{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  CHSH Ungleichung\n",
    "\n",
    "Simulation der Verletzung der CHSH-Ungleichung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit\n",
    "from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, transpile, execute, Aer, IBMQ\n",
    "from qiskit.tools.visualization import circuit_drawer\n",
    "from qiskit.visualization import plot_histogram\n",
    "\n",
    "#import python stuff\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "backend = Aer.get_backend('qasm_simulator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_circuit(theta0, theta1):\n",
    "    qc = QuantumCircuit(2,2)\n",
    "\n",
    "    qc.h(0)\n",
    "    qc.cx(0, 1)\n",
    "    \n",
    "    qc.barrier() \n",
    "\n",
    "    if theta0 != 0 :\n",
    "        qc.ry(2*theta0, 0)\n",
    "\n",
    "    if theta1 != 0 :\n",
    "        qc.ry(2*theta1, 1)\n",
    "\n",
    "\n",
    "    qc.barrier()    \n",
    "    qc.measure(range(2),range(2))\n",
    "    \n",
    "    return qc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = -np.pi/8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.852x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc1 = make_circuit(0, theta )\n",
    "qc1.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.852x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc2 = make_circuit(0, -theta )\n",
    "qc2.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.852x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc3 = make_circuit(2*theta, theta )\n",
    "qc3.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 507.852x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc4 = make_circuit(2*theta, -theta )\n",
    "qc4.draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of shots used by the simulation and by the run on a quantum computer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# global variable\n",
    "shots=5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Cor(counts, shots):\n",
    "    result = 0;\n",
    "    \n",
    "    if '00' in counts:\n",
    "        result += counts['00']\n",
    "    if '01' in counts:\n",
    "        result -= counts['01']\n",
    "    if '10' in counts:\n",
    "        result -= counts['10']\n",
    "    if '11' in counts:\n",
    "        result += counts['11']\n",
    "\n",
    "    return result/shots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'11': 2139, '00': 2120, '10': 373, '01': 368}\n",
      "\n",
      "E1 = +0.70360\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job1 = execute(qc1, backend, shots=shots)\n",
    "result1 = job1.result()\n",
    "counts1 = result1.get_counts(qc1)\n",
    "E1 = Cor(counts1, shots)\n",
    "print(counts1)\n",
    "print()\n",
    "print(\"E1 = {:+.5f}\".format(E1));\n",
    "plot_histogram(counts1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'11': 2120, '00': 2166, '10': 364, '01': 350}\n",
      "\n",
      "E2 = +0.71440\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job2 = execute(qc2, backend, shots=shots)\n",
    "result2 = job2.result()\n",
    "counts2 = result2.get_counts(qc2)\n",
    "E2 = Cor(counts2, shots)\n",
    "print(counts2)\n",
    "print()\n",
    "print(\"E2 = {:+.5f}\".format(E2));\n",
    "plot_histogram(counts2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'11': 2088, '00': 2153, '10': 354, '01': 405}\n",
      "\n",
      "E3 = +0.69640\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job3 = execute(qc3, backend, shots=shots)\n",
    "result3 = job3.result()\n",
    "counts3 = result3.get_counts(qc3)\n",
    "E3 = Cor(counts3, shots)\n",
    "print(counts3)\n",
    "print()\n",
    "print(\"E3 = {:+.5f}\".format(E3));\n",
    "plot_histogram(counts3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'01': 2148, '10': 2110, '00': 343, '11': 399}\n",
      "\n",
      "E4 = -0.70320\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job4 = execute(qc4, backend, shots=shots)\n",
    "result4 = job4.result()\n",
    "counts4 = result4.get_counts(qc4)\n",
    "E4 = Cor(counts4, shots)\n",
    "print(counts4)\n",
    "print()\n",
    "print(\"E4 = {:+.5f}\".format(E4));\n",
    "plot_histogram(counts4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E1: 0.703600\n",
      "E2: 0.714400\n",
      "E3: 0.696400\n",
      "E4: -0.703200\n",
      "Correlation 2.817600\n",
      "Theoretical Value 2.828427\n"
     ]
    }
   ],
   "source": [
    "print(\"E1: {:>3f}\".format(E1))\n",
    "print(\"E2: {:>3f}\".format(E2))\n",
    "print(\"E3: {:>3f}\".format(E3))\n",
    "print(\"E4: {:>3f}\".format(E4))\n",
    "E = E1 + E2 + E3 - E4\n",
    "print(\"Korrelation {:>3f}\".format(E) )\n",
    "print(\"Theoretischer Wert {:>3f}\".format(2*np.sqrt(2)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}