Quantum_Pachinko.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "\n",
    "# Import Libraries"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from qiskit import ClassicalRegister\n",
    "from qiskit import QuantumRegister\n",
    "from qiskit import QuantumCircuit\n",
    "from qiskit import Aer, transpile\n",
    "from qiskit.tools.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Create Small Pachinko"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 371.107x367.889 with 1 Axes>",
      "image/png": "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"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize  register\n",
    "cr = ClassicalRegister(1, name='c')\n",
    "qr = QuantumRegister(3, name='q')\n",
    "small_pac = QuantumCircuit(qr, cr, name='small_pachinko')\n",
    "# Initialize rotation gates\n",
    "val = np.pi / 2\n",
    "small_pac.x(0)\n",
    "small_pac.cry(val, control_qubit=0, target_qubit=1)\n",
    "small_pac.cry(val, control_qubit=0, target_qubit=2)\n",
    "# Draw circuit\n",
    "small_pac.draw('mpl', style='bw', plot_barriers=False, justify='none', fold=-1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Simulate Small Circuit"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.504\n",
      "1 0.496\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": 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"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup simulator backend\n",
    "simulator = Aer.get_backend('aer_simulator')\n",
    "\n",
    "def simulate_circuit(circuit, shots=10_000):\n",
    "    \"\"\"\n",
    "    Simulate the given quantum circuit using the aer_simulator backend from Qiskit.\n",
    "\n",
    "    Parameters:\n",
    "    - circuit (QuantumCircuit): The quantum circuit to simulate.\n",
    "    - shots (int): The number of shots for the simulation.\n",
    "\n",
    "    Returns:\n",
    "    - dict: A dictionary with measurement outcomes as keys and their frequencies as values.\n",
    "    \"\"\"\n",
    "    # Transpile the circuit for the simulator backend\n",
