diff --git a/circuit_knitting/cutting/__init__.py b/circuit_knitting/cutting/__init__.py
index c05d275b3..2bf820b20 100644
--- a/circuit_knitting/cutting/__init__.py
+++ b/circuit_knitting/cutting/__init__.py
@@ -34,6 +34,7 @@
     :template: autosummary/class_no_inherited_members.rst
 
     PartitionedCuttingProblem
+    CutInfo
     CuttingExperimentResults
 
 Quasi-Probability Decomposition (QPD)
@@ -80,6 +81,7 @@
     cut_gates,
     decompose_gates,
     PartitionedCuttingProblem,
+    CutInfo,
 )
 from .cutting_evaluation import execute_experiments, CuttingExperimentResults
 from .cutting_reconstruction import reconstruct_expectation_values
@@ -92,5 +94,6 @@
     "execute_experiments",
     "reconstruct_expectation_values",
     "PartitionedCuttingProblem",
+    "CutInfo",
     "CuttingExperimentResults",
 ]
diff --git a/circuit_knitting/cutting/cutting_decomposition.py b/circuit_knitting/cutting/cutting_decomposition.py
index 971969a71..ffb8a7f28 100644
--- a/circuit_knitting/cutting/cutting_decomposition.py
+++ b/circuit_knitting/cutting/cutting_decomposition.py
@@ -28,15 +28,34 @@
 from ..utils.observable_grouping import observables_restricted_to_subsystem
 from ..utils.transforms import separate_circuit
 from .qpd.qpd_basis import QPDBasis
-from .qpd.instructions import TwoQubitQPDGate
+from .qpd.instructions import BaseQPDGate, SingleQubitQPDGate, TwoQubitQPDGate
 
 
 class PartitionedCuttingProblem(NamedTuple):
     """The result of decomposing and separating a circuit and observable(s)."""
 
-    subcircuits: dict[str | int, QuantumCircuit]
-    bases: list[QPDBasis]
-    subobservables: dict[str | int, QuantumCircuit] | None = None
+    subcircuits: dict[Hashable, QuantumCircuit]
+    cuts: list[CutInfo]
+    subobservables: dict[Hashable, QuantumCircuit] | None = None
+
+
+class CutInfo(NamedTuple):
+    """
+    The decomposition and circuit index information associated with one cut.
+
+    If the cut is associated with more than one subcircuit, the ``gates`` field should
+    be represented as a list of length-2 tuples containing the partition labels and
+    subcircuit instruction indices to the associated gates.
+
+    If the cut is associated with more than one :class:`~circuit_knitting.cutting.qpd.SingleQubitQPDGate` in an unseparated
+    circuit, the ``gates`` may be specified as a list of circuit indices to those gates.
+
+    If the cut is associated with a single :class:`~circuit_knitting.cutting.qpd.BaseQPDGate` instance in an unseparated circuit, the ``gates``
+    may be specified by a single index to the gate.
+    """
+
+    basis: QPDBasis
+    gates: list[tuple[Hashable, int]] | list[int] | int
 
 
 def partition_circuit_qubits(
@@ -195,6 +214,7 @@ def partition_problem(
         ValueError: An input observable acts on a different number of qubits than the input circuit.
         ValueError: An input observable has a phase not equal to 1.
         ValueError: The input circuit should contain no classical bits or registers.
+        ValueError: The input circuit should contain no :class:`~circuit_knitting.cutting.qpd.SingleQubitQPDGate` instances.
     """
     if len(partition_labels) != circuit.num_qubits:
         raise ValueError(
@@ -221,6 +241,10 @@ def partition_problem(
     bases = []
     i = 0
     for inst in qpd_circuit.data:
+        if isinstance(inst.operation, SingleQubitQPDGate):
+            raise ValueError(
+                "Input circuit may not contain SingleQubitQPDGate instances."
+            )
         if isinstance(inst.operation, TwoQubitQPDGate):
             bases.append(inst.operation.basis)
             inst.operation.label = inst.operation.label + f"_{i}"
@@ -228,7 +252,17 @@ def partition_problem(
 
