1. grover algorithm - find 3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ed2e5f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit\n",
    "from qiskit import QuantumCircuit\n",
    "from qiskit.visualization import plot_histogram\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "03ac00b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import execute, Aer\n",
    "\n",
    "simulator = Aer.get_backend('qasm_simulator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "69cead21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5570d759",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a729eaeb",
   "metadata": {},
   "source": [
    "## Tool Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c89e5a93",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.quantum_info import DensityMatrix, Operator\n",
    "\n",
    "# Quantum circuit to unitary matrix\n",
    "def getDensityMatrix(circuit):\n",
    "    return DensityMatrix(circuit).data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b9cc9a5",
   "metadata": {},
   "source": [
    "### - dagger and tensor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "da15479a",
   "metadata": {},
   "outputs": [],
   "source": [
    "state_0 = np.array([1, 0]) # |0>\n",
    "state_1 = np.array([0, 1]) # |1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "d1147e90",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import reduce\n",
    "\n",
    "Dag = lambda matrix: matrix.conj().T # Dag(M) = M†\n",
    "Kron = lambda *matrices: reduce(np.kron, matrices) # Kron(state_0, state_0) is |00>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b145aad9",
   "metadata": {},
   "source": [
    "### - pretty print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e668ad39",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pm(matrix):\n",
    "    for row in range(len(matrix)):\n",
    "        for col in range (len(matrix[row])):\n",
    "            print(\"{:.3f}\".format(matrix[row][col]), end = \" \")\n",
    "        print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec005135",
   "metadata": {},
   "source": [
    "# 1. Find <3> in 2-qubits database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6c2e88fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://qiskit.org/textbook/ch-algorithms/grover.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76b17ecd",
   "metadata": {},
   "source": [
    "## 1.1 set up starting state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "86a0a790",
   "metadata": {},
   "outputs": [],
   "source": [
    "def initCircuit(n):\n",
    "    circuit = QuantumCircuit(n, n)\n",
    "    \n",
    "    for i in range(n):\n",
    "        circuit.h(i)    \n",
    "    circuit.barrier()\n",
    "    \n",
    "    return circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "bcf5192f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 287.294x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputCircuit_2q = initCircuit(2)\n",
    "inputCircuit_2q.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc4aff92",
   "metadata": {},
   "source": [
    "## 1.2 creating Oracle for n=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "18472482",
   "metadata": {},
   "outputs": [],
   "source": [
    "def createOracle_3():\n",
    "    circuit = QuantumCircuit(2, 2)\n",
    "\n",
    "    # Oracle for find 3\n",
    "\n",
    "    # U_f \n",
    "    circuit.cz(0, 1)\n",
    "    circuit.barrier()\n",
    "    \n",
    "    return circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "628a6683",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 287.294x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oracleCircuit_3 = createOracle_3()\n",
    "oracleCircuit_3.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e3d512c",
   "metadata": {},
   "source": [
    "### - test Oralce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a8233208",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.000+0.000j 0.000+0.000j 0.000+0.000j 0.000+0.000j \n",
      "0.000+0.000j 1.000+0.000j 0.000+0.000j 0.000+0.000j \n",
      "0.000+0.000j 0.000+0.000j 1.000+0.000j 0.000+0.000j \n",
      "0.000+0.000j 0.000+0.000j 0.000+0.000j -1.000+0.000j \n"
     ]
    }
   ],
   "source": [
    "O_3 = Operator(oracleCircuit_3).data\n",
    "pm(O_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "cf2bbb5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# all possible states\n",
    "state_00 = Kron(state_0, state_0)\n",
    "state_01 = Kron(state_0, state_1)\n",
    "state_10 = Kron(state_1, state_0)\n",
    "state_11 = Kron(state_1, state_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "75db7cd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O|00> [1.+0.j 0.+0.j 0.+0.j 0.+0.j]\n",
      "O|01> [0.+0.j 1.+0.j 0.+0.j 0.+0.j]\n",
      "O|10> [0.+0.j 0.+0.j 1.+0.j 0.+0.j]\n",
      "O|11> [ 0.+0.j  0.+0.j  0.+0.j -1.+0.j]\n"
     ]
    }
   ],
   "source": [
    "print(\"O|00>\", O_3 @ state_00)\n",
    "print(\"O|01>\", O_3 @ state_01)\n",
    "print(\"O|10>\", O_3 @ state_10)\n",
    "print(\"O|11>\", O_3 @ state_11) # flip phase"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d6f8b5",
   "metadata": {},
   "source": [
    "## 1.3 Diffuser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "200652cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# H R H, where R = 2|0><0| - I "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c8bf213",
   "metadata": {},
   "source": [
    "### 1.3.1 R = 2|0><0| - I  (Conditional phase shift gate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "3cc106fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def createR_2q():\n",
    "    circuit = QuantumCircuit(2, 2)\n",
    "    \n",
    "    circuit.z(0)\n",
    "    circuit.z(1)\n",
    "    circuit.cz(0, 1)\n",
    "    \n",
    "    return circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "1acfcf1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 287.294x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_2q = createR_2q()\n",
    "R_2q.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ce25a76",
   "metadata": {},
   "source": [
