qiskit_lesson_8.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x1c098c2b160>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a 3 qubit circuit\n",
    "nqubits = 3\n",
    "circuit = QuantumCircuit(nqubits, nqubits)\n",
    "circuit.h(0)\n",
    "circuit.cx(0,1)\n",
    "circuit.cx(1,2)\n",
    "circuit.measure([0,1,2], [0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 501.977x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "circuit.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#runnin on the quantum simulator (no noise)\n",
    "simulator = Aer.get_backend('qasm_simulator')\n",
    "sim_result = execute(circuit, backend = simulator, shots=1024).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(sim_result.get_counts(circuit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rapha\\anaconda3\\lib\\site-packages\\qiskit\\providers\\ibmq\\ibmqfactory.py:192: UserWarning: Timestamps in IBMQ backend properties, jobs, and job results are all now in local time instead of UTC.\n",
      "  warnings.warn('Timestamps in IBMQ backend properties, jobs, and job results '\n"
     ]
    }
   ],
   "source": [
    "IBMQ.load_account()\n",
    "provider = IBMQ.get_provider(hub='ibm-q')\n",
    "device = provider.get_backend('ibmqx2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5f899eac77d4ec0012afbbf0\n",
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "#running on real quantum computer (we get noise here)\n",
    "job = execute(circuit, backend = device, shots=1024)\n",
    "print(job.job_id())\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "job_monitor(job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device_result = job.result()\n",
    "plot_histogram(device_result.get_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.ignis.mitigation.measurement import (complete_meas_cal, CompleteMeasFitter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting calibration circuit for a 3 qubit quantum circuit\n",
    "cal_circuits, state_labels = complete_meas_cal(qr = circuit.qregs[0], circlabel = 'measerromitigaationcal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 441.777x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal_circuits[2].draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cal_circuits) #this is 8 since I have 3 qubits (2^3=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#execute the calibration circuits on a real quantum computer\n",
