Steane_Code_error_detection_ancilla readings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Steane code error detection ancilla readings\n",
    "====================================\n",
    "\n",
    "1. Set up logical zero for Steane code based  on the parity matrix in the book by Nielsen MA, Chuang IL. Quantum Computation and Quantum Information, 10th Anniversary Edition. Cambridge University Press; 2016. p. 474\n",
    "\n",
    "2. Set up the ancilla\n",
    "\n",
    "3. Force an error and confirm that the ancilla identify this error even with noise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the necessary function modules, including the SteaneCodeLogicalQubit class.  The methods of this class are called in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import(\n",
    "  QuantumCircuit,\n",
    "  QuantumRegister,\n",
    "  ClassicalRegister,\n",
    "  execute,\n",
    "  Aer)\n",
    "\n",
    "from qiskit.providers.aer.noise import NoiseModel\n",
    "from qiskit.providers.aer.noise.errors import pauli_error, depolarizing_error\n",
    "from qiskit.compiler import transpile\n",
    "\n",
    "from circuits import SteaneCodeLogicalQubit\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from helper_functions import (\n",
    "    find_individual_ancilla_values,\n",
    "    find_ancilla_values,\n",
    "    find_parity,\n",
    "    get_noise,\n",
    "    mean_of_list,\n",
    "    calculate_standard_error,\n",
    "    get_parity_check_matrix,\n",
    "    get_codewords,\n",
    "    get_parity_check_matrix,\n",
    "    get_codewords\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define constants so the process flow can be controlled from one place:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SINGLE_GATE_SET = ['id', 'ry', 'rx']\n",
    "TWO_GATE_SET = ['rxx']\n",
    "BASIS_GATE_SET = SINGLE_GATE_SET + TWO_GATE_SET\n",
    "ANCILLA_QUBITS = 3\n",
    "ERROR_REDUCTION = 1\n",
    "\n",
    "NOISE = True                                              #Test with noise\n",
    "SHOTS = 10000                                             #Number of shots to run   \n",
    "MEASURE_NOISE = 0.0046 * ERROR_REDUCTION                  #Measurement noise not relevant\n",
    "SINGLE_GATE_DEPOLARISING = 0.000366 * ERROR_REDUCTION     #Single gate noise   \n",
    "TWO_GATE_DEPOLARISING = 0.022 * ERROR_REDUCTION \n",
    "ITERATIONS = 25\n",
    "SIMULATOR = Aer.get_backend('qasm_simulator')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We specify the parity check matrix, since this defines the Steane code.  It is validated before the logical qubit is initiated to check that it is orthogonal to the valid codewords."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0001111', '0110011', '1010101']\n"
     ]
    }
   ],
   "source": [
    "parity_check_matrix = get_parity_check_matrix()\n",
