], "partition_subexpt": [11, 13], "partitioned_problem": [7, 8, 9, 11, 13, 14], "partitionedcuttingproblem": 37, "pass": [4, 5, 6, 8, 9, 18, 33], "pass_manag": [11, 12, 13], "past": [9, 17], "path": [17, 18, 27], "pauli": [2, 4, 5, 7, 8, 9, 11, 12, 13, 14, 18, 37, 60, 61], "paulilist": [9, 11, 12, 13, 14, 18, 21, 31, 33, 37, 46, 60, 61], "pedro": 16, "pemmaraju": 16, "per": [6, 18, 19, 44], "percentag": [4, 5, 55], "perform": [3, 6, 10, 17, 18, 35], "permiss": 17, "persist": 17, "ph": 6, "phase": [6, 11, 12, 37, 46], "phrase": 16, "phy": 50, "physic": 6, "pi": [4, 5, 6, 9, 13, 18, 35], "piec": [6, 46], "pin": 18, "pip": 16, "piveteau": 6, "place": [6, 16, 17, 18, 31], "plan": [6, 17], "platform": 16, "pleas": [6, 16], "plot": [4, 5, 6], "plot_histogram": [4, 5], "podman": 17, "point": [4, 6], "polici": 16, "portion": 6, "posit": [6, 12, 46], "possibl": [16, 18, 62], "possibli": [11, 12, 13], "post": [6, 24], "postprocess": [4, 5], "potenti": 52, "powershel": 17, "practic": 6, "pre": 16, "preced": 35, "precis": [2, 63], "prefer": 6, "prefix": 18, "prematur": 18, "prepar": [4, 5, 7, 17], "present": [4, 5, 6], "preset_passmanag": [11, 12, 13], "previou": [4, 13], "previous": 18, "primari": [17, 18], "primarili": 6, "primit": [4, 5, 6, 7, 9, 18, 46, 62], "primitiveresult": [18, 46], "print": [4, 8, 9, 11, 12, 13, 14, 25], "prior": 35, "prob": [4, 5], "probabilist": [12, 18, 38, 39], "probabl": [2, 4, 5, 6, 8, 18, 24, 28, 29, 30, 33, 43, 48, 49, 50, 51, 54, 58, 62, 63], "probdistribut": [4, 5], "problem": [6, 8, 12, 18], "process": [4, 6, 16, 24], "processor": 16, "prod": [11, 12, 13, 14], "produc": [4, 5], "program": [3, 4], "project": [4, 5, 6, 62, 63], "proport": 6, "provabl": [6, 25], "provid": [4, 5, 6, 12, 17, 18, 25, 29, 30, 37, 43, 61, 62, 65], "ps1": 17, "pull": [17, 18], "purpos": 18, "py": [4, 5, 11, 13, 52, 53], "python": [17, 18], "python3": [11, 13, 17], "q": 12, "q_0": 35, "q_1": 35, "qasm": [4, 5], "qc": [11, 35, 63, 65], "qc_0": [9, 13], "qc_1": [9, 13], "qc_w_ancilla": 14, "qiskit": [6, 7, 8, 9, 14, 16, 17, 18, 26, 35, 43, 45, 46, 62], "qiskit_a": [9, 11, 12, 13, 62], "qiskit_ibm_runtim": [4, 5, 11, 12, 13], "qiskit_natur": 18, "qiskitruntimeservic": [4, 5, 26, 29], "qpd": [8, 16, 18, 37], "qpd_base": 44, "qpd_basis_cx": 8, "qpd_circuit": 12, "qpd_measur": 18, "qpdbasi": [8, 12, 18, 21, 22, 38, 41, 44, 45], "qpdbasis_from_g": 18, "qpdbasis_from_instruct": 18, "qpdgate": 40, "qpu": 14, "qpy": 18, "quant": 6, "quantiti": [4, 6], "quantum": [2, 6, 16, 18, 30, 35, 39, 54], "quantum_info": [7, 8, 9, 11, 12, 13, 14], "quantumcircuit": [4, 5, 6, 7, 9, 13, 18, 21, 22, 23, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 43, 64, 65], "quasi": [8, 10, 18, 33, 51], "quasi_dist": 18, "quasiprob": [11, 12, 13, 16, 38, 39, 40, 41, 44, 49, 50, 51], "quasiprobabilist": 39, "qubit": [4, 5, 8, 10, 12, 15, 16, 18, 19, 23, 25, 27, 31, 32, 35, 36, 37, 38, 40, 41, 45, 46, 59, 60, 61, 64, 65, 67], "qubit_id": 40, "qubit_map": [64, 65], "qubits_per_subcircuit": [14, 19], "r": 16, "rais": [22, 25, 31, 32, 33, 36, 37, 40, 41, 43, 45, 46, 55, 56, 57, 58, 65], "random": 14, "random_circuit": 14, "randomli": 43, "rang": [4, 5, 9, 13, 40, 55], "rangl": [4, 6], "rapidli": 16, "rather": [6, 10, 18, 33], "raw": 4, "re": [4, 5, 9, 11, 12, 13, 17, 18], "reach": 6, "real": [6, 9, 11, 12, 13, 39], "realiz": 6, "recombin": 6, "recommend": 17, "recompos": 28, "reconstruct": [6, 8, 16, 18, 24, 27, 28, 30, 46], "reconstruct_expectation_valu": [9, 11, 12, 13, 18], "reconstruct_full_distribut": [4, 5], "reconstructed_dict_bitstr": [4, 5], "reconstructed_distribut": [4, 5], "reconstructed_expv": [9, 11, 12, 13], "reconstructed_output": 30, "reconstructed_prob": [4, 5], "reduc": [6, 16, 18], "redund": 6, "ref": 6, "refer": [11, 12, 13, 18], "regist": [18, 22, 37, 43], "rel": [9, 11, 12, 13, 40], "relat": 62, "releas": 16, "reli": 18, "remain": [6, 8, 9, 18, 44, 62], "remaind": [13, 35], "remov": [4, 5, 18, 24, 25, 26, 27, 28, 29, 30, 33, 48, 49, 50, 51, 54, 55, 56, 57, 58, 67], "removefinalreset": [18, 33], "removeresetinzerost": [18, 33], "renam": 18, "rep": [4, 8, 11], "repeat": 6, "rephras": 18, "replac": [6, 13, 18, 22, 23, 36, 37, 43], "repositori": [16, 17], "repres": [4, 5, 6, 33, 35, 60], "represent": [6, 13, 23, 39], "request": 18, "requir": [6, 12, 15, 18, 31], "research": [3, 16], "reset": [6, 13, 18, 33, 35, 62, 63, 66, 67], "resolv": 18, "respect": [6, 11, 13, 19], "restrict": [6, 18, 61], "result": [6, 7, 8, 9, 11, 12, 13, 16, 18, 21, 26, 27, 29, 30, 32, 35, 37, 44, 46, 48, 64], "retain": [4, 5], "retriev": 12, "retriv": [11, 13], "return": [4, 7, 12, 18, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 39, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 65], "retval": [31, 43], "reus": [9, 13, 18], "rev": 50, "right": [6, 13], "rigor": [6, 33], "rivero": 16, "root": [4, 5, 6, 16], "rossmannek": 16, "rotat": 6, "round": [9, 11, 12, 13], "rtype": [52, 53], "run": [4, 5, 6, 7, 10, 17, 18, 26, 33, 46, 66], "runnabl": [18, 32], "runner": [11, 13], "runtim": [4, 6, 17, 18, 26, 29], "rvert": 6, "rx": [5, 9, 13, 35], "rxxgate": [6, 18], "ry": [4, 5], "ryi": 35, "ryygat": [6, 18], "rz": 35, "rzxgate": 6, "rzzgate": [16, 18], "s_i": 4, "s_j": 4, "sake": 18, "same": [6, 8, 9, 11, 12, 33, 37, 43, 46, 65], "sampl": [10, 16, 18, 32, 33, 42, 43, 44, 46], "sample_": [33, 46], "sampler": [4, 5, 9, 16, 18, 46, 62], "samplerresult": [18, 46], "samplerv2": [9, 11, 12, 13], "sampling_overhead": [14, 32], "sastri": 6, "save": 17, "scale": [4, 5, 8, 11, 12, 14, 55], "scheme": 18, "schmitt": 6, "scholten": 16, "script": 17, "sdggate": 6, "search": [17, 18, 32], "sec": [6, 23], "second": [4, 5, 6, 9, 13, 18, 35, 44], "second_q": 18, "second_quant": 18, "section": [4, 17], "see": [4, 5, 6, 13, 17, 18, 23, 31, 45], "seed": [14, 20], "seelam": 16, "seetharami": 16, "send": 6, "sent": 6, "separ": [6, 12, 15, 18, 21, 33, 37, 46, 65], "separate_circuit": 64, "separatedcircuit": 65, "seq": 8, "sequenc": [8, 22, 24, 25, 26, 27, 29, 30, 33, 36, 37, 39, 43, 44, 46, 61, 65], "sergei": 16, "seri": 17, "serial": 18, "server": 17, "serverless": 18, "servic": [4, 26, 29], "session": [11, 13], "set": [4, 6, 8, 11, 12, 13, 14, 15, 17, 18, 33, 47, 52, 59, 60], "seven": 13, "sever": [4, 5, 18], "sgate": 6, "shankar": 6, "share": [11, 13, 17, 35], "short": [6, 17], "shorten": 18, "shot": [4, 5, 6, 9, 11, 13, 48], "should": [3, 6, 17, 18, 22, 31, 37, 38, 40, 43, 45, 46], "show": [5, 6, 7, 8], "shown": [5, 6, 46], "side": 5, "sign": 6, "signific": 18, "significantli": 18, "similar": [4, 5], "simpl": [8, 17], "simplest": 35, "simul": [4, 5, 6, 7, 8, 11, 12, 13, 18, 39], "simultan": 6, "sin": 6, "sinc": [4, 5, 9, 18, 24, 25, 26, 27, 28, 29, 30, 35, 48, 49, 50, 51, 54, 55, 56, 57, 58], "singl": [6, 8, 10, 11, 13, 16, 18, 23, 36, 40, 46, 66], "singlequbitqpdg": [6, 18, 33, 37], "sitdikov": 16, "site": [11, 13], "six": [6, 8], "size": [4, 6, 11, 12, 13, 14], "skip": 17, "small": [4, 5, 8, 52], "smaller": [4, 5, 6, 16], "smolin": 50, "so": [4, 6, 8, 9, 13, 17, 18, 33, 46, 52], "solut": [14, 18], "solv": 18, "some": [4, 6, 9, 11, 12, 13, 18, 33, 45, 62], "someth": [6, 18], "soon": 18, "sooner": [4, 5, 18, 24, 25, 26, 27, 28, 29, 30, 48, 49, 50, 51, 54, 55, 56, 57, 58], "sort": [4, 5], "sourc": [4, 5, 6, 13, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67], "space": 6, "span": [11, 13, 36, 37, 65], "sparsepauliop": [7, 8, 9, 11, 12, 13, 14, 18], "special": [6, 45], "specif": [18, 43, 44], "specifi": [3, 4, 5, 6, 10, 14, 15, 18, 19, 20, 22, 25, 32, 33, 37, 45], "spell": 9, "sphinx_autodoc_typehints_typ": [52, 53], "split": [13, 18], "sqrt": 6, "squar": [4, 5, 6, 56, 57], "stage": 18, "standalon": 40, "standard_g": 8, "start": [17, 37, 65], "state": [4, 5, 6, 13, 35], "statevector": [4, 5, 7, 18], "statist": 57, "stefan": 16, "step": [4, 5, 9, 11, 12, 13, 17, 18, 49], "still": 18, "stop": 18, "str": [24, 25, 26, 27, 29, 30, 32, 38], "straight": 17, "strike": 18, "string": 13, "style": 14, "su2_gat": 4, "sub": [18, 46], "subcircuit": [6, 7, 8, 9, 11, 12, 13, 18, 19, 21, 24, 25, 26, 27, 28, 29, 30, 32, 37, 64, 65], "subcircuit_0_entry_0": 27, "subcircuit_1_entry_13": 27, "subcircuit_2_entry_7": 27, "subcircuit_entri": 27, "subcircuit_entry_idx": 27, "subcircuit_entry_prob": 24, "subcircuit_idx": [27, 29], "subcircuit_inst": [27, 29], "subcircuit_instance_idx": [27, 29], "subcircuit_instance_prob": [4, 5, 28, 29], "subcircuit_vertic": [5, 25], "subdirectori": 17, "subeperi": 12, "subexperi": [6, 7, 8, 9, 14, 18, 33, 46], "submit": [11, 12, 13, 18, 46], "submodul": 18, "subobserv": [7, 8, 9, 11, 13, 21], "suboptim": 18, "subramanian": 16, "subset": 6, "substanti": 16, "subsystem": 61, "sudo": 17, "suffici": 6, "suit": 17, "sum": [4, 6, 27, 46], "sum_i": [6, 55, 56, 57, 58], "summat": [24, 27], "summation_term": [24, 27], "summation_term_0": 27, "summation_term_1": 27, "superset": 31, "support": [4, 5, 6, 7, 11, 13, 16, 18, 45, 61, 62, 63], "sutter": 6, "swap": [6, 13, 15, 18, 35], "swapgat": [6, 18], "symbol": 35, "system": [13, 17, 35], "t": 5, "tabl": 16, "tail": [6, 8, 44], "takashi": 16, "take": [6, 8, 17, 18, 46, 49, 50, 60], "taken": [6, 11, 12, 13], "tang": [4, 5, 16], "tanvi": 16, "target": [11, 13, 54, 55, 56, 57, 58], "techniqu": [3, 4, 5, 6, 11, 12, 13, 18], "temm": 6, "term": [11, 12, 13, 16, 18, 24, 27, 46], "termin": 17, "terra": [18, 62], "test": [8, 11, 13], "than": [4, 5, 6, 9, 10, 11, 12, 13, 18, 24, 25, 26, 27, 28, 29, 30, 32, 33, 36, 37, 48, 49, 50, 51, 54, 55, 56, 57, 58, 62, 65], "thei": [4, 5, 7, 8, 9, 11, 12, 16, 17, 18, 33, 40], "them": [9, 11, 13, 16, 17, 18, 46], "themselv": 25, "theori": 6, "therefor": [8, 18], "theta": [6, 18], "theta_i": 6, "theta_x": 6, "theta_z": 6, "thi": [3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 17, 18, 29, 30, 31, 33, 35, 37, 38, 39, 40, 45, 46, 52, 62], "thing": 17, "think": 6, "third": 6, "those": [4, 5, 6, 8, 16, 18, 31, 35, 44, 65], "thread": [4, 5, 24, 28], "three": [6, 11, 12, 13, 17, 18], "threshold": [6, 8, 18, 44], "through": [6, 7, 27], "thu": 6, "time": [4, 6, 9, 18, 52], "titl": 16, "tmp": [4, 5], "to_matrix": 45, "todai": 6, "togeth": [4, 5, 11, 13, 37, 43, 46, 64], "token": 17, "too": 17, "tool": [4, 5, 16, 17, 18], "toolbox": [3, 4, 5, 11, 13, 17, 18, 24, 25, 26, 27, 28, 29, 30, 48, 49, 50, 51, 54, 55, 56, 57, 58], "top": 18, "tos": 18, "total": [14, 25, 43], "tox": [11, 13, 17], "track": [6, 18], "tradeoff": 6, "transfer": [6, 13], "transform": [6, 13, 18, 22, 23], "transform_cuts_to_mov": 31, "transpil": [11, 13, 33], "transpiled_qc": 12, "transpiled_qpd_circuit": 12, "travi": 16, "treat": 6, "tree": [4, 5], "trigger": 8, "true": [4, 8, 11, 12, 25], "truth": [4, 5, 30], "try": [4, 6, 25], "tupl": [22, 24, 27, 29, 30, 32, 33, 37, 39, 44, 46, 64, 65], "turn": 35, "tutori": [8, 9, 11, 12, 13, 16, 17, 18, 35], "two": [4, 5, 8, 9, 10, 11, 12, 15, 16, 18, 23, 32, 35, 36, 41, 45, 49, 55, 56, 57, 58], "twoqubitqpdg": [9, 18, 22, 23, 36, 37], "twoqubitweyldecomposit": 18, "txt": [4, 5], "type": [2, 6, 9, 11, 12, 17, 18, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 42, 43, 44, 45, 46, 61, 63, 65], "typic": 35, "u": 6, "unbound": 45, "uncom": [4, 5], "under": [4, 5], "unifi": 6, "uniform": 18, "uniqu": [6, 8, 10, 11, 12, 13, 33, 44, 46, 52, 53], "unitari": [6, 8, 62], "uniti": 4, "unlik": 9, "unnecessari": 33, "unsepar": 33, "unsupport": 36, "until": [9, 18], "unus": 62, "up": [4, 6, 8, 11, 12, 13, 17, 18, 65], "updat": [18, 49], "upfront": 6, "upgrad": 17, "upon": 18, "upstream": 62, "url": [16, 17], "us": [2, 3, 4, 5, 6, 7, 8, 10, 16, 18, 24, 25, 26, 27, 28, 29, 30, 31, 32, 35, 38, 39, 40, 41, 45, 46, 56], "user": [3, 7, 8, 9, 16, 17, 18, 46], "userwarn": [11, 13], "usual": 9, "util": [1, 4, 5, 7, 16, 18], "v": 23, "v0": [4, 5, 18, 24, 25, 26, 27, 28, 29, 30, 48, 49, 50, 51, 54, 55, 56, 57, 58], "v2": 18, "v_1": 6, "v_2": 6, "v_3": 6, "v_4": 6, "vala": 6, "valid": [18, 35, 51], "valu": [4, 6, 8, 18, 42, 44, 46, 49], "valueerror": [22, 25, 31, 32, 33, 36, 37, 40, 41, 43, 45, 46, 65], "valuesview": 53, "variabl": [38, 39, 40, 60], "variant": 4, "varieti": [6, 18, 30], "vast": 45, "vazquez": 16, "vec": 6, "vector": [24, 28, 29, 54, 55, 56, 57, 58], "venv": 17, "verbos": 25, "version": [4, 5, 6, 17, 18, 24, 25, 26, 27, 28, 29, 30, 48, 49, 50, 51, 54, 55, 56, 57, 58], "vertic": 25, "via": [16, 18, 55, 56, 58], "virtual": [6, 17], "visibl": 18, "visual": [4, 5, 17], "volum": [17, 54], "wa": [3, 4, 5, 18, 30, 35, 37, 46], "wai": [6, 13, 17, 25, 31, 35, 46], "want": 17, "warn": [11, 13], "we": [4, 5, 6, 8, 9, 11, 12, 13, 16, 17], "web": 17, "wei": 16, "weight": [16, 18, 33, 42, 44], "weighttyp": [33, 44, 46], "well": 17, "were": [9, 18, 46], "weyl": 6, "whalei": 6, "what": 18, "when": [6, 8, 11, 13, 16, 17, 18, 67], "where": [4, 6, 9, 33], "whether": [22, 25, 36, 48], "which": [4, 5, 6, 9, 11, 13, 17, 18, 31, 33, 35, 37, 38, 39, 40, 43, 44, 45, 46, 52, 62, 64, 65], "while": [6, 13, 18], "who": [17, 46], "whose": [4, 5, 8, 31, 35, 44], "width": 16, "window": 17, "wire": [3, 10, 15, 16, 17, 18, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 37, 40, 45, 67], "wire_cut": [4, 5, 18, 25, 26, 28], "wire_cutting_evalu": 29, "wire_cutting_post_process": [24, 27], "wire_cutting_verif": [4, 5, 30], "wise": [59, 60], "wish": [8, 9, 17, 18], "within": [6, 16, 33, 43], "without": [6, 18], "woerner": 16, "work": [4, 5, 11, 13, 17, 18, 35], "workflow": [4, 5, 6, 9, 18], "worst": 52, "worth": 6, "would": [6, 8, 13, 18], "wrap": [9, 18, 23], "written": [6, 62], "www": [4, 5], "x": [4, 6, 8, 12, 18, 35, 47, 65], "x86": 17, "x86_venv": 17, "x_i": [55, 56, 57, 58], "xi": 11, "xixi": [11, 12], "xxminusyyg": 6, "xxplusyyg": 6, "y": [4, 6], "y_i": [55, 56, 57, 58], "yaml": 17, "year": 16, "yet": [6, 17, 18], "you": [4, 5, 6, 8, 12, 16, 17, 18], "your": [16, 17, 18], "z": [4, 6], "zenodo": 16, "zero": [8, 49], "zgate": 6, "zhang": 6, "zi": 11, "ziii": [9, 13, 14], "ziiii": 9, "ziiiiii": [9, 13, 14], "ziiiiiii": 13, "ziiiiiiii": 9, "zizz": [11, 12], "zx": 61, "zz": [6, 7, 11], "zzii": [11, 12], "zzxx": 61, "zzzz": 8}, "titles": ["Circuit Cutting", "Circuit Knitting Toolbox API References", "Utilities", "CutQC (legacy circuit cutting implementation)", "CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding", "CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting", "Explanatory material for the circuit cutting module", "How to generate exact quasiprobability distributions from Sampler", "How to generate exact sampling coefficients", "How to place wire cuts using a single-qubit <code class=\"docutils literal notranslate\"><span class=\"pre\">CutWire</span></code> instruction", "Circuit Cutting How-To Guides", "Gate Cutting to Reduce Circuit Width", "Gate Cutting to Reduce Circuit Depth", "Wire Cutting Phrased as a Two-Qubit <code class=\"docutils literal notranslate\"><span class=\"pre\">Move</span></code> Instruction", "Automatically find cuts using CKT", "Circuit Cutting Tutorials", "Circuit Knitting Toolbox", "Installation Instructions", "Release Notes", "circuit_knitting.cutting.DeviceConstraints", "circuit_knitting.cutting.OptimizationParameters", "circuit_knitting.cutting.PartitionedCuttingProblem", "circuit_knitting.cutting.cut_gates", "circuit_knitting.cutting.cut_wires", "circuit_knitting.cutting.cutqc.build", "circuit_knitting.cutting.cutqc.cut_circuit_wires", "circuit_knitting.cutting.cutqc.evaluate_subcircuits", "circuit_knitting.cutting.cutqc.generate_summation_terms", "circuit_knitting.cutting.cutqc.reconstruct_full_distribution", "circuit_knitting.cutting.cutqc.run_subcircuit_instances", "circuit_knitting.cutting.cutqc.verify", "circuit_knitting.cutting.expand_observables", "circuit_knitting.cutting.find_cuts", "circuit_knitting.cutting.generate_cutting_experiments", "circuit_knitting.cutting.instructions.CutWire", "circuit_knitting.cutting.instructions.Move", "circuit_knitting.cutting.partition_circuit_qubits", "circuit_knitting.cutting.partition_problem", "circuit_knitting.cutting.qpd.BaseQPDGate", "circuit_knitting.cutting.qpd.QPDBasis", "circuit_knitting.cutting.qpd.SingleQubitQPDGate", "circuit_knitting.cutting.qpd.TwoQubitQPDGate", "circuit_knitting.cutting.qpd.WeightType", "circuit_knitting.cutting.qpd.decompose_qpd_instructions", "circuit_knitting.cutting.qpd.generate_qpd_weights", "circuit_knitting.cutting.qpd.qpdbasis_from_instruction", "circuit_knitting.cutting.reconstruct_expectation_values", "circuit_knitting.utils.bitwise.bit_count", "circuit_knitting.utils.conversion.dict_to_array", "circuit_knitting.utils.conversion.naive_probability_distribution", "circuit_knitting.utils.conversion.nearest_probability_distribution", "circuit_knitting.utils.conversion.quasi_to_real", "circuit_knitting.utils.iteration.unique_by_eq", "circuit_knitting.utils.iteration.unique_by_id", "circuit_knitting.utils.metrics.HOP", "circuit_knitting.utils.metrics.MAPE", "circuit_knitting.utils.metrics.MSE", "circuit_knitting.utils.metrics.chi2_distance", "circuit_knitting.utils.metrics.cross_entropy", "circuit_knitting.utils.observable_grouping.CommutingObservableGroup", "circuit_knitting.utils.observable_grouping.ObservableCollection", "circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem", "circuit_knitting.utils.simulation.ExactSampler", "circuit_knitting.utils.simulation.simulate_statevector_outcomes", "circuit_knitting.utils.transforms.SeparatedCircuits", "circuit_knitting.utils.transforms.separate_circuit", "circuit_knitting.utils.transpiler_passes.ConsolidateResets", "circuit_knitting.utils.transpiler_passes.RemoveFinalReset"], "titleterms": {"0": 18, "1": [4, 17, 18], "2": [5, 8, 17, 18], "3": [17, 18], "4": [14, 18], "5": 18, "6": 18, "7": 18, "To": 10, "accord": 11, "account": [9, 14], "add": 14, "after": 12, "against": 9, "all": 8, "an": [6, 9, 11, 12, 13], "ancilla": 14, "api": 1, "ar": 8, "automat": [0, 4, 14], "backend": [11, 12, 13, 14], "baseqpdg": 38, "been": 13, "bit_count": 47, "bitwis": [2, 47], "bug": 18, "build": 24, "calcul": [11, 12, 13, 14], "can": 14, "chi2_dist": 57, "choos": [11, 13], "chosen": [11, 12, 13], "circuit": [0, 1, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16], "circuit_knit": [2, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67], "cite": 16, "ckt": [6, 14], "cnot": 8, "coeffici": 8, "commutingobservablegroup": 59, "compar": [9, 11, 12, 13], "consolidatereset": 66, "content": 16, "convers": [2, 48, 49, 50, 51], "creat": [4, 5, 11, 12, 13, 14], "cross_entropi": 58, "crzgate": 14, "current": 6, "cut": [0, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46], "cut_circuit_wir": 25, "cut_gat": 22, "cut_wir": 23, "cutqc": [0, 3, 4, 5, 24, 25, 26, 27, 28, 29, 30], "cutwir": [9, 34], "decompos": [4, 5, 9, 11, 13], "decompose_qpd_instruct": 43, "decomposit": [0, 6], "demonstr": [8, 12], "deprec": 18, "depth": 12, "desir": 13, "develop": 16, "deviceconstraint": 