Day2/Error mitigation on real devices.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "import matplotlib\n",
    "from qiskit import IBMQ\n",
    "\n",
    "# Import Qiskit classes\n",
    "import qiskit \n",
    "from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister, Aer\n",
    "from qiskit.providers.aer import noise\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "\n",
    "# Import measurement calibration functions\n",
    "from qiskit.ignis.mitigation.measurement import (complete_meas_cal, tensored_meas_cal,\n",
    "                                                 CompleteMeasFitter, TensoredMeasFitter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'token': 'a68ed5f01ae762a863d05960b20488a24b834174d145ba4bea63975b06d4bd534f10e0659a8ec4f79c23ff61daa64294b82d531d6cf08fcdcdba7661371f9a09', 'url': 'https://auth.quantum-computing.ibm.com/api'}\n",
      "[<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>, <AccountProvider for IBMQ(hub='ibm-q-hub-ntu', group='ntu-internal', project='default')>]\n",
      "[<IBMQSimulator('ibmq_qasm_simulator') from IBMQ(hub='ibm-q', group='open', project='main')>, <IBMQBackend('ibmqx4') from IBMQ(hub='ibm-q', group='open', project='main')>, <IBMQBackend('ibmqx2') from IBMQ(hub='ibm-q', group='open', project='main')>, <IBMQBackend('ibmq_16_melbourne') from IBMQ(hub='ibm-q', group='open', project='main')>]\n"
     ]
    }
   ],
   "source": [
    "# Load account from disk\n",
    "provider = IBMQ.load_account()\n",
    "# List the account currently in the session\n",
    "print(IBMQ.active_account())\n",
    "# List all available providers\n",
    "print(IBMQ.providers())\n",
    "# List all available providers \n",
    "print(provider.backends())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Error mitigation of Bell state on real devices\n",
    "$$ \\mid \\phi^+ \\rangle = \\frac{\\mid{00} \\rangle + \\mid{11} \\rangle}{\\sqrt{2}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 541.8x258.86 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate Bell state circuit\n",
    "qr = QuantumRegister(2) \n",
    "cr = ClassicalRegister(2) \n",
    "bell = QuantumCircuit(qr, cr)\n",
    "\n",
    "bell.h(qr[0]) \n",
    "bell.cx(qr[0], qr[1])\n",
    "\n",
    "bell.measure(qr[0],cr[0]) \n",
    "bell.measure(qr[1],cr[1])\n",
    "\n",
    "bell.draw(output='mpl') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['00', '01', '10', '11']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The qubits to be calibrated\n",
    "qubit_list = [0,1]\n",
    "# Generate calibration circuit\n",
    "meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list, qr=qr, circlabel='mcal')\n",
    "state_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (a) Bell state with open devices (ibmqx4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute the calibration circuits\n",
    "job = qiskit.execute(meas_calibs, backend = provider.get_backend('ibmqx4'), shots=1000)\n",
    "cal_results = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.951 0.094 0.294 0.027]\n",
      " [0.025 0.882 0.004 0.263]\n",
      " [0.023 0.002 0.683 0.074]\n",
      " [0.001 0.022 0.019 0.636]]\n"
     ]
    }
   ],
   "source": [
    "# Calculate the calibration matrix\n",
    "meas_fitter = CompleteMeasFitter(cal_results, state_labels, qubit_list=qubit_list, circlabel='mcal')\n",
    "print(meas_fitter.cal_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the calibration matrix\n",
    "meas_fitter.plot_calibration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute the Bell state circuits\n",
    "job = qiskit.execute(bell, backend = provider.get_backend('ibmqx4'), shots=1000)\n",
    "results = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the filter object\n",
    "meas_filter = meas_fitter.filter\n",
    "\n",
    "# Results with mitigation\n",
    "mitigated_results = meas_filter.apply(results)\n",
    "mitigated_counts = mitigated_results.get_counts(0)\n",
    "\n",
    "# Plot the results with and without error mitigation\n",
    "plot_histogram([results.get_counts(), mitigated_counts], legend=['raw', 'mitigated'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (b) Bell state with NTU-hub's 20-qubit device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "provider = IBMQ.get_provider(hub='ibm-q-hub-ntu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute the calibration circuits\n",
    "job = qiskit.execute(meas_calibs, backend = provider.get_backend('ibmq_poughkeepsie'), shots=1000)\n",
    "cal_results = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.973 0.068 0.061 0.005]\n",
      " [0.009 0.92  0.001 0.053]\n",
      " [0.018 0.001 0.93  0.048]\n",
      " [0.    0.011 0.008 0.894]]\n"
     ]
    }
   ],
   "source": [
    "# Calculate the calibration matrix\n",
    "meas_fitter = CompleteMeasFitter(cal_results, state_labels, qubit_list=qubit_list, circlabel='mcal')\n",
    "print(meas_fitter.cal_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the calibration matrix\n",
    "meas_fitter.plot_calibration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute the Bell state circuits\n",
    "job = qiskit.execute(bell, backend = provider.get_backend('ibmq_poughkeepsie'), shots=1000)\n",
    "results = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the filter object\n",
