Steane_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Steane code\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. Perform a logical Hadamard\n",
    "\n",
    "3. Peform a logical CX to entangle the two logical qubits \n",
    "\n",
    "4. Set up ancilla based on the parity matrix\n",
    "\n",
    "5. Set up an error correcting circuit\n",
    "\n",
    "5. Decode the qubit"
   ]
  },
  {
   "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",
    "    get_parity_check_matrix,\n",
    "    get_codewords,\n",
    "    count_valid_output_strings\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",
    "FIRST_LOGICAL = True                     #Set up first logical qubit?\n",
    "SECOND_LOGICAL = False                   #Set up second logical qubit?  NB, error correction is not possible with two logical qubits\n",
    "\n",
    "FIRST_LOGICAL_ONE = True\n",
    "SECOND_LOGICAL_ONE = False\n",
    "\n",
    "FIRST_LOGICAL_X = False                  #Apply logical X operation? \n",
    "SECOND_LOGICAL_X = False                 #Apply logical X operation? \n",
    "\n",
    "FIRST_LOGICAL_H = False                  #Hadamard on first logical qbit\n",
    "SECOND_LOGICAL_H = False                 #Hadamard on second logical qbit\n",
    "\n",
    "FIRST_DECODE = False                     #Decode the first logical qubit\n",
    "SECOND_DECODE = False                    #Decond the second logical qubit\n",
    "FIRST_SIMPLE_DECODE = True              #Simple decode for the first logical qubit\n",
    "SECOND_SIMPLE_DECODE = False             #Simple decode for the second logical qubit\n",
    "\n",
    "REDUCED = True                           #Reduce the encoding circuit if possible\n",
    "\n",
    "LOGICAL_CX = False                       #Logical CX\n",
    "CX_CONTROL = 1                           #Logical control bit\n",
    "CX_TARGET = 0                            #Logical target bit\n",
    "\n",
    "CORRECT_ERROR = False                     #Correct errors\n",
    "ANCILLAS = False                          #Ancillas are active?\n",
    "MCT = False                               #Use MCT\n",
    "EXTEND_ANCILLA = False                    #Correct errors (NB, can only do this with one logical QUBIT)\n",
    "TESTING = False                           #Test by applying and X or Z error to one of the qubits\n",
    "TEST_X_QUBIT = 0                         #Qbit to set bit flip- Starts at zero.\n",
    "TEST_X_LOGICAL = 0                       #logical Qbit to set bit flip- Starts at zero.\n",
    "TEST_Z_QUBIT = 6                         #Qbit to phase flip- Starts at zero.\n",
    "TEST_Z_LOGICAL = 0                       #logical Qbit to set phase flip- Starts at zero.\n",
    "\n",
    "NOISE = False                            #Test with noise\n",
    "SHOTS = 100                              #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",
    "\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",
      "7\n"
     ]
    }
   ],
   "source": [
    "parity_check_matrix = get_parity_check_matrix()\n",
    "print(parity_check_matrix)\n",
    "print(len(parity_check_matrix[0]))"
   ]
  },
  {
   "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": [
    "Create an instance of the logical qubits needed.  The parity check matrix above as an input.\n",
    "\n",
    "The number of qubits needed is an argument to the class and should be 1 or 2 at present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "if SECOND_LOGICAL:\n",
    "    qubit = SteaneCodeLogicalQubit(2, parity_check_matrix, codewords, ANCILLAS, EXTEND_ANCILLA)\n",
    "else:\n",
    "    qubit = SteaneCodeLogicalQubit(1, parity_check_matrix, codewords, ANCILLAS, EXTEND_ANCILLA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use a method to initialise the logical qubits as a logical zero as required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x23c7fcb1be0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if FIRST_LOGICAL:\n",
    "    qubit.set_up_logical_zero(0, REDUCED, FIRST_LOGICAL_ONE)\n",
    "if SECOND_LOGICAL:\n",
    "    qubit.set_up_logical_zero(0, REDUCED, SECOND_LOGICAL_ONE)\n",
    "qubit.barrier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If TESTING is active we test the ancilla with an X and Z error and check that the correct error syndrome is returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "if TESTING:\n",
    "    qubit.force_X_error(TEST_X_QUBIT,TEST_X_LOGICAL)   #force X error for testing\n",
    "    #qubit.force_Z_error(TEST_Z_QUBIT,TEST_Z_LOGICAL)   #force Z error for testing\n",
    "    qubit.barrier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up ancilla and make a logical measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "if ANCILLAS:\n",
    "    qubit.set_up_ancilla(0)\n",
    "    if SECOND_LOGICAL:\n",
    "        qubit.set_up_ancilla(1)\n",
    "    qubit.barrier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correct errors found in ancilla.  NB at present errors can only be corrected if one qubit is simulated due to memory constaints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "if CORRECT_ERROR:\n",
    "    qubit.correct_errors(0, MCT)\n",
    "    qubit.barrier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply logical X gate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "if FIRST_LOGICAL_X:\n",
    "    qubit.logical_gate_X(0)\n",
    "    qubit.barrier()\n",
    "if SECOND_LOGICAL_X:\n",
    "    qubit.logical_gate_X(1)\n",
    "    qubit.barrier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply logical Hadamard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "if FIRST_LOGICAL_H:\n",
    "    qubit.logical_gate_H(0)\n",
    "    qubit.barrier()\n",
    "if SECOND_LOGICAL_H:\n",
    "    qubit.logical_gate_H(1)\n",
    "    qubit.barrier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply logical CX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "if LOGICAL_CX:\n",
    "    qubit.logical_gate_CX(CX_CONTROL,CX_TARGET)\n",
    "    qubit.barrier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uncompute initialisation of logical qubit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "if FIRST_DECODE:\n",
    "    qubit.decode(0, REDUCED, FIRST_SIMPLE_DECODE)\n",
    "    if SECOND_DECODE:\n",
    "        qubit.decode(1, REDUCED, SECOND_SIMPLE_DECODE)\n",
    "    qubit.barrier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "if NOISE:\n",
    "    noise_model = get_noise(MEASURE_NOISE, SINGLE_GATE_DEPOLARISING, TWO_GATE_DEPOLARISING, SINGLE_GATE_ERRORS, TWO_GATE_ERRORS )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Measure qubits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x23c7fdc81f0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qubit.logical_measure_data(0)\n",
    "if ANCILLAS:\n",
    "    qubit.logical_measure_ancilla(0)\n",
    "if SECOND_LOGICAL:\n",
    "    qubit.logical_measure_data(1)\n",
    "    if ANCILLAS:\n",
    "        qubit.logical_measure_ancilla(1)\n",
    "qubit.barrier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Measurement noise\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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Execute results and print out.  The format is three bits for the X error address (logical Z), three bits for the Z error address (logical X) and seven bits for the Steane data bit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The different states can be counted.  The simulated result are {'0011001': 17, '0101010': 17, '0110100': 9, '1001100': 12, '1010010': 9, '1100001': 13, '0000111': 11, '1111111': 12}\n"
     ]
    }
   ],
   "source": [
    "counts = result.get_counts(qubit)\n",
    "print ('The different states can be counted.  The simulated result are', counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print out circuit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1492.71x505.68 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qubit.draw(output='mpl', filename = './circuits/Steane_code_circuit.jpg', fold = 44)"
   ]
  }
 ],
 "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
}