19, "dict_to_arrai": 48, "distant": 12, "distribut": 7, "docker": 17, "evalu": [4, 5], "evaluate_subcircuit": 26, "exact": [7, 8, 11, 12, 13], "exactli": 8, "exactsampl": 62, "exampl": 6, "expand": 14, "expand_observ": 31, "expect": [9, 11, 12, 13], "experi": [9, 14], "explanatori": 6, "extra": 9, "featur": 18, "find": [0, 4, 8, 14], "find_cut": 32, "fix": 18, "from": [7, 8, 11, 12, 13, 14, 17], "full": [4, 5], "gate": [6, 11, 12, 14], "gener": [6, 7, 8, 9, 11, 12, 13, 14], "generate_cutting_experi": [8, 33], "generate_qpd_weight": 44, "generate_summation_term": 27, "given": 14, "go": 13, "group": 2, "guid": [10, 16], "have": 13, "hop": 54, "how": [7, 8, 9, 10], "implement": 3, "incur": 14, "indic": 12, "instal": 17, "instruct": [9, 13, 17, 34, 35], "issu": 18, "iter": [2, 52, 53], "kak": 6, "kei": 6, "knit": [1, 16], "known": 18, "legaci": 3, "limit": 6, "locat": [13, 14], "make": 14, "manual": 5, "mape": 55, "materi": 6, "maximum": 14, "metric": [2, 54, 55, 56, 57, 58], "minimum": 8, "modul": 6, "more": 6, "move": [6, 9, 13, 35], "mse": 56, "naive_probability_distribut": 49, "nearest_probability_distribut": 50, "need": 8, "new": [13, 18], "note": [12, 18], "num_sampl": 8, "observ": [2, 8, 9, 11, 12, 13, 14], "observable_group": [2, 59, 60, 61], "observablecollect": 60, "observables_restricted_to_subsystem": 61, "obtain": 8, "one": 14, "oper": 6, "optimizationparamet": 20, "option": 17, "origin": [11, 12, 13], "other": 18, "output": [4, 5], "overhead": [6, 11, 12, 13, 14], "overview": 6, "partit": [11, 14], "partition_circuit_qubit": 36, "partition_problem": 37, "partitionedcuttingproblem": 21, "pass": 2, "per": 14, "phrase": [6, 13], "pip": 17, "place": [9, 13], "platform": 17, "pre": 17, "prelud": 18, "prepar": [9, 11, 12, 13], "primit": [11, 12, 13], "probabl": 0, "problem": [9, 11, 13], "project": 16, "qiskit": [4, 5, 11, 12, 13], "qpd": [0, 6, 12, 38, 39, 40, 41, 42, 43, 44, 45], "qpdbasi": 39, "qpdbasis_from_instruct": 45, "quantum": [4, 5], "quasi": 0, "quasi_to_r": 51, "quasiprob": [6, 7], "qubit": [6, 9, 11, 13, 14], "reconstruct": [4, 5, 9, 11, 12, 13], "reconstruct_expectation_valu": 46, "reconstruct_full_distribut": 28, "recov": 9, "reduc": [11, 12], "refer": [1, 6, 16], "releas": 18, "removefinalreset": 67, "replac": 12, "result": [4, 5], "retriev": 8, "return": 8, "run": [9, 11, 12, 13, 14], "run_subcircuit_inst": 29, "runtim": [5, 11, 12, 13], "rzzgate": 6, "sampl": [6, 8, 11, 12, 13, 14], "sampler": [7, 11, 12, 13], "separ": [9, 11, 13, 14], "separate_circuit": 65, "separatedcircuit": 64, "servic": 5, "set": 5, "shallow": 12, "simul": [2, 62, 63], "simulate_statevector_outcom": 63, "singl": [9, 14], "singlequbitqpdg": 40, "sourc": 17, "specifi": [9, 11, 12, 13], "subcircuit": [4, 5, 14], "subexperi": [11, 12, 13], "subobserv": 14, "support": 17, "swap": 12, "tabl": 6, "term": [6, 9], "them": 12, "thi": [14, 16], "toolbox": [1, 16], "transform": [2, 9, 64, 65], "transpil": [2, 12], "transpiler_pass": [2, 66, 67], "tutori": [3, 4, 5, 15], "two": [6, 13, 14], "twoqubitqpdg": [12, 41], "uncut": 9, "unique_by_eq": 52, "unique_by_id": 53, "up": 5, "updat": 9, "upgrad": 18, "us": [9, 11, 12, 13, 14, 17], "util": [2, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67], "valu": [9, 11, 12, 13], "verifi": [4, 5, 30], "via": 6, "visual": [9, 11, 12, 13], "weight": [6, 8], "weighttyp": [8, 42], "where": 13, "width": 11, "wire": [4, 5, 6, 9, 13, 14], "within": 17}})
\ No newline at end of file
diff --git a/stubs/circuit_knitting.cutting.DeviceConstraints.html b/stubs/circuit_knitting.cutting.DeviceConstraints.html
new file mode 100644
index 000000000..72c358836
--- /dev/null
+++ b/stubs/circuit_knitting.cutting.DeviceConstraints.html
@@ -0,0 +1,430 @@
+<!doctype html>
+<html class="no-js" lang="en">
+  <head><meta charset="utf-8"/>
+    <meta name="viewport" content="width=device-width,initial-scale=1"/>
+    <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
+<link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.qpd.QPDBasis" href="circuit_knitting.cutting.qpd.QPDBasis.html" /><link rel="prev" title="circuit_knitting.cutting.OptimizationParameters" href="circuit_knitting.cutting.OptimizationParameters.html" />
+
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.DeviceConstraints - Circuit Knitting Toolbox 0.7.0</title>
+      <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
+    <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
+    <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-ecosystem.css?v=745c5aa7" />
+    <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/jupyter-sphinx.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=36a5483c" />
+    
+    
+
+
+<style>
+  body {
+    --color-code-background: #ffffff;
+  --color-code-foreground: black;
+  
+  }
+  @media not print {
+    body[data-theme="dark"] {
+      --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+    }
+    @media (prefers-color-scheme: dark) {
+      body:not([data-theme="light"]) {
+        --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+      }
+    }
+  }
+</style><link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin><link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&family=IBM+Plex+Sans:ital,wght@0,400;0,600;1,400;1,600&display=swap" rel="stylesheet">
+<script src="../_static/js/web-components/top-nav-bar.js"></script></head>
+  <body>
+    
+    <script>document.body.dataset.theme = "light";</script>
+    
+<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
+  <symbol id="svg-toc" viewBox="0 0 24 24">
+    <title>Contents</title>
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32">
+      <defs><style>.cls-1{fill:none;}</style></defs>
+      <path d="M28,4H4A2,2,0,0,0,2,6V26a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V6A2,2,0,0,0,28,4ZM4,6H20V26H4ZM28,26H22V6h6Z"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-menu" viewBox="0 0 24 24">
+    <title>Menu</title>
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32">
+      <defs><style>.cls-1{fill:none;}</style></defs>
+      <rect x="4" y="6" width="24" height="2"/>
+      <rect x="4" y="24" width="24" height="2"/>
+      <rect x="4" y="12" width="24" height="2"/>
+      <rect x="4" y="18" width="24" height="2"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
+    <title>Expand</title>
+    <svg version="1.1" id="icon" xmlns="http://www.w3.org/2000/svg" x="0px" y="0px"
+         viewBox="0 0 32 32" style="enable-background:new 0 0 32 32;" xml:space="preserve"><polygon points="22,16 12,26 10.6,24.6 19.2,16 10.6,7.4 12,6 " stroke="currentColor"/>
+      <rect id="_x3C_Transparent_Rectangle_x3E_" fill="none" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-new-tab" viewBox="0 0 32 32">
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 32 32">
+      <defs>
+        <style>
+          .cls-1 {
+            fill: none;
+          }
+        </style>
+      </defs>
+      <path fill="#6929C4" d="M26,28H6a2.0027,2.0027,0,0,1-2-2V6A2.0027,2.0027,0,0,1,6,4H16V6H6V26H26V16h2V26A2.0027,2.0027,0,0,1,26,28Z"/>
+      <polygon fill="#6929C4" points="20 2 20 4 26.586 4 18 12.586 19.414 14 28 5.414 28 12 30 12 30 2 20 2"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-sun" viewBox="0 0 24 24">
+    <title>Light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      <circle cx="12" cy="12" r="5"></circle>
+      <line x1="12" y1="1" x2="12" y2="3"></line>
+      <line x1="12" y1="21" x2="12" y2="23"></line>
+      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
+      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
+      <line x1="1" y1="12" x2="3" y2="12"></line>
+      <line x1="21" y1="12" x2="23" y2="12"></line>
+      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
+      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon" viewBox="0 0 24 24">
+    <title>Dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
+    </svg>
+  </symbol>
+  <symbol id="svg-sun-half" viewBox="0 0 24 24">
+    <title>Auto light/dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+      <circle cx="12" cy="12" r="9" />
+      <path d="M13 12h5" />
+      <path d="M13 15h4" />
+      <path d="M13 18h1" />
+      <path d="M13 9h4" />
+      <path d="M13 6h1" />
+    </svg>
+  </symbol>
+</svg>
+
+<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
+<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
+<label class="overlay sidebar-overlay" for="__navigation">
+  <div class="visually-hidden">Hide navigation sidebar</div>
+</label>
+<label class="overlay toc-overlay" for="__toc">
+  <div class="visually-hidden">Hide table of contents sidebar</div>
+</label>
+
+
+
+<div class="page">
+  <header class="mobile-header">
+    <div class="header-left">
+      <label class="nav-overlay-icon" for="__navigation">
+        <div class="visually-hidden">Toggle site navigation sidebar</div>
+        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
+      </label>
+    </div>
+    <div class="header-center">
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
+    </div>
+    <div class="header-right">
+      <div class="theme-toggle-container theme-toggle-header">
+        <button class="theme-toggle">
+          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+        </button>
+      </div>
+      <label class="toc-overlay-icon toc-header-icon" for="__toc">
+        <div class="visually-hidden">Toggle table of contents sidebar</div>
+        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+      </label>
+    </div>
+  </header>
+  <aside class="sidebar-drawer">
+    <div class="sidebar-container">
+      
+      <div class="sidebar-sticky"><div class="sidebar-brand">
+  <a href="https://www.qiskit.org/ecosystem">
+    <div class="sidebar-logo-container">
+      <img class="sidebar-logo" src="../_static/images/ecosystem-logo.svg" alt="Qiskit Ecosystem logo"/>
+    </div>
+  </a>
+  
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
+</div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
+  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
+  <input type="hidden" name="check_keywords" value="yes">
+  <input type="hidden" name="area" value="default">
+</form>
+<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
+  <ul>
+<li class="toctree-l1"><a class="reference internal" href="../index.html">About Circuit Knitting Toolbox</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../install.html">Installation Instructions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Circuit Cutting</span></p>
+<ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../circuit_cutting/tutorials/index.html">Cutting Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of Cutting Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../circuit_cutting/how-tos/index.html">Cutting How-To Guides</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Cutting How-To Guides</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.html">How to generate exact quasiprobability distributions from Sampler</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.html">How to generate exact sampling coefficients</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/how-tos/how_to_specify_cut_wires.html">How to place wire cuts using a single-qubit <code class="docutils literal notranslate"><span class="pre">CutWire</span></code> instruction</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../circuit_cutting/cutqc/index.html">CutQC (legacy circuit cutting implementation)</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of CutQC (legacy circuit cutting implementation)</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.html">CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.html">CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">References</span></p>
+<ul class="current">
+<li class="toctree-l1 current has-children"><a class="reference internal" href="../apidocs/index.html">API References</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of API References</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
+<li class="toctree-l2 current has-children"><a class="reference internal" href="../apidocs/circuit_cutting.html">Circuit Cutting</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Circuit Cutting</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cut_wires.html">circuit_knitting.cutting.cut_wires</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.expand_observables.html">circuit_knitting.cutting.expand_observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.partition_circuit_qubits.html">circuit_knitting.cutting.partition_circuit_qubits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.partition_problem.html">circuit_knitting.cutting.partition_problem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cut_gates.html">circuit_knitting.cutting.cut_gates</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.generate_cutting_experiments.html">circuit_knitting.cutting.generate_cutting_experiments</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.reconstruct_expectation_values.html">circuit_knitting.cutting.reconstruct_expectation_values</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3 current current-page"><a class="current reference internal" href="#">circuit_knitting.cutting.DeviceConstraints</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.TwoQubitQPDGate.html">circuit_knitting.cutting.qpd.TwoQubitQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.WeightType.html">circuit_knitting.cutting.qpd.WeightType</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.generate_qpd_weights.html">circuit_knitting.cutting.qpd.generate_qpd_weights</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.decompose_qpd_instructions.html">circuit_knitting.cutting.qpd.decompose_qpd_instructions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.qpdbasis_from_instruction.html">circuit_knitting.cutting.qpd.qpdbasis_from_instruction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.run_subcircuit_instances.html">circuit_knitting.cutting.cutqc.run_subcircuit_instances</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.generate_summation_terms.html">circuit_knitting.cutting.cutqc.generate_summation_terms</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.build.html">circuit_knitting.cutting.cutqc.build</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.verify.html">circuit_knitting.cutting.cutqc.verify</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.cut_circuit_wires.html">circuit_knitting.cutting.cutqc.cut_circuit_wires</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.evaluate_subcircuits.html">circuit_knitting.cutting.cutqc.evaluate_subcircuits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.reconstruct_full_distribution.html">circuit_knitting.cutting.cutqc.reconstruct_full_distribution</a></li>
+</ul>
+</li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../apidocs/utils.html">Utilities</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of Utilities</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.bitwise.bit_count.html">circuit_knitting.utils.bitwise.bit_count</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.conversion.quasi_to_real.html">circuit_knitting.utils.conversion.quasi_to_real</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.conversion.nearest_probability_distribution.html">circuit_knitting.utils.conversion.nearest_probability_distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.conversion.naive_probability_distribution.html">circuit_knitting.utils.conversion.naive_probability_distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.conversion.dict_to_array.html">circuit_knitting.utils.conversion.dict_to_array</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.iteration.unique_by_id.html">circuit_knitting.utils.iteration.unique_by_id</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.iteration.unique_by_eq.html">circuit_knitting.utils.iteration.unique_by_eq</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.metrics.chi2_distance.html">circuit_knitting.utils.metrics.chi2_distance</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.metrics.MSE.html">circuit_knitting.utils.metrics.MSE</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.metrics.MAPE.html">circuit_knitting.utils.metrics.MAPE</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.metrics.cross_entropy.html">circuit_knitting.utils.metrics.cross_entropy</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.metrics.HOP.html">circuit_knitting.utils.metrics.HOP</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.html">circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.observable_grouping.CommutingObservableGroup.html">circuit_knitting.utils.observable_grouping.CommutingObservableGroup</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.observable_grouping.ObservableCollection.html">circuit_knitting.utils.observable_grouping.ObservableCollection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.simulation.simulate_statevector_outcomes.html">circuit_knitting.utils.simulation.simulate_statevector_outcomes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.simulation.ExactSampler.html">circuit_knitting.utils.simulation.ExactSampler</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.transforms.separate_circuit.html">circuit_knitting.utils.transforms.separate_circuit</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.transforms.SeparatedCircuits.html">circuit_knitting.utils.transforms.SeparatedCircuits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.transpiler_passes.RemoveFinalReset.html">circuit_knitting.utils.transpiler_passes.RemoveFinalReset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.transpiler_passes.ConsolidateResets.html">circuit_knitting.utils.transpiler_passes.ConsolidateResets</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../release-notes.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference external" href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox">GitHub</a></li>
+</ul>
+
+</div></div>
+      </div>
+      
+    </div>
+  </aside>
+  <div class="main">
+    <div class="content">
+      <div class="article-container">
+        <a href="#" class="back-to-top muted-link">
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
+            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
+          </svg>
+          <span>Back to top</span>
+        </a>
+        <div class="content-icon-container">
+          <div class="edit-this-page">
+  <a class="muted-link" href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/edit/main/docs/stubs/circuit_knitting.cutting.DeviceConstraints.rst" title="Edit this page">
+    <svg aria-hidden="true" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+      <path d="M4 20h4l10.5 -10.5a1.5 1.5 0 0 0 -4 -4l-10.5 10.5v4" />
