QiskitExperimentFinal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Qiskit 라이브러리 불러오기\n",
    "import qiskit\n",
    "from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, transpile, Aer, IBMQ\n",
    "from qiskit.providers.aer import QasmSimulator\n",
    "from qiskit.tools.visualization import circuit_drawer\n",
    "from qiskit.tools.monitor import job_monitor, backend_monitor, backend_overview\n",
    "from qiskit.providers.aer import noise\n",
    "from qiskit.visualization import plot_histogram\n",
    "\n",
    "# 기타 라이브러리 불러오기\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from math import pi\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bell Test on IBMQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ibmqfactory.load_account:WARNING:2021-11-26 14:41:26,637: Credentials are already in use. The existing account in the session will be replaced.\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "# Quantum Experience에서 양자 컴퓨터를 불러오기\n",
    "IBMQ.load_account()\n",
    "provider = IBMQ.get_provider('ibm-q')\n",
    "lima = provider.get_backend('ibmq_lima')\n",
    "'''\n",
    "\n",
    "# 양자 시뮬레이터 로드\n",
    "simulator = QasmSimulator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60\n"
     ]
    }
   ],
   "source": [
    "# 벨의 부등식 시험을 위한 양자 회로 제작하기\n",
    "\n",
    "circuits = []\n",
    "theta_arr = np.linspace(0,pi*2,20)\n",
    "\n",
    "for theta in theta_arr:\n",
    "    # A, B 측정 양자 회로\n",
    "    circuit = QuantumCircuit(2, 2)\n",
    "\n",
    "    circuit.h(0)\n",
    "    circuit.cx(0, 1)\n",
    "\n",
    "    circuit.ry(theta, 1)\n",
    "\n",
    "    circuit.measure([0,1], [0, 1])\n",
    "\n",
    "    circuits.append(circuit)\n",
    "\n",
    "    # A, C 측정 양자 회로\n",
    "    circuit = QuantumCircuit(2, 2)\n",
    "\n",
    "    circuit.h(0)\n",
    "    circuit.cx(0, 1)\n",
    "\n",
    "    circuit.ry(-theta, 1)\n",
    "\n",
    "    circuit.measure([0,1], [0, 1])\n",
    "\n",
    "    circuits.append(circuit)\n",
    "\n",
    "    # B, C 측정 양자 회로\n",
    "    circuit = QuantumCircuit(2, 2)\n",
    "\n",
    "    circuit.h(0)\n",
    "    circuit.cx(0, 1)\n",
    "\n",
    "    circuit.ry(theta, 0)\n",
    "    circuit.ry(-theta, 1)\n",
    "\n",
    "    circuit.measure([0,1], [0, 1])\n",
    "\n",
    "    circuits.append(circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 양자 시뮬레이터로 시험하기\n",
    "result_ideal = simulator.run(circuits).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "# Quantum Experience에서 양자 컴퓨터로 시험하기\n",
    "transpiled_circuits = transpile(circuits, lima)\n",
    "job_real = lima.run(transpiled_circuits, shots=1024)\n",
    "job_monitor(job_real)\n",
    "result_real = job_real.result()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 양자 시뮬레이터 계산 결과를 확률로 반환하는 함수\n",
    "def getProbability(result):\n",
    "    res = []\n",
    "    for i in range(20):\n",
    "        temp = 0\n",
    "        for j in range(i*3, i*3+3):\n",
    "            temp += result[j].get('11', 0)\n",
    "            temp += result[j].get('00', 0)\n",
    "        temp /= 3*1024\n",
    "        res.append(temp)\n",
    "\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 숨은 변수 이론에 따른 확률의 하한\n",
    "plt.axhline(y=1/3, color='r', linestyle=':')\n",
    "plt.annotate(\"Classical Lower Bound\", (1-0.4, 1/3+0.03))\n",
    "\n",
    "# 양자역학에 따른 이론적 확률 그래프\n",
    "theta_arr = np.linspace(0, 2*pi, 100)\n",
    "plt.plot(theta_arr/pi, (np.cos(theta_arr/2)**2*2+np.cos(theta_arr)**2)/3, '--k', label = \"Theory\")\n",
    "\n",
    "# 시뮬레이션에 따른 확률 그래프\n",
    "theta_arr = np.linspace(0, 2*pi, 20)\n",
    "plt.plot(theta_arr/pi, getProbability(result_ideal.get_counts()), 'g', label = \"Simuation\")\n",
    "\n",
    "'''\n",
    "# 양자 컴퓨터로 수행한 롹률 그래프\n",
    "plt.plot(theta_arr/pi, getProbability(result_real.get_counts()), 'xb', label = \"Experiment\")\n",
    "'''\n",
    "\n",
    "plt.legend(loc=\"center\")\n",
    "plt.savefig(\"MyBellTest.svg\")\n",
    "plt.ylabel(\"Probabilty\")\n",
