projectq-grover-sudoku.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from projectq import MainEngine\n",
    "from projectq.meta import Compute, Control, Dagger, Uncompute\n",
    "from projectq.ops import All, CNOT, H, Measure, X, Z\n",
    "from projectq.types import Qureg, Qubit"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "def xor(q1: Qubit, q2: Qubit, output: Qubit):\n",
    "    CNOT | (q1, output)\n",
    "    CNOT | (q2, output)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "def sudoku_oracle(e: MainEngine, inputs: Qureg[Qubit], tmp: Qureg[Qubit], output: Qubit):\n",
    "    with Compute(e):\n",
    "        xor(inputs[0], inputs[1], tmp[0])\n",
    "        xor(inputs[1], inputs[3], tmp[1])\n",
    "        xor(inputs[0], inputs[2], tmp[2])\n",
    "        xor(inputs[2], inputs[3], tmp[3])\n",
    "    with Control(e, tmp):\n",
    "        X | output\n",
    "    Uncompute(e)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "def reflect_zero(e: MainEngine, inputs: Qureg[Qubit]):\n",
    "    with Compute(e):\n",
    "        All(X) | inputs\n",
    "    with Control(e, inputs[:len(inputs) - 1]):\n",
    "        Z | inputs[-1]\n",
    "    Uncompute(e)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "from collections.abc import Callable\n",
    "\n",
    "def reflect_initial(e: MainEngine, init_op: Callable[[MainEngine, Qureg], None], qubits: Qureg):\n",
    "    with Compute(e):\n",
    "        with Dagger(e):\n",
    "            init_op(e, qubits)\n",
    "\n",
    "    reflect_zero(e, qubits)\n",
    "    Uncompute(e)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "def grover_circuit():\n",
    "    eng = MainEngine()\n",
    "\n",
    "    inputs = eng.allocate_qureg(4)\n",
    "    tmp = eng.allocate_qureg(4)\n",
    "    pkb = eng.allocate_qubit()\n",
    "\n",
    "    def prepare_initial_state(_: MainEngine, qubits: Qureg):\n",
    "        All(H) | qubits\n",
    "\n",
    "    prepare_initial_state(eng, inputs)\n",
    "\n",
    "    X | pkb\n",
    "    H | pkb\n",
    "\n",
    "    for _ in range(2):\n",
    "        sudoku_oracle(eng, inputs, tmp, pkb)\n",
    "        reflect_initial(eng, prepare_initial_state, inputs)\n",
    "\n",
    "    All(Measure) | inputs\n",
    "    # garbage\n",
    "    All(Measure) | tmp\n",
    "    Measure | pkb\n",
    "\n",
    "    eng.flush()\n",
    "\n",
    "    return map(lambda x: int(x), inputs)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [''.join(map(lambda x: str(x), grover_circuit())) for _ in range(512)]\n",
    "plt.hist(sorted(data), density=True, bins=20)\n",
    "plt.ylabel('Prob')\n",
    "plt.xlabel('Data')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
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