+      <line x1="13.5" y1="6.5" x2="17.5" y2="10.5" />
+    </svg>
+    <span class="visually-hidden">Edit this page</span>
+  </a>
+</div><div class="theme-toggle-container theme-toggle-content">
+            <button class="theme-toggle">
+              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+            </button>
+          </div>
+          <label class="toc-overlay-icon toc-content-icon" for="__toc">
+            <div class="visually-hidden">Toggle table of contents sidebar</div>
+            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+          </label>
+        </div>
+        <article role="main">
+          <section id="circuit-knitting-cutting-deviceconstraints">
+<h1>circuit_knitting.cutting.DeviceConstraints<a class="headerlink" href="#circuit-knitting-cutting-deviceconstraints" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="circuit_knitting.cutting.DeviceConstraints">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">DeviceConstraints</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qubits_per_subcircuit</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/cutting/automated_cut_finding.html#DeviceConstraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.cutting.DeviceConstraints" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<p>Specify the constraints (qubits per subcircuit) that must be respected.</p>
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code>(qubits_per_subcircuit)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_qpu_width</span></code>()</p></td>
+<td><p>Return the number of qubits per subcircuit.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">qubits_per_subcircuit</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</dd></dl>
+
+</section>
+
+        </article>
+      </div>
+      <footer>
+        <div class="related-pages">
+          <a class="next-page" href="circuit_knitting.cutting.qpd.QPDBasis.html">
+              <div class="page-info">
+                <div class="context">
+                  <span>Next</span>
+                </div>
+                <div class="title">circuit_knitting.cutting.qpd.QPDBasis</div>
+              </div>
+              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
+            </a>
+          <a class="prev-page" href="circuit_knitting.cutting.OptimizationParameters.html">
+              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
+              <div class="page-info">
+                <div class="context">
+                  <span>Previous</span>
+                </div>
+                
+                <div class="title">circuit_knitting.cutting.OptimizationParameters</div>
+                
+              </div>
+            </a>
+        </div>
+        <div class="bottom-of-page">
+          <div class="left-details">
+            <div class="copyright">
+                Copyright &#169; 2023
+            </div>
+            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
+            
+            <a href="https://github.com/pradyunsg/furo">Furo</a>
+            
+          </div>
+          <div class="right-details">
+            <div class="icons">
+              <a class="muted-link " href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox" aria-label="GitHub">
+                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
+                </svg>
+            </a>
+              
+            </div>
+          </div>
+        </div>
+        
+      </footer>
+    </div>
+    <aside class="toc-drawer">
+      
+      
+      <div class="toc-sticky toc-scroll">
+        <div class="toc-title-container">
+          <span class="toc-title">
+            On this page
+          </span>
+        </div>
+        <div class="toc-tree-container">
+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">circuit_knitting.cutting.DeviceConstraints</a><ul>
+<li><a class="reference internal" href="#circuit_knitting.cutting.DeviceConstraints"><code class="docutils literal notranslate"><span class="pre">DeviceConstraints</span></code></a></li>
+</ul>
+</li>
+</ul>
+
+          </div>
+        </div>
+      </div>
+      
+      
+    </aside>
+  </div>
+</div><script src="../_static/jquery.js?v=5d32c60e"></script>
+    <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
+    <script src="../_static/doctools.js?v=9a2dae69"></script>
+    <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
+    <script src="../_static/copybutton.js?v=f281be69"></script>
+    <script src="../_static/scripts/qiskit-sphinx-theme.js?v=4d77b8ca"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/@jupyter-widgets/html-manager@^1.0.1/dist/embed-amd.js"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    </body>
+</html>
\ No newline at end of file
diff --git a/stubs/circuit_knitting.cutting.OptimizationParameters.html b/stubs/circuit_knitting.cutting.OptimizationParameters.html
new file mode 100644
index 000000000..71d72ed11
--- /dev/null
+++ b/stubs/circuit_knitting.cutting.OptimizationParameters.html
@@ -0,0 +1,433 @@
+<!doctype html>
+<html class="no-js" lang="en">
+  <head><meta charset="utf-8"/>
+    <meta name="viewport" content="width=device-width,initial-scale=1"/>
+    <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
+<link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.DeviceConstraints" href="circuit_knitting.cutting.DeviceConstraints.html" /><link rel="prev" title="circuit_knitting.cutting.find_cuts" href="circuit_knitting.cutting.find_cuts.html" />
+
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.OptimizationParameters - Circuit Knitting Toolbox 0.7.0</title>
+      <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
+    <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
+    <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-ecosystem.css?v=745c5aa7" />
+    <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/jupyter-sphinx.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=36a5483c" />
+    
+    
+
+
+<style>
+  body {
+    --color-code-background: #ffffff;
+  --color-code-foreground: black;
+  
+  }
+  @media not print {
+    body[data-theme="dark"] {
+      --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+    }
+    @media (prefers-color-scheme: dark) {
+      body:not([data-theme="light"]) {
+        --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+      }
+    }
+  }
+</style><link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin><link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&family=IBM+Plex+Sans:ital,wght@0,400;0,600;1,400;1,600&display=swap" rel="stylesheet">
+<script src="../_static/js/web-components/top-nav-bar.js"></script></head>
+  <body>
+    
+    <script>document.body.dataset.theme = "light";</script>
+    
+<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
+  <symbol id="svg-toc" viewBox="0 0 24 24">
+    <title>Contents</title>
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32">
+      <defs><style>.cls-1{fill:none;}</style></defs>
+      <path d="M28,4H4A2,2,0,0,0,2,6V26a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V6A2,2,0,0,0,28,4ZM4,6H20V26H4ZM28,26H22V6h6Z"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-menu" viewBox="0 0 24 24">
+    <title>Menu</title>
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32">
+      <defs><style>.cls-1{fill:none;}</style></defs>
+      <rect x="4" y="6" width="24" height="2"/>
+      <rect x="4" y="24" width="24" height="2"/>
+      <rect x="4" y="12" width="24" height="2"/>
+      <rect x="4" y="18" width="24" height="2"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
+    <title>Expand</title>
+    <svg version="1.1" id="icon" xmlns="http://www.w3.org/2000/svg" x="0px" y="0px"
+         viewBox="0 0 32 32" style="enable-background:new 0 0 32 32;" xml:space="preserve"><polygon points="22,16 12,26 10.6,24.6 19.2,16 10.6,7.4 12,6 " stroke="currentColor"/>
+      <rect id="_x3C_Transparent_Rectangle_x3E_" fill="none" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-new-tab" viewBox="0 0 32 32">
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 32 32">
+      <defs>
+        <style>
+          .cls-1 {
+            fill: none;
+          }
+        </style>
+      </defs>
+      <path fill="#6929C4" d="M26,28H6a2.0027,2.0027,0,0,1-2-2V6A2.0027,2.0027,0,0,1,6,4H16V6H6V26H26V16h2V26A2.0027,2.0027,0,0,1,26,28Z"/>
+      <polygon fill="#6929C4" points="20 2 20 4 26.586 4 18 12.586 19.414 14 28 5.414 28 12 30 12 30 2 20 2"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-sun" viewBox="0 0 24 24">
+    <title>Light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      <circle cx="12" cy="12" r="5"></circle>
+      <line x1="12" y1="1" x2="12" y2="3"></line>
+      <line x1="12" y1="21" x2="12" y2="23"></line>
+      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
+      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
+      <line x1="1" y1="12" x2="3" y2="12"></line>
+      <line x1="21" y1="12" x2="23" y2="12"></line>
+      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
+      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon" viewBox="0 0 24 24">
+    <title>Dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
+    </svg>
+  </symbol>
+  <symbol id="svg-sun-half" viewBox="0 0 24 24">
+    <title>Auto light/dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+      <circle cx="12" cy="12" r="9" />
+      <path d="M13 12h5" />
+      <path d="M13 15h4" />
+      <path d="M13 18h1" />
+      <path d="M13 9h4" />
+      <path d="M13 6h1" />
+    </svg>
+  </symbol>
+</svg>
+
+<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
+<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
+<label class="overlay sidebar-overlay" for="__navigation">
+  <div class="visually-hidden">Hide navigation sidebar</div>
+</label>
+<label class="overlay toc-overlay" for="__toc">
+  <div class="visually-hidden">Hide table of contents sidebar</div>
+</label>
+
+
+
+<div class="page">
+  <header class="mobile-header">
+    <div class="header-left">
+      <label class="nav-overlay-icon" for="__navigation">
+        <div class="visually-hidden">Toggle site navigation sidebar</div>
+        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
+      </label>
+    </div>
+    <div class="header-center">
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
+    </div>
+    <div class="header-right">
+      <div class="theme-toggle-container theme-toggle-header">
+        <button class="theme-toggle">
+          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+        </button>
+      </div>
+      <label class="toc-overlay-icon toc-header-icon" for="__toc">
+        <div class="visually-hidden">Toggle table of contents sidebar</div>
+        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+      </label>
+    </div>
+  </header>
+  <aside class="sidebar-drawer">
+    <div class="sidebar-container">
+      
+      <div class="sidebar-sticky"><div class="sidebar-brand">
+  <a href="https://www.qiskit.org/ecosystem">
+    <div class="sidebar-logo-container">
+      <img class="sidebar-logo" src="../_static/images/ecosystem-logo.svg" alt="Qiskit Ecosystem logo"/>
+    </div>
+  </a>
+  
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
+</div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
+  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
+  <input type="hidden" name="check_keywords" value="yes">
+  <input type="hidden" name="area" value="default">
+</form>
+<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
+  <ul>
+<li class="toctree-l1"><a class="reference internal" href="../index.html">About Circuit Knitting Toolbox</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../install.html">Installation Instructions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Circuit Cutting</span></p>
+<ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../circuit_cutting/tutorials/index.html">Cutting Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of Cutting Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../circuit_cutting/how-tos/index.html">Cutting How-To Guides</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Cutting How-To Guides</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.html">How to generate exact quasiprobability distributions from Sampler</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.html">How to generate exact sampling coefficients</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/how-tos/how_to_specify_cut_wires.html">How to place wire cuts using a single-qubit <code class="docutils literal notranslate"><span class="pre">CutWire</span></code> instruction</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../circuit_cutting/cutqc/index.html">CutQC (legacy circuit cutting implementation)</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of CutQC (legacy circuit cutting implementation)</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.html">CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.html">CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">References</span></p>
+<ul class="current">
+<li class="toctree-l1 current has-children"><a class="reference internal" href="../apidocs/index.html">API References</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of API References</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
+<li class="toctree-l2 current has-children"><a class="reference internal" href="../apidocs/circuit_cutting.html">Circuit Cutting</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Circuit Cutting</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cut_wires.html">circuit_knitting.cutting.cut_wires</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.expand_observables.html">circuit_knitting.cutting.expand_observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.partition_circuit_qubits.html">circuit_knitting.cutting.partition_circuit_qubits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.partition_problem.html">circuit_knitting.cutting.partition_problem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cut_gates.html">circuit_knitting.cutting.cut_gates</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.generate_cutting_experiments.html">circuit_knitting.cutting.generate_cutting_experiments</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.reconstruct_expectation_values.html">circuit_knitting.cutting.reconstruct_expectation_values</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3 current current-page"><a class="current reference internal" href="#">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.TwoQubitQPDGate.html">circuit_knitting.cutting.qpd.TwoQubitQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.WeightType.html">circuit_knitting.cutting.qpd.WeightType</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.generate_qpd_weights.html">circuit_knitting.cutting.qpd.generate_qpd_weights</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.decompose_qpd_instructions.html">circuit_knitting.cutting.qpd.decompose_qpd_instructions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.qpdbasis_from_instruction.html">circuit_knitting.cutting.qpd.qpdbasis_from_instruction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.run_subcircuit_instances.html">circuit_knitting.cutting.cutqc.run_subcircuit_instances</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.generate_summation_terms.html">circuit_knitting.cutting.cutqc.generate_summation_terms</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.build.html">circuit_knitting.cutting.cutqc.build</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.verify.html">circuit_knitting.cutting.cutqc.verify</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.cut_circuit_wires.html">circuit_knitting.cutting.cutqc.cut_circuit_wires</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.evaluate_subcircuits.html">circuit_knitting.cutting.cutqc.evaluate_subcircuits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.reconstruct_full_distribution.html">circuit_knitting.cutting.cutqc.reconstruct_full_distribution</a></li>
+</ul>
+</li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../apidocs/utils.html">Utilities</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of Utilities</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.bitwise.bit_count.html">circuit_knitting.utils.bitwise.bit_count</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.conversion.quasi_to_real.html">circuit_knitting.utils.conversion.quasi_to_real</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.conversion.nearest_probability_distribution.html">circuit_knitting.utils.conversion.nearest_probability_distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.conversion.naive_probability_distribution.html">circuit_knitting.utils.conversion.naive_probability_distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.conversion.dict_to_array.html">circuit_knitting.utils.conversion.dict_to_array</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.iteration.unique_by_id.html">circuit_knitting.utils.iteration.unique_by_id</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.iteration.unique_by_eq.html">circuit_knitting.utils.iteration.unique_by_eq</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.metrics.chi2_distance.html">circuit_knitting.utils.metrics.chi2_distance</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.metrics.MSE.html">circuit_knitting.utils.metrics.MSE</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.metrics.MAPE.html">circuit_knitting.utils.metrics.MAPE</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.metrics.cross_entropy.html">circuit_knitting.utils.metrics.cross_entropy</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.metrics.HOP.html">circuit_knitting.utils.metrics.HOP</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.html">circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.observable_grouping.CommutingObservableGroup.html">circuit_knitting.utils.observable_grouping.CommutingObservableGroup</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.observable_grouping.ObservableCollection.html">circuit_knitting.utils.observable_grouping.ObservableCollection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.simulation.simulate_statevector_outcomes.html">circuit_knitting.utils.simulation.simulate_statevector_outcomes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.simulation.ExactSampler.html">circuit_knitting.utils.simulation.ExactSampler</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.transforms.separate_circuit.html">circuit_knitting.utils.transforms.separate_circuit</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.transforms.SeparatedCircuits.html">circuit_knitting.utils.transforms.SeparatedCircuits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.transpiler_passes.RemoveFinalReset.html">circuit_knitting.utils.transpiler_passes.RemoveFinalReset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.transpiler_passes.ConsolidateResets.html">circuit_knitting.utils.transpiler_passes.ConsolidateResets</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../release-notes.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference external" href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox">GitHub</a></li>
+</ul>
+
+</div></div>
+      </div>
+      
+    </div>
+  </aside>
+  <div class="main">
+    <div class="content">
+      <div class="article-container">
+        <a href="#" class="back-to-top muted-link">
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
+            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
+          </svg>
+          <span>Back to top</span>
+        </a>
+        <div class="content-icon-container">
+          <div class="edit-this-page">
+  <a class="muted-link" href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/edit/main/docs/stubs/circuit_knitting.cutting.OptimizationParameters.rst" title="Edit this page">
+    <svg aria-hidden="true" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+      <path d="M4 20h4l10.5 -10.5a1.5 1.5 0 0 0 -4 -4l-10.5 10.5v4" />
+      <line x1="13.5" y1="6.5" x2="17.5" y2="10.5" />
+    </svg>
+    <span class="visually-hidden">Edit this page</span>
+  </a>
+</div><div class="theme-toggle-container theme-toggle-content">
+            <button class="theme-toggle">
+              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+            </button>
+          </div>
+          <label class="toc-overlay-icon toc-content-icon" for="__toc">
+            <div class="visually-hidden">Toggle table of contents sidebar</div>
+            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+          </label>
+        </div>
+        <article role="main">
+          <section id="circuit-knitting-cutting-optimizationparameters">
+<h1>circuit_knitting.cutting.OptimizationParameters<a class="headerlink" href="#circuit-knitting-cutting-optimizationparameters" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="circuit_knitting.cutting.OptimizationParameters">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">OptimizationParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1024</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_backjumps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/cutting/automated_cut_finding.html#OptimizationParameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.cutting.OptimizationParameters" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<p>Specify parameters that control the optimization.</p>
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code>([seed, max_gamma, max_backjumps])</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_backjumps</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_gamma</span></code></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">seed</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</dd></dl>
+
+</section>
+
+        </article>
+      </div>
+      <footer>
+        <div class="related-pages">
+          <a class="next-page" href="circuit_knitting.cutting.DeviceConstraints.html">
+              <div class="page-info">
+                <div class="context">
+                  <span>Next</span>
+                </div>
+                <div class="title">circuit_knitting.cutting.DeviceConstraints</div>
+              </div>
+              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
+            </a>
+          <a class="prev-page" href="circuit_knitting.cutting.find_cuts.html">
+              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
+              <div class="page-info">
+                <div class="context">
+                  <span>Previous</span>
+                </div>
+                
+                <div class="title">circuit_knitting.cutting.find_cuts</div>
+                
+              </div>
+            </a>
+        </div>
+        <div class="bottom-of-page">
+          <div class="left-details">
+            <div class="copyright">
+                Copyright &#169; 2023
+            </div>
+            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
+            
+            <a href="https://github.com/pradyunsg/furo">Furo</a>
+            
+          </div>
+          <div class="right-details">
+            <div class="icons">
+              <a class="muted-link " href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox" aria-label="GitHub">
+                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
+                </svg>
+            </a>
+              
+            </div>
+          </div>
+        </div>
+        
+      </footer>
+    </div>
+    <aside class="toc-drawer">
+      
+      
+      <div class="toc-sticky toc-scroll">
+        <div class="toc-title-container">
+          <span class="toc-title">
+            On this page
+          </span>
+        </div>
+        <div class="toc-tree-container">
+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">circuit_knitting.cutting.OptimizationParameters</a><ul>
+<li><a class="reference internal" href="#circuit_knitting.cutting.OptimizationParameters"><code class="docutils literal notranslate"><span class="pre">OptimizationParameters</span></code></a></li>
+</ul>
+</li>
+</ul>
+
+          </div>
+        </div>
+      </div>
+      
+      
+    </aside>
+  </div>
+</div><script src="../_static/jquery.js?v=5d32c60e"></script>
+    <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
+    <script src="../_static/doctools.js?v=9a2dae69"></script>
+    <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
+    <script src="../_static/copybutton.js?v=f281be69"></script>
+    <script src="../_static/scripts/qiskit-sphinx-theme.js?v=4d77b8ca"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/@jupyter-widgets/html-manager@^1.0.1/dist/embed-amd.js"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    </body>
+</html>
\ No newline at end of file
diff --git a/stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html b/stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html
index c009d820d..9310f9560 100644
--- a/stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html
+++ b/stubs/circuit_knitting.cutting.PartitionedCuttingProblem.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.instructions.CutWire" href="circuit_knitting.cutting.instructions.CutWire.html" /><link rel="prev" title="circuit_knitting.cutting.reconstruct_expectation_values" href="circuit_knitting.cutting.reconstruct_expectation_values.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.PartitionedCuttingProblem - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.PartitionedCuttingProblem - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3 current current-page"><a class="current reference internal" href="#">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -414,7 +418,7 @@ <h1>circuit_knitting.cutting.PartitionedCuttingProblem<a class="headerlink" href
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.cut_gates.html b/stubs/circuit_knitting.cutting.cut_gates.html
index 1481f9cf9..503b65596 100644
--- a/stubs/circuit_knitting.cutting.cut_gates.html
+++ b/stubs/circuit_knitting.cutting.cut_gates.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.generate_cutting_experiments" href="circuit_knitting.cutting.generate_cutting_experiments.html" /><link rel="prev" title="circuit_knitting.cutting.partition_problem" href="circuit_knitting.cutting.partition_problem.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cut_gates - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cut_gates - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -408,7 +412,7 @@ <h1>circuit_knitting.cutting.cut_gates<a class="headerlink" href="#circuit-knitt
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.cut_wires.html b/stubs/circuit_knitting.cutting.cut_wires.html
index 2bb4750ab..d72855425 100644
--- a/stubs/circuit_knitting.cutting.cut_wires.html
+++ b/stubs/circuit_knitting.cutting.cut_wires.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.expand_observables" href="circuit_knitting.cutting.expand_observables.html" /><link rel="prev" title="Circuit Cutting" href="../apidocs/circuit_cutting.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cut_wires - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cut_wires - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -404,7 +408,7 @@ <h1>circuit_knitting.cutting.cut_wires<a class="headerlink" href="#circuit-knitt
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.cutqc.build.html b/stubs/circuit_knitting.cutting.cutqc.build.html
index c2db28654..bc32602e8 100644
--- a/stubs/circuit_knitting.cutting.cutqc.build.html
+++ b/stubs/circuit_knitting.cutting.cutqc.build.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.cutqc.verify" href="circuit_knitting.cutting.cutqc.verify.html" /><link rel="prev" title="circuit_knitting.cutting.cutqc.generate_summation_terms" href="circuit_knitting.cutting.cutqc.generate_summation_terms.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cutqc.build - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cutqc.build - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -309,6 +313,9 @@ <h1>circuit_knitting.cutting.cutqc.build<a class="headerlink" href="#circuit-kni
 <dt class="sig sig-object py" id="circuit_knitting.cutting.cutqc.build">
 <span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">summation_terms</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subcircuit_entry_probs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_cuts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_threads</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/cutting/cutqc/wire_cutting_post_processing.html#build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.cutting.cutqc.build" title="Link to this definition">¶</a></dt>
 <dd><p>Reconstruct the full probability distribution from the subcircuits.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting.cutqc.wire_cutting_post_processing.build()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting</span></code> package.</p>
+</div>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -409,7 +416,7 @@ <h1>circuit_knitting.cutting.cutqc.build<a class="headerlink" href="#circuit-kni
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires.html b/stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires.html
index 1b1bd7ce0..608231794 100644
--- a/stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires.html
+++ b/stubs/circuit_knitting.cutting.cutqc.cut_circuit_wires.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.cutqc.evaluate_subcircuits" href="circuit_knitting.cutting.cutqc.evaluate_subcircuits.html" /><link rel="prev" title="circuit_knitting.cutting.cutqc.verify" href="circuit_knitting.cutting.cutqc.verify.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cutqc.cut_circuit_wires - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cutqc.cut_circuit_wires - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -309,6 +313,9 @@ <h1>circuit_knitting.cutting.cutqc.cut_circuit_wires<a class="headerlink" href="
 <dt class="sig sig-object py" id="circuit_knitting.cutting.cutqc.cut_circuit_wires">
 <span class="sig-name descname"><span class="pre">cut_circuit_wires</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">circuit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subcircuit_vertices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_subcircuit_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_subcircuit_cuts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_subcircuit_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_cuts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_subcircuits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/cutting/cutqc/wire_cutting.html#cut_circuit_wires"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.cutting.cutqc.cut_circuit_wires" title="Link to this definition">¶</a></dt>
 <dd><p>Decompose the circuit into a collection of subcircuits.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting.cutqc.wire_cutting.cut_circuit_wires()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting</span></code> package.</p>
+</div>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -415,7 +422,7 @@ <h1>circuit_knitting.cutting.cutqc.cut_circuit_wires<a class="headerlink" href="
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits.html b/stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits.html
index 654de539d..d3aa93322 100644
--- a/stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits.html
+++ b/stubs/circuit_knitting.cutting.cutqc.evaluate_subcircuits.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.cutqc.reconstruct_full_distribution" href="circuit_knitting.cutting.cutqc.reconstruct_full_distribution.html" /><link rel="prev" title="circuit_knitting.cutting.cutqc.cut_circuit_wires" href="circuit_knitting.cutting.cutqc.cut_circuit_wires.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cutqc.evaluate_subcircuits - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cutqc.evaluate_subcircuits - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -309,6 +313,9 @@ <h1>circuit_knitting.cutting.cutqc.evaluate_subcircuits<a class="headerlink" hre
 <dt class="sig sig-object py" id="circuit_knitting.cutting.cutqc.evaluate_subcircuits">
 <span class="sig-name descname"><span class="pre">evaluate_subcircuits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cuts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">service</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/cutting/cutqc/wire_cutting.html#evaluate_subcircuits"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.cutting.cutqc.evaluate_subcircuits" title="Link to this definition">¶</a></dt>
 <dd><p>Evaluate the subcircuits.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting.cutqc.wire_cutting.evaluate_subcircuits()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting</span></code> package.</p>
+</div>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -405,7 +412,7 @@ <h1>circuit_knitting.cutting.cutqc.evaluate_subcircuits<a class="headerlink" hre
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.cutqc.generate_summation_terms.html b/stubs/circuit_knitting.cutting.cutqc.generate_summation_terms.html
index e4da66005..088fde5cf 100644
--- a/stubs/circuit_knitting.cutting.cutqc.generate_summation_terms.html
+++ b/stubs/circuit_knitting.cutting.cutqc.generate_summation_terms.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.cutqc.build" href="circuit_knitting.cutting.cutqc.build.html" /><link rel="prev" title="circuit_knitting.cutting.cutqc.run_subcircuit_instances" href="circuit_knitting.cutting.cutqc.run_subcircuit_instances.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cutqc.generate_summation_terms - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cutqc.generate_summation_terms - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -321,6 +325,9 @@ <h1>circuit_knitting.cutting.cutqc.generate_summation_terms<a class="headerlink"
 <div class="line">Add coefficient*subcircuit_instance to subcircuit_entry.</div>
 </div>
 <p>subcircuit_instances[subcircuit_idx][init,meas] = subcircuit_instance_idx.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting.cutqc.wire_cutting_post_processing.generate_summation_terms()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting</span></code> package.</p>
+</div>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -417,7 +424,7 @@ <h1>circuit_knitting.cutting.cutqc.generate_summation_terms<a class="headerlink"
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.html b/stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.html
index 39cca41e7..75f38a0e1 100644
--- a/stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.html
+++ b/stubs/circuit_knitting.cutting.cutqc.reconstruct_full_distribution.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="Utilities" href="../apidocs/utils.html" /><link rel="prev" title="circuit_knitting.cutting.cutqc.evaluate_subcircuits" href="circuit_knitting.cutting.cutqc.evaluate_subcircuits.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cutqc.reconstruct_full_distribution - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cutqc.reconstruct_full_distribution - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -309,6 +313,9 @@ <h1>circuit_knitting.cutting.cutqc.reconstruct_full_distribution<a class="header
 <dt class="sig sig-object py" id="circuit_knitting.cutting.cutqc.reconstruct_full_distribution">
 <span class="sig-name descname"><span class="pre">reconstruct_full_distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">circuit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subcircuit_instance_probabilities</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cuts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_threads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/cutting/cutqc/wire_cutting.html#reconstruct_full_distribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.cutting.cutqc.reconstruct_full_distribution" title="Link to this definition">¶</a></dt>
 <dd><p>Reconstruct the full probabilities from the subcircuit evaluations.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting.cutqc.wire_cutting.reconstruct_full_distribution()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting</span></code> package.</p>
+</div>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -405,7 +412,7 @@ <h1>circuit_knitting.cutting.cutqc.reconstruct_full_distribution<a class="header
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances.html b/stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances.html
index dcc687de1..c2cd3cc8b 100644
--- a/stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances.html
+++ b/stubs/circuit_knitting.cutting.cutqc.run_subcircuit_instances.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.cutqc.generate_summation_terms" href="circuit_knitting.cutting.cutqc.generate_summation_terms.html" /><link rel="prev" title="circuit_knitting.cutting.qpd.qpdbasis_from_instruction" href="circuit_knitting.cutting.qpd.qpdbasis_from_instruction.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cutqc.run_subcircuit_instances - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cutqc.run_subcircuit_instances - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -312,6 +316,9 @@ <h1>circuit_knitting.cutting.cutqc.run_subcircuit_instances<a class="headerlink"
 <p>Using the backend(s) provided, this executes all the subcircuits to generate the
 resultant probability vectors.
