tutorial_aqcel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "backend_name = 'ibmq_quito'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import icepp\n",
    "import math\n",
    "from decimal import Decimal, ROUND_HALF_UP, ROUND_HALF_EVEN\n",
    "\n",
    "from pytket.extensions.qiskit import IBMQBackend, AerStateBackend, AerBackend\n",
    "\n",
    "from qiskit import *\n",
    "from qiskit.visualization import plot_histogram\n",
    "IBMQ.load_account()\n",
    "\n",
    "# Fill your provider information\n",
    "# Example: provider = IBMQ.get_provider(hub=ibm-q'',group='open',project='main')\n",
    "provider = IBMQ.get_provider(hub='',group='',project='')\n",
    "\n",
    "backend = provider.get_backend(backend_name)\n",
    "sim_b = AerBackend()\n",
    "dev_b = IBMQBackend(backend_name=backend_name, hub='',group='',project='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = QuantumCircuit(3,3)\n",
    "test.h(0)\n",
    "test.cx(0,1)\n",
    "test.cx(1,2)\n",
    "test.ccx(0,1,2)\n",
    "test.measure(0,0)\n",
    "test.measure(1,1)\n",
    "test.measure(2,2)\n",
    "\n",
    "test.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Only Qiskit + Measurement error mitigation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "transpiled_qcs = transpile(test,backend,optimization_level=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28 , 41\n",
      "Gate counts: OrderedDict([('cx', 16), ('rz', 15), ('sx', 6), ('measure', 3), ('barrier', 1)])\n"
     ]
    }
   ],
   "source": [
    "qc = transpiled_qcs\n",
    "print(qc.depth(), ',', qc.__len__())\n",
    "print('Gate counts:', qc.count_ops())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'000': 533, '011': 491}\n"
     ]
    }
   ],
   "source": [
    "simulation_counts = icepp.simulator(qc).aer_simulator(shots=1024)\n",
    "print(simulation_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job id : 26039791684a4096a3e48c1351dfa5f0-16489925437400389\n",
      "Job Status: job has successfully run\n",
      "Raw counts: {'000': 464, '001': 37, '010': 38, '011': 401, '100': 27, '101': 10, '110': 26, '111': 21}\n",
      "Measurement error mitigated counts {'000': 471.72592455643814, '001': 6.2036136696156285, '010': 24.700060269777527, '011': 440.032979218709, '100': 25.46042169956512, '101': 8.90372486505393, '110': 28.244140188478312, '111': 18.729135532362267}\n"
     ]
    }
   ],
   "source": [
    "all_mitigated_counts = icepp.pass_manager(qc, level=1, backend=backend, backend_tket=dev_b, shots=1024).auto_manager()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_counts, meas_mit_counts= all_mitigated_counts[0], all_mitigated_counts[1]\n",
    "\n",
    "legend = ['raw counts','measreument error mitigated','simulation']\n",
    "plot_histogram([raw_counts, meas_mit_counts, simulation_counts], legend=legend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AQCEL circuit optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "aqcel_results_cc = icepp.pass_manager(test, level=2, backend=backend, backend_tket=dev_b, shots=1024, measure_type='cc').auto_manager()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'011': 495, '000': 529}\n"
     ]
