{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entanglement Distillation\n",
    "\n",
    "Aus zwei nicht-maximal verschränkten Qubit-Paare wird ein ein verschränktes Qubi-Paar erzeugt, das mit einer bestimmten Wahrscheinlichkeit eine größere Verschränkung aufzeigt."
   ]
  },
  {
   "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.circuit import QuantumCircuit,Parameter\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ausgangszustände"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|Psi_1> = alpha |0> + beta |1>\n",
      "alpha   : 0.632981\n",
      "beta    : 0.774167\n",
      "alpha^2 : 0.400665\n",
      "beta^2  : 0.599335\n",
      "\n",
      "|Psi_2> = alpha |0> + beta |1>\n",
      "alpha   : 0.648337\n",
      "beta    : 0.761353\n",
      "alpha^2 : 0.420341\n",
      "beta^2  : 0.579659\n"
     ]
    }
   ],
   "source": [
    "backend = Aer.get_backend('qasm_simulator')\n",
    "phi1 = Parameter('Phi 1')\n",
    "phi2 = Parameter('Phi 2')\n",
    "\n",
    "# Dejustierung\n",
    "phi1_value =  np.pi/4 + 0.1\n",
    "phi2_value =  np.pi/4 + 0.08\n",
    "print(\"|Psi_1> = alpha |0> + beta |1>\")\n",
    "print(\"alpha   : {:>5f}\".format(np.cos(phi1_value)) )\n",
    "print(\"beta    : {:>5f}\".format(np.sin(phi1_value)) )\n",
    "print(\"alpha^2 : {:>5f}\".format(np.cos(phi1_value)**2) )\n",
    "print(\"beta^2  : {:>5f}\".format(np.sin(phi1_value)**2) )\n",
    "print()\n",
    "print(\"|Psi_2> = alpha |0> + beta |1>\")\n",
    "print(\"alpha   : {:>5f}\".format(np.cos(phi2_value)) )\n",
    "print(\"beta    : {:>5f}\".format(np.sin(phi2_value)) )\n",
    "print(\"alpha^2 : {:>5f}\".format(np.cos(phi2_value)**2) )\n",
    "print(\"beta^2  : {:>5f}\".format(np.sin(phi2_value)**2) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Concurrence System 1 : 0.980067\n",
      "Concurrence System 2 : 0.987227\n"
     ]
    }
   ],
   "source": [
    "# Concurrence\n",
    "C1 = 2*np.cos(phi1_value)*np.sin(phi1_value)\n",
    "C2 = 2*np.cos(phi2_value)*np.sin(phi2_value)\n",
    "print(\"Concurrence System 1 : {:>5f}\".format(C1))\n",
    "print(\"Concurrence System 2 : {:>5f}\".format(C2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analytische Berechnung der Koeffizienten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p(|00>) : 0.515826\n",