    "plt.xlabel(\"Theta\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CHSH test on IBMQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80\n"
     ]
    }
   ],
   "source": [
    "circuits = []\n",
    "theta_arr = np.linspace(0,pi*2,20)\n",
    "\n",
    "for theta in theta_arr:\n",
    "    # A, B 측정 양자 회로\n",
    "    circuit = QuantumCircuit(2, 2)\n",
    "\n",
    "    circuit.h(0)\n",
    "    circuit.cx(0, 1)\n",
    "\n",
    "    circuit.ry(theta, 1)\n",
    "\n",
    "    circuit.measure([0,1], [0, 1])\n",
    "\n",
    "    circuits.append(circuit)\n",
    "\n",
    "    # A', B 측정 양자 회로\n",
    "    circuit = QuantumCircuit(2, 2)\n",
    "\n",
    "    circuit.h(0)\n",
    "    circuit.cx(0, 1)\n",
    "\n",
    "    circuit.ry(2*theta, 0)\n",
    "    circuit.ry(theta, 1)\n",
    "\n",
    "    circuit.measure([0,1], [0, 1])\n",
    "\n",
    "    circuits.append(circuit)\n",
    "\n",
    "    # A, B' 측정 양자 회로\n",
    "    circuit = QuantumCircuit(2, 2)\n",
    "\n",
    "    circuit.h(0)\n",
    "    circuit.cx(0, 1)\n",
    "\n",
    "    circuit.ry(3*theta, 1)\n",
    "\n",
    "    circuit.measure([0,1], [0, 1])\n",
    "\n",
    "    circuits.append(circuit)\n",
    "\n",
    "    # A', B' 측정 양자 회로\n",
    "    circuit = QuantumCircuit(2, 2)\n",
    "\n",
    "    circuit.h(0)\n",
    "    circuit.cx(0, 1)\n",
    "\n",
    "    circuit.ry(2*theta, 0)\n",
    "    circuit.ry(3*theta, 1)\n",
    "\n",
    "    circuit.measure([0,1], [0, 1])\n",
    "\n",
    "    circuits.append(circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 양자 시뮬레이터로 시험하기\n",
    "result_ideal = simulator.run(circuits, shots=1024).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "# Quantum Experience에서 양자 컴퓨터로 시험하기\n",
    "transpiled_circuits = transpile(circuits, lima)\n",
    "job_real = lima.run(transpiled_circuits, shots=1024)\n",
    "job_monitor(job_real)\n",
    "result_real = job_real.result()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 양자 시뮬레이터 계산 결과를 상관계수로 반환하는 함수\n",
    "def getCorrelation(result):\n",
    "    temp = 0\n",
    "    temp += result.get('11', 0)\n",
    "    temp += result.get('00', 0)\n",
    "    temp -= result.get('01', 0)\n",
    "    temp -= result.get('10', 0)\n",
    "    return temp/1024\n",
    "\n",
    "\n",
    "# 양자 시뮬레이터 계산 결과를 S로 반환하는 함수\n",
    "def getS(result):\n",
    "    res = []\n",
    "    for i in range(20):\n",
    "        temp = 0\n",
    "        temp += getCorrelation(result[i*4])\n",
    "        temp += getCorrelation(result[i*4+1])\n",
    "        temp -= getCorrelation(result[i*4+2])\n",
    "        temp += getCorrelation(result[i*4+3])\n",
    "        res.append(temp)\n",
    "\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 숨은 변수 이론에 따른 S의 상한과 하한\n",
    "plt.axhline(y=2, color='r', linestyle=':')\n",
    "plt.annotate(\"Classical Upper Bound\", (1-.35, 2-0.4))\n",
    "plt.axhline(y=-2, color='r', linestyle=':')\n",
    "plt.annotate(\"Classical Lower Bound\", (1-.35, -2+0.4))\n",
    "\n",
    "# 양자역학에 따른 이론적 확률 그래프\n",
    "theta_arr = np.linspace(0, 2*pi, 100)\n",
    "f = lambda x : np.cos(x) - np.cos(3*x) + np.cos(-x) + np.cos(x)\n",
    "plt.plot(theta_arr/pi, f(theta_arr), '--k', label = \"Theory\")\n",
    "\n",
    "# 시뮬레이션에 따른 확률 그래프\n",
    "theta_arr = np.linspace(0, 2*pi, 20)\n",
    "Cress = getS(result_ideal.get_counts())\n",
    "plt.plot(theta_arr/pi, Cress, 'g', label = \"Simation\")\n",
    "\n",
    "\n",
    "'''\n",
    "# 양자 컴퓨터로 수행한 롹률 그래프\n",
    "plt.plot(theta_arr/pi, getS(result_real.get_counts()), 'xb', label = \"Experiment\")\n",
    "'''\n",
    "\n",
    "plt.legend(loc=\"center\")\n",
    "plt.savefig(\"MyCHSHTest.svg\")\n",
    "plt.ylabel(\"S\")\n",
    "plt.xlabel(\"Theta\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "27a9bf0a9ba9953446d34736fb6a54965fa84e25618090c488bbfe37455588ed"
  },
  "kernelspec": {
   "display_name": "Qiskit v0.32.1 (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": "3.8.10"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}