    "print(parity_check_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The allowed codewords for the zero logical from Nielsen and Chuang is stored in a list.  Note, the order of qubits in the list is reversed compared to the normal output of Qiskit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0000000', '1010101', '0110011', '1100110', '0001111', '1011010', '0111100', '1101001']\n"
     ]
    }
   ],
   "source": [
    "codewords = get_codewords()\n",
    "print(codewords)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "runs = {'I':{},\n",
    "        'X1':{'test_x_qubit': 0},\n",
    "        'X2':{'test_x_qubit': 1},\n",
    "        'X3':{'test_x_qubit': 2},\n",
    "        'X4':{'test_x_qubit': 3},\n",
    "        'X5':{'test_x_qubit': 4},\n",
    "        'X6':{'test_x_qubit': 5},\n",
    "        'X7':{'test_x_qubit': 6},\n",
    "        'Z1':{'test_z_qubit': 0},\n",
    "        'Z2':{'test_z_qubit': 1},\n",
    "        'Z3':{'test_z_qubit': 2},\n",
    "        'Z4':{'test_z_qubit': 3},\n",
    "        'Z5':{'test_z_qubit': 4},\n",
    "        'Z6':{'test_z_qubit': 5},\n",
    "        'Z7':{'test_z_qubit': 6}\n",
    "       }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Error detection\n",
    "---------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error detection circuit is added to the encoding circuit and transpiled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run with 10000 shots and 25 iterations\n",
      "\n",
      "Error introduced I\n",
      "For ancilla x1 the mean is 0.2145 and the standard_error is 0.0011\n",
      "For ancilla x2 the mean is 0.2409 and the standard_error is 0.0010\n",
      "For ancilla x3 the mean is 0.2712 and the standard_error is 0.0011\n",
      "For ancilla z1 the mean is 0.1079 and the standard_error is 0.0007\n",
      "For ancilla z2 the mean is 0.1334 and the standard_error is 0.0008\n",
      "For ancilla z3 the mean is 0.1639 and the standard_error is 0.0008\n",
      "\n",
      "Error introduced X1\n",
      "For ancilla x1 the mean is 0.2149 and the standard_error is 0.0008\n",
      "For ancilla x2 the mean is 0.2401 and the standard_error is 0.0009\n",
      "For ancilla x3 the mean is 0.7283 and the standard_error is 0.0010\n",
      "For ancilla z1 the mean is 0.1068 and the standard_error is 0.0005\n",
      "For ancilla z2 the mean is 0.1329 and the standard_error is 0.0005\n",
      "For ancilla z3 the mean is 0.1652 and the standard_error is 0.0006\n",
      "\n",
      "Error introduced X2\n",
      "For ancilla x1 the mean is 0.2142 and the standard_error is 0.0010\n",
      "For ancilla x2 the mean is 0.7599 and the standard_error is 0.0008\n",
      "For ancilla x3 the mean is 0.2726 and the standard_error is 0.0007\n",
      "For ancilla z1 the mean is 0.1068 and the standard_error is 0.0006\n",
      "For ancilla z2 the mean is 0.1331 and the standard_error is 0.0008\n",
      "For ancilla z3 the mean is 0.1641 and the standard_error is 0.0008\n",
      "\n",
      "Error introduced X3\n",
      "For ancilla x1 the mean is 0.2146 and the standard_error is 0.0008\n",
