qiskit/advanced/ignis/7_quantum_volume.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantum Volume\n",
    "---\n",
    "\n",
    "* **Last Updated:** August 6, 2019\n",
    "* **Requires:** qiskit-terra 0.8, qiskit-ignis 0.1.1, qiskit-aer 0.2\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "**Quantum Volume (QV)** is a method to verify device performance and a metric to quantify the computational power of a quantum device.  The method is based on the paper \"Validating quantum computers using randomized model circuits\" (https://arxiv.org/abs/1811.12926). \n",
    "\n",
    "This notebook gives an example for how to use the ``ignis.verification.quantum_volume`` module. This particular example shows how to run up to depth 6 quantum volume circuits and will run them using the noisy Aer simulator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import general libraries (needed for functions)\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "#Import Qiskit classes classes\n",
    "import qiskit\n",
    "from qiskit.providers.aer.noise import NoiseModel\n",
    "from qiskit.providers.aer.noise.errors.standard_errors import depolarizing_error, thermal_relaxation_error\n",
    "\n",
    "#Import the qv function.\n",
    "import qiskit.ignis.verification.quantum_volume as qv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select the Parameters of the QV Run\n",
    "\n",
    "In this example we have 6 qubits Q0,Q1,Q3,Q5,Q7,Q10. We are going to look at subsets up to the full set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Qubit list\n",
    "qubit_lists = [[0,1,3],[0,1,3,5],[0,1,3,5,7],[0,1,3,5,7,10]]\n",
    "ntrials = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate QV sequences\n",
    "\n",
    "We generate the quantum volume sequences. We start with a small example (so it doesn't take too long to run).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "qv_circs, qv_circs_nomeas = qv.qv_circuits(qubit_lists, ntrials)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pass the first trial of the nomeas through the transpiler to illustrate the circuit\n",
    "qv_circs_nomeas[0] = qiskit.compiler.transpile(qv_circs_nomeas[0], basis_gates=['u1','u2','u3','cx'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, we print the circuit corresponding to the first QV sequence. Note that the ideal circuits are run on the first n qubits (where n is the number of qubits in the subset)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         ┌────────────────────────────┐                                   »\n",
      "qr_0: |0>┤ U3(1.7877,-2.0036,-3.9633) ├───────────────────────────────────»\n",
      "         ├───────────────────────────┬┘┌───┐ ┌──────────────────────┐┌───┐»\n",
      "qr_1: |0>┤ U3(2.6017,4.6083,-1.6628) ├─┤ X ├─┤ U3(0.15983,-pi/2,pi) ├┤ X ├»\n",
      "         ├───────────────────────────┤ └─┬─┘┌┴──────────────────────┤└─┬─┘»\n",
      "qr_2: |0>┤ U3(2.0742,1.1379,-2.1532) ├───■──┤ U3(1.1421,pi/1,-pi/1) ├──■──»\n",
      "         └───────────────────────────┘      └───────────────────────┘     »\n",
      " cr_0: 0 ═════════════════════════════════════════════════════════════════»\n",
      "                                                                          »\n",
      " cr_1: 0 ═════════════════════════════════════════════════════════════════»\n",
      "                                                                          »\n",
      " cr_2: 0 ═════════════════════════════════════════════════════════════════»\n",
      "                                                                          »\n",
      "«                                                                       »\n",
      "«qr_0: ──────────────────────────────────────────────────────────────■──»\n",
      "«          ┌────────────────┐    ┌───┐┌───────────────────────────┐┌─┴─┐»\n",
      "«qr_1: ────┤ U2(pi/2,-pi/1) ├────┤ X ├┤ U3(1.7165,0.70413,3.0776) ├┤ X ├»\n",
      "«      ┌───┴────────────────┴───┐└─┬─┘├───────────────────────────┤└───┘»\n",
      "«qr_2: ┤ U3(0.21311,pi/2,-pi/1) ├──■──┤ U3(2.8987,4.5382,0.61844) ├─────»\n",
      "«      └────────────────────────┘     └───────────────────────────┘     »\n",
      "«cr_0: ═════════════════════════════════════════════════════════════════»\n",
      "«                                                                       »\n",
      "«cr_1: ═════════════════════════════════════════════════════════════════»\n",
      "«                                                                       »\n",
      "«cr_2: ═════════════════════════════════════════════════════════════════»\n",
      "«                                                                       »\n",
      "«       ┌───────────────────┐      ┌─────────────────────┐     »\n",
      "«qr_0: ─┤ U3(1.0783,pi,-pi) ├───■──┤ U3(0.14702,-pi/2,0) ├──■──»\n",
      "«      ┌┴───────────────────┴┐┌─┴─┐└──┬────────────────┬─┘┌─┴─┐»\n",
      "«qr_1: ┤ U3(0.0303,-pi/2,pi) ├┤ X ├───┤ U2(pi/2,-pi/1) ├──┤ X ├»\n",
      "«      └─────────────────────┘└───┘   └────────────────┘  └───┘»\n",
      "«qr_2: ────────────────────────────────────────────────────────»\n",
      "«                                                              »\n",