     # Separate the decomposed circuit into its subcircuits
     qpd_circuit_dx = qpd_circuit.decompose(TwoQubitQPDGate)
-    separated_circs = separate_circuit(qpd_circuit_dx, partition_labels)
+    subcircuits = separate_circuit(qpd_circuit_dx, partition_labels).subcircuits
+
+    # Gather the basis and location information for the cuts
+    cuts_dict = defaultdict(list)
+    for label in subcircuits.keys():
+        circuit = subcircuits[label]
+        for i, inst in enumerate(circuit.data):
+            if isinstance(inst.operation, BaseQPDGate):
+                cut_num = int(inst.operation.label.split("_")[-1])
+                cuts_dict[cut_num].append((label, i))
+    cuts = [CutInfo(basis, cuts_dict[cut_num]) for cut_num, basis in enumerate(bases)]
 
     # Decompose the observables, if provided
     subobservables_by_subsystem = None
@@ -238,15 +272,15 @@ def partition_problem(
         )
 
     return PartitionedCuttingProblem(
-        separated_circs.subcircuits,  # type: ignore
-        bases,
+        subcircuits,
+        cuts,
         subobservables=subobservables_by_subsystem,
     )
 
 
 def decompose_observables(
-    observables: PauliList, partition_labels: Sequence[str | int]
-) -> dict[str | int, PauliList]:
+    observables: PauliList, partition_labels: Sequence[Hashable]
+) -> dict[Hashable, PauliList]:
     """
     Decompose a list of observables with respect to some qubit partition labels.
 
diff --git a/circuit_knitting/cutting/cutting_reconstruction.py b/circuit_knitting/cutting/cutting_reconstruction.py
index 260e2d1de..67fbcd4e6 100644
--- a/circuit_knitting/cutting/cutting_reconstruction.py
+++ b/circuit_knitting/cutting/cutting_reconstruction.py
@@ -72,7 +72,7 @@ def reconstruct_expectation_values(
         for label, subobservable in observables.items():
             if any(obs.phase != 0 for obs in subobservable):
                 raise ValueError("An input observable has a phase not equal to 1.")
-        subobservables_by_subsystem = observables
+        subobservables_by_subsystem = observables  # type: ignore
         expvals = np.zeros(len(list(observables.values())[0]))
 