    }
   ],
   "source": [
    "simulation_counts = icepp.simulator(aqcel_results_cc[0]).aer_simulator(shots=1024)\n",
    "print(simulation_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 327.397x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aqcel_results_cc[0].draw('mpl',fold=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job id : 6d278b1825394bae8b1c74c1c1ff039c-1648992570321526\n",
      "Job Status: job has successfully run\n",
      "Raw counts: {'000': 508, '001': 23, '010': 17, '011': 457, '100': 8, '110': 1, '111': 10}\n",
      "Measurement error mitigated counts {'000': 517.8309068329967, '011': 502.53425137410665, '100': 1.962249807085925e-13, '110': 1.1246559239452836e-13, '111': 3.634841792897358}\n"
     ]
    }
   ],
   "source": [
    "aqcel_counts_cc = icepp.pass_manager(aqcel_results_cc[1], level=1, backend=backend, backend_tket=dev_b, shots=1024).auto_manager()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram([aqcel_counts_cc[0], aqcel_counts_cc[1],simulation_counts])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     ┌───┐ ░ ┌─┐\n",
      "q_0: ┤ H ├─░─┤M├\n",
      "     └───┘ ░ └╥┘\n",
      "q_1: ──────░──╫─\n",
      "           ░  ║ \n",
      "q_2: ──────░──╫─\n",
      "           ░  ║ \n",
      "c: 1/═════════╩═\n",
      "              0 \n",
      "Job id : 2801d9de33754488bf7ebb8c49aa5c25-1648992589229192\n",
      "Job Status: job has successfully run\n",
      "Raw counts: {'0': 511, '1': 513}\n",
      "Measurement error mitigated counts {'0': 491.90082597320855, '1': 532.0991740267914}\n",
      "Final counts after applying the threshold : {'0': 491.90082597320855, '1': 532.0991740267914}\n",
      "Threshold : 102.4\n",
      "     ┌───┐      ░    \n",
      "q_0: ┤ H ├──■───░────\n",
      "     └───┘┌─┴─┐ ░ ┌─┐\n",
      "q_1: ─────┤ X ├─░─┤M├\n",
      "          └───┘ ░ └╥┘\n",
      "q_2: ───────────░──╫─\n",
      "                ░  ║ \n",
      "c: 1/══════════════╩═\n",
      "                   0 \n",
      "Job id : 39afa4e297e148a78f36c4cef4a7bece-1648992611747947\n",
      "Job Status: job has successfully run\n",
      "Raw counts: {'0': 513, '1': 511}\n",
      "Measurement error mitigated counts {'0': 491.04721905158533, '1': 532.9527809484146}\n",
      "Final counts after applying the threshold : {'0': 491.04721905158533, '1': 532.9527809484146}\n",
      "Threshold : 102.4\n",
      "     ┌───┐           ░ ┌─┐   \n",
      "q_0: ┤ H ├──■────────░─┤M├───\n",
      "     └───┘┌─┴─┐      ░ └╥┘┌─┐\n",
      "q_1: ─────┤ X ├──■───░──╫─┤M├\n",
      "          └───┘┌─┴─┐ ░  ║ └╥┘\n",
      "q_2: ──────────┤ X ├─░──╫──╫─\n",
      "               └───┘ ░  ║  ║ \n",
      "c: 2/═══════════════════╩══╩═\n",
      "                        0  1 \n",
      "Job id : 76484569bb834c66bc6b76953686f310-1648992629945919\n",
      "Job Status: job has successfully run\n",
      "Raw counts: {'00': 497, '01': 18, '10': 35, '11': 474}\n",
      "Measurement error mitigated counts {'00': 503.1749470708502, '10': 5.341186656876105, '11': 515.4838662722743}\n",
      "Final counts after applying the threshold : {'00': 503.1749470708502, '11': 515.4838662722743}\n",
      "Threshold : 102.4\n"