      "«cr_0: ════════════════════════════════════════════════════════»\n",
      "«                                                              »\n",
      "«cr_1: ════════════════════════════════════════════════════════»\n",
      "«                                                              »\n",
      "«cr_2: ════════════════════════════════════════════════════════»\n",
      "«                                                              »\n",
      "«      ┌───────────────────────────┐      ┌───────────────────────┐     »\n",
      "«qr_0: ┤ U3(2.8944,1.0704,-5.1004) ├───■──┤ U3(1.4527,pi/1,-pi/1) ├──■──»\n",
      "«      ├───────────────────────────┴┐┌─┴─┐├───────────────────────┤┌─┴─┐»\n",
      "«qr_1: ┤ U3(1.1185,-4.9707,-2.2819) ├┤ X ├┤ U3(0.039741,-pi/2,pi) ├┤ X ├»\n",
      "«      └────────────────────────────┘└───┘└───────────────────────┘└───┘»\n",
      "«qr_2: ─────────────────────────────────────────────────────────────────»\n",
      "«                                                                       »\n",
      "«cr_0: ═════════════════════════════════════════════════════════════════»\n",
      "«                                                                       »\n",
      "«cr_1: ═════════════════════════════════════════════════════════════════»\n",
      "«                                                                       »\n",
      "«cr_2: ═════════════════════════════════════════════════════════════════»\n",
      "«                                                                       »\n",
      "«      ┌─────────────────────┐      ┌────────────────────────────┐\n",
      "«qr_0: ┤ U3(0.84618,-pi/2,0) ├──■───┤ U3(0.74559,0.32465,4.1543) ├\n",
      "«      └──┬────────────────┬─┘┌─┴─┐┌┴────────────────────────────┤\n",
      "«qr_1: ───┤ U2(pi/2,-pi/1) ├──┤ X ├┤ U3(1.4136,-0.56678,-2.9162) ├\n",
      "«         └────────────────┘  └───┘└─────────────────────────────┘\n",
      "«qr_2: ───────────────────────────────────────────────────────────\n",
      "«                                                                 \n",
      "«cr_0: ═══════════════════════════════════════════════════════════\n",
      "«                                                                 \n",
      "«cr_1: ═══════════════════════════════════════════════════════════\n",
      "«                                                                 \n",
      "«cr_2: ═══════════════════════════════════════════════════════════\n",
      "«                                                                 \n"
     ]
    }
   ],
   "source": [
    "print(qv_circs_nomeas[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate the ideal circuits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The quantum volume method requires that we know the ideal output for each circuit, so use the statevector simulator in Aer to get the ideal result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating trial 0\n",
      "Simulating trial 1\n",
      "Simulating trial 2\n",
      "Simulating trial 3\n",
      "Simulating trial 4\n",
      "Simulating trial 5\n",
      "Simulating trial 6\n",
      "Simulating trial 7\n",
      "Simulating trial 8\n",
      "Simulating trial 9\n",
      "Simulating trial 10\n",
      "Simulating trial 11\n",
      "Simulating trial 12\n",
      "Simulating trial 13\n",
      "Simulating trial 14\n",
      "Simulating trial 15\n",
      "Simulating trial 16\n",
      "Simulating trial 17\n",
      "Simulating trial 18\n",
      "Simulating trial 19\n",
      "Simulating trial 20\n",
      "Simulating trial 21\n",
      "Simulating trial 22\n",
      "Simulating trial 23\n",
      "Simulating trial 24\n",
      "Simulating trial 25\n",
      "Simulating trial 26\n",
      "Simulating trial 27\n",
      "Simulating trial 28\n",
      "Simulating trial 29\n",
      "Simulating trial 30\n",
      "Simulating trial 31\n",
      "Simulating trial 32\n",
      "Simulating trial 33\n",
      "Simulating trial 34\n",
      "Simulating trial 35\n",
      "Simulating trial 36\n",
      "Simulating trial 37\n",
      "Simulating trial 38\n",
      "Simulating trial 39\n",
      "Simulating trial 40\n",
      "Simulating trial 41\n",
      "Simulating trial 42\n",
      "Simulating trial 43\n",
      "Simulating trial 44\n",
      "Simulating trial 45\n",
      "Simulating trial 46\n",
      "Simulating trial 47\n",
      "Simulating trial 48\n",
      "Simulating trial 49\n"
     ]
    }
   ],
   "source": [
    "#The Unitary is an identity (with a global phase)\n",
    "backend = qiskit.Aer.get_backend('statevector_simulator')\n",
    "ideal_results = []\n",
    "for trial in range(ntrials):\n",
    "    print('Simulating trial %d'%trial)\n",
    "    ideal_results.append(qiskit.execute(qv_circs_nomeas[trial], backend=backend, optimization_level=0).result())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, load the ideal results into a quantum volume fitter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "qv_fitter = qv.QVFitter(qubit_lists=qubit_lists)\n",
    "qv_fitter.add_statevectors(ideal_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the noise model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define a noise model for the simulator. To simulate decay, we add depolarizing error probabilities to the CNOT and U gates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_model = NoiseModel()\n",