 subcircuit_instance_probs[subcircuit_idx][subcircuit_instance_idx] = measured probability</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting.cutqc.wire_cutting_evaluation.run_subcircuit_instances()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting</span></code> package.</p>
+</div>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -410,7 +417,7 @@ <h1>circuit_knitting.cutting.cutqc.run_subcircuit_instances<a class="headerlink"
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.cutqc.verify.html b/stubs/circuit_knitting.cutting.cutqc.verify.html
index a3f95d369..39cd0215a 100644
--- a/stubs/circuit_knitting.cutting.cutqc.verify.html
+++ b/stubs/circuit_knitting.cutting.cutqc.verify.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.cutqc.cut_circuit_wires" href="circuit_knitting.cutting.cutqc.cut_circuit_wires.html" /><link rel="prev" title="circuit_knitting.cutting.cutqc.build" href="circuit_knitting.cutting.cutqc.build.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.cutqc.verify - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.cutqc.verify - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -312,6 +316,9 @@ <h1>circuit_knitting.cutting.cutqc.verify<a class="headerlink" href="#circuit-kn
 <p>Executes the original circuit, then measures the distributional differences between this exact
 result (ground truth) and the reconstructed result from the subcircuits.
 Provides a variety of metrics to evaluate the differences in the distributions.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting.cutqc.wire_cutting_verification.verify()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0. Use the wire cutting or automated cut-finding functionality in the <code class="docutils literal notranslate"><span class="pre">circuit_knitting.cutting</span></code> package.</p>
+</div>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -407,7 +414,7 @@ <h1>circuit_knitting.cutting.cutqc.verify<a class="headerlink" href="#circuit-kn
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.expand_observables.html b/stubs/circuit_knitting.cutting.expand_observables.html
index b57eb861a..795ff1e19 100644
--- a/stubs/circuit_knitting.cutting.expand_observables.html
+++ b/stubs/circuit_knitting.cutting.expand_observables.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.partition_circuit_qubits" href="circuit_knitting.cutting.partition_circuit_qubits.html" /><link rel="prev" title="circuit_knitting.cutting.cut_wires" href="circuit_knitting.cutting.cut_wires.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.expand_observables - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.expand_observables - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -423,7 +427,7 @@ <h1>circuit_knitting.cutting.expand_observables<a class="headerlink" href="#circ
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.find_cuts.html b/stubs/circuit_knitting.cutting.find_cuts.html
new file mode 100644
index 000000000..2dae1e28e
--- /dev/null
+++ b/stubs/circuit_knitting.cutting.find_cuts.html
@@ -0,0 +1,440 @@
+<!doctype html>
+<html class="no-js" lang="en">
+  <head><meta charset="utf-8"/>
+    <meta name="viewport" content="width=device-width,initial-scale=1"/>
+    <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
+<link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.OptimizationParameters" href="circuit_knitting.cutting.OptimizationParameters.html" /><link rel="prev" title="circuit_knitting.cutting.instructions.Move" href="circuit_knitting.cutting.instructions.Move.html" />
+
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.find_cuts - Circuit Knitting Toolbox 0.7.0</title>
+      <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
+    <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
+    <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-ecosystem.css?v=745c5aa7" />
+    <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/jupyter-sphinx.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=36a5483c" />
+    
+    
+
+
+<style>
+  body {
+    --color-code-background: #ffffff;
+  --color-code-foreground: black;
+  
+  }
+  @media not print {
+    body[data-theme="dark"] {
+      --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+    }
+    @media (prefers-color-scheme: dark) {
+      body:not([data-theme="light"]) {
+        --color-code-background: #202020;
+  --color-code-foreground: #d0d0d0;
+  
+      }
+    }
+  }
+</style><link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin><link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600&family=IBM+Plex+Sans:ital,wght@0,400;0,600;1,400;1,600&display=swap" rel="stylesheet">
+<script src="../_static/js/web-components/top-nav-bar.js"></script></head>
+  <body>
+    
+    <script>document.body.dataset.theme = "light";</script>
+    
+<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
+  <symbol id="svg-toc" viewBox="0 0 24 24">
+    <title>Contents</title>
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32">
+      <defs><style>.cls-1{fill:none;}</style></defs>
+      <path d="M28,4H4A2,2,0,0,0,2,6V26a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V6A2,2,0,0,0,28,4ZM4,6H20V26H4ZM28,26H22V6h6Z"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-menu" viewBox="0 0 24 24">
+    <title>Menu</title>
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32">
+      <defs><style>.cls-1{fill:none;}</style></defs>
+      <rect x="4" y="6" width="24" height="2"/>
+      <rect x="4" y="24" width="24" height="2"/>
+      <rect x="4" y="12" width="24" height="2"/>
+      <rect x="4" y="18" width="24" height="2"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
+    <title>Expand</title>
+    <svg version="1.1" id="icon" xmlns="http://www.w3.org/2000/svg" x="0px" y="0px"
+         viewBox="0 0 32 32" style="enable-background:new 0 0 32 32;" xml:space="preserve"><polygon points="22,16 12,26 10.6,24.6 19.2,16 10.6,7.4 12,6 " stroke="currentColor"/>
+      <rect id="_x3C_Transparent_Rectangle_x3E_" fill="none" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-new-tab" viewBox="0 0 32 32">
+    <svg id="icon" xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 32 32">
+      <defs>
+        <style>
+          .cls-1 {
+            fill: none;
+          }
+        </style>
+      </defs>
+      <path fill="#6929C4" d="M26,28H6a2.0027,2.0027,0,0,1-2-2V6A2.0027,2.0027,0,0,1,6,4H16V6H6V26H26V16h2V26A2.0027,2.0027,0,0,1,26,28Z"/>
+      <polygon fill="#6929C4" points="20 2 20 4 26.586 4 18 12.586 19.414 14 28 5.414 28 12 30 12 30 2 20 2"/>
+      <rect id="_Transparent_Rectangle_" data-name="&lt;Transparent Rectangle&gt;" class="cls-1" width="32" height="32"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-sun" viewBox="0 0 24 24">
+    <title>Light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      <circle cx="12" cy="12" r="5"></circle>
+      <line x1="12" y1="1" x2="12" y2="3"></line>
+      <line x1="12" y1="21" x2="12" y2="23"></line>
+      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
+      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
+      <line x1="1" y1="12" x2="3" y2="12"></line>
+      <line x1="21" y1="12" x2="23" y2="12"></line>
+      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
+      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon" viewBox="0 0 24 24">
+    <title>Dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
+    </svg>
+  </symbol>
+  <symbol id="svg-sun-half" viewBox="0 0 24 24">
+    <title>Auto light/dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+      <circle cx="12" cy="12" r="9" />
+      <path d="M13 12h5" />
+      <path d="M13 15h4" />
+      <path d="M13 18h1" />
+      <path d="M13 9h4" />
+      <path d="M13 6h1" />
+    </svg>
+  </symbol>
+</svg>
+
+<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
+<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
+<label class="overlay sidebar-overlay" for="__navigation">
+  <div class="visually-hidden">Hide navigation sidebar</div>
+</label>
+<label class="overlay toc-overlay" for="__toc">
+  <div class="visually-hidden">Hide table of contents sidebar</div>
+</label>
+
+
+
+<div class="page">
+  <header class="mobile-header">
+    <div class="header-left">
+      <label class="nav-overlay-icon" for="__navigation">
+        <div class="visually-hidden">Toggle site navigation sidebar</div>
+        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
+      </label>
+    </div>
+    <div class="header-center">
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
+    </div>
+    <div class="header-right">
+      <div class="theme-toggle-container theme-toggle-header">
+        <button class="theme-toggle">
+          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+        </button>
+      </div>
+      <label class="toc-overlay-icon toc-header-icon" for="__toc">
+        <div class="visually-hidden">Toggle table of contents sidebar</div>
+        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+      </label>
+    </div>
+  </header>
+  <aside class="sidebar-drawer">
+    <div class="sidebar-container">
+      
+      <div class="sidebar-sticky"><div class="sidebar-brand">
+  <a href="https://www.qiskit.org/ecosystem">
+    <div class="sidebar-logo-container">
+      <img class="sidebar-logo" src="../_static/images/ecosystem-logo.svg" alt="Qiskit Ecosystem logo"/>
+    </div>
+  </a>
+  
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
+</div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
+  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
+  <input type="hidden" name="check_keywords" value="yes">
+  <input type="hidden" name="area" value="default">
+</form>
+<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
+  <ul>
+<li class="toctree-l1"><a class="reference internal" href="../index.html">About Circuit Knitting Toolbox</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../install.html">Installation Instructions</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Circuit Cutting</span></p>
+<ul>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../circuit_cutting/tutorials/index.html">Cutting Tutorials</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of Cutting Tutorials</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../circuit_cutting/how-tos/index.html">Cutting How-To Guides</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of Cutting How-To Guides</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/how-tos/how_to_generate_exact_quasi_dists_from_sampler.html">How to generate exact quasiprobability distributions from Sampler</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/how-tos/how_to_generate_exact_sampling_coefficients.html">How to generate exact sampling coefficients</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/how-tos/how_to_specify_cut_wires.html">How to place wire cuts using a single-qubit <code class="docutils literal notranslate"><span class="pre">CutWire</span></code> instruction</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../circuit_cutting/cutqc/index.html">CutQC (legacy circuit cutting implementation)</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of CutQC (legacy circuit cutting implementation)</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/cutqc/tutorials/tutorial_1_automatic_cut_finding.html">CutQC Tutorial 1: Circuit Cutting with Automatic Cut Finding</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/cutqc/tutorials/tutorial_2_manual_cutting.html">CutQC Tutorial 2: Circuit Cutting with Manual Wire Cutting</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">References</span></p>
+<ul class="current">
+<li class="toctree-l1 current has-children"><a class="reference internal" href="../apidocs/index.html">API References</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of API References</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
+<li class="toctree-l2 current has-children"><a class="reference internal" href="../apidocs/circuit_cutting.html">Circuit Cutting</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of Circuit Cutting</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cut_wires.html">circuit_knitting.cutting.cut_wires</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.expand_observables.html">circuit_knitting.cutting.expand_observables</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.partition_circuit_qubits.html">circuit_knitting.cutting.partition_circuit_qubits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.partition_problem.html">circuit_knitting.cutting.partition_problem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cut_gates.html">circuit_knitting.cutting.cut_gates</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.generate_cutting_experiments.html">circuit_knitting.cutting.generate_cutting_experiments</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.reconstruct_expectation_values.html">circuit_knitting.cutting.reconstruct_expectation_values</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3 current current-page"><a class="current reference internal" href="#">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.TwoQubitQPDGate.html">circuit_knitting.cutting.qpd.TwoQubitQPDGate</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.WeightType.html">circuit_knitting.cutting.qpd.WeightType</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.generate_qpd_weights.html">circuit_knitting.cutting.qpd.generate_qpd_weights</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.decompose_qpd_instructions.html">circuit_knitting.cutting.qpd.decompose_qpd_instructions</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.qpdbasis_from_instruction.html">circuit_knitting.cutting.qpd.qpdbasis_from_instruction</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.run_subcircuit_instances.html">circuit_knitting.cutting.cutqc.run_subcircuit_instances</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.generate_summation_terms.html">circuit_knitting.cutting.cutqc.generate_summation_terms</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.build.html">circuit_knitting.cutting.cutqc.build</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.verify.html">circuit_knitting.cutting.cutqc.verify</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.cut_circuit_wires.html">circuit_knitting.cutting.cutqc.cut_circuit_wires</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.evaluate_subcircuits.html">circuit_knitting.cutting.cutqc.evaluate_subcircuits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.cutqc.reconstruct_full_distribution.html">circuit_knitting.cutting.cutqc.reconstruct_full_distribution</a></li>
+</ul>
+</li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="../apidocs/utils.html">Utilities</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of Utilities</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.bitwise.bit_count.html">circuit_knitting.utils.bitwise.bit_count</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.conversion.quasi_to_real.html">circuit_knitting.utils.conversion.quasi_to_real</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.conversion.nearest_probability_distribution.html">circuit_knitting.utils.conversion.nearest_probability_distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.conversion.naive_probability_distribution.html">circuit_knitting.utils.conversion.naive_probability_distribution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.conversion.dict_to_array.html">circuit_knitting.utils.conversion.dict_to_array</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.iteration.unique_by_id.html">circuit_knitting.utils.iteration.unique_by_id</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.iteration.unique_by_eq.html">circuit_knitting.utils.iteration.unique_by_eq</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.metrics.chi2_distance.html">circuit_knitting.utils.metrics.chi2_distance</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.metrics.MSE.html">circuit_knitting.utils.metrics.MSE</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.metrics.MAPE.html">circuit_knitting.utils.metrics.MAPE</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.metrics.cross_entropy.html">circuit_knitting.utils.metrics.cross_entropy</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.metrics.HOP.html">circuit_knitting.utils.metrics.HOP</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.html">circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.observable_grouping.CommutingObservableGroup.html">circuit_knitting.utils.observable_grouping.CommutingObservableGroup</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.observable_grouping.ObservableCollection.html">circuit_knitting.utils.observable_grouping.ObservableCollection</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.simulation.simulate_statevector_outcomes.html">circuit_knitting.utils.simulation.simulate_statevector_outcomes</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.simulation.ExactSampler.html">circuit_knitting.utils.simulation.ExactSampler</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.transforms.separate_circuit.html">circuit_knitting.utils.transforms.separate_circuit</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.transforms.SeparatedCircuits.html">circuit_knitting.utils.transforms.SeparatedCircuits</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.transpiler_passes.RemoveFinalReset.html">circuit_knitting.utils.transpiler_passes.RemoveFinalReset</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.utils.transpiler_passes.ConsolidateResets.html">circuit_knitting.utils.transpiler_passes.ConsolidateResets</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../release-notes.html">Release Notes</a></li>
+<li class="toctree-l1"><a class="reference external" href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox">GitHub</a></li>
+</ul>
+
+</div></div>
+      </div>
+      
+    </div>
+  </aside>
+  <div class="main">
+    <div class="content">
+      <div class="article-container">
+        <a href="#" class="back-to-top muted-link">
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
+            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
+          </svg>
+          <span>Back to top</span>
+        </a>
+        <div class="content-icon-container">
+          <div class="edit-this-page">
+  <a class="muted-link" href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/edit/main/docs/stubs/circuit_knitting.cutting.find_cuts.rst" title="Edit this page">
+    <svg aria-hidden="true" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+      <path d="M4 20h4l10.5 -10.5a1.5 1.5 0 0 0 -4 -4l-10.5 10.5v4" />
+      <line x1="13.5" y1="6.5" x2="17.5" y2="10.5" />
+    </svg>
+    <span class="visually-hidden">Edit this page</span>
+  </a>
+</div><div class="theme-toggle-container theme-toggle-content">
+            <button class="theme-toggle">
+              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+            </button>
+          </div>
+          <label class="toc-overlay-icon toc-content-icon" for="__toc">
+            <div class="visually-hidden">Toggle table of contents sidebar</div>
+            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+          </label>
+        </div>
+        <article role="main">
+          <section id="circuit-knitting-cutting-find-cuts">
+<h1>circuit_knitting.cutting.find_cuts<a class="headerlink" href="#circuit-knitting-cutting-find-cuts" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="circuit_knitting.cutting.find_cuts">
+<span class="sig-name descname"><span class="pre">find_cuts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">circuit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraints</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/cutting/automated_cut_finding.html#find_cuts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.cutting.find_cuts" title="Link to this definition">¶</a></dt>
+<dd><p>Find cut locations in a circuit, given optimization parameters and cutting constraints.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>circuit</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.circuit.QuantumCircuit" title="(in Qiskit v1.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuantumCircuit</span></code></a></span>) – The circuit to cut. The input circuit may not contain gates acting
+on more than two qubits.</p></li>
+<li><p><strong>optimization</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html#circuit_knitting.cutting.OptimizationParameters" title="circuit_knitting.cutting.automated_cut_finding.OptimizationParameters"><code class="xref py py-class docutils literal notranslate"><span class="pre">OptimizationParameters</span></code></a></span>) – Options for controlling optimizer behavior. Currently, the optimal
+cuts are chosen using Dijkstra’s best-first search algorithm.</p></li>
+<li><p><strong>constraints</strong> (<span class="sphinx_autodoc_typehints-type"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html#circuit_knitting.cutting.DeviceConstraints" title="circuit_knitting.cutting.automated_cut_finding.DeviceConstraints"><code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceConstraints</span></code></a></span>) – Constraints on how the circuit may be partitioned</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.circuit.QuantumCircuit" title="(in Qiskit v1.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuantumCircuit</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>]]</span></p>
+</dd>
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><p>A circuit containing <a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html#circuit_knitting.cutting.qpd.BaseQPDGate" title="circuit_knitting.cutting.qpd.BaseQPDGate"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseQPDGate</span></code></a> instances. The subcircuits
+resulting from cutting these gates will be runnable on the devices meeting
+the <code class="docutils literal notranslate"><span class="pre">constraints</span></code>.</p>
+<dl class="simple">
+<dt>A metadata dictionary:</dt><dd><ul class="simple">
+<li><p>cuts: A list of length-2 tuples describing each cut in the output circuit.
