qiskit/advanced/aer/4_custom_gate_noise.ipynb

{
 "cells": [
  {
   "attachments": {
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     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![qiskit_header.png](attachment:qiskit_header.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Qiskit Aer: Applying noise to custom unitary gates\n",
    "\n",
    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This notebook shows how to add custom unitary gates to a quantum circuit, and use them for noise simulations in Qiskit Aer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T17:09:18.154548Z",
     "start_time": "2019-08-19T17:09:15.909907Z"
    }
   },
   "outputs": [],
   "source": [
    "from qiskit import execute, QuantumCircuit, QuantumRegister, ClassicalRegister\n",
    "from qiskit.quantum_info import Operator, average_gate_fidelity\n",
    "from qiskit.providers.aer import QasmSimulator\n",
    "from qiskit.providers.aer.noise import NoiseModel, amplitude_damping_error\n",
    "\n",
    "from qiskit.tools.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating matrix operators\n",
    "\n",
    "We can use the `Operator` class in `qiskit.quantum_info` to represent arbitrary matrix operators. If the operator is unitary it can then be added to a quantum circuit and used for simulation on Qiskit Aer.\n",
    "\n",
    "Lets create two operators below for a CNOT gate and an iSWAP gate:\n",
    "\n",
    "$$\\mbox{CNOT} = \\left(\\begin{array} \n",
    "& 1 & 0 & 0 & 0 \\\\ \n",
    "0 & 0 & 0 & 1 \\\\ \n",
    "0 & 0 & 1 & 0 \\\\ \n",
    "0 & 1 & 0 & 0\n",
    "\\end{array}\\right), \\quad\n",
    "\\mbox{iSWAP} = \\left(\\begin{array} \n",
    "& 1 & 0 & 0 & 0 \\\\ \n",
    "0 & 0 & i & 0 \\\\ \n",
    "0 & i & 0 & 0 \\\\ \n",
    "0 & 0 & 0 & 1\n",
    "\\end{array}\\right)$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T17:09:18.170527Z",
     "start_time": "2019-08-19T17:09:18.166252Z"
    }
   },
   "outputs": [],
   "source": [
    "# CNOT matrix operator with qubit-0 as control and qubit-1 as target\n",
    "cx_op = Operator([[1, 0, 0, 0],\n",
    "                  [0, 0, 0, 1],\n",
    "                  [0, 0, 1, 0],\n",
    "                  [0, 1, 0, 0]])\n",
    "\n",
    "# iSWAP matrix operator\n",
    "iswap_op = Operator([[1, 0, 0, 0],\n",
    "                     [0, 0, 1j, 0],\n",
    "                     [0, 1j, 0, 0],\n",
    "                     [0, 0, 0, 1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** The matrix is specified with respect to the tensor product $U_{b}\\otimes U_{a}$ for qubits specified by list `[a, b]`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using operators in circuits\n",
    "\n",
    "Let us demonstrate how these can be used in a circuit. We will consider an example of implementing a CNOT gate decomposed in terms of single-qubit gates and the iSWAP gate as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T17:09:55.343221Z",
     "start_time": "2019-08-19T17:09:55.332156Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ┌─────┐     ┌────────┐┌─────┐┌───┐┌─────┐┌────────┐     \n",
      "q_0: |0>┤ Sdg ├─────┤0       ├┤ Sdg ├┤ H ├┤ Sdg ├┤0       ├─────\n",
      "        ├─────┤┌───┐│  iswap │└─────┘└───┘└─────┘│  iswap │┌───┐\n",
      "q_1: |0>┤ Sdg ├┤ H ├┤1       ├───────────────────┤1       ├┤ S ├\n",
      "        └─────┘└───┘└────────┘                   └────────┘└───┘\n"
     ]
    }
   ],
   "source": [
    "# CNOT in terms of iSWAP and single-qubit gates\n",
    "cx_circ = QuantumCircuit(2)\n",
    "\n",
    "# Add gates\n",
    "cx_circ.sdg(1)\n",
    "cx_circ.h(1)\n",
    "cx_circ.sdg(0)\n",
    "cx_circ.unitary(iswap_op, [0, 1], label='iswap')\n",
    "cx_circ.sdg(0)\n",
    "cx_circ.h(0)\n",
    "cx_circ.sdg(0)\n",
    "cx_circ.unitary(iswap_op, [0, 1], label='iswap')\n",
    "cx_circ.s(1)\n",
    "\n",
    "print(cx_circ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we have assigned an optional *label* of `\"iswap\"` to the unitary when it is inserted. This allows us to identify this unitary in a Qiskit Aer `NoiseModel` so that we can add errors to these custom unitary gates in noisy circuit simulations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can confirm this circuit returns the correct output using the `Operator` class as a simulator for the circuit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T17:09:58.954826Z",
     "start_time": "2019-08-19T17:09:58.948275Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Operator([[1.+0.j 0.+0.j 0.+0.j 0.+0.j]\n",
      " [0.+0.j 0.+0.j 0.+0.j 1.+0.j]\n",
      " [0.+0.j 0.+0.j 1.+0.j 0.+0.j]\n",
      " [0.+0.j 1.+0.j 0.+0.j 0.+0.j]], input_dims=(2, 2), output_dims=(2, 2))\n"
     ]
    }
   ],
   "source": [
    "# Simulate the unitary for the circuit using Operator:\n",
    "unitary = Operator(cx_circ)\n",
    "print(unitary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And to confirm the output is correct we can compute the average gate fidelity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T17:10:01.198369Z",
     "start_time": "2019-08-19T17:10:01.184222Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Gate Fidelity: F = 1.000000\n"
     ]
    }
   ],
   "source": [
    "f_ave = average_gate_fidelity(cx_op, unitary)\n",
    "print(\"Average Gate Fidelity: F = {:f}\".format(f_ave))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a custom unitary in a noise model\n",
    "\n",
    "The Qiskit Aer `QasmSimulator` supports simulation of arbitrary unitary operators directly as specified by the `\"unitary\"` in the basis gates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T17:10:03.174651Z",
     "start_time": "2019-08-19T17:10:03.168643Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'unitary' in QasmSimulator().configuration().basis_gates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This allows us to add noise models to arbitrary unitaries in our simulation when we identify them using the optional `label` argument of `QuantumCircuit.unitary`.\n",
    "\n",
    "We will now do this by creating a `NoiseModel` that includes a quantum error channel on our custom iSWAP gate. For our example we will create a 2-qubit error consisting of two single-qubit amplitude damping channels with different damping parameters. For now we will assume all the other circuit instructions are ideal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T17:10:05.585654Z",
     "start_time": "2019-08-19T17:10:05.574669Z"
    }
   },
   "outputs": [],
   "source": [
    "# Error parameters\n",
    "param_q0 = 0.05  # damping parameter for qubit-0\n",
    "param_q1 = 0.1   # damping parameter for qubit-1\n",
    "\n",
    "# Construct the error\n",
    "qerror_q0 = amplitude_damping_error(param_q0)\n",
    "qerror_q1 = amplitude_damping_error(param_q1)\n",
    "iswap_error = qerror_q1.tensor(qerror_q0)\n",
    "\n",
    "# Build the noise model by adding the error to the \"iswap\" gate\n",
    "noise_model = NoiseModel()\n",
    "noise_model.add_all_qubit_quantum_error(iswap_error, 'iswap')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that when we add an error to a custom label such as `\"iswap\"` the `NoiseModel` does not know what gate this label is supposed to apply to, so we must manually add the desired gate string to the noise model `basis_gates`. This ensures that the compiler will unroll to the correct basis gates for the noise model simulation. This can done using the `NoiseModel.add_basis_gates` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T17:10:06.301854Z",
     "start_time": "2019-08-19T17:10:06.298595Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cx', 'id', 'u3', 'unitary']\n"
     ]
    }
   ],
   "source": [
    "noise_model.add_basis_gates(['unitary'])\n",