    "    transpiled_circuit = transpile(circuit, simulator)\n",
    "\n",
    "    # Run the simulation\n",
    "    job = simulator.run(transpiled_circuit, shots=shots)\n",
    "\n",
    "    # Get the results\n",
    "    result = job.result()\n",
    "\n",
    "    # Get counts of measurement outcomes\n",
    "    counts = result.get_counts(transpiled_circuit)\n",
    "\n",
    "    return counts\n",
    "\n",
    "# Create a copy of the 'small_pac' circuit\n",
    "markov_circuit = small_pac.copy()\n",
    "\n",
    "# Measure qubit at index 2 into classical bit at index 0\n",
    "markov_circuit.measure(2, 0)\n",
    "\n",
    "# Set the number of shots for the simulation\n",
    "nshots = 10_000\n",
    "\n",
    "# Simulate the circuit\n",
    "counts = simulate_circuit(markov_circuit, nshots)\n",
    "\n",
    "# Print the normalized results\n",
    "for key in sorted(counts.keys()):\n",
    "    print(key, counts[key] / nshots)\n",
    "\n",
    "# Visualize the results using a histogram\n",
    "plot_histogram(counts, title='Small Pachinko')\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Create Big Pachinko"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1894.34x1371.22 with 1 Axes>",
      "image/png": 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SznP88cdn06ZN+c53vpOTTz65fXulUskXvvCFrFixIv/4j/+Y/v37F5gS9o1ifA88//zzSXZeRiV5fz2lBx98MG+99VYuvPDCnHfeeXnxxRdzzz335IorrujqqAAAAADsg4aGhvzRH/1RfvKTn+SXfumXkry/YsBXv/rVHcry7qpnz54577zzsnr16vbVEH7pl34p3/3udzNhwoRu+00BysNSKnvgw4rxJJk6deoef3IvAAAAAPs3JTB0P+4Y3wMfVYwDAEB3tWzZslx22WV55513Mn369AwdOjSjRo3K6aefnk2bNhUdr0uYAwCA7kMxvgdWrFiRarWaKVOmFB0FAAC61P3335/p06cnSS655JK8+OKLefbZZzNt2rRcfPHFxYbrIuYAAKD7UIwDAAB566230tDQkL59+2b06NEZNmxYevbsmYsvvjjvvvtu1qxZk9NOOy01NTWZPHly+4+Ujxs3Lps3by42fAcxBwAA5aEYBwAA0rt375x//vm56qqrsn79+txyyy0ZN25cFi9enJUrV2b8+PE55JBDdjrv1ltvzbRp0wpI3PHMAQBAeSjGAQCAJMn69etz/PHHJ0mefvrp9t8/8MADOeuss3Y6/qabbsqmTZsyf/78Ls3ZmcwBAEA5KMYBAIAkuy6Fq9VqHnnkkUyaNGmHYxcuXJj77rsvDz/8cGpra4uI2ynMAQBAOSjGAQCAbNmyJZVKJUceeWSS5LnnnsuIESPy5JNPZtiwYamvr28/9utf/3qWLFmSRx99NL179y4occczBwAA5XFw0QEAAIDiPfPMM+13Sifvr7e9aNGi9OvXL9OnT2/f3tjYmLlz52bQoEGZMGFCkqRnz5554oknujpyhzMHAADloRgHAAAyderUTJ06tf3x2rVrkyTDhw/PypUr27c3NDSkWq12eb6uYA4AAMpDMQ4AAOzWhg0bio5QOHMAAND9KMYBAKAbam1tLTrCbnVVNnMAAMDuKMYBAKAbOuKII4qOUDhzAADA7vQoOgAAAAAAAHQld4wDAEA3UVtbm61btxYdY4/U1tZ2+PXKPgcAAHw0xTgAAHQTlUoldXV1RccolDkAAODjsJQKAAAAAAClohgHAAAAAKBUFOMAAAAAAJSKYhwAAAAAgFJRjAMAAAAAUCqKcQAAAAAASkUxDgAAAABAqSjGAQAAAAAoFcU4AAAAAAClohgHAAAAAKBUFOMAAAAAAJSKYhwAAAAAgFJRjAMAAAAAUCqKcQAAAAAASkUxDgAAAABAqSjGAQAAAAAoFcU4AAAAAAClcnDRAQAAAOgY1Wo127Zt6/DrtrW1tX9tbW3t0GvX1tamUql06DUBAD6KYhwAAKCb2LZtW+rr6zvt+k1NTR1+/a1bt6aurq5DrwkA8FEspQIAAAAAQKm4YxwAAKAbev311/fbO7FbW1tzxBFHFB0DACgxxTgAAEA3VFdXt98W4wAARbOUCgAAAAAApaIYBwAAAACgVBTjAAAAAACUimIcAAAAAIBSUYwDAAAAAFAqinEAAICSW7VqVSqVyg6/6uvrc8IJJ+Tmm2/O9u3bi44IANChDi46AAAAAPuH8847L5MnT061Wk1zc3O+/e1v5+qrr87GjRtz1113FR0PAKDDKMYBAABIkpxwwgmZNWtW++PZs2fn2GOPzeLFi3PjjTemf//+BaYDAOg4llIBAABgl+rq6jJu3LhUq9W8/PLLRccBAOgwinEAAAB264NCvE+fPgUnAQDoOIrxD9HS0pJ58+Zl8ODBqampyVFHHZUrr7wyra2tueiii1KpVHL77bcXHRMAAKBDbNu2LS0tLfnXf/3XPP/887n88svzzDPPZOzYsRk6dGjR8QCgy33wU1NPPPFE1q9fn5/+9KdFR+pyb7zxRp555pk8+eSTeeWVV4qO02GsMb4b69evz6RJk9Lc3Jy6urocd9xxee2113Lbbbfl5ZdfzhtvvJEkGT16dLFBAQAAOsgNN9yQG264YYdtZ599dv7iL/6ioEQAUIzW1tbcc889+cY3vpH169e3b6+pqcl5552X2bNnZ8yYMcUF7AKrV6/OokWLcu+99+bdd99t337SSSflsssuy7nnnpuePXsWmHDfuGN8F1paWnLmmWemubk5c+fOTVNTU9atW5fm5uYsWLAgy5cvz9q1a1OpVDJy5Mii4wIAAHSISy65JI8++mh+8IMfZMGCBenTp08aGxtTU1PTfsyMGTNy7rnn7nDeG2+8kYEDB+aee+7p6sgA0OF+8pOf5HOf+1x++7d/e4dSPEneeeed/OVf/mVOPPHE3HjjjalWq8WE7ERtbW25+uqrc8opp+Rv/uZvdijFk+Sf/umf8qUvfSmnnnpq/vVf/7WglPtOMb4LV1xxRRobGzNnzpwsXLgwvXr1at83b968jBo1Ktu3b88xxxyTww47rMCkAAAAHWfIkCGZOHFiJk2alHnz5uXBBx/M2rVrc+mll7Yfs2jRoqxZsyZLlixp33b55Zfn5JNPzsyZM4uIDQAdpqWlJb/yK7+SDRs2fOSx/+2//bd87Wtf64JUXWvevHm5+eabP/K4f/7nf84ZZ5yRrVu3dkGqjqcY/wUbN27M0qVL069fv8yfP3+Xx3z2s59NkowaNWqH7X//93+fcePGpaamJocffnguvfTSUq47BAAAdA/jx4/PBRdckKVLl+bxxx9P8v6HcN59992ZM2dOXnvttSxbtiyrVq3KHXfcUXBaANh3119/fTZt2vSxj/+DP/iD/PjHP+7ERF3rqaeeyp/92Z997OPXrVuXhQsXdmKizqMY/wVLlixJW1tbZs6cmfr6+l0ec+ihhybZsRj/h3/4h5xxxhk58sgjc//99+fGG2/