     ]
    }
   ],
   "source": [
    "aqcel_results_qc = icepp.pass_manager(test, level=2, backend=backend, backend_tket=dev_b, shots=1024, measure_type='qc', threshold_type=['constant',0.1], zne='off').auto_manager()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KhXbOYql+91cPQGpoTkFEDAoFETFo+CByjWJjY5u8zZGCYgBCe95k3G/pdh2hUBC5RldzOfiaT3T+Yf4M474r0PBBRAwKBRExKBRExKBQEBGDQkFEDAoFETEoFETEoFAQEYNCQUQMCgURMSgURMSgUBARg0JBRAz6lGQj5syZQ15enlPajo2NvapP4IlcC4VCI/Ly8oyLh4q0dRo+iIhBoSAiBoWCiBgUCiJiUCiIiEGhICIGhYKL6Nq1q7NLEAF0nkKzslgsJCQkMGLECOLi4ggICMBisfDNN9+wa9cusrKyyMzMpKqqytguOTmZ9PR0xowZw+7du51UvUg1l+4pVFVV8cILL9CrVy86depETEwM2dnZREZGMmOGa3xHfo1p06Zx4MABPvroI5566inGjRtHTEwM0dHRjBkzhgULFrBp0yYOHTrEzJkzsViqr+WenJzMypUr8fX15fbbb3fysxBx8Z5CSkoKGRkZLFy4kLi4OHJyckhKSuK7775j3rx5zi4PAH9/f1avXs0dd9wBQGFhIWvWrOHzzz/nyJEj2O12QkJCuPXWW0lKSiI0NJSXX36ZKVOmsGHDBp5//nmsVitPPPEES5cudfKzEXHhUFi7di2rV68mKyuL+Ph4ABITE9m1axcZGRkMGDDAyRVCcHAwWVlZhIaGcvLkSebOncuaNWuorKw01tuzZw8bNmxg4cKFTJgwgZdeeonbb7+dYcOGYbFYeOKJJ1i8eLGTnoWIyWWHD2lpaYwdO7Y2EGqEh4fj6elJdHQ0p0+f5q677iIiIoKYmBjGjBnDoUOHWqU+b29vNm3aRGhoKDt27MBms/H6669fEQiXstvtvP3226SlpWG327FYLJw+fZrly5e3Ss0ijnDJUCgqKuLLL79k8uTJVzxWUFCAzWajY8eOWCwW5syZQ35+Pnv27OGuu+4iOTm5VWp87rnniIqKYv/+/YwePZpvv/3Woe2Sk5P505/+hMViobCwkO7du/Piiy+2cLUijnPZUIDq8fqlzp8/T3Z2du3QoVu3bowaNar28SFDhnD06FGH2rBYLA7d6vqEZFhYGHPnzuXixYs8+OCDlJSUONRmzaRizRzC6NGjKSsr46GHHqpzOJSdne1wne31dunr2dTXtr3V7SiXDAVfX18A8vPzjeXp6ekUFxcTFxdX53ZLlizh3nvvbeny+NWvfoXVauWNN95g586dDm1zeSAsXryYgwcP8pe//AWAmTNntmTJIg5zyVAIDQ0lOjqatLQ0XnvtNTZv3kxqaiqvvPIKQJ2h8Oyzz3Lo0CHS09MdasNutzt0u3xOAyApKQmg9g+6MXUFQo1ly5YBcP/992O1mi9HfHy8w3W219ulr2dTX9v2VrejXDIUrFYr69atw2azkZqaSnJyMr6+vsyaNQsPDw+io6ON9RctWsQ///lPNm3aROfOnVu0Nn9/fwIDAykpKSE3N7fR9RsKBICvv/6aY8eO4ePjQ2RkZEuVLeIwlz0kGRERwZYtW4xlU6dOJSoqCm9v79plzz77LBs3biQzM5Nu3bq1eF02mw2AL774otH0bSwQauTl5RESEoLNZuPAgQPNXrNIU7hsKNQlNzeXwYMH1/5/3759PPPMM4SFhZGQkFC7vCW/U/HYsWM8++yzjU5ohoeHs2LFikYDAWDNmjXk5eVx8ODB5i5XpMncJhRKS0vJz883JuRsNluTxkrN4dChQzzzzDMOrZeamkq3bt0aPTHprbfeaqbqRK6d24SCj49PgycGuaIVK1Y4uwSRJnPJiUYRcR6FgogYFAoiYlAoiIhBoSAiBoWCiBgUCiJiUCiIiMFtTl5yltjY2Kva7khBMQChPW8y7rdG2yLXQqHQiCVLllzVdguer/6KtT/Mn2HcF3F1Gj6IiEGhICIGhYKIGBQKImJQKIiIQaEgIgaFgogYFAoiYlAoiIhBoSAiBoWCiBgUCiJiUCiIiEGhICIGhUI9xowZQ2xsLP369WPSpEmcPXu2VdotLCxk5MiR9OnTB5vNxm9+85tWabe9ysrKwmazER4ezvTp093igkOPPvooQUFBdOjQMt98oFCox7p168jLy2Pv3r0EBQXx4osvtkq7HTp04Pnnn+fAgQPs3r2bTz75hPfee69V2m5vqqqqmD59OuvWrePQoUOcPXuWN954w9llNWry5MkOXfH8aikU6tG1a1eg+henrKwMi8XSKu3edNNNDBw4EAAvLy/69+9PQUFBq7Td3uzYsYOAgACioqIASElJ4e2333ZyVY0bNmwY/v7+LbZ/hUIDfv7zn+Pn58fBgwd57LHHWr39U6dO8e677zJ69OhWb7s9KCoqIjg4uPb/PXv2pLCw0IkVuQZ9HVsD3nnnHSoqKkhJSWH9+vVMmzat3nU35+ziy4NXXp7+T6++fcX9iJ8FMS5hUINtV1RUMGnSJB599FF69+59dU+gjfrHhzkcKSw2ltX1cwaIjQonflBMnftpzSuWH//me9a/n33F8rrq7ujlyYMT76Bzp46tVt+l1FNohJeXF1OmTOGdd95pcL3b+kdR+uN5ik+cpPjEydrll98/c7aUoXF9G9xXZWUlDzzwALGxsU7pobi6Ybf049SZsw3+nItPnOR8WTmDYvvUu5/g4GCjZ1BQUEBQUFCL1Bzo70vgjb6N/n4UnzhJXN8IpwUCKBTq9MMPP1BcXP1OVFVVxYYNG7DZbA1u09m7E5PHxTe673vHDOP667o0uM6MGTO47rrr+OMf/+h40e1I967XMX7U0AbXsQCT70ygU0evetcZOHAgRUVF7N+/H4BVq1YxYcKE5izVcNfI2+je9boG1+kTHsLA6MgWq8ERCoU6/PDDD4wfP57o6Giio6O5ePEiTz31VKPbRYQGM7h/VL2Px0aFE9MnrMF9fPrpp7zyyivk5ubSv39/