      "For ancilla x2 the mean is 0.7605 and the standard_error is 0.0009\n",
      "For ancilla x3 the mean is 0.7288 and the standard_error is 0.0007\n",
      "For ancilla z1 the mean is 0.1068 and the standard_error is 0.0005\n",
      "For ancilla z2 the mean is 0.1337 and the standard_error is 0.0007\n",
      "For ancilla z3 the mean is 0.1645 and the standard_error is 0.0008\n",
      "\n",
      "Error introduced X4\n",
      "For ancilla x1 the mean is 0.7868 and the standard_error is 0.0009\n",
      "For ancilla x2 the mean is 0.2386 and the standard_error is 0.0008\n",
      "For ancilla x3 the mean is 0.2720 and the standard_error is 0.0009\n",
      "For ancilla z1 the mean is 0.1062 and the standard_error is 0.0007\n",
      "For ancilla z2 the mean is 0.1331 and the standard_error is 0.0007\n",
      "For ancilla z3 the mean is 0.1639 and the standard_error is 0.0007\n",
      "\n",
      "Error introduced X5\n",
      "For ancilla x1 the mean is 0.7856 and the standard_error is 0.0009\n",
      "For ancilla x2 the mean is 0.2376 and the standard_error is 0.0008\n",
      "For ancilla x3 the mean is 0.7289 and the standard_error is 0.0008\n",
      "For ancilla z1 the mean is 0.1062 and the standard_error is 0.0007\n",
      "For ancilla z2 the mean is 0.1330 and the standard_error is 0.0007\n",
      "For ancilla z3 the mean is 0.1638 and the standard_error is 0.0006\n",
      "\n",
      "Error introduced X6\n",
      "For ancilla x1 the mean is 0.7848 and the standard_error is 0.0008\n",
      "For ancilla x2 the mean is 0.7609 and the standard_error is 0.0008\n",
      "For ancilla x3 the mean is 0.2742 and the standard_error is 0.0006\n",
      "For ancilla z1 the mean is 0.1076 and the standard_error is 0.0006\n",
      "For ancilla z2 the mean is 0.1340 and the standard_error is 0.0005\n",
      "For ancilla z3 the mean is 0.1663 and the standard_error is 0.0007\n",
      "\n",
      "Error introduced X7\n",
      "For ancilla x1 the mean is 0.7859 and the standard_error is 0.0008\n",
      "For ancilla x2 the mean is 0.7613 and the standard_error is 0.0011\n",
      "For ancilla x3 the mean is 0.7273 and the standard_error is 0.0008\n",
      "For ancilla z1 the mean is 0.1071 and the standard_error is 0.0005\n",
      "For ancilla z2 the mean is 0.1327 and the standard_error is 0.0006\n",
      "For ancilla z3 the mean is 0.1632 and the standard_error is 0.0008\n",
      "\n",
      "Error introduced Z1\n",
      "For ancilla x1 the mean is 0.2144 and the standard_error is 0.0007\n",
      "For ancilla x2 the mean is 0.2385 and the standard_error is 0.0010\n",
      "For ancilla x3 the mean is 0.2728 and the standard_error is 0.0009\n",
      "For ancilla z1 the mean is 0.1067 and the standard_error is 0.0006\n",
      "For ancilla z2 the mean is 0.1326 and the standard_error is 0.0007\n",
      "For ancilla z3 the mean is 0.8347 and the standard_error is 0.0009\n",
      "\n",
      "Error introduced Z2\n",