      "state after measure |00>:\n",
      "0.571400|00> + 0.820672|11>\n",
      "Measurement values\n",
      " => 0.168416\n",
      " => 0.347410\n",
      "Concurrence : 0.937864\n",
      "-----------------------------\n",
      "p(|11>) : 0.484174\n",
      "state after measure |11>:\n",
      "0.692590|00> + 0.721331|11>\n",
      "Measurement values\n",
      " => 0.232249\n",
      " => 0.251925\n",
      "Concurrence : 0.999174\n"
     ]
    }
   ],
   "source": [
    "# Kontrollrechnung\n",
    "\n",
    "alpha1 = np.cos(phi1_value)\n",
    "beta1  = np.sin(phi1_value)\n",
    "\n",
    "alpha2 = np.cos(phi2_value)\n",
    "beta2  = np.sin(phi2_value)\n",
    "\n",
    "p00 = (alpha1*alpha2)**2 + (beta1*beta2)**2\n",
    "p11 = (alpha1*beta2)**2 + (beta1*alpha2)**2\n",
    "\n",
    "len00 = np.sqrt(p00)\n",
    "len11 = np.sqrt(p11)\n",
    "\n",
    "\n",
    "print(\"p(|00>) : {:>5f}\".format(p00) )\n",
    "print(\"state after measure |00>:\")\n",
    "print(\"{:>5f}|00> + {:>5f}|11>\".format( alpha1*alpha2/len00, beta1*beta2/len00) )\n",
    "print(\"Measurement values\")\n",
    "print(\" => {:>5f}\".format((alpha1*alpha2)**2) )\n",
    "print(\" => {:>5f}\".format((beta1*beta2)**2) )\n",
    "print(\"Concurrence : {:>5f}\".format(2* alpha1*alpha2/len00*beta1*beta2/len00) )\n",
    "\n",
    "print(\"-----------------------------\")\n",
    "print(\"p(|11>) : {:>5f}\".format(p11) )\n",
    "print(\"state after measure |11>:\")\n",
    "print(\"{:>5f}|00> + {:>5f}|11>\".format( alpha1*beta2/len11, beta1*alpha2/len11) )\n",
    "print(\"Measurement values\")\n",
    "print(\" => {:>5f}\".format((alpha1*beta2)**2) )\n",
    "print(\" => {:>5f}\".format((beta1*alpha2)**2) )\n",
    "print(\"Concurrence : {:>5f}\".format(2* alpha1*beta2/len11 * beta1*alpha2/len11) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation der beiden verschränkten Systeme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 327.252x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit1 = QuantumCircuit(2,2)\n",
    "circuit1.ry(2*phi1,0)\n",
    "circuit1.cx(0,1)\n",
    "circuit1.measure([0,1],[0,1])\n",