MsmXLMn369G75IxUAAEA5XHfddTnooINy/fXXt28744wzcu6552bWrFmZPXt2Fi9enL59+xaYEgD23c9+9rP8z//5P/fonGq1mjvvvLOTEnW9RYsW7fE53/zmN3dabuVAoBj/BStWrEiSTJgwYbfHNDY2JtmxGP/qV7+aIUOG5Lvf/W4mTZqU3/qt38pdd92VVatWZfny5Z0bGgAAoJMMHjw4M2bMyGOPPZbVq1e3b1+4cGE2bdqUSZMmZcqUKQUmBICO8d3vfnevlgW5++67u8WNsa2trTsslfZxvfbaa/m7v/u7TkjUuQ4uOsD+5pVXXkmSHH300bvcv3379qxZsybJjsX4E088kd/8zd9Mjx7/73sNv/qrv5okeeCBBzJ16tQ9zjJmzJg0Nzfv8Xk/r62tbZ/Oh442ZMiQHf6eAADQcTrr9f+1116bJUuW5Prrr8/KlSuTJHV1dRk0aFBGjBixT9c+EF4fNjU1tX9taGgoOE0xyj4HZR9/Yg6Mvxzj//d///e9Oq+lpSVHHnnkfv//2UfZvn173nnnnb0694ILLtjt6hudacCAAXnqqaf26lzF+C9obW1Nkrz99tu73L906dK0tLSkV69e+dSnPtW+/aCDDsonPvGJHY495JBDUqlUPtZi/bvS3NycLVu27NW5sL/64D9TAAD2H6eeeuqH3uk2bNiwvPfee53y3AfS68O2trbSv0cr+xyUffyJOTD+co//wxxI/591hp/+9KcH3GctKsZ/wYABA/Lmm29m3bp1Oemkk3bY19TUlGuuuSZJMnLkyFQqlfZ9Q4cOzRNPPLHD8WvXrk21Ws0bb7yx11n2VVtbW+n/YrJ/GThw4AH/HVQAgP3Vgfj6/0B4fdjU1JS2trb06NEjAwcOLDpOIco+B2Uff2IOjL8c49+6detelbuVSiUDBw7coSs8EO3L64hPfvKTqa2t7eBEH21f+lPF+C+YOHFiNm7cmAULFuT000/P0KFDk7xfcl9wwQVpaWlJkowePXqH86644op86Utfyh//8R/n0ksvTWNjY2bPnp2DDjpor1/k7e2PAfy81tbWQn6MAXbnpZdeSl1dXdExAAC6pQPx9f+B8PqwoaEhW7ZsycCBA9s/c6psyj4HZR9/Yg6Mvxzjb25uzlFHHZXt27fv0XkXXXRRvvnNb3ZSqq41bdq0fP/739+jcw477LD85Cc/OeBeg+zf35YvwLx589K3b9+8+uqrGT58eEaMGJEhQ4Zk7NixGTRoUE477bQkO64vniSzZs3K7//+7+e///f/nv79+2fMmDGZMGFCRo8e3a2/kwYAAJTXqlWr8nu/93tFxwCADjFgwID8+q//+h6fN3v27E5IU4y9GcuFF154wJXiiWJ8Jw0NDVm9enWmTJmSmpqabN68OX369Mmdd96Z5cuX50c/+lGSnYvxSqWSP/mTP0lLS0ueffbZvP766/mzP/uzvPTSSxk/fnwRQwEAAAAA9sBXv/rVfPKTn/zYx1900UU5/vjjOzFR1zr99NMzbdq0j338kUcemd///d/vxESdx1IquzBs2LA89NBDO23funVrNm/enB49euQzn/nMLs/t1atXRo4cmST55je/mbfffju/+Zu/2al5AQAAAIB9N3To0Dz88MOZMmVK/u3f/u1Djz3vvPPyjW98o4uSdY0ePXrkO9/5Tn791389f/d3f/ehxzY0NOSRRx7JkUce2UXpOpY7xvfAhg0bUq1WM2TIkJ0Wk3/qqacyf/78PPLII1m+fHl+93d/N5deemkWLFiQT3/60wUlBgAAAAD2xOc+97k89dRT+Z3f+Z0cdthhO+0fM2ZMvvWtb+Wv//qvc8ghhxSQsHPV1tbmwQcfzKJFizJ8+PCd9vft2zfz5s3L2rVrc9xxxxWQsGO4Y3wPPP/880l2XkYlSXr27JkHH3ww8+fPz/bt2zNixIgsXbo0X/jCF7o6JgAAAACwD4455pjcdtttuemmm/L3f//3+c3f/M289dZb6d+/f9auXVt0vE538MEH57LLLsull16af/qnf8rUqVPz5ptvpk+fPmlsbExNTU3REfeZYnwPfFgxPmLEiDz++ONdHQkAAAAA6CT19fWZPn165syZk7feeiuf+MQnio7UpSqVSsaPH5/a2tq8+eabOfTQQ7tFKZ5YSmWPfFgxDgAA0J0tW7Ysl112Wd55551Mnz49Q4cOzahRo3L66adn06ZNRccDANgjivE9sGLFilSr1UyZMqXoKAAAAF3q/vvvz/Tp05Mkl1xySV588cU8++yzmTZtWi6++OJiwwEA7CHFOAAAAHnrrbfS0NCQvn37ZvTo0Rk2bFh69uyZiy++OO+++27WrFmT0047LTU1NZk8eXIqlUqSZNy4cdm8eXOx4QEA9pBiHAAAgPTu3Tvnn39+rrrqqqxfvz633HJLxo0bl8WLF2flypUZP358DjnkkJ3Ou/XWWzNt2rQCEgMA7D3FOAAAAEmS9evX5/jjj0+SPP300+2/f+CBB3LWWWftdPxNN92UTZs2Zf78+V2aEwBgXynGAQAASLLrYrxareaRRx7JpEmTdjh24cKFue+++/Lwww+ntra2iLgAAHtNMQ4AAEC2bNmSSqWSI488Mkny3HPPZcSIEXnyySczbNiw1NfXtx/79a9/PUuWLMmjjz6a3r17F5QYAGDvHVx0AAAAAIr3zDPPtN8tnry/5viiRYvSr1+/TJ8+vX17Y2Nj5s6dm0GDBmXChAlJkp49e+aJJ57o6sgAAHtNMQ4AAECmTp2aqVOntj9eu3ZtkmT48OFZuXJl+/aGhoZUq9UuzwcA0JEU4wAAAOzWhg0bio4AANDhFOMAAADdUGtra9ERdmt/zgYAlINiHAAAoBs64ogjio4AALDf6lF0AAAAAAAA6EruGAcAAOgmamtrs3Xr1qJj7JHa2tqiIwAAJaQYBwAA6CYqlUrq6uqKjgEAsN+zlAoAAAAAAKWiGAcAAAAAoFQU4wAAAAAAlIpiHAAAAACAUlGMAwAAAABQKopxAAAAAABKRTEOAAAAAECpKMYBAAAAACgVxTgAAAAAAKWiGAcAAAAAoFQU4wAAAAAAlIpiHAAAAACAUlGMAwAAAABQKopxAAAAAABKRTEOAAAAAECpKMYBAAAAACgVxTgAAAAAAKWiGAcAAAAAoFQOLjoAAAAAdJRqtZpt27Z16DXb2trav7a2tnbotWtra1OpVDr0mgDAR1OMAwAA0G1s27Yt9fX1nXLtpqamDr/21q1bU1dX16HXBAA+mqVUAAAAAAAoFXeMAwAA0C29/vrr++Xd2K2trTniiCOKjgEApaYYBwAAoFuqq6vbL4txAKB4llIBAAAAAKBUFOMAAAAAAJSKYhwAAAAAgFJRjAMAAAAAUCqKcQAAAEpt1apVqVQqO/yqr6/PCSeckJtvvjnbt28vOiIA0MEOLjoAAAAA7A/OO++8TJ48OdVqNc3Nzfn2t7+dq6++Ohs3bsxdd91VdDwAoAMpxgEAACDJCSeckFmzZrU/nj17do499tgsXrw4N954Y/r3719gOgCgI1lKBQAAAHahrq4u48aNS7Vazcsvv1x0HACgAynGAQAAYDc+KMT79OlTcBIAoCNZSgUAAACSbNu2LS0tLe1rjN9xxx155plnMnbs2AwdOrToeABAByrlHeMtLS2ZN29eBg8enJqamhx11FG58sor09ramosuuiiVSiW333570TEBAADoQjfccEP69++fww8/PCNHjsyiRYty9tln53vf+17R0QCADla6O8bXr1+fSZMmpbm5OXV1dTnuuOPy2muv5bbbbsvLL7+cN954I0kyevToYoMCAADQpS655JKcc845effdd/P8889nwYIFaWxsTE1NTfsxM2bMSFtbW/72b/+2fdsbb7yR4cOHZ+HChZk5c2YR0QGAPVSqO8ZbWlpy5plnprm5OXPnzk1TU1PWrVuX5ubmLFiwIMuXL8/atWtTqVQycuTIouMCAADQhYYMGZKJEydm0qRJmTdvXh588MGsXbs2l156afsxixYtypo1a7JkyZL2bZdffnlOPvlkpTgAHEBKVYxfccUVaWxszJw5c7Jw4cL06tWrfd+8efMyatSobN++Pcccc0wOO+ywApMCAABQtPHjx+eCCy7I0qVL8/jjjyd5/0M477