      "For ancilla x1 the mean is 0.2135 and the standard_error is 0.0008\n",
      "For ancilla x2 the mean is 0.2374 and the standard_error is 0.0009\n",
      "For ancilla x3 the mean is 0.2721 and the standard_error is 0.0008\n",
      "For ancilla z1 the mean is 0.1067 and the standard_error is 0.0005\n",
      "For ancilla z2 the mean is 0.8668 and the standard_error is 0.0006\n",
      "For ancilla z3 the mean is 0.1650 and the standard_error is 0.0007\n",
      "\n",
      "Error introduced Z3\n",
      "For ancilla x1 the mean is 0.2142 and the standard_error is 0.0006\n",
      "For ancilla x2 the mean is 0.2394 and the standard_error is 0.0009\n",
      "For ancilla x3 the mean is 0.2720 and the standard_error is 0.0006\n",
      "For ancilla z1 the mean is 0.1067 and the standard_error is 0.0007\n",
      "For ancilla z2 the mean is 0.8660 and the standard_error is 0.0007\n",
      "For ancilla z3 the mean is 0.8356 and the standard_error is 0.0007\n",
      "\n",
      "Error introduced Z4\n",
      "For ancilla x1 the mean is 0.2148 and the standard_error is 0.0008\n",
      "For ancilla x2 the mean is 0.2389 and the standard_error is 0.0006\n",
      "For ancilla x3 the mean is 0.2722 and the standard_error is 0.0010\n",
      "For ancilla z1 the mean is 0.8929 and the standard_error is 0.0007\n",
      "For ancilla z2 the mean is 0.1319 and the standard_error is 0.0007\n",
      "For ancilla z3 the mean is 0.1624 and the standard_error is 0.0006\n",
      "\n",
      "Error introduced Z5\n",
      "For ancilla x1 the mean is 0.2147 and the standard_error is 0.0009\n",
      "For ancilla x2 the mean is 0.2400 and the standard_error is 0.0009\n",
      "For ancilla x3 the mean is 0.2722 and the standard_error is 0.0010\n",
      "For ancilla z1 the mean is 0.8923 and the standard_error is 0.0006\n",
      "For ancilla z2 the mean is 0.1311 and the standard_error is 0.0006\n",
      "For ancilla z3 the mean is 0.8354 and the standard_error is 0.0005\n",
      "\n",
      "Error introduced Z6\n",
      "For ancilla x1 the mean is 0.2139 and the standard_error is 0.0009\n",
      "For ancilla x2 the mean is 0.2395 and the standard_error is 0.0009\n",
      "For ancilla x3 the mean is 0.2707 and the standard_error is 0.0006\n",
      "For ancilla z1 the mean is 0.8923 and the standard_error is 0.0007\n",
      "For ancilla z2 the mean is 0.8672 and the standard_error is 0.0008\n",
      "For ancilla z3 the mean is 0.1635 and the standard_error is 0.0009\n",
      "\n",
      "Error introduced Z7\n",
      "For ancilla x1 the mean is 0.2143 and the standard_error is 0.0009\n",
      "For ancilla x2 the mean is 0.2400 and the standard_error is 0.0008\n",
      "For ancilla x3 the mean is 0.2713 and the standard_error is 0.0009\n",
      "For ancilla z1 the mean is 0.8928 and the standard_error is 0.0006\n",
      "For ancilla z2 the mean is 0.8670 and the standard_error is 0.0007\n",
      "For ancilla z3 the mean is 0.8366 and the standard_error is 0.0007\n"
     ]
    }