     subsystem_observables = {
diff --git a/docs/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb b/docs/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb
index b667736b2..de20f76b1 100644
--- a/docs/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb
+++ b/docs/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb
@@ -51,7 +51,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 831.997x294.311 with 1 Axes>"
       ]
@@ -63,7 +63,7 @@
    ],
    "source": [
     "circuit = EfficientSU2(4, entanglement=\"linear\", reps=2).decompose()\n",
-    "circuit.assign_parameters([0.8] * len(circuit.parameters), inplace=True)\n",
+    "circuit.assign_parameters([0.2] * len(circuit.parameters), inplace=True)\n",
     "\n",
     "circuit.draw(\"mpl\", scale=0.8)"
    ]
@@ -105,7 +105,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "subcircuits, bases, subobservables = partition_problem(\n",
+    "subcircuits, cuts, subobservables = partition_problem(\n",
     "    circuit=circuit, partition_labels=\"AABB\", observables=observables\n",
     ")"
    ]
@@ -118,8 +118,8 @@
     "`partition_problem` returns:\n",
     "\n",
     "- `Dict` mapping partition labels to subcircuits\n",
-    "- `Dict` mapping partition labels to subobservables\n",
-    "- The ``QPDBasis`` instances from each gate decomposition"
+    "- `List` containing the decomposition and location information for each cut\n",
+    "- `Dict` mapping partition labels to subobservables"
    ]
   },
   {
@@ -160,7 +160,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 913.147x160.533 with 1 Axes>"
       ]
@@ -182,7 +182,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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5Qb9v1mfRraF3M49sWyUX8E8+3rBSzlYQrPfdCJcbsrfAiq0n35yl25kw/GJoY8Il8az0GbJTHVANMI+V68DeI/DOetj2s8vkxbSAgefBFd08jXewWeVz5K//d3bLxxshtWcaICYmhrlz5zJ37tx6yzdv3kxGRka9Rhqoa5abkpCQQFpaGhs3bqxrpjds2EBsbCwdOnTwR+giYhKnw3PL8Cu6wbod8PY6z/LfXQ2d2pobm4gER7t4uH8g5O2Dl5Z5lo28FH7REcKbPi1KpFEh10w3pLS0lKKiIgYPHtys7dxzzz1MmzaNvn37EhERweTJkxkzZoxXJx8Gw/tfvMJHX76Gw+HkweGz6XhGRt1j67Z+wIKPfk94WARdUjMZN+xlEyP1jt3yCYR/ZE/lzU+nNvr4u39q3lnn9/z5fPYeLmjwsSszb2f8TXOatX2rCQ+D8044jJsU0/i6VmWncWOnXAJFNcD/Wp8w7rudGXqNtN3GjR3ysUUznZOTAzT/zoeTJk3iwIEDnH/++bhcLm6++WamT5/uhwib7+gPh3hv7WxmjvucPQd3MPOd+3n+vk/rHl/48WT+cOc7tE1IY9Ir17Fj9zeWvH/9j+yWT6D8svd99L/wVwHb/p/ufp+aRi7N1aqldzc5kOCx07ixUy6BpBogJ7LbuLFLPmqmTxAeHs7MmTMteV3pbbvWcUHnAYSHRZDW9hyOlB/A5XLVTWvpkNKd8opSauPO5Hh1BTGtEk2O+NTslk+gxLVKCsjlq37ULjE9YNsW/7PTuLFTLoGkGiAnstu4sUs+tmims7KyyMrKMjuMgDpWcYjYqJ8+RFEtYimvPELsfz5YV/S8lcdeGUTLyGh6dh5I2wTv78RoBrvlIxIMdho3dspFJFjsNm7skk8Q7z4vzRETlUhZRWndvyuOHyO6ZXzdv19anMWs333Jgonfg8PBms1Lgh+kD+yWj0gw2Gnc2CkXkWCx27ixSz5qpkPEuWddSs7OVdTW1lB8IJf46OR6Vy5xOsKIjkoAICG6DUd/sMB1aU7BbvmIBIOdxo2dchEJFruNG7vkY4tpHqeDuFZJXHfJPTw8ux8Oh5Nxw2bx5dYPOVZxiIE9b+WOa6bw6JyBRIS3ICYqkVEWv5e93fIRCQY7jRs75SISLHYbN3bJJ+Ru2mIndrqwuVVyAd2wwSxWvlnDiUp/gMmLPb9PHgYJrcyLxUqfITvVAdUA84RCHbBSDQDrfI500xbjNM1DRERERMQgNdMiIiIiIgZpzrSJ/jOn3nT+iMMquUDwYrFSzlag98N3VnrP7FQHVAPMo/fEd1Z5z/wVh93y8YbmTIvIacVq8yVFJLhUA8TfNM1DRERERMQgNdMiIiIiIgapmRYRERERMUjNtIiIiIiIQWqmRUREREQMUjMtIiIiImKQmmkREREREYPUTIuIiIiIGKRmWkRERETEIDXTIiIiIiIGqZkWERERETFIzbSIiIiIiEFqpkVEREREDFIzLSIiIiJikJppERERERGDws0O4HS2cTFUlJodBUQlQI9hzduGVXIB/+QjEgx2GzdWyUc1QEKJ3caN3fLxhpppE1WUQvlBs6PwDzvlIhIsdhs3dstHJBjsNm7slo83NM1DRERERMQgNdMiIiIiIgapmRYRERERMUhzpkXE9iqrIXcvFB6CXSfM5Xt3A3RpBx3bQEq8efGJSODtKYUd+yFv30/L3voCzmoNaUlwdjtoEWFaeBLC1EyLiG3tOwort8JXO+F4zcmPr8/3/AB0SIY+XSGzAzgdQQxSRALG5YKv8mH19vpfpH/03W7PD0CLcLikE/Q/F5JjgxqmhDg10yHiuUVjWLZ+AQBOh5OkuDPo0Xkgd18/jeT4VJOj842dchFrqnXB8u/gw2+gxuXdc/IPeH7W5sItvaz3x9RO48ZOuYh17T0Ki9bCzgPerX+8Bv5vO6zNg19eCP3OAafFJsPaaezYKReLfUzkVDI69uWtp/bwxhO7ePzWf5C7ewN/XDjC7LAMsVMuYi2V1TB3Oby30ftG+kR5++C593/aW2Uldho3dspFrGdzETz/vveN9IlqamHJ1zBvJVQ1cETLbHYaO3bJRc10CAkPiyQpLoXk+FQu6NSPwZfey7cFaymvPGp2aD6zUy5iHVU18LflsL2k8XWcDoiP8vw0Np2jqgZeWQnb9gQmTqPsNG7slItYy5ZieG2VpyluiDc1ADxfqF9ZCdWNbMcsdho7dsnltGimp02bxogRI+jUqRMOh4MOHTqYHVKzHTiym1U5/8LpDMPpDDM7nGaxUy5irqVfe04wOpXYljBluOcntmXj69W64PXVcOQH/8boL3YaN3bKRcx1qAz+vhpc7sbX8bYGgOeL+Xsb/RqiX9lp7IRyLqfFnOlJkyaRlJREZmYmpaWlZodj2KYdK7jhiRjcbhfHqysAuLnfBKIiowF4+u83c1HXaxjc614Acos3MPUftzJn/AYiI5qoGEHWVC6rcxazcNmUes/Zte9bsm6cwQ2X3R/0eMX6tpfAmu/9u82KKnh7HdzTHxwWOClRNUA1QBrndsOiLxo+2bg5Vm2FC9OgU1v/btco1QHr1YHTopnOy8ujU6dOAHTv3p2ysjKTIzLm3LRLeXTUAqpqKlm56W02fP8Jd137TN3jWUNm8NCsPvTJGE5sVBIz3rmfsUNfttzggaZz6ZMxjD4Zw+r+vWbzEl77YBJXX3ynGeGKxbnd8O+vA7PtLcWQu89z2SyzqQaoBkjjtu459RQvo9zAvzfA+EH+37YRqgPWqwMhPc1j06ZNDBkyhPj4eOLi4hg6dCh79uwhNjaWUaNG1a33YyMd6lpERJGa3IWOKd0ZM+hpUpI68vKScXWPJ8enclO/h/nbe4/wv1/8jfbJXck8+0oTI25cU7mcaH9pES8tfoAnbltEy8hWQY5UQkHBQSg6HLjtr9keuG37QjVANUAatzqA4zT/ABQdCtz2faE6YL06ELLNdHZ2Nr169WLbtm08+eSTTJ06laKiIq677jrKysro0aOH2SEG3OirJ/PRV/PZVvhV3bIbL3uAgr1beGv5s/z2hhdMjM43DeUC4HK5ePbN2xl1xWN0OvMCk6ITq1u3I7Db/6bQc5UQq1ENEPEoq4RviwP7Gl/uDOz2jVIdMF9INtP79+9n5MiRZGZmsmHDBh555BHGjh1LdnY2u3btAjgtmun2bc6md7cbmP/hE3XLnE4nv+x1H5ecez0JMW1MjM43DeUC8Eb2M7RqGcfQPg1/UxUB2GXg8le+cLmts1fqRKoBIh6FhzzTMQIp0HXGKNUB84XknOnp06dz+PBh5s+fT1RUVN3y+Ph4MjMzyc7ONqWZrqmpoaTE+wlb1dXtgObdu3TEgEcYP+tyNuWt4MLOAwBwOJw4HN5/T6qurqaoaG+z4ghELpt3ruHDda8ye7xvk2H9kY+EjloX7C5NBX46Q9DpaPws/biohn8/0bHKk68GkLOjlJbV/j3fwio1wBOL+XVANUCM2lIQC8TXW9ZYHfCmBsDJdaDwkItdhbv9fodUq9QBf42bUK4DKSkphIf73ho73G53oL/M+V379u3p0qULK1asOOmxq666is2bNzfa1P54AmJ+fn6Dj7/99tvMnDmTjRs3kpyc3Oh6DSkqKiItLc3r9edN2EyHlPO9Xt9bH335OtuLvmLcsJe9Wj+/ZAu/eaF7s17T37mUVZRy/18ymTDiVXp0ucKn5/ojHwkdreLa8pu/1i+Y8VGey14Z9Yd34EhF/WVfvTudNW89ZnyjDbBKDQDr1QHVAPFFv9F/oeegB+stC0QdmP2bOKoqjhnfaAOsUgf8NW5CuQ4UFhbSvn17n54DIbhnuqSkhOLiYkaOHHnSYy6Xi5ycHHr27Gl4+4mJiYwdO5a9e/fy4osvNidUaYZ3187m0NE9zP73Q/WWX3PxndzU76FGniWnpSBds85htfsK25xqgPjCQZCuXenjER9pnlCpAyG3ZzovL48uXbowYcIE/vznP9d7bPHixQwfPpyJEyfy7LPPNvj8pvZM/2jJkiWMHz/epz3Tvk7z2PF+O6qONu/Qjj9ExlXT6frmHdqxSi7gn3wkdFTXOpix+kx8meYx4TrP7y98AEcrTl6noWkefTocoVe6f/dI2W3cWCUf1YDTz+qdcXy+K67eslNN82iqBsDJdcDpcPNgn2LC/NxP223chHI+Rqd5hNye6bS0NMLCwli5cmW95QUFBYwb55mYbtbJh+Hh4T4dHiiMgKoAxuOtiIgIQ4c1TmSVXMA/+UhoafsN7Dvh7rMu98mHZxtytMK79QDO6xBP+9T4plf0gd3GjVXyUQ04/XRzwee76i/zpg74UgPOSHCQfpb/P1d2Gzd2y8cbIddMR0ZGcscddzB//nyGDBnC4MGDKSwsZN68ebRr147i4uKTmumFCxdSUFAAeK4EUlVVxTPPeC4Knp6ezujRo4Odhoj4UVpS/WY6UK8hItZ0VuvAv4ZqgDQm5JppgJkzZxIREcHSpUv59NNP6d27N4sXL+bpp58mNzeXrl271lv/1VdfPWlP9lNPPQVA//791UyLhLie6bA+P3DbP7sdxJ7irH8RMVdiNKQnQ0EAL1/XMz1w25bQFpLNdExMDHPnzmXu3Ln1lm/evJmMjAycPztRqKGrfoiIfZx3pueP6eHywGz/8q5NryMi5upzduCa6TaxcHZKYLYtoS8km+mGlJaWUlRUxODBg5u1ndraWqqrq6mursbtdlNZWYnD4aBFixZ+itS49794hY++fA2Hw8mDw2fT8YyMusfWbf2ABR/9nvCwCLqkZvp0SSyz2C0fMY/TCddmwJuf+3/bqYmQYaHpt3YaN3bKRczXMx2yv4WSI/7f9rUX4PfrSxtlt3Fjh3xsc42XnJwcoPknHy5cuJCoqCh+9atfsWvXLqKiojjnnHP8EGHzHP3hEO+tnc0L969kwohX+evS+tfTXPjxZP5w5zvMGPsZew7msWP3NyZF6h275SPmu6QTnHuGf7fpdMCtvfH72ftG2Wnc2CkXsYbwMLill/+vlpnRHjItMsXDbuPGLvlY5E9E8/mrmR4zZgxut7vejy+XxwuUbbvWcUHnAYSHRZDW9hyOlB/A5XLVPd4hpTvlFaXUumo5Xl1BTKtEE6Ntmt3yEfM5HJ4/pEnRp17vWKXnZgx/eMfz+6kMu8izZ9oq7DRu7JSLWEd6MtzYxK0mfKkBybHwq0uDdjn7Jtlt3NglH9tM88jKyiIrK8vsMALmWMUhYqN++hBFtYilvPIIsf/5YF3R81Yee2UQLSOj6dl5IG0TvL8Toxnslo9YQ3wreOAq+Gs2HGzkzt/eXjZvaCb0Nf+gVD12Gjd2ykWs5YpuUFML/7up4ce9rQFtYiHrysavWW8Gu40bu+Rjmz3TdhcTlUhZRWndvyuOHyO65U/XvH1pcRazfvclCyZ+Dw4HazYvCX6QPrBbPmIdrWPgoUHGz7yPj4J7B8CAbn4Nyy/sNG7slItYz9Xd4e5+EGewEb64I4wf5Dmx2UrsNm7sko+a6RBx7lmXkrNzFbW1NRQfyCU+OrneVUucjjCioxIASIhuw9EfDpoUqXfslo9YS0xLuLMP/LofpHt5/dlWkZ49WhN/CeelBjY+o+w0buyUi1hTRppnPPc/F1p6eUO+jm3gNwPg9ssg2vzrDpzEbuPGLvnYZpqH3cW1SuK6S+7h4dn9cDicjBs2iy+3fsixikMM7Hkrd1wzhUfnDCQivAUxUYmMGvi42SGfkt3yEWu6IM3zU3gIthR5/runFI7XeE4qTGjluRFDxzae9SItXhHtNG7slItYV3QLz7kP118Im3ZB/n5PHThSAS4XtIiAMxKgfZLnREMrnSPRELuNG7vk43C73e6mV5NAWDsfyi3wJSu6NfS+q3nbsEou4J98RILBbuPGKvmoBkgosdu4sVs+3tA0DxERERERg9RMi4iIiIgYpGZaRERERMQgi59uY2//OUHVdP6Iwyq5gLViETkVK31W7VQHrBKHiDes8nn1Vxx2y8cbOgFRRERERMQgTfMQERERETFIzbSIiIiIiEFqpkVEREREDFIzLSIiIiJikJppERERERGD1EyLiIiIiBikZlpERERExCA10yIiIiIiBqmZFhERERExSM20iIiIiIhBaqZFRERERAxSMy0iIiIiYpCaaRERERERg9RMi4iIiIgYpGZaRERERMQgNdMiIiIiIgapmRYRERERMUjNtIiIiIiIQWqmRUREREQMUjMtIiIiImKQmmkREREREYPUTIuIiIiIGPT/LR8CNclNkhIAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 913.309x160.533 with 1 Axes>"
       ]
@@ -223,6 +223,7 @@
     }
    ],
    "source": [
+    "bases = [cut.basis for cut in cuts]\n",
     "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")"
    ]
   },
@@ -259,7 +260,7 @@
     "quasi_dists, coefficients = execute_experiments(\n",