+The tuples are formatted as <code class="docutils literal notranslate"><span class="pre">(cut_type:</span> <span class="pre">str,</span> <span class="pre">cut_id:</span> <span class="pre">int)</span></code>. The
+cut ID is the index of the cut gate or wire in the output circuit’s
+<code class="docutils literal notranslate"><span class="pre">data</span></code> field.</p></li>
+<li><p>sampling_overhead: The sampling overhead incurred from cutting the specified
+gates and wires.</p></li>
+</ul>
+</dd>
+</dl>
+</p>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – The input circuit contains a gate acting on more than 2 qubits.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
+        </article>
+      </div>
+      <footer>
+        <div class="related-pages">
+          <a class="next-page" href="circuit_knitting.cutting.OptimizationParameters.html">
+              <div class="page-info">
+                <div class="context">
+                  <span>Next</span>
+                </div>
+                <div class="title">circuit_knitting.cutting.OptimizationParameters</div>
+              </div>
+              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
+            </a>
+          <a class="prev-page" href="circuit_knitting.cutting.instructions.Move.html">
+              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
+              <div class="page-info">
+                <div class="context">
+                  <span>Previous</span>
+                </div>
+                
+                <div class="title">circuit_knitting.cutting.instructions.Move</div>
+                
+              </div>
+            </a>
+        </div>
+        <div class="bottom-of-page">
+          <div class="left-details">
+            <div class="copyright">
+                Copyright &#169; 2023
+            </div>
+            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
+            
+            <a href="https://github.com/pradyunsg/furo">Furo</a>
+            
+          </div>
+          <div class="right-details">
+            <div class="icons">
+              <a class="muted-link " href="https://github.com/Qiskit-Extensions/circuit-knitting-toolbox" aria-label="GitHub">
+                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
+                </svg>
+            </a>
+              
+            </div>
+          </div>
+        </div>
+        
+      </footer>
+    </div>
+    <aside class="toc-drawer">
+      
+      
+      <div class="toc-sticky toc-scroll">
+        <div class="toc-title-container">
+          <span class="toc-title">
+            On this page
+          </span>
+        </div>
+        <div class="toc-tree-container">
+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">circuit_knitting.cutting.find_cuts</a><ul>
+<li><a class="reference internal" href="#circuit_knitting.cutting.find_cuts"><code class="docutils literal notranslate"><span class="pre">find_cuts()</span></code></a></li>
+</ul>
+</li>
+</ul>
+
+          </div>
+        </div>
+      </div>
+      
+      
+    </aside>
+  </div>
+</div><script src="../_static/jquery.js?v=5d32c60e"></script>
+    <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
+    <script src="../_static/doctools.js?v=9a2dae69"></script>
+    <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
+    <script src="../_static/copybutton.js?v=f281be69"></script>
+    <script src="../_static/scripts/qiskit-sphinx-theme.js?v=4d77b8ca"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/@jupyter-widgets/html-manager@^1.0.1/dist/embed-amd.js"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    </body>
+</html>
\ No newline at end of file
diff --git a/stubs/circuit_knitting.cutting.generate_cutting_experiments.html b/stubs/circuit_knitting.cutting.generate_cutting_experiments.html
index dbc869140..5c9d7f8a5 100644
--- a/stubs/circuit_knitting.cutting.generate_cutting_experiments.html
+++ b/stubs/circuit_knitting.cutting.generate_cutting_experiments.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.reconstruct_expectation_values" href="circuit_knitting.cutting.reconstruct_expectation_values.html" /><link rel="prev" title="circuit_knitting.cutting.cut_gates" href="circuit_knitting.cutting.cut_gates.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.generate_cutting_experiments - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.generate_cutting_experiments - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -439,7 +443,7 @@ <h1>circuit_knitting.cutting.generate_cutting_experiments<a class="headerlink" h
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.instructions.CutWire.html b/stubs/circuit_knitting.cutting.instructions.CutWire.html
index 6791a57bc..2717b2258 100644
--- a/stubs/circuit_knitting.cutting.instructions.CutWire.html
+++ b/stubs/circuit_knitting.cutting.instructions.CutWire.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.instructions.Move" href="circuit_knitting.cutting.instructions.Move.html" /><link rel="prev" title="circuit_knitting.cutting.PartitionedCuttingProblem" href="circuit_knitting.cutting.PartitionedCuttingProblem.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.instructions.CutWire - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.instructions.CutWire - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3 current current-page"><a class="current reference internal" href="#">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -408,7 +412,7 @@ <h1>circuit_knitting.cutting.instructions.CutWire<a class="headerlink" href="#ci
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.instructions.Move.html b/stubs/circuit_knitting.cutting.instructions.Move.html
index 84a8d1d29..46f868c9b 100644
--- a/stubs/circuit_knitting.cutting.instructions.Move.html
+++ b/stubs/circuit_knitting.cutting.instructions.Move.html
@@ -3,10 +3,10 @@
   <head><meta charset="utf-8"/>
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
-<link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.qpd.QPDBasis" href="circuit_knitting.cutting.qpd.QPDBasis.html" /><link rel="prev" title="circuit_knitting.cutting.instructions.CutWire" href="circuit_knitting.cutting.instructions.CutWire.html" />
+<link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.find_cuts" href="circuit_knitting.cutting.find_cuts.html" /><link rel="prev" title="circuit_knitting.cutting.instructions.CutWire" href="circuit_knitting.cutting.instructions.CutWire.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.instructions.Move - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.instructions.Move - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3 current current-page"><a class="current reference internal" href="#">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -379,12 +383,12 @@ <h1>circuit_knitting.cutting.instructions.Move<a class="headerlink" href="#circu
       </div>
       <footer>
         <div class="related-pages">
-          <a class="next-page" href="circuit_knitting.cutting.qpd.QPDBasis.html">
+          <a class="next-page" href="circuit_knitting.cutting.find_cuts.html">
               <div class="page-info">
                 <div class="context">
                   <span>Next</span>
                 </div>
-                <div class="title">circuit_knitting.cutting.qpd.QPDBasis</div>
+                <div class="title">circuit_knitting.cutting.find_cuts</div>
               </div>
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
             </a>
@@ -451,7 +455,7 @@ <h1>circuit_knitting.cutting.instructions.Move<a class="headerlink" href="#circu
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.partition_circuit_qubits.html b/stubs/circuit_knitting.cutting.partition_circuit_qubits.html
index 2f3cced82..47db9d85b 100644
--- a/stubs/circuit_knitting.cutting.partition_circuit_qubits.html
+++ b/stubs/circuit_knitting.cutting.partition_circuit_qubits.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.partition_problem" href="circuit_knitting.cutting.partition_problem.html" /><link rel="prev" title="circuit_knitting.cutting.expand_observables" href="circuit_knitting.cutting.expand_observables.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.partition_circuit_qubits - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.partition_circuit_qubits - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -414,7 +418,7 @@ <h1>circuit_knitting.cutting.partition_circuit_qubits<a class="headerlink" href=
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.partition_problem.html b/stubs/circuit_knitting.cutting.partition_problem.html
index 6987676a9..f6b552a0f 100644
--- a/stubs/circuit_knitting.cutting.partition_problem.html
+++ b/stubs/circuit_knitting.cutting.partition_problem.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.cut_gates" href="circuit_knitting.cutting.cut_gates.html" /><link rel="prev" title="circuit_knitting.cutting.partition_circuit_qubits" href="circuit_knitting.cutting.partition_circuit_qubits.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.partition_problem - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.partition_problem - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -427,7 +431,7 @@ <h1>circuit_knitting.cutting.partition_problem<a class="headerlink" href="#circu
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html b/stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html
index 8eadd82c1..3e3eed551 100644
--- a/stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html
+++ b/stubs/circuit_knitting.cutting.qpd.BaseQPDGate.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.qpd.SingleQubitQPDGate" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html" /><link rel="prev" title="circuit_knitting.cutting.qpd.QPDBasis" href="circuit_knitting.cutting.qpd.QPDBasis.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.qpd.BaseQPDGate - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.qpd.BaseQPDGate - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3 current current-page"><a class="current reference internal" href="#">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -426,7 +430,7 @@ <h1>circuit_knitting.cutting.qpd.BaseQPDGate<a class="headerlink" href="#circuit
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.qpd.QPDBasis.html b/stubs/circuit_knitting.cutting.qpd.QPDBasis.html
index 70e2a964b..2b491e351 100644
--- a/stubs/circuit_knitting.cutting.qpd.QPDBasis.html
+++ b/stubs/circuit_knitting.cutting.qpd.QPDBasis.html
@@ -3,10 +3,10 @@
   <head><meta charset="utf-8"/>
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
-<link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.qpd.BaseQPDGate" href="circuit_knitting.cutting.qpd.BaseQPDGate.html" /><link rel="prev" title="circuit_knitting.cutting.instructions.Move" href="circuit_knitting.cutting.instructions.Move.html" />
+<link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.qpd.BaseQPDGate" href="circuit_knitting.cutting.qpd.BaseQPDGate.html" /><link rel="prev" title="circuit_knitting.cutting.DeviceConstraints" href="circuit_knitting.cutting.DeviceConstraints.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.qpd.QPDBasis - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.qpd.QPDBasis - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3 current current-page"><a class="current reference internal" href="#">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -382,14 +386,14 @@ <h1>circuit_knitting.cutting.qpd.QPDBasis<a class="headerlink" href="#circuit-kn
               </div>
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
             </a>
-          <a class="prev-page" href="circuit_knitting.cutting.instructions.Move.html">
+          <a class="prev-page" href="circuit_knitting.cutting.DeviceConstraints.html">
               <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
               <div class="page-info">
                 <div class="context">
                   <span>Previous</span>
                 </div>
                 
-                <div class="title">circuit_knitting.cutting.instructions.Move</div>
+                <div class="title">circuit_knitting.cutting.DeviceConstraints</div>
                 
               </div>
             </a>
@@ -445,7 +449,7 @@ <h1>circuit_knitting.cutting.qpd.QPDBasis<a class="headerlink" href="#circuit-kn
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html b/stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html
index 248d7a37d..3e042e3ea 100644
--- a/stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html
+++ b/stubs/circuit_knitting.cutting.qpd.SingleQubitQPDGate.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.qpd.TwoQubitQPDGate" href="circuit_knitting.cutting.qpd.TwoQubitQPDGate.html" /><link rel="prev" title="circuit_knitting.cutting.qpd.BaseQPDGate" href="circuit_knitting.cutting.qpd.BaseQPDGate.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.qpd.SingleQubitQPDGate - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.qpd.SingleQubitQPDGate - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3 current current-page"><a class="current reference internal" href="#">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -423,7 +427,7 @@ <h1>circuit_knitting.cutting.qpd.SingleQubitQPDGate<a class="headerlink" href="#
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.qpd.TwoQubitQPDGate.html b/stubs/circuit_knitting.cutting.qpd.TwoQubitQPDGate.html
index f98c17927..d0db82a62 100644
--- a/stubs/circuit_knitting.cutting.qpd.TwoQubitQPDGate.html
+++ b/stubs/circuit_knitting.cutting.qpd.TwoQubitQPDGate.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.qpd.WeightType" href="circuit_knitting.cutting.qpd.WeightType.html" /><link rel="prev" title="circuit_knitting.cutting.qpd.SingleQubitQPDGate" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.qpd.TwoQubitQPDGate - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.qpd.TwoQubitQPDGate - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -416,7 +420,7 @@ <h1>circuit_knitting.cutting.qpd.TwoQubitQPDGate<a class="headerlink" href="#cir
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.qpd.WeightType.html b/stubs/circuit_knitting.cutting.qpd.WeightType.html
index 3535323a8..23f532eb0 100644
--- a/stubs/circuit_knitting.cutting.qpd.WeightType.html
+++ b/stubs/circuit_knitting.cutting.qpd.WeightType.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.qpd.generate_qpd_weights" href="circuit_knitting.cutting.qpd.generate_qpd_weights.html" /><link rel="prev" title="circuit_knitting.cutting.qpd.TwoQubitQPDGate" href="circuit_knitting.cutting.qpd.TwoQubitQPDGate.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.qpd.WeightType - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.qpd.WeightType - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -307,7 +311,7 @@
 <h1>circuit_knitting.cutting.qpd.WeightType<a class="headerlink" href="#circuit-knitting-cutting-qpd-weighttype" title="Link to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="circuit_knitting.cutting.qpd.WeightType">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">WeightType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/cutting/qpd/qpd.html#WeightType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.cutting.qpd.WeightType" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">WeightType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/cutting/qpd/weights.html#WeightType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.cutting.qpd.WeightType" title="Link to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/enum.html#enum.Enum" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Enum</span></code></a></p>
 <p>Type of weight associated with a QPD sample.</p>
 <p class="rubric">Attributes</p>
@@ -403,7 +407,7 @@ <h1>circuit_knitting.cutting.qpd.WeightType<a class="headerlink" href="#circuit-
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.html b/stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.html
index ebf2c15c0..a05e05905 100644
--- a/stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.html
+++ b/stubs/circuit_knitting.cutting.qpd.decompose_qpd_instructions.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.qpd.qpdbasis_from_instruction" href="circuit_knitting.cutting.qpd.qpdbasis_from_instruction.html" /><link rel="prev" title="circuit_knitting.cutting.qpd.generate_qpd_weights" href="circuit_knitting.cutting.qpd.generate_qpd_weights.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.qpd.decompose_qpd_instructions - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.qpd.decompose_qpd_instructions - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -307,7 +311,7 @@
 <h1>circuit_knitting.cutting.qpd.decompose_qpd_instructions<a class="headerlink" href="#circuit-knitting-cutting-qpd-decompose-qpd-instructions" title="Link to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="circuit_knitting.cutting.qpd.decompose_qpd_instructions">
-<span class="sig-name descname"><span class="pre">decompose_qpd_instructions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">circuit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">instruction_ids</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/cutting/qpd/qpd.html#decompose_qpd_instructions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.cutting.qpd.decompose_qpd_instructions" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">decompose_qpd_instructions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">circuit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">instruction_ids</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/cutting/qpd/decompose.html#decompose_qpd_instructions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.cutting.qpd.decompose_qpd_instructions" title="Link to this definition">¶</a></dt>
 <dd><p>Replace all QPD instructions in the circuit with local Qiskit operations and measurements.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -422,7 +426,7 @@ <h1>circuit_knitting.cutting.qpd.decompose_qpd_instructions<a class="headerlink"