    "p1Q = 0.002\n",
    "p2Q = 0.02\n",
    "noise_model.add_all_qubit_quantum_error(depolarizing_error(p1Q, 1), 'u2')\n",
    "noise_model.add_all_qubit_quantum_error(depolarizing_error(2*p1Q, 1), 'u3')\n",
    "noise_model.add_all_qubit_quantum_error(depolarizing_error(p2Q, 2), 'cx')\n",
    "#noise_model = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute on Aer simulator\n",
    "\n",
    "We can execute the QV sequences either using a Qiskit Aer Simulator (with some noise model) or using an IBMQ provider, \n",
    "and obtain a list of results, `result_list`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running trial 0\n",
      "Running trial 1\n",
      "Running trial 2\n",
      "Running trial 3\n",
      "Running trial 4\n",
      "Running trial 5\n",
      "Running trial 6\n",
      "Running trial 7\n",
      "Running trial 8\n",
      "Running trial 9\n",
      "Running trial 10\n",
      "Running trial 11\n",
      "Running trial 12\n",
      "Running trial 13\n",
      "Running trial 14\n",
      "Running trial 15\n",
      "Running trial 16\n",
      "Running trial 17\n",
      "Running trial 18\n",
      "Running trial 19\n",
      "Running trial 20\n",
      "Running trial 21\n",
      "Running trial 22\n",
      "Running trial 23\n",
      "Running trial 24\n",
      "Running trial 25\n",
      "Running trial 26\n",
      "Running trial 27\n",
      "Running trial 28\n",
      "Running trial 29\n",
      "Running trial 30\n",
      "Running trial 31\n",
      "Running trial 32\n",
      "Running trial 33\n",
      "Running trial 34\n",
      "Running trial 35\n",
      "Running trial 36\n",
      "Running trial 37\n",
      "Running trial 38\n",
      "Running trial 39\n",
      "Running trial 40\n",
      "Running trial 41\n",
      "Running trial 42\n",
      "Running trial 43\n",
      "Running trial 44\n",
      "Running trial 45\n",
      "Running trial 46\n",
      "Running trial 47\n",
      "Running trial 48\n",
      "Running trial 49\n"
     ]
    }
   ],
   "source": [
    "backend = qiskit.Aer.get_backend('qasm_simulator')\n",
    "basis_gates = ['u1','u2','u3','cx'] # use U,CX for now\n",
    "shots = 1024\n",
    "exp_results = []\n",
    "for trial in range(ntrials):\n",
    "    print('Running trial %d'%trial)\n",
    "    exp_results.append(qiskit.execute(qv_circs[trial], basis_gates=basis_gates, backend=backend, noise_model=noise_model, backend_options={'max_parallel_experiments': 0}).result())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the experimental data into the fitter. The data will keep accumulating if this is re-run (unless the fitter is re-instantiated)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "qv_fitter.add_data(exp_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "ax = plt.gca()\n",
    "\n",
    "# Plot the essence by calling plot_rb_data\n",
    "qv_fitter.plot_qv_data(ax=ax, show_plt=False)\n",
    "\n",
    "# Add title and label\n",
    "ax.set_title('Quantum Volume for up to %d Qubits \\n and %d Trials'%(len(qubit_lists[-1]), ntrials), fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantum Volume\n",
    "List statistics for each depth. For each depth list if the depth was successful or not and with what confidence interval. For a depth to be successful the confidence interval must be > 97.5%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Width/depth 3 greater than 2/3 (0.798105) with confidence 0.989703 (successful). Quantum volume 8\n",
      "Width/depth 4 greater than 2/3 (0.710391) with confidence 0.752264 (unsuccessful).\n",
      "Width/depth 5 greater than 2/3 (0.716895) with confidence 0.784759 (unsuccessful).\n",
      "Width/depth 6 less than 2/3 (unsuccessful).\n"
     ]
    }
   ],
   "source": [
    "qv_success_list = qv_fitter.qv_success()\n",
    "qv_list = qv_fitter.ydata\n",
    "for qidx, qubit_list in enumerate(qubit_lists):\n",
    "    if qv_list[0][qidx]>2/3:\n",
    "        if qv_success_list[qidx][0]:\n",
    "            print(\"Width/depth %d greater than 2/3 (%f) with confidence %f (successful). Quantum volume %d\"%\n",
    "                  (len(qubit_list),qv_list[0][qidx],qv_success_list[qidx][1],qv_fitter.quantum_volume()[qidx]))\n",
    "        else:\n",
    "            print(\"Width/depth %d greater than 2/3 (%f) with confidence %f (unsuccessful).\"%\n",
    "                  (len(qubit_list),qv_list[0][qidx],qv_success_list[qidx][1]))\n",
    "    else:\n",
    "        print(\"Width/depth %d less than 2/3 (unsuccessful).\"%len(qubit_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T01:35:08.040110Z",
     "start_time": "2019-08-22T01:35:08.030947Z"
    }
   },
   "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>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.3</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:35:08 2019 EDT</td></tr></table>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "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, 2019.</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": []
  }
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