    "cal_job = execute(cal_circuits,\n",
    "                 backend = device,\n",
    "                 shots = 1024,\n",
    "                 optimization_level = 0\n",
    "                 )\n",
    "print(cal_job.job_id())\n",
    "job_monitor(cal_job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cal_results = cal_job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ISGujICgiIhJS2DbNUNSmKSIimSxsm2YYatMUEZGM1qw2TRERkdZMAVFERCQkjdMUEREJSeM0RUREQtI4TRERkZAUBEVEREJqdtA0s3wz+62ZlUVeTzZ43qaIiEhGalbQNLMrgDeA3sCCyKsn8LqZXRn/7ImIiKSPsM/TrHMH8AN3vzN6oZl9F/gp8FS8MiYiIpJumls9eyzw+xjLnwV6tDw7IiIi6au5QfMvwJkxlp8JLA2zAzO73swqzexjM1thZmc0kbavmXmM17kN0o2N7OtjM1tvZtOaUSYREZFQmjth+0LgLjMrAF6LLDsdmAj8e4h9XQrMBK4Hlkf+XWhmw9z9n01sei5QEfV5R9Q++xG0rT4GXAmMBh4ys/fc/bnD5UlERCSszzph+7WRV7QHgYcOs69bgCfc/dHI5xsjd43XAd9tYrvt7r61kXXTgM3ufmPk89tmVgjcCihoiohI3By2etbd24R8NTkbkJllA6cAixqsWgR84TDZ+KOZbTOzV83s4gbrimLs8yWgwMyOOsx+RUREQmtu79mW6E4wzV51g+XVwJcb2WYvwR3jq8ABYALwjJld5e51PXV7Af8dY59Zke/cEr3CzOrvknNyciguLgagf//+dOrUiYqKoBa4W7duDB8+nGXLlgGQlZXF6NGjKS8vZ/fu3QAUFBRQXV3Nxo0bARg4cCDt2rVj5cqVAPTo0YNBgwaxfPlyANq1a0dRURFlZWXs3bsXgMLCQjZt2kRVVRUAgwcPpm3btqxevTooXK9e9OvXj5KSEgDat29PYWEhpaWl7Nu3D4CioiIqKyvZujW4GR82bBi1tbWsWbMGgD59+pCbm0tpaSkAHTt2pKCggJKSEmpqagAYPXo0a9euZdu2bQCMGDGCmpoa1q1bB0BeXh49e/akrKwMgM6dO5Ofn8/y5cs5cOAAAGPGjGHVqlVs374dgJEjR7Jnzx7Wr18PQN++fenatSvl5eUAdOnShZEjR7J06VLcHTNj7NixVFRUsHPnTgDy8/PZsWMHGzZs0O+U5r8TdCaViouL9Tvp/1NcfqemmLs3meCQDcy6ErQxHgdkR69z9x83sV0OUAWMcfdXopb/ELjc3YeE/P6HgNHuflLk81rgSXf/SVSasUAx0LuJal0KCgq87qQVkZaZ+kBqv//R6an9fskcZrbC3QtirWvWnaaZnQ68ANQQDD+pIpjooAbYADQaNIH3gVqCO8NoPTj07rMppcDVUZ+3NrLPA8D2ZuxXRESkSc0dcnIf8DTQB/gYOIvgjrMMuKepDd19P7ACGNdg1Tjgr83IwygOrnIt4dDq3XFAmbt/0oz9ioiINKm5bZonAde4u5tZLdDO3deb2e3A7wgCalPuB540s9cJ2imnATnALAAzuws4zd3Pjny+CvgE+BvwKXAB8G3g9qh9zgJuMLMHgF8DXwQmAZc3s2wiIiJNam7Q3B/1vho4HniboMNOzuE2dvdnzKwb8H2Cat2VwHh3fzeSpDdwQoPNvh/5nlpgLTA5qhMQ7l5pZuOBXxAMXdkM3KQxmiIiEm/NDZrlwKkEwasY+KmZ9SSYVODNMDtw94doZDynu09q8Hk2MDvEPpcCetKKiIgkVHPbNL9