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.html b/stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.html
index b46a576da..cf518d185 100644
--- a/stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.html
+++ b/stubs/circuit_knitting.cutting.qpd.generate_qpd_weights.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.qpd.decompose_qpd_instructions" href="circuit_knitting.cutting.qpd.decompose_qpd_instructions.html" /><link rel="prev" title="circuit_knitting.cutting.qpd.WeightType" href="circuit_knitting.cutting.qpd.WeightType.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.qpd.generate_qpd_weights - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.qpd.generate_qpd_weights - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -307,7 +311,7 @@
 <h1>circuit_knitting.cutting.qpd.generate_qpd_weights<a class="headerlink" href="#circuit-knitting-cutting-qpd-generate-qpd-weights" title="Link to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="circuit_knitting.cutting.qpd.generate_qpd_weights">
-<span class="sig-name descname"><span class="pre">generate_qpd_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qpd_bases</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/cutting/qpd/qpd.html#generate_qpd_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.cutting.qpd.generate_qpd_weights" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">generate_qpd_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qpd_bases</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/cutting/qpd/weights.html#generate_qpd_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.cutting.qpd.generate_qpd_weights" title="Link to this definition">¶</a></dt>
 <dd><p>Generate weights from the joint quasiprobability distribution.</p>
 <p>Each weight whose absolute value is above a threshold of <code class="docutils literal notranslate"><span class="pre">1</span> <span class="pre">/</span>
 <span class="pre">num_samples</span></code> will be evaluated exactly. The remaining weights – those in
@@ -321,7 +325,7 @@ <h1>circuit_knitting.cutting.qpd.generate_qpd_weights<a class="headerlink" href=
 </ul>
 </dd>
 <dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/constants.html#Ellipsis" title="(in Python v3.12)"><code class="xref py py-data docutils literal notranslate"><span class="pre">...</span></code></a>], <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, <a class="reference internal" href="circuit_knitting.cutting.qpd.WeightType.html#circuit_knitting.cutting.qpd.WeightType" title="circuit_knitting.cutting.qpd.qpd.WeightType"><code class="xref py py-class docutils literal notranslate"><span class="pre">WeightType</span></code></a>]]</span></p>
+<dd class="field-even"><p><span class="sphinx_autodoc_typehints-type"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/constants.html#Ellipsis" title="(in Python v3.12)"><code class="xref py py-data docutils literal notranslate"><span class="pre">...</span></code></a>], <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, <a class="reference internal" href="circuit_knitting.cutting.qpd.WeightType.html#circuit_knitting.cutting.qpd.WeightType" title="circuit_knitting.cutting.qpd.weights.WeightType"><code class="xref py py-class docutils literal notranslate"><span class="pre">WeightType</span></code></a>]]</span></p>
 </dd>
 <dt class="field-odd">Returns<span class="colon">:</span></dt>
 <dd class="field-odd"><p><p>A mapping from a given decomposition to its weight.
@@ -413,7 +417,7 @@ <h1>circuit_knitting.cutting.qpd.generate_qpd_weights<a class="headerlink" href=
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction.html b/stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction.html
index 8eda5bd58..06a4fe2e5 100644
--- a/stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction.html
+++ b/stubs/circuit_knitting.cutting.qpd.qpdbasis_from_instruction.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.cutqc.run_subcircuit_instances" href="circuit_knitting.cutting.cutqc.run_subcircuit_instances.html" /><link rel="prev" title="circuit_knitting.cutting.qpd.decompose_qpd_instructions" href="circuit_knitting.cutting.qpd.decompose_qpd_instructions.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.qpd.qpdbasis_from_instruction - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.qpd.qpdbasis_from_instruction - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -307,7 +311,7 @@
 <h1>circuit_knitting.cutting.qpd.qpdbasis_from_instruction<a class="headerlink" href="#circuit-knitting-cutting-qpd-qpdbasis-from-instruction" title="Link to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="circuit_knitting.cutting.qpd.qpdbasis_from_instruction">
-<span class="sig-name descname"><span class="pre">qpdbasis_from_instruction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gate</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/cutting/qpd/qpd.html#qpdbasis_from_instruction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.cutting.qpd.qpdbasis_from_instruction" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">qpdbasis_from_instruction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gate</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/cutting/qpd/decompositions.html#qpdbasis_from_instruction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.cutting.qpd.qpdbasis_from_instruction" title="Link to this definition">¶</a></dt>
 <dd><p>Generate a <a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html#circuit_knitting.cutting.qpd.QPDBasis" title="circuit_knitting.cutting.qpd.QPDBasis"><code class="xref py py-class docutils literal notranslate"><span class="pre">QPDBasis</span></code></a> object, given a supported operation.</p>
 <p>All two-qubit gates which implement the <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.circuit.Gate#to_matrix" title="(in Qiskit v1.0)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">to_matrix()</span></code></a> method are
 supported.  This should include the vast majority of gates with no unbound
@@ -409,7 +413,7 @@ <h1>circuit_knitting.cutting.qpd.qpdbasis_from_instruction<a class="headerlink"
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.cutting.reconstruct_expectation_values.html b/stubs/circuit_knitting.cutting.reconstruct_expectation_values.html
index c2bea98f0..5e68e5c12 100644
--- a/stubs/circuit_knitting.cutting.reconstruct_expectation_values.html
+++ b/stubs/circuit_knitting.cutting.reconstruct_expectation_values.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.cutting.PartitionedCuttingProblem" href="circuit_knitting.cutting.PartitionedCuttingProblem.html" /><link rel="prev" title="circuit_knitting.cutting.generate_cutting_experiments" href="circuit_knitting.cutting.generate_cutting_experiments.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.cutting.reconstruct_expectation_values - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.cutting.reconstruct_expectation_values - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -312,7 +316,7 @@ <h1>circuit_knitting.cutting.reconstruct_expectation_values<a class="headerlink"
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>results</strong> (<span class="sphinx_autodoc_typehints-type">SamplerResult | dict[Hashable, SamplerResult]</span>) – <p>The results from running the cutting subexperiments. If the cut circuit
+<li><p><strong>results</strong> (<span class="sphinx_autodoc_typehints-type">SamplerResult | PrimitiveResult | dict[Hashable, SamplerResult | PrimitiveResult]</span>) – <p>The results from running the cutting subexperiments. If the cut circuit
 was not partitioned between qubits and run separately, this argument should be
 a <a class="reference external" href="https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.SamplerResult" title="(in Qiskit v1.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">SamplerResult</span></code></a> instance or a dictionary mapping
 a single partition to the results. If the circuit was partitioned and its
@@ -430,7 +434,7 @@ <h1>circuit_knitting.cutting.reconstruct_expectation_values<a class="headerlink"
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.bitwise.bit_count.html b/stubs/circuit_knitting.utils.bitwise.bit_count.html
index fd1e87c77..786b6cd52 100644
--- a/stubs/circuit_knitting.utils.bitwise.bit_count.html
+++ b/stubs/circuit_knitting.utils.bitwise.bit_count.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.conversion.quasi_to_real" href="circuit_knitting.utils.conversion.quasi_to_real.html" /><link rel="prev" title="Utilities" href="../apidocs/utils.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.bitwise.bit_count - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.bitwise.bit_count - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -389,7 +393,7 @@ <h1>circuit_knitting.utils.bitwise.bit_count<a class="headerlink" href="#circuit
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.conversion.dict_to_array.html b/stubs/circuit_knitting.utils.conversion.dict_to_array.html
index 736d4300a..ff997e64a 100644
--- a/stubs/circuit_knitting.utils.conversion.dict_to_array.html
+++ b/stubs/circuit_knitting.utils.conversion.dict_to_array.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.iteration.unique_by_id" href="circuit_knitting.utils.iteration.unique_by_id.html" /><link rel="prev" title="circuit_knitting.utils.conversion.naive_probability_distribution" href="circuit_knitting.utils.conversion.naive_probability_distribution.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.conversion.dict_to_array - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.conversion.dict_to_array - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -309,6 +313,9 @@ <h1>circuit_knitting.utils.conversion.dict_to_array<a class="headerlink" href="#
 <dt class="sig sig-object py" id="circuit_knitting.utils.conversion.dict_to_array">
 <span class="sig-name descname"><span class="pre">dict_to_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distribution_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_prob</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/utils/conversion.html#dict_to_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.utils.conversion.dict_to_array" title="Link to this definition">¶</a></dt>
 <dd><p>Convert dictionary of shot results to array of distribution.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.utils.conversion.dict_to_array()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0.</p>
+</div>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -401,7 +408,7 @@ <h1>circuit_knitting.utils.conversion.dict_to_array<a class="headerlink" href="#
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.conversion.naive_probability_distribution.html b/stubs/circuit_knitting.utils.conversion.naive_probability_distribution.html
index 5a8ccd3ac..d9691684f 100644
--- a/stubs/circuit_knitting.utils.conversion.naive_probability_distribution.html
+++ b/stubs/circuit_knitting.utils.conversion.naive_probability_distribution.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.conversion.dict_to_array" href="circuit_knitting.utils.conversion.dict_to_array.html" /><link rel="prev" title="circuit_knitting.utils.conversion.nearest_probability_distribution" href="circuit_knitting.utils.conversion.nearest_probability_distribution.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.conversion.naive_probability_distribution - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.conversion.naive_probability_distribution - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -312,6 +316,9 @@ <h1>circuit_knitting.utils.conversion.naive_probability_distribution<a class="he
 <p>Takes a quasiprobability distribution and does the following two steps:
 1. Update all negative probabilities to 0
 2. Normalize</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.utils.conversion.naive_probability_distribution()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0.</p>
+</div>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>quasiprobability</strong> – The input quasiprobabilities</p>
@@ -400,7 +407,7 @@ <h1>circuit_knitting.utils.conversion.naive_probability_distribution<a class="he
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.conversion.nearest_probability_distribution.html b/stubs/circuit_knitting.utils.conversion.nearest_probability_distribution.html
index b097e17ef..59e7c3ed2 100644
--- a/stubs/circuit_knitting.utils.conversion.nearest_probability_distribution.html
+++ b/stubs/circuit_knitting.utils.conversion.nearest_probability_distribution.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.conversion.naive_probability_distribution" href="circuit_knitting.utils.conversion.naive_probability_distribution.html" /><link rel="prev" title="circuit_knitting.utils.conversion.quasi_to_real" href="circuit_knitting.utils.conversion.quasi_to_real.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.conversion.nearest_probability_distribution - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.conversion.nearest_probability_distribution - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -313,6 +317,9 @@ <h1>circuit_knitting.utils.conversion.nearest_probability_distribution<a class="
 it to the closest probability distribution as defined by
 the L2-norm.</p>
 <p>Method from Smolin et al., Phys. Rev. Lett. 108, 070502 (2012).</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.utils.conversion.nearest_probability_distribution()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0.</p>
+</div>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>quasiprobability</strong> – The input quasiprobabilities</p>
@@ -401,7 +408,7 @@ <h1>circuit_knitting.utils.conversion.nearest_probability_distribution<a class="
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.conversion.quasi_to_real.html b/stubs/circuit_knitting.utils.conversion.quasi_to_real.html
index fd70602b7..10c06a10a 100644
--- a/stubs/circuit_knitting.utils.conversion.quasi_to_real.html
+++ b/stubs/circuit_knitting.utils.conversion.quasi_to_real.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.conversion.nearest_probability_distribution" href="circuit_knitting.utils.conversion.nearest_probability_distribution.html" /><link rel="prev" title="circuit_knitting.utils.bitwise.bit_count" href="circuit_knitting.utils.bitwise.bit_count.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.conversion.quasi_to_real - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.conversion.quasi_to_real - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -309,6 +313,9 @@ <h1>circuit_knitting.utils.conversion.quasi_to_real<a class="headerlink" href="#
 <dt class="sig sig-object py" id="circuit_knitting.utils.conversion.quasi_to_real">
 <span class="sig-name descname"><span class="pre">quasi_to_real</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">quasiprobability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/circuit_knitting/utils/conversion.html#quasi_to_real"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#circuit_knitting.utils.conversion.quasi_to_real" title="Link to this definition">¶</a></dt>
 <dd><p>Convert a quasi probability to a valid probability distribution.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.utils.conversion.quasi_to_real()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0.</p>
+</div>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
@@ -400,7 +407,7 @@ <h1>circuit_knitting.utils.conversion.quasi_to_real<a class="headerlink" href="#
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.iteration.unique_by_eq.html b/stubs/circuit_knitting.utils.iteration.unique_by_eq.html
index 6ece6d2e6..8a3c57cba 100644
--- a/stubs/circuit_knitting.utils.iteration.unique_by_eq.html
+++ b/stubs/circuit_knitting.utils.iteration.unique_by_eq.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.metrics.chi2_distance" href="circuit_knitting.utils.metrics.chi2_distance.html" /><link rel="prev" title="circuit_knitting.utils.iteration.unique_by_id" href="circuit_knitting.utils.iteration.unique_by_id.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.iteration.unique_by_eq - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.iteration.unique_by_eq - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -406,7 +410,7 @@ <h1>circuit_knitting.utils.iteration.unique_by_eq<a class="headerlink" href="#ci
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.iteration.unique_by_id.html b/stubs/circuit_knitting.utils.iteration.unique_by_id.html
index 4887d561d..83a681706 100644
--- a/stubs/circuit_knitting.utils.iteration.unique_by_id.html
+++ b/stubs/circuit_knitting.utils.iteration.unique_by_id.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.iteration.unique_by_eq" href="circuit_knitting.utils.iteration.unique_by_eq.html" /><link rel="prev" title="circuit_knitting.utils.conversion.dict_to_array" href="circuit_knitting.utils.conversion.dict_to_array.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.iteration.unique_by_id - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.iteration.unique_by_id - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -399,7 +403,7 @@ <h1>circuit_knitting.utils.iteration.unique_by_id<a class="headerlink" href="#ci
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.metrics.HOP.html b/stubs/circuit_knitting.utils.metrics.HOP.html
index 94db2f9b5..9c5b2b072 100644
--- a/stubs/circuit_knitting.utils.metrics.HOP.html
+++ b/stubs/circuit_knitting.utils.metrics.HOP.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem" href="circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.html" /><link rel="prev" title="circuit_knitting.utils.metrics.cross_entropy" href="circuit_knitting.utils.metrics.cross_entropy.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.metrics.HOP - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.metrics.HOP - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -311,6 +315,9 @@ <h1>circuit_knitting.utils.metrics.HOP<a class="headerlink" href="#circuit-knitt
 <dd><p>Compute the Heavy Output Probability (HOP).</p>
 <p>The HOP is an important metric for quantum volume experiments and is defined at the