     "    circuits=subcircuits,\n",
     "    subobservables=subobservables,\n",
-    "    num_samples=1500,\n",
+    "    num_samples=50,\n",
     "    samplers=samplers,\n",
     ")"
    ]
@@ -324,10 +325,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Simulated expectation values: [0.18906385, 0.19080251, 0.27544928, 0.43844813, 0.10740376, 0.70435834]\n",
-      "Exact expectation values: [0.17153613, 0.1815846, 0.30958691, 0.44036036, 0.08173037, 0.70623815]\n",
-      "Errors in estimation: [0.01752771, 0.00921791, -0.03413764, -0.00191223, 0.02567339, -0.00187982]\n",
-      "Relative errors in estimation: [0.10218089, 0.05076374, -0.11026835, -0.00434243, 0.31412298, -0.00266173]\n"
+      "Simulated expectation values: [0.74385685, 0.83981293, 0.85681641, 0.19941306, 0.7699222, 0.12753934]\n",
+      "Exact expectation values: [0.75617717, 0.84065011, 0.88047906, 0.20063312, 0.74926376, 0.12404645]\n",
+      "Errors in estimation: [-0.01232032, -0.00083717, -0.02366265, -0.00122005, 0.02065844, 0.00349288]\n",
+      "Relative errors in estimation: [-0.01629291, -0.00099586, -0.02687475, -0.00608102, 0.02757165, 0.02815787]\n"
      ]
     }
    ],
diff --git a/releasenotes/notes/cut-info-49e59b465b64d48f.yaml b/releasenotes/notes/cut-info-49e59b465b64d48f.yaml
new file mode 100644
index 000000000..b7d005b25
--- /dev/null
+++ b/releasenotes/notes/cut-info-49e59b465b64d48f.yaml
@@ -0,0 +1,6 @@
+---
+upgrade:
+  - |
+    Introduction of `~circuit_knitting.cutting.CutInfo` class, which contains the decomposition and gate index information for a single cut.
+
+    Removed the ``bases`` field from the :class:`~circuit_knitting.cutting.PartitionedCuttingProblem` class in favor of a ``cuts`` field. Users may now extract the :class:`~circuit_knitting.cutting.qpd.QPDBasis` instances from the ``basis`` field of each `~circuit_knitting.cutting.CutInfo` instance in ``cuts``.
diff --git a/test/cutting/test_backwards_compatibility.py b/test/cutting/test_backwards_compatibility.py
index c53b4b44d..9ac862f1e 100644
--- a/test/cutting/test_backwards_compatibility.py
+++ b/test/cutting/test_backwards_compatibility.py
@@ -51,9 +51,10 @@ def test_v0_2_cutting_width_workflow():
     circuit = EfficientSU2(4, entanglement="linear", reps=2).decompose()
     circuit.assign_parameters([0.8] * len(circuit.parameters), inplace=True)
     observables = PauliList(["ZZII", "IZZI", "IIZZ", "XIXI", "ZIZZ", "IXIX"])
-    subcircuits, bases, subobservables = partition_problem(
+    subcircuits, cuts, subobservables = partition_problem(
         circuit=circuit, partition_labels="AABB", observables=observables
     )
+    bases = [cut_info.basis for cut_info in cuts]
     assert np.prod([basis.overhead for basis in bases]) == pytest.approx(81)
 
