Steane_Code_encoding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Steane code\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"
   ]
  },
  {
   "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",
    "    get_parity_check_matrix,\n",
    "    get_codewords,\n",
    "    print_time\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 = 0.022            #Two gate noise\n",
    "ITERATIONS = 25\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": "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": [
    {
     "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": [
    "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",
      "Reduced encoding gates used\n",
      "Results after transpilation to ion computer gate set\n",
      "  Without post selection\n",
      "   Over all iterations invalid strings occurred on 0.1920 of runs\n",
      "   The standard deviation 0.0044 and the standard error is 0.0009\n",
      "\n",
      "  With post selection\n",
      "   Over all iterations invalid strings occurred on 0.0005 of runs\n",
      "   The standard deviation 0.0002 and the standard error is 0.0000\n",
      "\n",
      "  With simple decoding\n",
      "   Over all iterations invalid strings occurred on 0.0860 of runs\n",
      "   The standard deviation 0.0031 and the standard error is 0.0006\n",
      "\n",
      "Results without transpilation\n",
      "  Without post selection\n",
      "   Over all iterations invalid strings occurred on 0.1864 of runs\n",
      "   The standard deviation 0.0045 and the standard error is 0.0009\n",
      "\n",
      "  With post selection\n",
      "   Over all iterations invalid strings occurred on 0.0005 of runs\n",
      "   The standard deviation 0.0002 and the standard error is 0.0000\n",
      "\n",
      "  With simple decoding\n",
      "   Over all iterations invalid strings occurred on 0.0830 of runs\n",
      "   The standard deviation 0.0036 and the standard error is 0.0007\n",
      "\n",
      "Full encoding gates used\n",
      "Results after transpilation to ion computer gate set\n",
      "  Without post selection\n",
      "   Over all iterations invalid strings occurred on 0.2095 of runs\n",
      "   The standard deviation 0.0046 and the standard error is 0.0009\n",
      "\n",
      "  With post selection\n",
      "   Over all iterations invalid strings occurred on 0.0012 of runs\n",
      "   The standard deviation 0.0003 and the standard error is 0.0001\n",
      "\n",
      "  With simple decoding\n",
      "   Over all iterations invalid strings occurred on 0.0953 of runs\n",
      "   The standard deviation 0.0035 and the standard error is 0.0007\n",
      "\n",
      "Results without transpilation\n",
      "  Without post selection\n",
      "   Over all iterations invalid strings occurred on 0.2052 of runs\n",
      "   The standard deviation 0.0040 and the standard error is 0.0008\n",
      "\n",
      "  With post selection\n",
      "   Over all iterations invalid strings occurred on 0.0012 of runs\n",
      "   The standard deviation 0.0004 and the standard error is 0.0001\n",
      "\n",
      "  With simple decoding\n",
      "   Over all iterations invalid strings occurred on 0.0929 of runs\n",
      "   The standard deviation 0.0029 and the standard error is 0.0006\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f'Run with {SHOTS} shots and {ITERATIONS} iterations')\n",
    "for reduced in [True, False]:\n",
    "    if reduced:\n",
    "        print('Reduced encoding gates used')\n",
    "    else:\n",
    "        print('Full encoding gates used')\n",
    "    #print_time()\n",
    "    for ion_gate_set in [True, False]:\n",
    "        results_list = []\n",
    "        results_list_ps = []\n",
    "        results_list_simple = []\n",
    "        if ion_gate_set:\n",
    "            print('Results after transpilation to ion computer gate set')   \n",
    "        else:\n",
    "            print('Results without transpilation')\n",
    "        #print_time()\n",
    "        for iteration in range(ITERATIONS):\n",
    "            #print('starting set up circuit')\n",
    "            #print_time()\n",
    "            qubit = SteaneCodeLogicalQubit(1, parity_check_matrix, codewords, ancilla = False, )\n",
    "            qubit.set_up_logical_zero(0, reduced)\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, 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",
    "                #print('results available')\n",
    "                #print_time()\n",
    "            _, invalid, _ = count_valid_output_strings(counts, codewords,\n",
    "                                                                          post_selection = False)\n",
    "            invalid_rate = invalid / SHOTS\n",
    "            results_list.append(invalid_rate)\n",
    "            #print(f'There were {valid} valid output strings and {invalid} invalid output string')\n",
    "            #print(f'Invalid strings occurred on {invalid_rate:.4f} of runs')\n",
    "            _, invalid_ps, _= count_valid_output_strings(counts, codewords,\n",
    "                                                         post_selection = True)\n",
    "            invalid_rate_ps = invalid_ps / SHOTS\n",
    "            results_list_ps.append(invalid_rate_ps)\n",
    "            _, invalid_simple, _= count_valid_output_strings(counts, '0',\n",
    "                                                        post_selection = False,\n",
    "                                                        simple = True)\n",
    "            invalid_rate_simple = invalid_simple / SHOTS\n",
    "            results_list_simple.append(invalid_rate_simple)\n",
    "            \n",
    "        mean = mean_of_list(results_list)\n",
    "        mean_ps = mean_of_list(results_list_ps)\n",
    "        mean_simple = mean_of_list(results_list_simple)                                                \n",
    "        standard_deviation, standard_error = calculate_standard_error(results_list)\n",
    "        standard_deviation_ps, standard_error_ps = calculate_standard_error(results_list_ps)\n",
    "        standard_deviation_simple, standard_error_simple = calculate_standard_error(results_list_simple)                                              \n",
    "        print('  Without post selection')\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()\n",
    "        print('  With post selection')\n",
    "        print(f'   Over all iterations invalid strings occurred on {mean_ps:.4f} of runs')\n",
    "        print(f'   The standard deviation {standard_deviation_ps:.4f} and the standard error is {standard_error_ps:.4f}')\n",
    "        print()      \n",
    "        print('  With simple decoding')\n",
    "        print(f'   Over all iterations invalid strings occurred on {mean_simple:.4f} of runs')\n",
    "        print(f'   The standard deviation {standard_deviation_simple:.4f} and the standard error is {standard_error_simple:.4f}')\n",
    "        print()                                                      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw last circuit processed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1372.31x505.68 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qubit.draw(output = 'mpl', filename = './circuits/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 = './circuits/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
}