   ],
   "source": [
    "print(f'Run with {SHOTS} shots and {ITERATIONS} iterations')\n",
    "#initialise lists for graphs results\n",
    "values_graph = [[] for i in range(ANCILLA_QUBITS * 2)]\n",
    "standard_error_graph= [[] for i in range(ANCILLA_QUBITS * 2)]\n",
    "labels_list = [str(j) + str(i + 1) for j in ['x', 'z'] for i in range(3)]\n",
    "cases_list = []\n",
    "\n",
    "for cases, parameters in runs.items():\n",
    "    print()\n",
    "    print(f'Error introduced {cases}')\n",
    "    cases_list.append(cases)\n",
    "    test_x = True\n",
    "    if 'test_x_qubit' in parameters:\n",
    "        x_bit = parameters.get('test_x_qubit')\n",
    "    else:\n",
    "        test_x = False\n",
    "    test_z = True\n",
    "    if 'test_z_qubit' in parameters:\n",
    "        z_bit = parameters.get('test_z_qubit')\n",
    "    else:\n",
    "        test_z = False\n",
    "    results_list = [[] for i in range(ANCILLA_QUBITS * 2)]\n",
    "    for iteration in range(ITERATIONS):    \n",
    "        qubit = SteaneCodeLogicalQubit(1, parity_check_matrix, codewords)\n",
    "        qubit.set_up_logical_zero()\n",
    "        qubit.barrier()\n",
    "        if test_x:\n",
    "            qubit.force_X_error(x_bit)   #force X error for testing\n",
    "        if test_z:\n",
    "            qubit.force_Z_error(z_bit)   #force Z error for testing\n",
    "        qubit.barrier()\n",
    "        qubit.set_up_ancilla()\n",
    "        qubit.barrier()\n",
    "        if NOISE:\n",
    "            noise_model = get_noise(MEASURE_NOISE, SINGLE_GATE_DEPOLARISING, \n",
    "                                    TWO_GATE_DEPOLARISING, SINGLE_GATE_SET, TWO_GATE_SET)\n",
    "        qubit.logical_measure_data()\n",
    "        qubit.logical_measure_ancilla()\n",
    "        qubit.barrier()\n",
    "        qt = transpile(qubit, basis_gates = BASIS_GATE_SET)\n",
    "        if NOISE:\n",
    "            result = execute(qt, SIMULATOR, noise_model = noise_model, shots = SHOTS).result()\n",
    "        else:\n",
    "            result = execute(qt, SIMULATOR, shots = SHOTS).result()\n",
    "        counts = result.get_counts(qt)\n",
    "        # calculate location of ancilla \n",
    "        for i in ['x', 'z']:\n",
    "            if i == 'x':\n",
    "                location = 0        \n",
    "            elif i == 'z':\n",
    "                location = 1\n",
    "            else:\n",
    "                raise Exception ('Unexpected ancilla value') \n",
    "            # find ancilla values for relevant location\n",
    "            ancilla_values = find_ancilla_values(counts, ANCILLA_QUBITS, location) \n",
    "            #i is first part of ancilla label\n",
    "            individual_ancilla_values = find_individual_ancilla_values(ancilla_values, 0, ANCILLA_QUBITS, i)\n",
    "            index = 0\n",
    "            for labels in labels_list:\n",
    "                if labels in individual_ancilla_values.keys():\n",