    "print(noise_model.basis_gates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default the basis gates of a noise model are `['cx','id','u3']` plus any standard `QasmSimulator` basis gates that are added to the noise model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating a custom unitary noise model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us first take our previous CX circuit and add an initial Hadamard gate and final measurement to create a Bell-state preparation circuit that we may simulator on the `QasmSimulator` both for the ideal and noisy case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T17:10:26.234163Z",
     "start_time": "2019-08-19T17:10:26.224218Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         ┌───┐ ┌─────┐┌────────┐┌─────┐┌───┐┌─────┐┌────────┐     ┌─┐   \n",
      "q_0: |0>─┤ H ├─┤ Sdg ├┤0       ├┤ Sdg ├┤ H ├┤ Sdg ├┤0       ├─────┤M├───\n",
      "        ┌┴───┴┐└┬───┬┘│  iswap │└─────┘└───┘└─────┘│  iswap │┌───┐└╥┘┌─┐\n",
      "q_1: |0>┤ Sdg ├─┤ H ├─┤1       ├───────────────────┤1       ├┤ S ├─╫─┤M├\n",
      "        └─────┘ └───┘ └────────┘                   └────────┘└───┘ ║ └╥┘\n",
      " c_0: 0 ═══════════════════════════════════════════════════════════╩══╬═\n",
      "                                                                      ║ \n",
      " c_1: 0 ══════════════════════════════════════════════════════════════╩═\n",
      "                                                                        \n"
     ]
    }
   ],
   "source": [
    "# Bell state circuit where iSWAPS should be inserted at barrier locations\n",
    "bell_circ = QuantumCircuit(2, 2, name='bell')\n",
    "bell_circ.h(0)\n",
    "bell_circ = bell_circ + cx_circ \n",
    "bell_circ.measure([0,1], [0,1])\n",
    "print(bell_circ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ideal output\n",
    "\n",
    "Let's first see the ideal output. Since this generates a Bell-state we expect two peaks for 00 and 11."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T17:10:28.605669Z",
     "start_time": "2019-08-19T17:10:28.467516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute on the simulator without noise\n",
    "job = execute(bell_circ, QasmSimulator(),\n",
    "              basis_gates=noise_model.basis_gates)\n",
    "ideal_result = job.result()\n",
    "ideal_counts = ideal_result.get_counts(bell_circ)\n",
    "plot_histogram(ideal_counts, title='Ideal output for iSWAP bell-state preparation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Noisy circuit execution\n",
    "\n",
    "Finally, let's now simulate it with our custom noise model. Since there is a small amplitude damping error on the two-qubit gates we expect small additional peaks for the 01 and 10 outcome probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T17:10:31.078094Z",
     "start_time": "2019-08-19T17:10:30.946144Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute on the simulator without noise\n",
    "job = execute(bell_circ, QasmSimulator(),\n",
    "              basis_gates=noise_model.basis_gates,\n",
    "              noise_model=noise_model)\n",
    "noise_result = job.result()\n",
    "noise_counts = noise_result.get_counts(bell_circ)\n",
    "plot_histogram(noise_counts, title='Noisy output for iSWAP bell-state preparation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T17:10:53.298595Z",
     "start_time": "2019-08-19T17:10:53.290949Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.12.0.dev0+c028a28</td></tr><tr><td>Aer</td><td>0.4.0</td></tr><tr><td>Ignis</td><td>0.3.0.dev0+829207f</td></tr><tr><td>Aqua</td><td>0.7.0.dev0+2776c5d</td></tr><tr><td>IBM Q Provider</td><td>0.4.6rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.3 | packaged by conda-forge | (default, Jul  1 2019, 14:38:56) \n",
       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>6</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Wed Feb 05 11:43:27 2020 EST</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2020.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}