5. Worst Case.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9dbe00cf",
   "metadata": {},
   "source": [
    "# 5. Worst Case when t/N = 1/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eb157015",
   "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": 2,
   "id": "a2c9a734",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import execute, Aer\n",
    "\n",
    "simulator = Aer.get_backend('qasm_simulator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "284d2416",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e4cade26",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dada2d3d",
   "metadata": {},
   "source": [
    "## Tool Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6eb8f430",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.quantum_info import DensityMatrix, Statevector, Operator\n",
    "\n",
    "def getDensityMatrix(circuit):\n",
    "    return DensityMatrix(circuit).data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "527b0d3b",
   "metadata": {},
   "source": [
    "### - dagger and tensor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "38c1c38f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import reduce\n",
    "\n",
    "Dag = lambda matrix: matrix.conj().T\n",
    "Kron = lambda *matrices: reduce(np.kron, matrices)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33246453",
   "metadata": {},
   "source": [
    "### - pretty print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cfe27469",
   "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": "2470269e",
   "metadata": {},
   "source": [
    "## 5.1 set up starting state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b6c322de",
   "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": 12,
   "id": "7ac85e30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 287.294x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputCircuit_2q = initCircuit(2)\n",
    "inputCircuit_2q.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a1bfdf0",
   "metadata": {},
   "source": [
    "## 5.2 Creating oracle for even"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "657a2c1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def createEvenOracle():\n",
    "    circuit = QuantumCircuit(2, 2)\n",
    "    \n",
    "    circuit.x(0)\n",
    "    circuit.x(1)\n",
    "    circuit.cz(0, 1)\n",
    "    circuit.x(1)\n",
    "    circuit.cz(0, 1)\n",
    "    circuit.x(0)\n",
    "    circuit.barrier()\n",
    "    \n",
    "    return circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9f731e2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 621.739x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evenOracle = createEvenOracle()\n",
    "evenOracle.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ef22e43",
   "metadata": {},
   "source": [
    "### - test Oracle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "02c7512e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.+0.j,  0.+0.j,  0.+0.j,  0.+0.j],\n",
       "       [ 0.+0.j,  1.+0.j,  0.+0.j,  0.+0.j],\n",
       "       [ 0.+0.j,  0.+0.j, -1.+0.j,  0.+0.j],\n",
       "       [ 0.+0.j,  0.+0.j,  0.+0.j,  1.+0.j]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Operator(evenOracle).data # find 0 and 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d5b6eb1",
   "metadata": {},
   "source": [
    "## 5.3 Diffuser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "640262f5",
   "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": 20,
   "id": "80c1b82e",
   "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": 21,
   "id": "25dd3739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 538.128x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diffuserCircuit_2q = createDiffuser_2q()\n",
    "diffuserCircuit_2q.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e86301c0",
   "metadata": {},
   "source": [
    "## 5.4 Measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ce13ea52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1207.02x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grover_even = initCircuit(2).compose(createEvenOracle()).compose(createDiffuser_2q())\n",
    "grover_even.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "20be54c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x268490a3d60>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grover_even.measure([0, 1], [0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "45ecf542",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'10': 2544, '11': 2509, '01': 2441, '00': 2506}\n"
     ]
    }
   ],
   "source": [
    "job = execute(grover_even, simulator, shots = 10000)\n",
    "results = job.result()\n",
    "counts = results.get_counts(grover_even)\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "72837488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(counts, figsize=(5, 5), color=\"#66CCCC\", title=\"origin grover - find evens\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa117a01",
   "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
}