    "circuit1.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'11': 6024, '00': 3976}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runcircuit1 = circuit1.bind_parameters({phi1: phi1_value })\n",
    "\n",
    "job = execute(runcircuit1, backend, shots=10000)\n",
    "result = job.result()\n",
    "counts = result.get_counts(runcircuit1)\n",
    "print(counts)\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 327.252x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit2 = QuantumCircuit(2,2)\n",
    "circuit2.ry(2*phi2,0)\n",
    "circuit2.cx(0,1)\n",
    "circuit2.measure([0,1],[0,1])\n",
    "circuit2.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'00': 4165, '11': 5835}\n"
     ]
    },
    {
     "data": {
      "image/png": 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nQjMz/zkilgLjKJ1Z/rG86rfAjQW2vzcihgM3ULr8upzSZdb15SYdjtdsx6uALwOjgN9Tug86MzOfPIh9SZLUrk5dnu1rvDwrHZ28PNs3dcfl2U5PblCeBu/piPhDREwsL/twRPzloRYqSVJv1tnJDa6jdGn1y0DLQZPPAe9vaxtJkvqKzp5pvhe4MjNvB5paLK8H2h7cJ0lSH9HZ0Hw1rXq0lr0EDDz0ciRJ6r06G5prgMo2lr8FePkzpSRJ6kM6O07zVuDOiDiO0j3Nmoh4J/AhSvO/SpLUZ3V2nOZdEdGf0vMwj6M00cFzwLWZee9hqO+IYPf1vqmruq9L6js6e6ZJZs4D5kXE8cAxmbml68uSJKn36XRo7pWZL3RlIZIk9XYHDM2IeIrS47y2R8SvgHanEOro0WCSJB3pipxp3g80tnh/9M67J0k6qh0wNDPz4y3ef+ywViNJUi/W2Wn0HouIV7WxfEj5QdSSJPVZnZ3cYDYwoI3lxwLnHHI1kiT1YoV6z0ZEy1mATouIbS0+9wPOBxq6sjBJknqbokNO6ih1AErgkTbW7wau6aqiJEnqjYqG5gRK0+atAf4EeL7Fuj3Alsxs7uLaJEnqVQqFZmauL7/t9EOrJUnqK4pMbvA24N8z86Xy+3Zl5ne6rDJJknqZImea3wZGAVvK79uTlDoFSZLUJxWZ3OCYtt5LknS0MQQlSSqo6D3NQrynKUnqy4re0yzCe5qSpD6tU/c0JUk6mhmIkiQV5DhNSZIKcpymJEkFOU5TkqSCDEFJkgrqdGhGRGVE3B0RdeXXPa2etylJUp/UqdCMiEuBnwMnAv9Rfo0EnoyIv+n68iRJ6j2KPk9zr5uBGzPz0y0XRsRHgU8BX+uqwiRJ6m06e3n2BOBbbSy/Dxhx6OVIktR7dTY0fwLMbmP5bGDhoRYjSVJv1tkJ2x8C/iEiqoCflZedBbwN+FiXVydJUi9ysBO2zy2/WvoC8MVDrkiSpF7KCdslSSrIQJQkqaDODjkhIoYCbwZOAga0XJeZn+iiuiRJ6nU6FZoRcRbwA6CR0vCTBkoTHTQC6wBDU5LUZ3X28uxngX8DxgAvAm+gdMZZB3yma0uTJKl36WxongbcmZkJNAMVmbkZ+DAOOZEk9XGdDc09Ld5vBl5dfr8LGN0lFUmS1Et1tiNQPXAmsApYAHwqIkYCfwM81bWlSZLUu3T2TPN/A8+V398APE9pUoOhvHyyA0mS+pROnWlmZl2L989TGnoiSdJR4aAmN4iI10TEn5dfEzu57dURsTYiXoyIpRFxTgdtZ0XEExGxNSJ2R8SvI+KDbbR7e0SsiIjG8p8XHsxxSZLUkc4+hHp4RDwArAYeKL9WR8SDETG8wPYXA7cDnwbOAJ4AHoqIk9rZZBdwBzATmELpmZ0fj4irW+yzBriX0lCY08t/3hcR1Z05NkmSDqSzZ5pfAU4GzgGOLb9mAhOAeQW2vx6Yn5nzMvOZzLwG2Ahc1VbjzFyamd/MzKczc21mfg34Yfnn73Ud8JPMvLm8z5spdVK6rpPHJklShzobmucDV2bm45nZVH49DvzP8rp2RcQA4PXAI61WPQKcXeSHR8QZ5bYtn91Z08Y+f1h0n5IkFdXZISfPA//VxvI/AFsPsO3xQD9K4ztb2gy8saMNI2IDpWn7+gMfz8wvtVg9qp19jmpnX/seazZ69GgWLFgAwMSJExk8eDDLli0DYPjw4UydOpVFixYB0L9/f2bMmEF9fT07duwAoKqqis2bNwOv6ah8HaHq6urYtWsXANXV1WzYsIGGhgYAJk+eTL9+/VixYgUAo0aNYsKECdTW1gIwcOBAqqurWbJkCbt37wagpqaGtWvXsmnTJgCmTJlCc3MzK1euBGDMmDGMHTuWJUuWADBo0CCqqqqora2lsbERgBkzZrBq1Sq2bNkCwLRp02hsbGT16tUAjBs3jpEjR1JXV+qzN2TIECorK1m8eDFNTU0AzJw5k6effpqtW0tf2enTp7Nz507WrFkDwPjx4xk2bBj19fUADB06lOnTp7Nw4UIyk4hg1qxZLFu2jO3btwNQWVnJtm3bWLduHXBo36dnn30WgFNOOYWKigqWL18OwIgRI5g0aRKLFy8GoKKigpqamoP6PUFFJ/9v0JFg48aNXfJ96kiUJvcpJiLeA1wKvDMzG8rLxgBfBb6ZmV/pYNvRlOaqnZWZi1osvwm4NDMnd7DtBGAQpQdefwb428y8p7xuD3BFZt7dov1lwLzM7PCbUVVVlXv/cjkUV952yLtQLzTvup6uQIeL39m+qau+sxGxNDOr2lp3wDPNiPgV0DJZJwDrIqKh/HnvPLQjKN3zbM8LlKbeG9lq+UhgU0c1ZOba8ttflSdT+BhwT3nZpoPZpyRJnVXk8uy3u+IHZeaeiFgKnAfc12LVecD9ndjVMex/baW2vI/PttrnEwdZqiRJbTpgaGbmx7vw530euCcingQeB95Lac7aLwFExN3ln3lZ+fM1wFpgZXn7mcAHgS+22OftwKKI+AilITAXAucCM7qwbkmSOv8QaoCIeAOlcZMJPJ2ZC4psl5n3lsdz3kDpOZzLgbdk5vpyk9bjNftRuoc5HmgCfgt8hHLIlvf5RERcQmkM5yfKbS7OzCUHc2ySJLWnsw+hHgN8l9LQkb1z0I6OiDrgwsx8rt2NyzLzi+x/pthy3exWn28Dbiuwz2/TRZeRJUlqT2fHad5BqTPPyZk5LjPHAaeUl93R1cVJktSbdPby7HnA7Ba9WcnMNRFxLfBol1YmSVIvczATtrc1sLP4YE9Jko5QnQ3NR4EvRMS4vQvKk63fhmeakqQ+rrOheS3wSmBNRKyPiPWUequ+srxOkqQ+q7P3NLcCfwLMBl5bXvZMZv64K4uSJKk3KhyaEdEP+D0wPTN/BPzosFUlSVIvVPjybGY2A+uBAYevHEmSeq/O3tP8JHBLRBx/OIqRJKk36+w9zQ9SespJQ/kZl/s9WzMzT+uqwiRJ6m06G5rfpjQmMw5DLZIk9WqFQjMijqP06K2/AF5BaUzmNZn5wuErTZKk3qXoPc2PA+8GfgB8A3gj8E+HqSZJknqlopdn3wa8JzO/CRAR/wY8HhH9yr1qJUnq84qeaY4Dfrr3Q2Y+Sen5lqMPR1GSJPVGRUOzH7Cn1bImDvIh1pIkHYmKhl4AX4uIxhbLjgXmRcQf9i7IzDldWZwkSb1J0dD8ahvLvtaVhUiS1NsVCs3M/B+HuxBJknq7g3kItSRJRyVDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSqo20MzIq6OiLUR8WJELI2Iczpoe2JEfD0ifh0RzRExv402746IbON17GE9EEnSUadbQzMiLgZuBz4NnAE8ATwUESe1s0kF8AJwC7Ckg13/ATix5SszX+yquiVJgu4/07wemJ+Z8zLzmcy8BtgIXNVW48xcl5nXZuZ8YFsH+83M3NTy1fWlS5KOdt0WmhExAHg98EirVY8AZx/i7gdGxPqI2BAR34+IMw5xf5IkvUz/bvxZxwP9gM2tlm8G3ngI+10JXA4sAwYDfws8HhHTM3N168YRMReYCzB69GgWLFgAwMSJExk8eDDLli0DYPjw4UydOpVFixYB0L9/f2bMmEF9fT07duwAoKqqis2bNwOvOYTy1VvV1dWxa9cuAKqrq9mwYQMNDQ0ATJ48mX79