    "                    result = individual_ancilla_values.get(labels) / SHOTS\n",
    "                    results_list[index].append(result)\n",
    "                index = index + 1 \n",
    "    index = 0\n",
    "    for labels in labels_list:\n",
    "        #find mean and standard deviation for all iterations\n",
    "        mean = mean_of_list(results_list[index])\n",
    "        standard_devation, standard_error = calculate_standard_error(results_list[index])  \n",
    "        values_graph[index].append(mean)\n",
    "        standard_error_graph[index].append(standard_error)\n",
    "        print(f'For ancilla {labels} the mean is {mean:.4f} and the standard_error is {standard_error:.4f}')\n",
    "        index = index + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph constants\n",
    "graph_labels_list = [str(j) + ' Operator ' +  str(i + 1) for j in ['Z', 'X'] for i in range(3)]\n",
    "YMIN = 0\n",
    "YMAX = 1.0\n",
    "YTICK = 0.2\n",
    "WIDTH = 0.15\n",
    "GAP = .8\n",
    "BAR_WIDTH = 1\n",
    "x = np.arange(len(cases_list))  # the label locations\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(x - WIDTH * 5 / 2, values_graph[0], color = '#ffc199', width = WIDTH * GAP, label = graph_labels_list[0])\n",
    "plt.bar(x - WIDTH * 3 / 2,values_graph[1], color = '#ff8333', width = WIDTH * GAP, label = graph_labels_list[1])\n",
    "plt.bar(x - WIDTH / 2, values_graph[2], color = '#cc5000', width = WIDTH * GAP, label = graph_labels_list[2])\n",
    "plt.bar(x + WIDTH / 2, values_graph[3], color = '#c199ff', width = WIDTH * GAP, label = graph_labels_list[3])\n",
    "plt.bar(x + WIDTH * 3 /2, values_graph[4], color = '#8333ff', width = WIDTH * GAP, label = graph_labels_list[4])\n",
    "plt.bar(x + WIDTH * 5 /2, values_graph[5], color = '#5000cc', width = WIDTH * GAP, label = graph_labels_list[5])\n",
    "\n",
    "plt.errorbar(x - WIDTH  * 5 / 2, values_graph[0], yerr = standard_error_graph[0], \n",
    "             color = 'black', fmt = 'none', elinewidth = BAR_WIDTH)\n",
    "plt.errorbar(x - WIDTH  * 3 / 2, values_graph[1], yerr = standard_error_graph[1], \n",
    "             color = 'black', fmt = 'none', elinewidth = BAR_WIDTH )\n",
    "plt.errorbar(x - WIDTH  / 2, values_graph[2], yerr = standard_error_graph[2], \n",
    "             color = 'black', fmt = 'none', elinewidth = BAR_WIDTH )\n",
    "plt.errorbar(x + WIDTH  / 2, values_graph[3], yerr = standard_error_graph[3], \n",
    "             color = 'black', fmt = 'none', elinewidth = BAR_WIDTH )\n",
    "plt.errorbar(x + WIDTH  * 3 / 2, values_graph[4], yerr = standard_error_graph[4], \n",
    "             color = 'black', fmt = 'none', elinewidth = BAR_WIDTH )\n",
    "plt.errorbar(x + WIDTH  * 5 / 2, values_graph[5], yerr = standard_error_graph[5], \n",
    "             color = 'black', fmt = 'none', elinewidth = BAR_WIDTH )\n",
    "\n",
    "ax.set_xlabel('Error introduced')\n",
    "ax.set_ylabel('Ancilla Population')\n",