HcCcHwR3gewSTGnTh0MkOREREMkqz7jTdvSzq/XvAeXHPkYiISJr6TJMbmNkJwNDIx9Xuvj5+WRIREUlPzR2n2Q34DcHMPJ/+72J7nqCDjsZFiohIxmpum+Z/AgOAM4DPRV5jgH7Ao01sJyIicsRrbvXsOcDZ7l4StexVM/s/HDr/q4iISEZp7p3me8CHMZZ/hKasExGRDNfcoPlj4AEz61O3IPL+5zQ976yIiMgR77DVs2b2FhD9KJR+wAYzq4p8rpuHtgdBm6eIiEhGCtOm+YeE50JEROQIcNig6e4/SkZGRERE0t1nndzgLGAYQbXtKncvjmemRERE0lFzJzfoA/wJOIX/nYM2x8zKgIvcfXOjG4uIiBzhmtt79j8IHtE1wN3z3D0PGBhZ9h/xzpyIiEg6aW717DjgTHevrFsQeQj1TcCSuOZMREQkzTT3TrMxnx4+iYiIyJGtuUFzCfAfZpZXt8DMjgNmojtNERHJcM0NmjcBHYD1ZvaumW0A/iey7KY4501ERCStNLdNcztwGvAlYAhgBM/T1GTtIiKS8UIHTTNrC3wAjHT3xcDihOVKREQkDYWunnX3WuBdIDtx2REREUlfzW3T/Alwt5l1T0RmRERE0llz2zRvJXjKSZWZbaLBszXd/aR4ZUxERCTdNDdo/oFgvllLQF5ERETSWqigaWYdgPuAC4GjCMZk3uju7ycwbyIiImklbJvmj4BJwAvAHODLwMMJypOIiEhaCls9OxG4xt3nApjZ08CrZtY20qtWREQk44W908wDXqn74O6vAweAnERkSkREJB2FDZptgf0Nlh3gMz7EWkRE5EgUNugZ8JSZ1UQt+xzwqJl9VLfA3SfEM3MiIiLpJGzQnB1j2VPxzIiIiEi6CxU03f3qRGdEREQk3cXrIdQiIiIZT0FTREQkJAVNERGRkBQ0RUREQlLQFBERCUlBU0REJCQFTRERkZAUNEVEREJS0BQREQlJQVNERCQkBU0REZGQFDRFRERCUtAUEREJSUFTREQkJAVNERGRkBQ0RUREQlLQFBERCUlBU0REJCQFTRERkZAUNEVEREJS0BQREQlJQVNERCSkpAdNM7vezCrN7GMzW2FmZzSR9kwzm2dmW8zsIzN708wmx0jjMV5DEl8aERFpTZIaNM3sUmAmcCdwMvBXYKGZHdfIJl8A3gIuBkYADwOPmNm/xEg7HOgd9VoX39yLiEhrl5Xk77sFeMLdH418vtHMzgWuA77bMLG739lg0cNm9iXg68DvGqzb5u7vxzvDIiIidZJ2p2lm2cApwKIGqxYR3FGG1RnYGWN5WaQad0kksIqIiMRVMu80uwNtgeoGy6uBL4fZgZl9FTgb+GLU4i0Ed6pvANnAN4ElZnamuy+LsY9rgWsBcnJyKC4uBqB///506tSJiooKALp168bw4cNZtizYRVZWFqNHj6a8vJzdu3cDUFBQQHV1NRs3bgRg4MCBtGvXjpUrVwLQo0cPBg0axPLlywFo164dRUVFlJWVsXfvXgAKCwvZtGkTVVVVAAwePJi2bduyevVqAHr16kW/fv0oKSkBoH379hQWFlJaWsq+ffsAKCoqorKykq1btwIwbNgwamtrWbNmDQB9+vQhNzeX0tJSADp27EhBQQElJSXU1NQAMHr0aNauXcu2bdsAGDFiBDU1NaxbF9Ry5+Xl0bNnT8rKygDo3Lkz+fn5LF++nAMHDgAwZswYVq1axfbt2wEYOXIke/bsYf369QD07duXrl27Ul5eDkCXLl0YOXIkS5cuxd0xM8aOHUtFRQU7dwbXRfn5+ezYsYMNGzbod0rz3ym4nk2d4uJi/U76/xSX36kp5u5NJogXM8sBqoAx7v5K1PIfApe7e5Mdd8zsi8BC4HZ3f/gwaRcAB9x9QlPpCgoKvO6kFZGWmfpAar//0emp/X7JHGa2wt0LYq1LZkeg94FaoFeD5T049O7zIGY2miBg/r/DBcyIUmDgZ8mkiIhIY5IWNN19P7ACGNdg1TiCXrQxmdkYgoD5I3cPey07iqDaVkREJG6S3Xv2fuBJM3sdeBWYBuQAswDM7C7gNHc/O/L5TOAF4CHgaTOru0utdff3ImmmAxuAVQRtmlcCFxL0sBUREYmbpAZNd3/GzLoB3ycYS7kSGO/u70aS9AZOiNpkEtABuDXyqvMu0DfyPhv4GdAH2EcQPM939wWJKYWIiLRWyb7TxN0fIrhzjLVuUozPk2KljUpzL3BvfHInIiLSOM09KyIiEpKCpoiISEgKmiIiIiEpaIqIiISkoCkiIhKSgqaIiEhICpoiIiIhKWiKiIiEpKApIiISkoKmiIhISAqaIiIiISloioiIhKSgKSIiEpKCpoiISEgKmiIiIiEpaIqIiISkoCkiIhKSgqaIiEhICpoiIiIhKWiKiIiEpKApIiISkoKmiIhISAqaIiIiISloioiIhKSgKSIiEpKCpoiISEgKmiIiIiEpaIqIiISkoCkiIhKSgqaIiEhICpoiIiIhKWhK3L344osMHjyYAQMGcPfddx+y3t256aabGDBgACeddBLl5eX163bt2sXFF1/MkCFDGDp0KCUlJQDcdtttDBkyhJNOOomLLrqIXbt2Ja08IiJ1FDQlrmpra/n2t7/NwoULWb16NXPmzGH16tUHpVm4cCHr1q1j3bp1PPLII1x33XX1626++WbOPfdc3nnnHSoqKhg6dCgA48aNY+XKlbz55psMGjSIu+66K6nlEhEBBU2Js9dff50BAwbQv39/srOzueyyy5g3b95BaebNm8e3vvUtzIzTTz+dXbt2sWXLFnbv3s2yZcu45pprAMjOzuaYY44B4Ctf+QpZWVkAnH766WzatCm5BRMRQUFT4qyqqoq8vLz6z7m5uVRVVYVKs379eo499liuvvpqTj75ZKZMmcKHH354yHc89thjnHfeeYkrhIhIIxQ0Ja7c/ZBlZhYqzYEDBygvL+e6667jb3/7G5///OcPaRO94447yMrK4oorrohvxuOsJe26EFRzn3zyyXz1q1+tX1ZRUUFRUREnnngiF1xwAbt37054OUTkYAqaEle5ubls3Lix/vOmTZvIyckJlSY3N5fc3FwKCwsBuPjiiw8KJrNnz+b555/n6aefPiQQp5OWtusCzJw5s749t86UKVO4++67eeutt7jooou47777El4WETmYgqbE1amnnsq6deuorKxk//79zJ07lwkTJhyUZsKECfz2t7/F3Xnttdc4+uij6d27N7169SIvL481a9YAsGTJEoYNGwYEd2733HMP8+fPp0OHDkkvV3O0pF0XgouIF154gSlTphy0zZo1axgzZgwQdIx67rnnklMgEamXleoMSGbJysril7/8Jeeccw61tbVMnjyZ4cOHM2vWLACmTZvG+PHjWbBgAQMGDKBDhw48/vjj9ds/+OCDXHHFFezfv5/+/fvXr7vhhhuoqalh3LhxQNAZqG6f6SZWm21paelh01RVVdG7d2+mT5/Ovffey549ew7aZsSIEcyfP5+vfe1rPPvsswfdrYtIcihoStyNHz+e8ePHH7Rs2rRp9e/NjF/96lcxtx01ahRlZWWHLP/HP/4R30wmUEvadZ9//nl69OjBKaecQnFx8UHrH3vsMW666SZ+/OMfM2HCBLKzs+OabxE5PAVNkThrSbvuH/7wB+bPn8+CBQv4+OOP2b17N1deeSVPPfUUQ4YMYdGiRQCsXbuWF154ITkFEpF6atMUibOWtOveddddbNq0iQ0bNjB37lzOOussnnrqKQC2bdsGwKeffspPf/rTg+7eRSQ5dKcpcTH1gdR+/6PTU/v90VrartuYOXPm1FdrT5w4kauvvjqh5RCRQ1mstpXWoqCgwGO1n0nzKWiKzgHJFGa2wt0LYq1T9ayIiEhIqp4ViQPdZYkc7MUXX+Tmm2+mtraWKVOmMGPGjIPWuzs333wzCxYsoEOHDjzxxBPk5+cDMHny5Pqe5CtXrqzfZseOHVx66aVs2LCBvn378vvf/54uXboktVy604yzlkyf1tS2Dz74IIMHD2b48OF85zvfSXg5REQ+q5bOijVp0iRefPHFQ/Z79913c/bZZ7Nu3TrOPvvsmH9jE01BM45acqI0te1f/vIX5s2bx5tvvsmqVau49dZbk142EZGwWjor1pgxY+jatesh+503bx5XXXUVAFdddRV//vOfE1+YBhQ046glJ0pT2z788MPMmDGDdu3aAdCjR4+kl01EEudwNVRHmpY87agp1dXV9O7dG4DevXvXD8NKJgXNOGrJidLUtmvXruWVV16hsLCQsWPH8sYbbyS4JCKSLGFqqI40LZkVK90paMZRS06UprY9cOAAO3fu5LXXXuO+++7jkksuiZleRI48YWqojjQtmRWrKT179qyvwt2yZUtKat0UNOOopY/Famzb3NxcJk6ciJlx2mmn0aZNG95///0