    "meas_filter = meas_fitter.filter\n",
    "\n",
    "# Results with mitigation\n",
    "mitigated_results = meas_filter.apply(results)\n",
    "mitigated_counts = mitigated_results.get_counts(0)\n",
    "\n",
    "# Plot the results with and without error mitigation\n",
    "plot_histogram([results.get_counts(), mitigated_counts], legend=['raw', 'mitigated'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Error mitigation on GHZ state on real devices\n",
    "\n",
    "As an example, we start with the 3-qubit GHZ state on the qubits Q2,Q3,Q4:\n",
    "\n",
    "$$ \\mid GHZ \\rangle = \\frac{\\mid{000} \\rangle + \\mid{111} \\rangle}{\\sqrt{2}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 722.4x379.26 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a 3Q GHZ state\n",
    "qr = QuantumRegister(3)\n",
    "cr = ClassicalRegister(3)\n",
    "\n",
    "ghz = QuantumCircuit(qr, cr)\n",
    "ghz.h(qr[0])\n",
    "ghz.cx(qr[0], qr[1])\n",
    "ghz.cx(qr[1], qr[2])\n",
    "ghz.measure(qr[0],cr[0])\n",
    "ghz.measure(qr[1],cr[1])\n",
    "ghz.measure(qr[2],cr[2])\n",
    "ghz.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['000', '001', '010', '011', '100', '101', '110', '111']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The qubits to be calibrated\n",
    "qubit_list = [0,1,2]\n",
    "# Generate calibration circuit\n",
    "meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list, qr=qr, circlabel='mcal')\n",
    "state_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (a) GHZ with open devices (ibmqx4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "provider = IBMQ.get_provider(hub='ibm-q')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute the calibration circuits\n",
    "job = qiskit.execute(meas_calibs, backend = provider.get_backend('ibmqx4'), shots=1000)\n",
    "cal_results = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.771 0.089 0.175 0.018 0.221 0.023 0.024 0.004]\n",
      " [0.023 0.691 0.007 0.168 0.003 0.183 0.    0.033]\n",
      " [0.012 0.005 0.533 0.061 0.003 0.    0.181 0.023]\n",
      " [0.    0.012 0.012 0.469 0.    0.008 0.004 0.139]\n",
      " [0.188 0.021 0.116 0.016 0.725 0.079 0.108 0.008]\n",
      " [0.003 0.176 0.004 0.101 0.033 0.691 0.004 0.097]\n",
      " [0.003 0.001 0.15  0.014 0.015 0.002 0.667 0.078]\n",
      " [0.    0.005 0.003 0.153 0.    0.014 0.012 0.618]]\n"
     ]
    }
   ],
   "source": [
    "# Calculate the calibration matrix\n",
    "meas_fitter = CompleteMeasFitter(cal_results, state_labels, qubit_list=qubit_list, circlabel='mcal')\n",
    "print(meas_fitter.cal_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the calibration matrix\n",
    "meas_fitter.plot_calibration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute the GHZ state circuits\n",
    "job = qiskit.execute(ghz, backend = provider.get_backend('ibmqx4'), shots=1000)\n",
    "results = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the filter object\n",
    "meas_filter = meas_fitter.filter\n",
    "\n",
    "# Results with mitigation\n",
    "mitigated_results = meas_filter.apply(results)\n",
    "mitigated_counts = mitigated_results.get_counts(0)\n",
    "\n",
    "# Plot the results with and without error mitigation\n",
    "plot_histogram([results.get_counts(), mitigated_counts], legend=['raw', 'mitigated'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (b) GHZ with NTU-hub's 20-qubit device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "provider = IBMQ.get_provider(hub='ibm-q-hub-ntu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute the calibration circuits\n",
    "job = qiskit.execute(meas_calibs, backend = provider.get_backend('ibmq_poughkeepsie'), shots=1000)\n",
    "cal_results = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.971 0.081 0.043 0.003 0.066 0.002 0.002 0.002]\n",
      " [0.007 0.899 0.    0.05  0.    0.057 0.    0.003]\n",
      " [0.015 0.002 0.946 0.074 0.001 0.    0.052 0.004]\n",
      " [0.    0.011 0.006 0.864 0.    0.001 0.    0.045]\n",
      " [0.006 0.    0.    0.    0.916 0.064 0.039 0.005]\n",
      " [0.001 0.007 0.    0.    0.01  0.864 0.    0.039]\n",
      " [0.    0.    0.005 0.    0.007 0.    0.899 0.075]\n",
      " [0.    0.    0.    0.009 0.    0.012 0.008 0.827]]\n"
     ]
    }
   ],
   "source": [
    "# Calculate the calibration matrix\n",
    "meas_fitter = CompleteMeasFitter(cal_results, state_labels, qubit_list=qubit_list, circlabel='mcal')\n",
    "print(meas_fitter.cal_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the calibration matrix\n",
    "meas_fitter.plot_calibration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute the GHZ state circuits\n",
    "job = qiskit.execute(ghz, backend = provider.get_backend('ibmq_poughkeepsie'), shots=1000)\n",
    "results = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the filter object\n",
    "meas_filter = meas_fitter.filter\n",
    "\n",
    "# Results with mitigation\n",
    "mitigated_results = meas_filter.apply(results)\n",
    "mitigated_counts = mitigated_results.get_counts(0)\n",
    "\n",
    "# Plot the results with and without error mitigation\n",
    "plot_histogram([results.get_counts(), mitigated_counts], legend=['raw', 'mitigated'])"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}