    "ax.set_title('Simulation of ancilla population - error introduced in Steane code')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(cases_list)\n",
    "ax.legend(prop={\"size\":7}, loc=\"upper left\", ncol=2)\n",
    "plt.ylim(YMIN, YMAX)\n",
    "plt.yticks(np.arange(YMIN, YMAX, YTICK))\n",
    "\n",
    "fname = \"Steane_code_ancilla_graph.png\"\n",
    "plt.savefig(fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3886.37x2010.68 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qubit.draw(output = 'mpl', filename = 'Steane_code_error_detection_circuit.jpg', fold = 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw transpiled circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sYR07fVC7/lin6mHRWr55tnYlr9X7Xz2pEVNjtH3vKqvjAllq2bKl1RHyxNTcKBymzg9TcwMAAMA5Jj7vmZgZAAAAAAAUPFYIBywyYmqMduxbJVdXd7m4uCqoTGXdFTtGre7obXU0ACgQ3PdQHJk6r03NnZO7Yp+yv35/xBZJUovInnJxcVGbuv3Upm6/Qs9UXK81CtaRI0esjpAnRTm3ie9FEzNnpyjPj+yYmhsAAADOMfF5z8TMAAAAAACg4FEQDljo7rZjdXfbp5WamqLElW9p4sy7VDW0rkIDqlodDQAKBPc9FEemzmtTc+dWqzv6WB3hlrnWQFFn4nvRxMwAAAAAAAAAAAAACp+L1QEASK6ubrqz0QNKTUvRbwc2Wh0HAAoc9z0UR6bOa1Nzm4hrDWfVqlXL6gh5YkpuE9+LJma+kSnz40am5gYAAIBzTHzeMzEzAAAAAAAoeBSEA0XAlZTLWrByqiQpLKC6xWkAoOBx30NxZOq8NjW3ibjWcNYXX3xhdYQ8MSW3ie9FEzPfyJT5cSNTcwMAADP5+vqqZs2aioiIUFBQULZ9bTab+vbtW0jJii8Tn/dMzAwAAAAAAAoeBeHI0datW2Wz2bRw4UJJ0u+//y6bzaZZs2bZ+2TWlhcpKSny8fGRzWbL9mv27NmZ7n/q1Ck99NBDKleunHx8fNSsWTP9+OOPN5WpIM1c9oK6jy2tzk95a/ripzW89wcKD6kjSfrz6G49/Hp9XUm5LEmalfSKZiweZ2VcALhp3PdQHJk6r7PL/eInd2n19gX2vs/M6K51vyyxKqrxTJ0jsM64cWbOgaKe28T3YnG6Vxf1+ZEVU3MDAABz1KpVS2+++aZ++eUXnT59Wtu3b9fWrVt18OBBHTx4ULNnz1b79u1ls9ns+9hsNn3wwQf69NNP9fLLL1uY3nwmPu+ZmBkAAAAAABQ8CsKRo+DgYK1atUpt27aVJK1fv16SFB0dbe+TWVtepKen69tvv9WqVasyfL3wwguSpKpVq6pNmzaZ7tutWzclJCTo1Vdf1bx58xQQEKB27dppw4YNN5WroNwVO0ZznzupOeOPqmGNTtq0+zv7ttCAqmoe2VOffTtRB4//rqSNn+mu2DEWpgWAm8d9D8WRqfM6u9xDur2uGYvH6sKls/phy5cq4eWn6NvbW5jWbKbOEVgnq1+ALeqKem4T34vF6V5d1OdHVkzNDQAAir5y5cpp9uzZ2rZtmx555BFVr15dly5d0s6dO7Vt2zadPHlSQUFB6tWrlxYvXqxNmzYpOjraXgx+//336/z58/rqq6+sPhWjmfi8Z2JmAAAAAABQ8CgIR478/f3VuHFjeXh4SLpa/F2mTBmFh4fb+2TWlhfu7u5q3Lhxhq/U1FS9+OKLqlixopYtWyZ/f/8M+y5YsEDLly/XjBkzdM8996ht27aaPXu2wsLCNGaM9T/Iz46vTxkN7/2B1uz8Siu3Jtrb+8T8W6t3LNCLn/TTkK6vy8PN08KUAApbalqq3lvwb/Uaf5u6Pu2rZz/qqVPnjlodK19w37s1Fec5LZk7rzPLXaZkOcU3f1z/TXxMM5c9r4e6TrE4ZfFg6hwBihsT34vcqwEAAIqXmJgYbdu2Tb169dK5c+c0depUNWrUSL6+vqpZs6Zq166tMmXKqFq1anrqqaeUnJysyMhIrV69WmvWrLEXg3fq1EnLly+3+nQAAAAAAABQBFAQbpikpCR1795d5cuXl5eXl0JDQzVo0CCdOnXK3ic6Olr9+vXTF198oUaNGsnHx0eVK1fW559/numY8+bNU9u2bVW6dGn5+PgoMjJS06ZNs29v1KiRevfubf9+/fr1qlevnsMYN7Y5k9NZ69atU6dOneTn56dly5apQoUKmfZLTEyUv7+/OnbsaG/z8PBQ3759tXTpUp07dy7Xxy5MpXzKqmeL4Zr29VNKS0uTJLm5uisyvKXOXjih2pWbW5wQQGH77LtJWrktUW8+ukafjvlDkvTSp/+0OFX+4b536ynuc1oyd15nlrtDgwH648gudW/2mEr5lLU4YfFh6hwBihsT34vcqwEAAIqHmJgYLVq0SAEBAVqyZIlq1qyphx9+WD/99JOuXLni0Hf37t2aOHGiqlevrldeeUUuLi5q0KCBLl++TDE4AAAAAAAAHFAQbphNmzapefPmeuedd7RkyRKNGzdOiYmJGjFihCQpJSVFW7du1Zo1a/Tmm2/qX//6lxISElSuXDkNGDBAp0+fdhhv7Nix9sLtadOm6YsvvlDXrl11/PhxSVJqaqq2bNniUOy9fv16RUdHO4xzY1tOOZ21efNmtW/fXl5eXlq2bJmqVKmSZd+tW7cqIiJCNpvNob127dpKSUnRzp07c3VsK8S3eFzHTx/U0vX/kyTt/Wubtu1dobpV22rhmvctTgegsC1c/Z7+0Xqkgv3DVcLbTw/Evay1v3ytQyf2WR0t33Dfu7XcCnNaMnde35hbkkL8qyo0oKqFqYonU+cICo+pRR2m5TbxvWjyvdq0+XGNqbkBAEDRFBgYqDlz5sjLy0vvvvuuOnbsqOTk5Bz3u3Tpkvz9/WWz2ZSeni4PDw/5+PgUQuLiz8TnPRMzAwAAAACAgudmdQDkzuOPP25/nZaWpqZNm2rTpk1atmyZJGn79u26dOmSqlWrpkWLFsnF5WrN/6VLl9StWzft27dPkZGRkqQvv/xSzz//vGbMmKF7773XPu6dd95pf71z505duHBBdevWlSTt3btXx48fV/369e19MmvLKaczdu7cqXbt2slms+mbb75RjRo1su1//Phx1apVK0N72bJl7duLkteGJGVoK+FVSl9OuJozLS1Nb3z5kB6N/6/CAqrr8f82VdOIbirjG1jISQFY4eyFkzp8cr+qhf59bw0JqCIfr1L67cAmBZapaGG6vOG+d2srjnNaMnde55Qb+cfUOQJrbdu2TeXKlbM6Rq4V5dwmvheL2726KM+P7JiaGwAAFE1vv/22/P39tXjxYg0ZMkTp6ek57mOz2fTBBx/o/vvv1/nz5/XRRx9pyJAhev/991WrVq0MCwEhd0x83jMxMwAAAAAAKHgUhBskNTVVn332maZOnapff/1Vhw8ftm+7toL3hg0bJEkTJkywF4NLfxdDh4SE2NvGjh2r1q1bOxSD3+jnn3+WJHtB+Pr16yXJofj7xjZncubkt99+U2xsrC5duqRly5bZi9jzy7hx4/TZZ59p9+7dmjVrlnr16uXUftdWXbhxFfIbvfrQd7qjSsxNZZy/aqqqhdZX9bCr13VAh+f09rxhGnP3pzlkTFKDfq1v6thFWX5c24JS3K+9CUydH5nlPn/pjCSphLefQ3tJr9I6f7FwfsiTmznNfQ/XK6pzWir+89rK+2BhX9uC4ux55Nc55GWOcN8z3xNPPJHt9ilTpjjVpzDllEeyLrdV9+ubeS+acr92VlGeH9kxNXdWRk56V9LVzyyuf12UmZhZMjO3iZmlv3NfY1Jmk661iZklM3ObmFkyM3dRzBwZGakePXro7NmzGjRoUJ6KwTt16qQffvhBdevWVePGjTVw4MAMzyLLly+3/FyLClOf90z8f0YAAAAAAFBwnPkcSaIg3Cj33nuvvvzySz3yyCMaOXKkAgIC5ObmptjYWHvB9IYNG1SmTBk1atTIYd+NGzcqLCxM/v7+kqT9+/dr+/btDit5Z2bDhg0KCQlRYODVVdHWr1+vsmXLKjw83N7nxjZncmZn//79atOmjU6fPq3Fixc7FJ9np2zZsjpx4kSG9mvF8NdWCpekjh07asCAAbr//vudGtsK3ZoNdfi+We3uala7uzVhABQ6H09fSdK5C6cc2s9ePCkfr1JWRCpw3PeKt1txTkvmz+sn+86wOkKxZ/ocAYoLk9+L3KsBAADMMGTIEEnS9OnT9ccff+TYP7Ni8GuL1kycOFGJiYkaMmQIhb8AAAAAAACQREG4MbZu3apPPvlEH3zwgQYOHGhvX7Fihc6cOWMvmt6wYUOmBdTr1693WJ37wIEDkqTQ0NBsj7thwwb76uCZjXNjm7M5s3Lw4EHFxsbqyJEjWrhwoZo2bZpt/+tFRERo3rx5Sk9Pd1j9Ytu2bXJzc1ONGjXsbbkZ93qtWrWSJCUlJWXbb91n0smcP88tEK1axSh9qnO/EWIiK69tTor7tTeBqfMjs9wlvUurXOkK2v3nz6oaGiVJOnhsj85fPK3w4DoFnPaq3Mxp7nu4XlGd01Lxn9emZDb1fn09U641iqadO3dmu33KlCkaPHhwtn0mT56cn5FylFNmybrcVr0fb+a9WNzuIUV5fmTH1NxZGfXSe5KurhJx/euizMTMkpm5Tcws/Z37GpMym3StTcwsmZnbxMySmbmtzhwTE2Mv3r6mQ4cOkq4WhOcku2JwSfrqq6905MgRVatWTeHh4dqzZ499W6tWrXL8GcatwtTnPRP/nxEAAAAAAFjPxeoAcM7+/fslyaGo+cqVKxo+fLgkqX79+kpPT9fGjRszFF2npaVp48aNDoXdISEhkq4WcGfnxv3Wr1+fYfzr25zJmZUjR44oNjZWycnJmjt3rmJiYrLNdqNu3brp6NGjWrx4scOxP/vsM7Vt21YlSpTI1XgAYLVOjQfr86SXdPD47zp38bTeXzhS0dU7KKhsJaujAXnCnAYA5zz77LNWR8gTU3OjcJg6P0zNDQAAipYyZcooPDxc58+f1+bNm7Ptm1MxuCSlpqbqp59+kpT9z12QMxOf90zMDAAAAAAACh4rhBvijjvukJeXl0aNGqWxY8fq2LFjmjx5sk6cOCFXV1dFRUVpz549On36tKKjox323bVrl86ePeuwsneFChXUunVrvfDCC3J1dVX9+vV1/PhxLV26VHfffbdatGihPXv26OTJk/b99u3bp2PHjjmMf2ObMzkzc+7cObVr1047duzQ6NGjVapUKa1evTpDv5CQEFWoUCHTMbp06aIWLVrovvvu08svv6zg4GC99dZb2r9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      "text/plain": [
       "<Figure size 3886.37x2010.68 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qt.draw(output = 'mpl', filename = 'Steane_code_error_detection_circuit_transpiled.jpg', fold = 60)"
   ]
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}