Quantum_Error_Mitigation_ibmq_burlington.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌───┐     ┌─┐   \n",
       "q_0: ┤ X ├──■──┤M├───\n",
       "     └───┘┌─┴─┐└╥┘┌─┐\n",
       "q_1: ─────┤ X ├─╫─┤M├\n",
       "          └───┘ ║ └╥┘\n",
       "c_0: ═══════════╩══╬═\n",
       "                   ║ \n",
       "c_1: ══════════════╩═\n",
       "                     </pre>"
      ],
      "text/plain": [
       "     ┌───┐     ┌─┐   \n",
       "q_0: ┤ X ├──■──┤M├───\n",
       "     └───┘┌─┴─┐└╥┘┌─┐\n",
       "q_1: ─────┤ X ├─╫─┤M├\n",
       "          └───┘ ║ └╥┘\n",
       "c_0: ═══════════╩══╬═\n",
       "                   ║ \n",
       "c_1: ══════════════╩═\n",
       "                     "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import qiskit as q\n",
    "%matplotlib inline\n",
    "\n",
    "#Quantum Circuit with 2 qubits and with 2 Classical Bits\n",
    "circuit = q.QuantumCircuit(2,2) #[0 ,0] -> qubits (initial state)\n",
    "\n",
    "#this is gonna make the first qubit !0 (not of 0). So [1 , 0]\n",
    "circuit.x(0)\n",
    "\n",
    "circuit.cx(0,1)\n",
    "\n",
    "circuit.measure([0,1] , [0,1])\n",
    "circuit.draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import IBMQ\n",
    "IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has been cancelled\n"
     ]
    }
   ],
   "source": [
    "from qiskit.tools.monitor import job_monitor\n",
    "\n",
    "provider = IBMQ.get_provider('ibm-q')\n",
    "backend = provider.get_backend(\"ibmq_burlington\")\n",
    "job = q.execute(circuit, backend=backend, shots = 3000)\n",
    "job_monitor(job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "provider = IBMQ.get_provider('ibm-q')\n",
    "backend = provider.get_backend(\"ibmq_london\")\n",
    "job = q.execute(circuit, backend=backend, shots = 3000)\n",
    "job_monitor(job)"
   ]
  },
  {
   "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": [
    "from qiskit.visualization import plot_histogram\n",
    "from matplotlib import style\n",
    "\n",
    "style.use(\"dark_background\")\n",
    "\n",
    "result = job.result()\n",
    "counts = result.get_counts(circuit)\n",
    "\n",
    "plot_histogram([counts])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.ignis.mitigation.measurement import (complete_meas_cal, CompleteMeasFitter )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cal_circuits, state_labels = complete_meas_cal(qr = circuit.qregs[0], circlabel = 'measerrormitigationcal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 418.992x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal_circuits[2].draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5f393fa30db6f0001abc031a\n",
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "cal_job = q.execute(\n",
    "                    cal_circuits,\n",
    "                    backend = backend,\n",
    "                    optimization_level = 0\n",
    ")\n",
    "\n",
    "print(cal_job.job_id())\n",
    "job_monitor(cal_job)\n",
    "cal_results = cal_job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "style.use(\"dark_background\")\n",
    "\n",
    "plot_histogram(cal_results.get_counts(cal_circuits[3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "meas_fitter = CompleteMeasFitter(cal_results, state_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "meas_fitter.plot_calibration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas_filter = meas_fitter.filter\n",
    "mitigated_result = meas_filter.apply(result)\n",
    "device_counts = result.get_counts(circuit)\n",
    "mitigated_counts = mitigated_result.get_counts(circuit)\n",
    "plot_histogram([device_counts, mitigated_counts], legend=['device , noisy (original state)' , 'device , mitigated (mitigated state)'])"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}