Steane_Code_single_qubit_error_sensitivity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Steane code single qubit error sensitivity\n",
    "=================================\n",
    "\n",
    "Sets 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",
    "Use this to estimate the simulation sensitivity to single qubit errors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The necessary function modules are imported , 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",
    "from helper_functions import (\n",
    "    count_valid_output_strings,\n",
    "    find_individual_ancilla_values,\n",
    "    find_ancilla_values,\n",
    "    find_parity,\n",
    "    get_noise,\n",
    "    mean_of_list,\n",
    "    calculate_standard_error\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": [
    "NOISE = True                             #Test with noise\n",
    "SHOTS = 10000                            #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 = 0.0204           #Two gate noise\n",
    "ITERATIONS = 25\n",
    "FACTOR = [1, 0, 2]\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": [],
   "source": [
    "parity_check_matrix = ['0001111',\n",
    "                       '0110011',\n",
    "                       '1010101'\n",
    "                      ]"
   ]
  },
  {
   "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 string is reversed compared to the normal output of Qiskit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "codewords = ['0000000',\n",
    "             '1010101',\n",
    "             '0110011',\n",
    "             '1100110',\n",
    "             '0001111',\n",
    "             '1011010',\n",
    "             '0111100',\n",
    "             '1101001'\n",
    "            ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encoding set up\n",
    "-----------------------\n",
    "The encoding circuit alone is set up and either transpiled to the native gate set for the Ion computer, or calculated with the standard quiskit noise gate set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run with 10000 shots and 25 iterations\n",
      "With single gate depolarising noise of 0.0003660\n",
      "Reduced encoding gates used\n",
      "Results after transpilation to ion computer gate set\n",
      "\n",
      " Over all iterations invalid strings occurred on 0.1800 of runs\n",
      " The standard deviation 0.0043 and the standard error is 0.0009\n",
      "\n",
      "With single gate depolarising noise of 0.0000000\n",
      "Reduced encoding gates used\n",
      "Results after transpilation to ion computer gate set\n",
      "\n",
      " Over all iterations invalid strings occurred on 0.1776 of runs\n",
      " The standard deviation 0.0038 and the standard error is 0.0008\n",
      "\n",
      "With single gate depolarising noise of 0.0007320\n",
      "Reduced encoding gates used\n",
      "Results after transpilation to ion computer gate set\n",
      "\n",
      " Over all iterations invalid strings occurred on 0.1841 of runs\n",
      " The standard deviation 0.0042 and the standard error is 0.0008\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f'Run with {SHOTS} shots and {ITERATIONS} iterations')\n",
    "for factor in FACTOR:\n",
    "    single_gate_depolarising = SINGLE_GATE_DEPOLARISING * factor\n",
    "    print(f'With single gate depolarising noise of {single_gate_depolarising:.7f}')\n",
    "    for reduced in [True]:\n",
    "        if reduced:\n",
    "            print('Reduced encoding gates used')\n",
    "        else:\n",
    "            print('Full encoding gates used')\n",
    "        for ion_gate_set in [True]:\n",
    "            results_list = []\n",
    "            if ion_gate_set:\n",
    "                print('Results after transpilation to ion computer gate set')   \n",
    "            else:\n",
    "                print('Results without transpilation')\n",
    "            for iteration in range(ITERATIONS):\n",
    "                qubit = SteaneCodeLogicalQubit(1, parity_check_matrix, codewords, ancilla = False)\n",
    "                qubit.set_up_logical_zero(0)\n",
    "                qubit.barrier()\n",
    "                if ion_gate_set:\n",
    "                    SINGLE_GATE_SET = ['id', 'ry', 'rx']\n",
    "                    TWO_GATE_SET = ['rxx']\n",
    "                else:\n",
    "                    SINGLE_GATE_SET = ['id', 'h', 'x', 'y', 'z,']\n",
    "                    TWO_GATE_SET = ['cx', 'cz']    \n",
    "                BASIS_GATE_SET = SINGLE_GATE_SET + TWO_GATE_SET\n",
    "                if NOISE:\n",
    "                    noise_model = get_noise(MEASURE_NOISE, single_gate_depolarising, \n",
    "                                            TWO_GATE_DEPOLARISING, \n",
    "                                           SINGLE_GATE_SET, TWO_GATE_SET)\n",
    "                #print('noise_model',noise_model)\n",
    "                qubit.logical_measure_data()\n",
    "                qubit.barrier()\n",
    "                if ion_gate_set:\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",
    "                    #print(counts)\n",
    "                else:\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",
    "                    #print(counts)\n",
    "                count_valid, count_invalid = count_valid_output_strings(counts, codewords)\n",
    "                invalid_rate = count_invalid / SHOTS\n",
    "                results_list.append(invalid_rate)\n",
    "                #print(f'There were {count_valid} valid output strings and {count_invalid} invalid output string')\n",
    "                #print(f'Invalid strings occurred on {invalid_rate:.4f} of runs')\n",
    "            mean = mean_of_list(results_list)\n",
    "            standard_deviation, standard_error = calculate_standard_error(results_list)\n",
    "            print()\n",
    "            print(f' Over all iterations invalid strings occurred on {mean:.4f} of runs')\n",
    "            print(f' The standard deviation {standard_deviation:.4f} and the standard error is {standard_error:.4f}')\n",
    "            print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw last circuit processed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1312.11x505.68 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qubit.draw(output = 'mpl', filename = 'Steane_code_encoding_circuit.jpg', fold = 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw last transpiled circuit processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1914.11x505.68 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qt.draw(output = 'mpl', filename = 'Steane_code_encoding_circuit_transpiled.jpg', fold = 40)"
   ]
  }
 ],
 "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
}