+rFixQoARo0axYQJE6itrQVg4MCBVFdXs2TJEnbv3g1ATU0Na9euZdOm0oWQKVOm0NzczMqVKwEYM2YMY8eOZcmS0p2IQYMGUVVVRW1tLY2NjQDMmDGDVatWsWXLFgCmTZtGY2Mjq1eX/lcfN24cI0eOpK6uDoAhQ4ZQWVnJ4sWLaWpqAmDmzJk8/fTTbN26FYDp06ezc+dO1qxZA8D48eMZNmwY9fX1AAwdOpTp06ezcOFCMpOIYNasWSxbtozt27cDUFlZybZt21i3bh1waN+nZ599FoBTTjmFiooKli9fDsCIESOYNGkSixcvBqCiooKampqD+j2V7vyor9m4cWOXfJ86Epl5GA+hxQ+KGA00ALMyc1GL5TcBl2bm5ANs/33ghcx89wHa9QN+CfwkM6/tqG1VVVXu/cvlUFx52yHvQr3QvOt6ugIdLn5n+6au+s5GxNLMrGprXXfe03wBaAZGtlo+Euiye5CZ2QzUAad01T4lSYJuDM3M3AMsBc5rteo8Sr1ou0REBHAapQ5GkiR1me68pwnweeCeiHgSeBx4LzAa+BJARNwNkJmX7d0gIk4vvx0C/LH8eU9mriiv/3vgZ8DqcptrKYVmmz1yJUk6WN0ampl5b0QMB26gNJ5yOfCWzFxfbtLWeM1ftPp8AbAeGF/+/Crgy8Ao4Pfl9jMz88kuLV6SdNTr7jNNMvOLwBfbWTe7jWVxgP39HfB3XVKcJEkdcO5ZSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5KkggxNSZIKMjQlSSrI0JQkqSBDU5Kkgro9NCPi6ohYGxEvRsTSiDjnAO1nldu9GBFrIuK9h7pPSZIORreGZkRcDNwOfBo4A3gCeCgiTmqn/QTgP8rtzgD+AfhCRLz9YPcpSdLB6u4zzeuB+Zk5LzOfycxrgI3AVe20fy/wXGZeU24/D/gq8MFD2KckSQclMrN7flDEAOAPwF9l5n0tlv8jMC0zZ7WxzSLgV5n5vhbL3gF8HTgOiIPY51xgbvnjZGBlFxze0eR44IWeLkJSYX5nO+/VmXlCWyv6d2MRxwP9gM2tlm8G3tjONqOAH7fRvn95f9HZfWbml4EvF65a+4mIusys6uk6JBXjd7Zr2XtWkqSCuvNM8wWgGRjZavlIYFM722xqp31TeX9xEPuUJOmgdNuZZmbuAZYC57VadR6lHq9tqW2nfV1mvnSQ+9Sh8dK2dGTxO9uFuq0jEOwbHnIPcDXwOKXese8Bpmbm+oi4GyAzLyu3nwAsB+YB/wz8KfBFSh1/7i+yz247OElSn9edl2fJzHsjYjhwA3AipUB8S4twO6lV+7UR8Rbg/1IaQvIccO3ewCy4T0mSukS3nmlKknQks/esJEkFGZqSJBVkaEqSVFC3dgTSkSkixgInUxoX+0dgZWY6DlbSUceOQOpQRFwFXA5MB/4L+A2wAfgZ8EBmroyIYzLzjz1YpiR1Cy/Pql3loTyfBh6kNJynhtJTZpqBy4A7ImJKZv4xIqLnKpUEEBGviIhJEVHR07X0VZ5pql0RcQ3wN5lZ3ca6GZSebzoG+JPM9CkKUg+LiOuAm4FvAd8Bfg48n5nNLdoMoTRRzI8z86WeqPNI5pmmOrIHGBwR0wAioqL8iDcyczFwKfAi8Gc9V6KkFi4GnqTUB+EBSlORfjYiZkTEfyu3+Wvg7w3Mg2NoqiPfptTx57qIGJyZjZm5JyKOAcjM/wR+B4ztwRolARFxAvASMC8zzwFeDfwL8OfAIuCxiPgwcB2wpKfqPNJ5eVZtanGP8r8DtwPDKF3y+SLwC0pBORP4J+B1mbmuB8qUVBYRJwKXACsy84et1p0BXFFePxQYl5kN3V/lkc/QVIci4lWU5gQ+G7iQ0r0QKD16LYB7MvNjPVKcpP1ExEAgM/PFlp3zsvwXfUTcTGlu7jN6qsYjneM09TIRMQJ4J/ABSs8t3U3pMuxi4FbgFZTumTycmat6qExJrWTm7r1hma3OiCLiOODtwF09UVtf4ZmmXiYi5gNTgX8HtlG6NPs6YBKwBbghM70nIvUS5R6xO1sHZas2x1LqKPSN8rOIdRAMTe2n/K/UnZQu4SxqsewkoJrSfZGJwF9mZn2PFSppn4j4Z0q9Zp8E1mfmjjbavCozf9fdtfU19p5Va1OAtZSGmwClyzyZuT4zvwVcQOlS7Tt6pjxJLUXEXwFXAp+jNBHJZyPiwoh4Tfke5957nV/dO