El0ZEkuGzVFOmu5bMitWUCRMmMHv2bCB4VODXvva1hJWhMZp6qA0AAAzxSURBVAqacdSSE6WpbS+88EJefvllIKiq3b9/P927d096+UQk/o7UasqmRM+KNXToUC655JL6WbHqZsYaP348/fv3Z8CAAUydOpWHHnqofvvLL7+coqIi1qxZQ25uLr/5zW8AmDFjBosXL2bgwIEsXrz4kGEsSSlbsr/QzK4HbgN6A6uA6e7+ShPpTwR+CZwG7AB+DfzEo840MxsL3A8MBzYD97p70p8b1ZLp0xrbFoIxS5MnT2bEiBFkZ2cze/bsI/4/lYgEPks15ZGgJU87mjNnTszl3bp1Y8mSJfHL5GeQ1KBpZpcCM4HrgeWRfxea2TB3/2eM9J2BxcAy4FRgMPAE8CHw80iafsAC4DHgSmA08JCZvefuSX9Kb0tOlFjbAmRnZ9dP2i0imSW6lqlPnz7MnTuX3/3ud6nOljQi2XeatwBPuPujkc83mtm5wHXAd2OkvwLoAFzl7vuAlWY2FLjFzO6P3G1OAza7+42Rbd42s0LgVkCPtheRtNZULdORpjXMjJW0oGlm2cApwM8arFoEfKGRzYqAVyIBs85LwE+AvkBlJM2iBtu9BFxlZke5+yctzPphtYYTRUQSp7FaJkk/yewI1B1oC1Q3WF4N9Gpkm16NpK9b11SarMh3ioiIxEUqJmxv2FXMYiw7XPqGy8OkCVaYXQtcG/m418zWNPHdydId+MxjSP7zX+OYk9Rp7cegtZcfdAxaVP4MkS7nwPGNrUhm0HwfqOXQu8oeHHqnWGdrI+mJ2qaxNAeA7Q136O6PAI+Ey3JymFlZY89uay1a+zFo7eUHHYPWXn44Mo5B0qpn3X0/sAIY12DVOOCvjWxWApxhZp9rkH4zsCEqzZdj7LMsGe2ZIiLSeiR7coP7gUlmNsXMhprZTCAHmAVgZneZWfQgnN8BHwFPmNkIM5sIzADujxqnOQvINbMHIvucAkzi0A5HIiIiLZLUNk13f8bMugHfJ5jcYCUw3t3fjSTpDZwQlf4DMxsH/AooA3YSjM+8PypNpZmNB35BMHRlM3BTKsZotkBaVRenSGs/Bq29/KBj0NrLD0fAMTBN/C0iIhKO5p4VEREJSUFTREQkJAVNERGRkBQ0U8z0uJJWT+eAyJFDQTPF3N3NLMvMUjE7k6QBnQMCYGZtot7rQipNqfdsiphZV+AC4CrgPeCfwBpgubu/k8q8SXLoHJCGzKyTu+9JdT6kcQqaKWJmfwSGEMyS1AnoCbQjmB7wj8Bv3P3T1OUw8czsImBFrGepRqXJcvcDScxW0ugc0DkAUPe4Q+Bk4B8EF09/J3jC08ZIGnP9sU4LCpopEPlPUgYUufubkWUdCab/uxSYAPwWuAn4JBP/s5jZYOBtoAZ4leAh4i+4+wdRadoCV5OBd146B3QOAJjZCcACggulVwkuonoRXDz9A/hPd2/46MOMYmY9Cc77Be6+o4l0SXnU4+GoTTM1xgBvAauh/kp6r7v/yd0vA74BTARGZeIfy4iLgNcInjizE/g18K6ZPWlmZ0Xa9/oRzBDyUeqymTA6B3QOANwKrAXOd/fvuvtFwIXAgwTB809mdk0qM5gE3ye4QPyHmT1rZuPNrF10AjM7Dri54fJUUNBMjRUEj575FoC7HzCzo6Impl8MlAOXpSh/yXA0UAHMd/dvACcCtxMcl5cI/pA8A6xqquruCKZzQOcABGUtd/c9ZtbWzNq6e7W7P+7uYwjm1p5qZh1SnM9EKgDuAf4N6AL8Cag0swfNLD+SZiowzd1rUpTHegqaKeDuZcDzwC/M7IeRxv9P3P3jyPr9BFeZlanMZ6JEegk+T1Dl9gGAu29w918TPLHmZIIrz5OJmmc4k+gc0DkQ8RJwtZkNcfdad681s2wzy46s/w1BW3dh6rKYOGaWA2wCKt39ceBcYCQwEzgDeMPM3iJo830wZRmNojbNFDKz7wA3A8cCywiusD4BLgGOI6iay9RqqXqxOnqY2RigGOjk7h+mJGNJYGa3EpwDPWnd58AhHV1awzlgZv0IOn0dA/zE3R9rsH4EQY3DMZl4HpjZ54GzgG3uXtpgXQdgBEEV9kSC82Bf8nN5MAXNFDOz44HzgPEEV5MfE1TNPeHuy1OZt2SLdPr4NDJu8VZgrLtfkOp8JYKZtanrGWtmwwiuqs8BioD9ZPg5EBmHaE31Dm4F54BFzvXOBNWT/wK0BRYB/00QMEYDf3f3b6Uup8nTyMXTE8AJ7n5GanJ1MAXNJIv8gRxG0J6zB/iru2+KWt8F2JXBnT8aHoOPgNfcvbJBmhOBvQ2XZ6pIdWW2u38cOQf2ZPIwizDMbCSwuxWdAyMILqDPBkYB64GngD+6+9ZU5i1RIue9N/b3zszaA/OAh939T0nNXCMUNJPIzGYAVwADCZ77uR1w4A1gDkHwOBB9F5JpGhyDKoJj8CnwN4KHjr+aqWWH+pqF7e6+t5H1GT8e73DHoDWIBIuvETTNdAA2AMuih1yY2dHRw29aIzM7Cihw95JU56WOgmaSRB6+vQG4zd1nmVkecBpBdVwB8DlghrsXpyyTCXaYY3AKwTH4rrsXZ+KFQ+QO8h2C8XjPAa8AWxqOPTOz0cA6d69Ofi4Tq5nH4H/cfUvyc5lYZtaJoIPPlwguGKsiqz4CXgbmuPvbkbQZeREVCYb9gHfToUdsc6j3bPJ8A3jH3WcBuPtGd3/O3es6gmwC5ptZ/1RmMsGaOgbTCf54zDez/pkWMCOuAI4COhIM5C8DZpnZOWZ2rJm1iVxIPEbQ9T4TNecYHJ3CfCbSTcBgYLy79yRoy7wfWEnQt+FeMzsWgnrLlOUysb5NULs0y8wuMLNekT4N9cyss5mdH9WTOC0oaCbPdqB7pEcgdWOyANy9AriSYKD7uanLYsK19mMwFHiWoHzdgR8RdK9fSHDn9SOCi4cemTj7TYSOQVD22e7+BoC7v+PuTxEEklsIZgV6MoX5S4ZLgdeBAcCfgRLgPjMbbWZ1F0v/AvwgMvwqbShoJs8LwLvAv5nZiXVjsupWRsbnHQC6pSqDSdBqj0GkOqqEoGrS3H2Pu//K3QuAQcDvCSYy+FfgZ6nLaeLoGATDqwjuKL9edzcZNalBrbsvA6YBuZGOUBknUu5PgEcjPWKPJ6iu/irBsKuXzex2goun0kZ3lCJq00yCqK7lXyQYoHsiwZX1b4A3ga7AF4AfAye7+4ZU5TVRdAzqg0ZHd98ZucNuQzDEpjayfgiwCjg+ukd1JtExADM7HXga+ANwf8O260j19NvAYHevirGLI5qZ9Sa4OFrt7i81WHcyMCWyvguQl27HQEEziSLjsdoRdHr5JnB+5PNWgjusX7r7zNTlMPFa6zGIumg4gWA4ybaodW3c/VMz+wEw1d2PS11OE0fHoL7XbBuCSejvBLIIguczwEbgJILHxQ1191NTlc9Eiwwl8cgQq/pnh9a14ZrZHQRtvienKo+NUdBMMDPrQRAcbgF2EExesJmgqvJlgplAjiMYapFxvSVBx6BB+bcRXBxsIWjb+6NHZrsxs/MJxqYuTVVeE0XH4FBmdgwwiaDtbhTBuO0agra+u7zBDDmZprGewZGZgMqBx939nuTnrGkKmgkWmc1iOPBfBAGjK0HHhyEEgeP/1nUIyFSt/Rg0Uv6TCcq/CbjPM//xT0+gY9CZ4A7bo5a1IRhq1ZFgBqAPMzlYxjoGMdJ8jqCj0Jx06wQECpoJFal22ENQzbAsatlxBFPmTQH6A99w97+lLKMJ1NqPQRPlzyMo/1SCjhCXZWL5Qcegjpn9muAu8nWC8Ym7Y6TpEmnvzdTxmWGOwTHuvivpmQtJvWcTaxjBUyrqr5Y88K67/56gt9gugsm5M1VrPwaNlf+f7v4sQfn3kLnlBx0DzOxygouDnxNMC3efmU00swGR9r26h5A/HulZnokBM9YxuMjMTog6Bu2B2RZMKZiWFDQTaz1B+80vzGxgpCqmXqTqYTbBfJOZqrUfg9ZeftAxgOBJHo8A+cBDBD3FnyJo0/2+mZ1F0OY73t3fSlkuEyvWMajrRVx3DCYB57n7ylRl8nAUNBPIg8fYfA9oT/BswG+ZWZ4Fj8Opa/AeSzBuKyO19mPQ2ssPOgaRsZmVBA9iWO/uP3P3E4FTgaXAVQRjVB8kQyc1yKRjoDbNJIhUNfwAmAB8SDDA+z2Ch+1uAaZk8NUloGPQ2ssPrfsYWDDnbk93f8eCaeE+adAh6FKChzbku/vfU5XPRMqUY6CgmUSRbvfnAxcSDLtYCTzrmTtd2CFa+zFo7eUHHYM6kWpqc/daM5sKzHT3DqnOVzIdicdAQTNFLAOf4tFcrf0YtPbyg45BHTO7BWjr7velOi+pcqQcAwVNEZEUi0wvWNuaLyCOlGOgoCkiIhKSes+KiIiEpKApIiISkoKmiIhISAqaIiIiISloioiIhKSgKSIiEtL/B5bugItqHI9AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(\n",