     samplers = {
diff --git a/test/cutting/test_cutting_decomposition.py b/test/cutting/test_cutting_decomposition.py
index 362fbbc03..3e0f2867c 100644
--- a/test/cutting/test_cutting_decomposition.py
+++ b/test/cutting/test_cutting_decomposition.py
@@ -28,6 +28,7 @@
 )
 from circuit_knitting.cutting.qpd import (
     QPDBasis,
+    SingleQubitQPDGate,
     TwoQubitQPDGate,
     BaseQPDGate,
 )
@@ -170,7 +171,22 @@ def test_partition_problem(self):
             compare_obs = {"A": PauliList(["XX"]), "B": PauliList(["ZZ"])}
 
             self.assertEqual(subobservables, compare_obs)
-
+        with self.subTest("test single qubit qpd gate input"):
+            # Split 4q HWEA in middle of qubits
+            partition_labels = "AABB"
+            circuit = self.circuit.copy()
+            circuit.data.append(
+                CircuitInstruction(
+                    SingleQubitQPDGate(QPDBasis.from_gate(CXGate()), qubit_id=0),
+                    qubits=[0],
+                )
+            )
+            with pytest.raises(ValueError) as e_info:
+                partition_problem(circuit, partition_labels)
+            assert (
+                e_info.value.args[0]
+                == "Input circuit may not contain SingleQubitQPDGate instances."
+            )
         with self.subTest("test mismatching inputs"):
             # Split 4q HWEA in middle of qubits
             partition_labels = "AB"