YmNjWbp0aZOfQ1s3oG8vbBE31/v4sFuiCesZ0OA+PDw8WLlyJZMmTSIsLAwfHx+mTp3azJX+pFNHL+67M4H6pqy7dO7ExLHDG53UfuSRRwgKCqKyspKgoCBmzZrVrHVa7K05sGoD7HZ7gy9aRcUFlv5XBt+fKjGWX+/ThTkpk5zaLWxrSn88z5JX1lN67ryx/Ebf7sz+5c/xbKHj+Nfq/aztZG/fc8XyqRPGYOt1c+sXdBn1FJooe/se/vvdTC7Wc5KLl5cn99+ZiPWy4Jh8Z7wCoZn5dPZm0mVDNg+rlfvvSnTZQAAYPWwg/v/Rw1g2sF+kSwQCtIFQ2Lt3LxMnTsTX15dOnTrRq1cvnnzyyRZpq7y8go+376HiwkU6eHjUu15wgB+JQ/rX/n9IXF963dwyE1jtXe+wntwa89PRmdG3DyTgRl8nVtS4Dh08mHL3CDw8qv/8enS9jrtH3ubkqn7iunHqgJ07dzJ8+HCCg4NZvHgxISEhHD16lJycnBZpL2fXPn4sK2fU0LhG1x1x2wAOHi6kvOIC4+JvbZF6pNqdI27j8LH/xaeLN8NvjXZ2OQ7x/48ejLn9FjZlbWfynQl0bGBCtLW59ZxCQkIC+/fv5+uvv649A9FRNZdyE2kvHL1sodsOH3788Ue2bt1KUlJSkwNBROrntsOH06dPU1VVddUnmzTlYq/l5RU8v2wtwQF+JE8ed1XtibgLtw2F7t27Y7VaOX78+FVtfzXDh4NHCjXsELfV5ocPnTt3Zvjw4axdu7bVvutApD1w64nGmqMPISEhPPHEE4SEhFBQUMDWrVtZuXJls7SxZdtu/vXxDmZNvZfgAL9m2aeIK3Pb4QNAXFwc27ZtY+HChcydO5eysjKCg4OZMmVKs+y/vLyCrZ9/QWRosAJB2g237im0tOITJ3nj3Uym3DVCoSDthkKhEVVVVVitbjv1ItJkCgURMegtUEQMCgURMSgURMSgUBARg0JBRAwKBRExKBRExKBQEBGDQkFEDAoFETEoFETEoFAQEYNCQUQMCgURMSgURMSgUBARg0JBRAwKBRExKBRExKBQEBGDQkFEDAoFETEoFETEoFAQEYNCQUQMCgURMSgURMTwfyCgKV4t+QBQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 327.397x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aqcel_results_qc[0].draw('mpl' ,fold=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job id : 0fd9ca60d8204d3c856f5ee3d6596c81-1648992652522648\n",
      "Job Status: job has successfully run\n",
      "Raw counts: {'000': 486, '001': 29, '010': 26, '011': 472, '100': 5, '111': 6}\n",
      "Measurement error mitigated counts {'000': 498.22313238901427, '011': 525.7768676109858, '100': 3.552713678800501e-15, '110': 1.7763568394002505e-15}\n"
     ]
    }
   ],
   "source": [
    "aqcel_counts_qc = icepp.pass_manager(aqcel_results_qc[1], level=1, backend=backend, backend_tket=dev_b, shots=1024).auto_manager()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "legend = ['raw counts','measreument error mitigated','AQCEL_cc','AQCEL_qc','simulation']\n",
    "plot_histogram([raw_counts, meas_mit_counts, aqcel_counts_cc[-1], aqcel_counts_qc[-1], simulation_counts], legend=legend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def fidelity(count1,count2,shots):\n",
    "    fidelity=0\n",
    "    for key in count1.keys():\n",
    "        if (key in count2) and (count1[key] >0) and (count2[key] >0):\n",
    "            fidelity += math.sqrt((count1[key]*count2[key]))/shots\n",
    "    return fidelity\n",
    "\n",
    "def all_fidelities(sim_counts, counts_list, shots):\n",
    "    fidelity_list = []\n",
    "    for counts in counts_list:\n",
    "        each_fidelity = fidelity(sim_counts, counts ,shots)\n",
    "        fidelity_list.append(each_fidelity)\n",
    "    return fidelity_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9182292235867031, 0.9433278097878576, 0.9981210766370407, 0.999978091548282]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_fidelities(sim_counts=simulation_counts, counts_list=all_mitigated_counts+[aqcel_counts_cc[-1],aqcel_counts_qc[-1]], shots=1024)"
   ]
  },
  {
   "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": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}