    "cal_results.get_counts(cal_circuits[3])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating the fitter and using the calibration results that we then use to create the results filter\n",
    "meas_fitter = CompleteMeasFitter(cal_results, state_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "meas_fitter.plot_calibration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "meas_filter = meas_fitter.filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filtering the previous results that we want to mitigate\n",
    "mitigated_result = meas_filter.apply(device_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "device_counts = device_result.get_counts(circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mitigated_counts = mitigated_result.get_counts(circuit)\n",
    "#comparing before and after mitigation\n",
    "plot_histogram([device_counts,mitigated_counts], legend=['device noisy', 'device, mitigated'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 501.977x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now we build a completely different 3 qubit circuit to run the same filter on\n",
    "circuit2 = QuantumCircuit(3,3)\n",
    "circuit2.x(1)\n",
    "circuit2.h(0)\n",
    "circuit2.cx(0,1)\n",
    "circuit2.cx(1,2)\n",
    "circuit2.measure([0,1,2],[0,1,2])\n",
    "circuit2.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#run it on the simulator without any noise\n",
    "plot_histogram(\n",
    "    execute(circuit2, backend=simulator, shots=1024).result().get_counts(circuit2)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#run it on a real quantum computer\n",
    "device_counts_2=execute(circuit2, backend=device, shots = 1024).result().get_counts(circuit2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(device_counts_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mitigating previous results using the same 3 qubit filter\n",
    "device_mitigated_counts_2 = meas_filter.apply(device_counts_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(device_mitigated_counts_2)"
   ]
  },
  {
   "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
}