Steane_code_FTb_encoding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Steane code fault tolerance encoding scheme b\n",
    "======================================= \n",
    "\n",
    "1. Set up two 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 fault tolerance as per scheme (b) from Goto H. Minimizing resource overheads for fault-tolerant preparation of encoded states of the Steane code. Sci Rep. 2016 Jan 27;6:19578. \n",
    "\n",
    "3. Find out if this scheme has a tolerance."
   ]
  },
  {
   "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",
    "\n",
    "from qiskit.providers.aer.noise import NoiseModel\n",
    "from qiskit.providers.aer.noise.errors import pauli_error, depolarizing_error\n",
    "\n",
    "from circuits import SteaneCodeLogicalQubit\n",
    "\n",
    "from helper_functions import (\n",
    "    get_noise, \n",
    "    count_valid_output_strings,\n",
    "    string_reverse,\n",
    "    process_FT_results, \n",
    "    mean_of_list,\n",
    "    calculate_standard_error,\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_ERRORS = ['x', 'y', 'z', 'h', 's', 'sdg']\n",
    "TWO_GATE_ERRORS = ['cx', 'cz']\n",
    "\n",
    "NOISE = True                             #Test with noise\n",
    "SHOTS = 250000                           #Number of shots to run    \n",
    "MEASURE_NOISE = 0.0046                   #Measurement noise not relevant\n",
    "SINGLE_GATE_DEPOLARISING = 0.000366      #Single gate noise   \n",
    "TWO_GATE_DEPOLARISING = 0.022\n",
    "ITERATIONS = 1\n",
    "POST_SELECTION = True\n",
    "\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": "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": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "if NOISE:\n",
    "    noise_model = get_noise(MEASURE_NOISE, SINGLE_GATE_DEPOLARISING, \n",
    "                            TWO_GATE_DEPOLARISING, SINGLE_GATE_ERRORS, TWO_GATE_ERRORS )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At the data validation stage\n",
      "There are 177305 strings rejected and 72695 strings submitted for processing\n",
      "Making 250000 in total submitted for data processing\n",
      "\n",
      "At the ancilla validation stage\n",
      "There are 0 strings rejected and 72695 strings submitted for validation\n",
      "Making 72695 in total submitted to check against ancilla\n",
      "\n",
      "Of these 72695 strings validated there are 64944 valid strings and 2 invalid_strings\n",
      "There were 7749 strings that were neither logical one or logical zero\n",
      "The error rate is 0.0000\n",
      "Unable to carry out standard error calcuation with one point. \n",
      "Standard error of 0 used.\n",
      "There are 177305 strings rejected and 72695 strings submitted for validation\n",
      "Of these 72695 strings processed there are 64944 valid strings and 2 invalid_strings\n",
      "There are 7749 strings outside the codeword\n",
      "The error rate is 0.000028 and the standard error is 0.000000 \n"
     ]
    }
   ],
   "source": [
    "rejected_accum = 0\n",
    "accepted_accum = 0\n",
    "valid_accum = 0\n",
    "invalid_accum = 0\n",
    "results = []\n",
    "for iteration in range(ITERATIONS):\n",
    "    qubit = SteaneCodeLogicalQubit(2, parity_check_matrix, codewords, \n",
    "                           ancilla = False, fault_tolerant_b = True, \n",
    "                           data_rounds = 3\n",
    "                          )\n",
    "    qubit.set_up_logical_zero(0)\n",
    "    for i in range(3):\n",
    "        qubit.barrier()\n",
    "        qubit.set_up_logical_zero(1)\n",
    "        qubit.barrier()\n",
    "        qubit.logical_gate_CX(0, 1)\n",
    "        qubit.barrier()\n",
    "        qubit.logical_measure_data_FT(logical_qubit = 1, measure_round = i + 1)\n",
    "        qubit.barrier() \n",
    "        qubit.logical_measure_data(0)\n",
    "    if NOISE:\n",
    "        result = execute(qubit, SIMULATOR, noise_model = noise_model, shots = SHOTS).result()\n",
    "    else:\n",
    "        result = execute(qubit, SIMULATOR, shots = SHOTS).result()\n",
    "    counts = result.get_counts(qubit)\n",
    "    error_rate, rejected, accepted, valid, invalid = process_FT_results(counts, codewords, verbose = True,\n",
    "                                                                data_start = 3, data_meas_qubits = 1,\n",
    "                                                                data_meas_repeats = 3, data_meas_strings = codewords,\n",
    "                                                                post_selection = POST_SELECTION\n",
    "                                                                       )\n",
    "    rejected_accum = rejected + rejected_accum\n",
    "    accepted_accum = accepted_accum + accepted\n",
    "    valid_accum = valid_accum + valid\n",
    "    invalid_accum =  invalid_accum + invalid    \n",
    "    results.append(error_rate)\n",
    "mean_error_rate = mean_of_list(results)\n",
    "outside_accum = accepted_accum - valid_accum - invalid_accum\n",
    "standard_deviation, standard_error = calculate_standard_error(results)\n",
    "print(f'There are {rejected_accum} strings rejected and {accepted_accum} strings submitted for validation')\n",
    "print(f'Of these {accepted_accum} strings processed there are {valid_accum} valid strings and {invalid_accum} invalid_strings')\n",
    "if POST_SELECTION:\n",
    "    print(f'There are {outside_accum} strings outside the codeword')\n",
    "print(f'The error rate is {mean_error_rate:.6f} and the standard error is {standard_error:.6f} ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2830.53x3395.28 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qubit.draw(output='mpl', filename = './circuits/Steane_code_circuit_encoding_FTb.jpg', fold = 43)"
   ]
  }
 ],
 "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
}