3xMB88zTe2n/OX6PnAcpVl/ftt6irzypAfvyczTu79CSS1FxDxKs3T9H+BtwLuA1wArgf8AHgUmA7dn5oCeqrOv8ExT+8nM3cD/BgYCdwOXRcS4iBgE+zoTzAKW91yVkgAioj+lK0O/y8w1mXlrZr4OOBNYSClAvwV8Abin5yrtOzzTVJvKl3FuBOZQmqi9FngeeCOwEbgiM3/VcxVKAoiIocDIzPx1ecaul1p2CIqIi4FvAJWZ+cseKrPPMDTVofLwk7cCf0FpyrzlwH2Z+euerEtS+8qzdkVmNkfElZQuzR7X03X1BYamCvMRYNKRJyKuB/pl5md7upa+wNCUpD4sIl4BNPsP3q5haEqSVJC9ZyVJKsjQlCSpIENTkqSCDE1JkgoyNCVJKsjQlCSpoP8HLDqVVFYJFlYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runcircuit2 = circuit2.bind_parameters({phi2: phi2_value })\n",
    "\n",
    "job = execute(runcircuit2, backend, shots=10000)\n",
    "result = job.result()\n",
    "counts = result.get_counts(runcircuit2)\n",
    "print(counts)\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Durchführung der Distillation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 781.82x385.28 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = QuantumCircuit()\n",
    "qr = QuantumRegister(4,'q')\n",
    "qc.add_register( qr )\n",
    "crResult = ClassicalRegister(2,'result')\n",
    "qc.add_register( crResult )\n",
    "crCond = ClassicalRegister(2,'cond')\n",
    "qc.add_register( crCond )\n",
    "\n",
    "\n",
    "qc.ry(2*phi1,0)\n",
    "qc.cx(0,1)\n",
    "\n",
    "qc.ry(2*phi2,2)\n",
    "qc.cx(2,3)\n",
    "\n",
    "qc.barrier()\n",
    "qc.cx(0,2)\n",
    "qc.cx(1,3)\n",
    "\n",
    "qc.barrier()\n",
    "qc.measure(qr[2],crCond[0])\n",
    "qc.measure(qr[3],crCond[1])\n",
    "\n",
    "\n",
    "qc.barrier()\n",
    "qc.measure(qr[0],crResult[0])\n",
    "qc.measure(qr[1],crResult[1])\n",
    "\n",
    "qc.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "qc = qc.bind_parameters({phi1: phi1_value, phi2: phi2_value })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'11 00': 4649, '00 11': 6984, '11 11': 5054, '00 00': 3313}\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": [
    "job = execute(qc, backend, shots=20000)\n",
    "result = job.result()\n",
    "counts = result.get_counts(qc)\n",
    "print(counts)\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Erhält man als Messergebis 11, so hat sich die Verschränkung des Qubit-Paars $q_0q_1$ verbessert"
   ]
  },
  {
   "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
}