    "### - test R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "6b07a4f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.000+0.000j 0.000+0.000j 0.000+0.000j 0.000+0.000j \n",
      "0.000+0.000j -1.000+0.000j 0.000+0.000j 0.000+0.000j \n",
      "0.000+0.000j 0.000+0.000j -1.000+0.000j 0.000+0.000j \n",
      "0.000+0.000j 0.000+0.000j 0.000+0.000j -1.000+0.000j \n"
     ]
    }
   ],
   "source": [
    "R_2q = Operator(R_2q).data\n",
    "pm(R_2q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8fc36148",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RO|00> [1.+0.j 0.+0.j 0.+0.j 0.+0.j]\n",
      "RO|01> [ 0.+0.j -1.+0.j  0.+0.j  0.+0.j]\n",
      "RO|10> [ 0.+0.j  0.+0.j -1.+0.j  0.+0.j]\n",
      "RO|11> [0.+0.j 0.+0.j 0.+0.j 1.+0.j]\n"
     ]
    }
   ],
   "source": [
    "# flip phase(no work on zero states) => Conditional Phase Shift gate\n",
    "print(\"RO|00>\", R_2q @ O_3 @ state_00) # zero state => would not flip\n",
    "print(\"RO|01>\", R_2q @ O_3 @ state_01)\n",
    "print(\"RO|10>\", R_2q @ O_3 @ state_10)\n",
    "print(\"RO|11>\", R_2q @ O_3 @ state_11)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9f093a0",
   "metadata": {},
   "source": [
    "### 1.3.2 diffuser circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "adfb63ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# H R H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "71b745da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def createDiffuser_2q():\n",
    "    circuit = QuantumCircuit(2, 2)\n",
    "    \n",
    "    circuit.h(0)\n",
    "    circuit.h(1)\n",
    "    circuit = circuit.compose(createR_2q())\n",
    "    circuit.h(0)\n",
    "    circuit.h(1)\n",
    "    circuit.barrier()\n",
    "    \n",
    "    return circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "e64d2a9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 538.128x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diffuserCircuit_2q = createDiffuser_2q()\n",
    "diffuserCircuit_2q.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfcc5f5a",
   "metadata": {},
   "source": [
    "### - test diffuser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "0e8b86cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.500+0.000j 0.500+0.000j 0.500+0.000j 0.500+0.000j \n",
      "0.500+0.000j -0.500+0.000j 0.500+0.000j 0.500+0.000j \n",
      "0.500+0.000j 0.500+0.000j -0.500+0.000j 0.500+0.000j \n",
      "0.500+0.000j 0.500+0.000j 0.500+0.000j -0.500+0.000j \n"
     ]
    }
   ],
   "source": [
    "diff_2q = Operator(diffuserCircuit_2q).data\n",
    "pm(diff_2q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "da40d247",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DO|DB> [0.+0.j 0.+0.j 0.+0.j 1.+0.j]\n"
     ]
    }
   ],
   "source": [
    "DB = 0.5 * state_00 + 0.5 * state_01 + 0.5 * state_10 + 0.5 * state_11\n",
    "print(\"DO|DB>\", diff_2q @ O_3 @ DB)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "541afe8d",
   "metadata": {},
   "source": [
    "## 1.4 Grover iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "39232969",
   "metadata": {},
   "outputs": [],
   "source": [
    "def createGroverIteration(oracle, diffuser):\n",
    "    return oracle.compose(diffuser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "8762c78b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 705.35x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groverIteration_2q = createGroverIteration(createOracle_3(), createDiffuser_2q())\n",
    "groverIteration_2q.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e5a57ea",
   "metadata": {},
   "source": [
    "## 1.5 Measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "40e89865",
   "metadata": {},
   "outputs": [],
   "source": [
    "groverIteration_2q = createGroverIteration(createOracle_3(), createDiffuser_2q())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b52fcbf",
   "metadata": {},
   "source": [
    "### 1.5.1 one grover iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "ce937dfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 872.572x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grover_2q_1 = initCircuit(2).compose(groverIteration_2q.copy())\n",
    "grover_2q_1.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "647e3192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x28b4d339100>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grover_2q_1.measure([0, 1], [0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "451de586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'11': 10000}\n"
     ]
    }
   ],
   "source": [
    "job = execute(grover_2q_1, simulator, shots = 10000)\n",
    "results = job.result()\n",
    "counts = results.get_counts(grover_2q_1)\n",
    "print(counts) # 100%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "dda3ec47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 100x500 with 1 Axes>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(counts, figsize=(1, 5), color=\"#CC3333\", title=\"one iteration - find 3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eea827b6",
   "metadata": {},
   "source": [
    "### 1.5.2 two grover iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "3cc7c08c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1457.85x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grover_2q_2 = initCircuit(2).compose(groverIteration_2q.copy()).compose(groverIteration_2q.copy())\n",
    "grover_2q_2.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a3322b42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x28b4cb1e310>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grover_2q_2.measure([0, 1], [0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "cd852ea6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'11': 2440, '10': 2540, '01': 2538, '00': 2482}\n"
     ]
    }
   ],
   "source": [
    "job = execute(grover_2q_2, simulator, shots = 10000)\n",
    "results = job.result()\n",
    "counts = results.get_counts(grover_2q_2)\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0020ac5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(counts, figsize=(7, 5), color=\"#FF9999\", title=\"two iterations - find 3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "224cb6ef",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}