 probability that one measures a bitstring above the median target probability.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.utils.metrics.HOP()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0.</p>
+</div>
 <p>Example:
 &gt;&gt;&gt; HOP(np.array([0.1, 0.1, 0.3, 0.5]), np.array([0.25, 0.25, 0.25, 0.25]))
 0.5</p>
@@ -405,7 +412,7 @@ <h1>circuit_knitting.utils.metrics.HOP<a class="headerlink" href="#circuit-knitt
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.metrics.MAPE.html b/stubs/circuit_knitting.utils.metrics.MAPE.html
index 13e9e5e6b..be2e53fec 100644
--- a/stubs/circuit_knitting.utils.metrics.MAPE.html
+++ b/stubs/circuit_knitting.utils.metrics.MAPE.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.metrics.cross_entropy" href="circuit_knitting.utils.metrics.cross_entropy.html" /><link rel="prev" title="circuit_knitting.utils.metrics.MSE" href="circuit_knitting.utils.metrics.MSE.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.metrics.MAPE - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.metrics.MAPE - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -312,6 +316,9 @@ <h1>circuit_knitting.utils.metrics.MAPE<a class="headerlink" href="#circuit-knit
 <p>The MAPE is a scaled metric in the range [0, 100] defining the percentage
 difference between two vectors via:
 $ sum_i frac{x_i - y_i}{x_i} $.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.utils.metrics.MAPE()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0.</p>
+</div>
 <p>Example:
 &gt;&gt;&gt; MAPE(np.array([0.1, 0.1, 0.3, 0.5]), np.array([0.25, 0.25, 0.25, 0.25]))
 91.66666666666659</p>
@@ -413,7 +420,7 @@ <h1>circuit_knitting.utils.metrics.MAPE<a class="headerlink" href="#circuit-knit
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.metrics.MSE.html b/stubs/circuit_knitting.utils.metrics.MSE.html
index 49866c161..02c10ee9d 100644
--- a/stubs/circuit_knitting.utils.metrics.MSE.html
+++ b/stubs/circuit_knitting.utils.metrics.MSE.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.metrics.MAPE" href="circuit_knitting.utils.metrics.MAPE.html" /><link rel="prev" title="circuit_knitting.utils.metrics.chi2_distance" href="circuit_knitting.utils.metrics.chi2_distance.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.metrics.MSE - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.metrics.MSE - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -312,6 +316,9 @@ <h1>circuit_knitting.utils.metrics.MSE<a class="headerlink" href="#circuit-knitt
 <p>The MSE is a common metric in fields such as deep learning and is used to
 measure the squared distance between two vectors via:
 $sum_i (x_i - y_i)^2$.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.utils.metrics.MSE()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0.</p>
+</div>
 <p>Example:
 &gt;&gt;&gt; MSE(np.array([0.1, 0.1, 0.3, 0.5]), np.array([0.25, 0.25, 0.25, 0.25]))
 0.0275</p>
@@ -413,7 +420,7 @@ <h1>circuit_knitting.utils.metrics.MSE<a class="headerlink" href="#circuit-knitt
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.metrics.chi2_distance.html b/stubs/circuit_knitting.utils.metrics.chi2_distance.html
index fa15f6eda..d2bd726d4 100644
--- a/stubs/circuit_knitting.utils.metrics.chi2_distance.html
+++ b/stubs/circuit_knitting.utils.metrics.chi2_distance.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.metrics.MSE" href="circuit_knitting.utils.metrics.MSE.html" /><link rel="prev" title="circuit_knitting.utils.iteration.unique_by_eq" href="circuit_knitting.utils.iteration.unique_by_eq.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.metrics.chi2_distance - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.metrics.chi2_distance - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -311,6 +315,9 @@ <h1>circuit_knitting.utils.metrics.chi2_distance<a class="headerlink" href="#cir
 <dd><p>Measure the Chi-square distance.</p>
 <p>The Chi-Square distance is a measure of statistically correlation between
 two feature vectors and is defined as $ sum_i frac{(x_i - y_i)^2}{x_i + y_i}$.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.utils.metrics.chi2_distance()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0.</p>
+</div>
 <p>Examples:
 &gt;&gt;&gt; chi2_distance(np.array([0.1, 0.1, 0.3, 0.5]), np.array([0.25, 0.25, 0.25, 0.25]))
 0.21645021645021645</p>
@@ -412,7 +419,7 @@ <h1>circuit_knitting.utils.metrics.chi2_distance<a class="headerlink" href="#cir
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.metrics.cross_entropy.html b/stubs/circuit_knitting.utils.metrics.cross_entropy.html
index a6fa57081..5852417c2 100644
--- a/stubs/circuit_knitting.utils.metrics.cross_entropy.html
+++ b/stubs/circuit_knitting.utils.metrics.cross_entropy.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.metrics.HOP" href="circuit_knitting.utils.metrics.HOP.html" /><link rel="prev" title="circuit_knitting.utils.metrics.MAPE" href="circuit_knitting.utils.metrics.MAPE.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.metrics.cross_entropy - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.metrics.cross_entropy - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -312,6 +316,9 @@ <h1>circuit_knitting.utils.metrics.cross_entropy<a class="headerlink" href="#cir
 <p>The cross entropy is a measure of the difference between two probability
 distributions, defined via:
 $ -sum_i x_i log y_i $.</p>
+<div class="deprecated">
+<p><span class="versionmodified deprecated">Deprecated since version 0.7.0: </span>The function <code class="docutils literal notranslate"><span class="pre">circuit_knitting.utils.metrics.cross_entropy()</span></code> is deprecated as of circuit-knitting-toolbox 0.7.0. It will be removed no sooner than CKT v0.8.0.</p>
+</div>
 <p>Example:
 &gt;&gt;&gt; cross_entropy(np.array([0.1, 0.1, 0.3, 0.5]), np.array([0.25, 0.25, 0.25, 0.25]))
 1.3862943611198906</p>
@@ -413,7 +420,7 @@ <h1>circuit_knitting.utils.metrics.cross_entropy<a class="headerlink" href="#cir
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.observable_grouping.CommutingObservableGroup.html b/stubs/circuit_knitting.utils.observable_grouping.CommutingObservableGroup.html
index 7a8ddb4e7..301fa058d 100644
--- a/stubs/circuit_knitting.utils.observable_grouping.CommutingObservableGroup.html
+++ b/stubs/circuit_knitting.utils.observable_grouping.CommutingObservableGroup.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.observable_grouping.ObservableCollection" href="circuit_knitting.utils.observable_grouping.ObservableCollection.html" /><link rel="prev" title="circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem" href="circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.observable_grouping.CommutingObservableGroup - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.observable_grouping.CommutingObservableGroup - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -426,7 +430,7 @@ <h1>circuit_knitting.utils.observable_grouping.CommutingObservableGroup<a class=
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.observable_grouping.ObservableCollection.html b/stubs/circuit_knitting.utils.observable_grouping.ObservableCollection.html
index ddd22af01..d1d40f3a3 100644
--- a/stubs/circuit_knitting.utils.observable_grouping.ObservableCollection.html
+++ b/stubs/circuit_knitting.utils.observable_grouping.ObservableCollection.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.simulation.simulate_statevector_outcomes" href="circuit_knitting.utils.simulation.simulate_statevector_outcomes.html" /><link rel="prev" title="circuit_knitting.utils.observable_grouping.CommutingObservableGroup" href="circuit_knitting.utils.observable_grouping.CommutingObservableGroup.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.observable_grouping.ObservableCollection - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.observable_grouping.ObservableCollection - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -438,7 +442,7 @@ <h1>circuit_knitting.utils.observable_grouping.ObservableCollection<a class="hea
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.html b/stubs/circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.html
index 4df42cc33..730dbeaf1 100644
--- a/stubs/circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.html
+++ b/stubs/circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.observable_grouping.CommutingObservableGroup" href="circuit_knitting.utils.observable_grouping.CommutingObservableGroup.html" /><link rel="prev" title="circuit_knitting.utils.metrics.HOP" href="circuit_knitting.utils.metrics.HOP.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.observable_grouping.observables_restricted_to_subsystem - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -409,7 +413,7 @@ <h1>circuit_knitting.utils.observable_grouping.observables_restricted_to_subsyst
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.simulation.ExactSampler.html b/stubs/circuit_knitting.utils.simulation.ExactSampler.html
index c2281821e..ed8d980f3 100644
--- a/stubs/circuit_knitting.utils.simulation.ExactSampler.html
+++ b/stubs/circuit_knitting.utils.simulation.ExactSampler.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.transforms.separate_circuit" href="circuit_knitting.utils.transforms.separate_circuit.html" /><link rel="prev" title="circuit_knitting.utils.simulation.simulate_statevector_outcomes" href="circuit_knitting.utils.simulation.simulate_statevector_outcomes.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.simulation.ExactSampler - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.simulation.ExactSampler - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -454,7 +458,7 @@ <h1>circuit_knitting.utils.simulation.ExactSampler<a class="headerlink" href="#c
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.simulation.simulate_statevector_outcomes.html b/stubs/circuit_knitting.utils.simulation.simulate_statevector_outcomes.html
index 8178e785f..7d32d0715 100644
--- a/stubs/circuit_knitting.utils.simulation.simulate_statevector_outcomes.html
+++ b/stubs/circuit_knitting.utils.simulation.simulate_statevector_outcomes.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.simulation.ExactSampler" href="circuit_knitting.utils.simulation.ExactSampler.html" /><link rel="prev" title="circuit_knitting.utils.observable_grouping.ObservableCollection" href="circuit_knitting.utils.observable_grouping.ObservableCollection.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.simulation.simulate_statevector_outcomes - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.simulation.simulate_statevector_outcomes - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -396,7 +400,7 @@ <h1>circuit_knitting.utils.simulation.simulate_statevector_outcomes<a class="hea
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.transforms.SeparatedCircuits.html b/stubs/circuit_knitting.utils.transforms.SeparatedCircuits.html
index 3e2c724ba..a5013f7ef 100644
--- a/stubs/circuit_knitting.utils.transforms.SeparatedCircuits.html
+++ b/stubs/circuit_knitting.utils.transforms.SeparatedCircuits.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.transpiler_passes.RemoveFinalReset" href="circuit_knitting.utils.transpiler_passes.RemoveFinalReset.html" /><link rel="prev" title="circuit_knitting.utils.transforms.separate_circuit" href="circuit_knitting.utils.transforms.separate_circuit.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.transforms.SeparatedCircuits - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.transforms.SeparatedCircuits - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -416,7 +420,7 @@ <h1>circuit_knitting.utils.transforms.SeparatedCircuits<a class="headerlink" hre
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.transforms.separate_circuit.html b/stubs/circuit_knitting.utils.transforms.separate_circuit.html
index beb5f6b71..70d66c8ad 100644
--- a/stubs/circuit_knitting.utils.transforms.separate_circuit.html
+++ b/stubs/circuit_knitting.utils.transforms.separate_circuit.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.transforms.SeparatedCircuits" href="circuit_knitting.utils.transforms.SeparatedCircuits.html" /><link rel="prev" title="circuit_knitting.utils.simulation.ExactSampler" href="circuit_knitting.utils.simulation.ExactSampler.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.transforms.separate_circuit - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.transforms.separate_circuit - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -434,7 +438,7 @@ <h1>circuit_knitting.utils.transforms.separate_circuit<a class="headerlink" href
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.transpiler_passes.ConsolidateResets.html b/stubs/circuit_knitting.utils.transpiler_passes.ConsolidateResets.html
index b97c1c87d..4d590b836 100644
--- a/stubs/circuit_knitting.utils.transpiler_passes.ConsolidateResets.html
+++ b/stubs/circuit_knitting.utils.transpiler_passes.ConsolidateResets.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="Release Notes" href="../release-notes.html" /><link rel="prev" title="circuit_knitting.utils.transpiler_passes.RemoveFinalReset" href="circuit_knitting.utils.transpiler_passes.RemoveFinalReset.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.transpiler_passes.ConsolidateResets - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.transpiler_passes.ConsolidateResets - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -432,7 +436,7 @@ <h1>circuit_knitting.utils.transpiler_passes.ConsolidateResets<a class="headerli
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>
diff --git a/stubs/circuit_knitting.utils.transpiler_passes.RemoveFinalReset.html b/stubs/circuit_knitting.utils.transpiler_passes.RemoveFinalReset.html
index 962ac056a..24b7945d8 100644
--- a/stubs/circuit_knitting.utils.transpiler_passes.RemoveFinalReset.html
+++ b/stubs/circuit_knitting.utils.transpiler_passes.RemoveFinalReset.html
@@ -5,8 +5,8 @@
     <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
 <link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="circuit_knitting.utils.transpiler_passes.ConsolidateResets" href="circuit_knitting.utils.transpiler_passes.ConsolidateResets.html" /><link rel="prev" title="circuit_knitting.utils.transforms.SeparatedCircuits" href="circuit_knitting.utils.transforms.SeparatedCircuits.html" />
 
-    <!-- Generated with Sphinx 7.3.6 and Furo 2024.01.29 -->
-        <title>circuit_knitting.utils.transpiler_passes.RemoveFinalReset - Circuit Knitting Toolbox 0.6.0</title>
+    <!-- Generated with Sphinx 7.3.7 and Furo 2024.01.29 -->
+        <title>circuit_knitting.utils.transpiler_passes.RemoveFinalReset - Circuit Knitting Toolbox 0.7.0</title>
       <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
     <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=82a976d7" />
     <link rel="stylesheet" type="text/css" href="../_static/styles/qiskit-sphinx-theme.css?v=fe84956c" />
@@ -145,7 +145,7 @@
       </label>
     </div>
     <div class="header-center">
-      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.6.0</div></a>
+      <a href="../index.html"><div class="brand">Circuit Knitting Toolbox 0.7.0</div></a>
     </div>
     <div class="header-right">
       <div class="theme-toggle-container theme-toggle-header">
@@ -172,7 +172,7 @@
     </div>
   </a>
   
-  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.6.0</span>
+  <span class="sidebar-brand-text">Circuit Knitting Toolbox 0.7.0</span>
 </div><form class="sidebar-search-container" method="get" action="../search.html" role="search">
   <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
   <input type="hidden" name="check_keywords" value="yes">
@@ -189,6 +189,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.html">Gate Cutting to Reduce Circuit Width</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/02_gate_cutting_to_reduce_circuit_depth.html">Gate Cutting to Reduce Circuit Depth</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/03_wire_cutting_via_move_instruction.html">Wire Cutting Phrased as a Two-Qubit <code class="docutils literal notranslate"><span class="pre">Move</span></code> Instruction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../circuit_cutting/tutorials/04_automatic_cut_finding.html">Automatically find cuts using CKT</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../circuit_cutting/explanation/index.html">Cutting Explanatory Material</a></li>
@@ -218,6 +219,9 @@
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.PartitionedCuttingProblem.html">circuit_knitting.cutting.PartitionedCuttingProblem</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.CutWire.html">circuit_knitting.cutting.instructions.CutWire</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.instructions.Move.html">circuit_knitting.cutting.instructions.Move</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.find_cuts.html">circuit_knitting.cutting.find_cuts</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.OptimizationParameters.html">circuit_knitting.cutting.OptimizationParameters</a></li>
+<li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.DeviceConstraints.html">circuit_knitting.cutting.DeviceConstraints</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.QPDBasis.html">circuit_knitting.cutting.qpd.QPDBasis</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.BaseQPDGate.html">circuit_knitting.cutting.qpd.BaseQPDGate</a></li>
 <li class="toctree-l3"><a class="reference internal" href="circuit_knitting.cutting.qpd.SingleQubitQPDGate.html">circuit_knitting.cutting.qpd.SingleQubitQPDGate</a></li>
@@ -432,7 +436,7 @@ <h1>circuit_knitting.utils.transpiler_passes.RemoveFinalReset<a class="headerlin
   </div>
 </div><script src="../_static/jquery.js?v=5d32c60e"></script>
     <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-    <script src="../_static/documentation_options.js?v=8fa8b3e9"></script>
+    <script src="../_static/documentation_options.js?v=fe7df9b0"></script>
     <script src="../_static/doctools.js?v=9a2dae69"></script>
     <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../_static/clipboard.min.js?v=a7894cd8"></script>