777syZMyevvfZali1bllWrVuWOO+4oOC0AsCdKU4xv3LgxS5cuTb9+/TJ//vxdHvPZz342STJq1Kj2bR8U6WPHjk3Pnj1TqVR2+xw//vGP82u/9mvp1atXPvnJT+ZLX/pS/u3f/q1jBwIAAECXue6663LQQQfl+uuvb992xhln5Nxzz82sWbMye/bsLF68OH379i0wJQCwp0pTjC9ZsiRtbW2ZOXNm6uvrd3nMoYcemmTHYnzTpk259957M2DAgJx44om7vf7PfvazTJgwIY2NjVmyZEnuuuuurF69OlOnTk1bW1vHDgYAAIAuMXjw4MyYMSOPPfZYVq9e3b594cKF2bRpUyZNmpQpU6YUmBAA2Bul+fDNFStWJEkmTJiw22MaGxuT7FiMn3LKKWlqakqSfOUrX8maNWt2ee5dd92VLVu25H/9r/+VX/7lX06SNDQ0ZPz48fn+97+f6dOn73HmMWPGpLm5eY/P+3lKefY3Q4YMSY8epfmeHAAAXawz3gNde+21WbJkSa6//vqsXLkySVJXV5dBgwZlxIgR+3TtA+X18Qfvi5uamtLQ0FBwmq5X9vEn5sD4yz3+xBzsr+MfMGBAnnrqqb06tzTF+CuvvJIkOfroo3e5f/v27e2l988X4x/3BcpDDz2Uk08+ub0UT5KTTjopgwYNyoMPPrhXxXhzc3O2bNmyx+fB/uyDf0gBAGB/ceqpp6Zare52/7Bhw/Lee+91ynMfaK+P29raSv0+tezjT8yB8Zd7/Ik56E7jL00x3tramiR5++23d7l/6dKlaWlpSa9evfKpT31qj6//wgsv5Jxzztlp+/Dhw/PCCy/s8fWS97/jsa/a2toOuBdadG8DBw48IO6IAQDgwHSgvQc6UF4fNzU1pa2tLT169MjAgQOLjtPlyj7+xBwYf7nHn5iD/XX8+9KflqYYHzBgQN58882sW7cuJ5100g77mpqacs011yRJRo4c+aEfsLk7b775Znr37r3T9j59+uTFF1/cq8x7+2MAP6+1tXW3a6pDEV566aXU1dUVHQMAgG7qQHsPdKC8Pm5oaMiWLVsycODA9mVIy6Ts40/MgfGXe/yJOeiO49//vy3dQSZOnJgkWbBgQX70ox+1b1+7dm0mTJiQlpaWJMno0aOLiAcAAMABZtWqVfm93/u9omMAAHuhNMX4vHnz0rdv37z66qsZPnx4RowYkSFDhmTs2LEZNGhQTjvttCQ7ri++Jz75yU/mrbfe2mn7G2+8kT59+uxLdAAAAAAAOlBpivGGhoasXr06U6ZMSU1NTTZv3pw+ffrkzjvvzPLly9vvIt/bYnzYsGG7XEv8hRdeyLBhw/YpOwAAAAAAHac0a4wn75fXDz300E7bt27dms2bN6dHjx75zGc+s1fXnjp1ar785S+nsbExDQ0NSZInnngiL7/8cr72ta/tU24AAAAAADpOqYrx3dmwYUOq1WqGDh2a2tranfYvW7YsSdrvCP/g8THHHJMxY8YkSS655JL8+Z//eaZNm5Y/+qM/yjvvvJN58+Zl7NixmTZtWheNBAAAAACAj6IYT/L8888n2f0yKuecc84uH1944YX51re+lSQ57LDDsmLFilx55ZWZMWNGDj744EydOjU333xzevQozYo1AAAAAAD7PcV4ProYr1arH+s6n/70p3e5VAsAAAAHtmXLluWxxx7LzTffnBkzZuSFF17IoYcemsMPPzzf+MY3Mnjw4KIjAgB7wK3M+ehiHAAAgHK7//77M3369CTvL6X54osv5tlnn820adNy8cUXFxsOANhjivEkK1asSLVazZQpU4qOAgAAQAHeeuutNDQ0pG/fvhk9enSGDRuWnj175uKLL867776bNWvW5LTTTktNTU0mT56cSqWSJBk3blw2b95cbHgAYI8pxgEAACi93r175/zzz89VV12V9evX55Zbbsm4ceOyePHirFy5MuPHj88hhxyy03m33nprpk2bVkBiAGBfKMYBAAAgyfr163P88ccnSZ5++un23z/wwAM566yzdjr+pptuyqZNmzJ//vwuzQkA7DvFOAAAAGTXxXi1Ws0jjzySSZMm7XDswoULc9999+Xhhx9ObW1tEXEBgH2gGAcAAKD0tmzZkkqlkiOPPDJJ8txzz2XEiBF58sknM2zYsNTX17cf+/Wvfz1LlizJo48+mt69exeUGADYFwcXHQAAAACK9swzz7TfLZ68v+b4okWL0q9fv0yfPr19e2NjY+bOnZtBgwZlwoQJSZKePXvmiSee6OrIAMA+UIwDAABQelOnTs3UqVPbH69duzZJMnz48KxcubJ9e0NDQ6rVapfnAwA6lmIcAAAAdmPDhg1FRwAAOoFiHAAAgG6ptbW16Ai7tL/mAoAyUYwDAADQLR1xxBFFRwAA9lM9ig4AAAAAAABdyR3jAAAAdBu1tbXZunVr0TE+ttra2qIjAEApKcYBAADoNiqVSurq6oqOAQDs5yylAgAAAABAqSjGAQAAAAAoFcU4AAAAAAClohgHAAAAAKBUFOMAAAAAAJSKYhwAAAAAgFJRjAMAAAAAUCqKcQAAAAAASkUxDgAAAABAqSjGAQAAAAAoFcU4AAAAAAClohgHAAAAAKBUFOMAAAAAAJSKYhwAAAAAgFJRjAMAAAAAUCqKcQAAAAAASkUxDgAAAABAqSjGAQAAAAAolYOLDgAAAAB0jGq1mm3btnX4ddva2tq/tra2dui1a2trU6lUOuRaZR8/AB+fYhwAAAC6iW3btqW+vr7Trt/U1NTh19+6dWvq6uo65FplHz8AH5+lVAAAAAAAKBV3jAMAAEA39Prrr++3dyK3trbmiCOO6NTnKPv4AfhwinEAAADohurq6vbbYrgrlH38AHw4S6kAAAAAAFAqinEAAAAAAEpFMQ4AAAAAQKkoxgEAAAAAKBXFOAAAAJTcqlWrUqlUdvhVX1+fE044ITfffHO2b99edMROVfbxA5TRwUUHAAAAAPYP5513XiZPnpxqtZrm5uZ8+9vfztVXX52NGzfmrrvuKjpepyv7+AHKRDEOAAAAJElOOOGEzJo1q/3x7Nmzc+yxx2bx4sW58cYb079//wLTdb6yjx+gTCylAgAAAOxSXV1dxo0bl2q1mpdffrnoOF2u7OMH6M4U4wAAAMBufVAI9+nTp+AkxSj7+AG6K0upAAAAAEmSbdu2paWlpX2N7TvuuCPPPPNMxo4dm6FDhxYdr9OVffwAZVLKO8ZbWloyb968DB48ODU1NTnqqKNy5ZVXprW1NRdddFEqlUpuv/32omMCAABAl7rhhhvSv3//HH744Rk5cmQWLVqUs88+O9/73veKjtYlyj5+gDIpXTG+fv36jBgxIl/72tfS3Nyc4447Lu+++25uu+22fPGLX8zGjRuTJKNHjy42KAAAAHSxSy65JI8++mh+8IMfZMGCBenTp08aGxtTU1PTfsyMGTNy7rnn7nDeG2+8kYEDB+aee+7p6sgdquzjByiTUhXjLS0tOfPMM9Pc3Jy5c+emqakp69atS3NzcxYsWJDly5dn7dq1qVQqGTlyZNFxAQAAoEsNGTIkEydOzKRJkzJv3rw8+OCDWbt2bS699NL2YxYtWpQ1a9ZkyZIl7dsuv/zynHzyyZk5c2YRsTtM2ccPUCalKsavuOKKNDY2Zs6cOVm4cGF69erVvm/evHkZNWpUtm/fnmOOOSaHHXZYgUkBAACgeOPHj88FF1yQpUuX5vHHH0/y/odQ3n333ZkzZ05ee+21LFu2LKtWrcodd9xRcNqOV/bxA3RnpSnGN27cmKVLl6Zfv36ZP3/+Lo/57Gc/myQZNWpU+7YPivSxY8emZ8+eqVQquzz34x4HAAAAB5LrrrsuBx10UK6//vr2bWeccUbOPffczJo1K7Nnz87ixYvTt2/fAlN2nrKPH6C7Kk0xvmTJkrS1tWXmzJmpr6/f5TGHHnpokh2L8U2bNuXee+/NgAEDcuKJJ+72+h/3OAAAADiQDB48ODNmzMhjjz2W1atXt29fuHBhNm3alEmTJmXKlCkFJuxcZR8/QHdVmmJ8xYoVSZIJEybs9pjGxsYkOxbjp5xySpqamvL9738/EydO3O25H/c4AAAAONBce+216dGjxw53TdfV1WXQoEEZMWJEgcm6RtnHD9AdHVx0gK7yyiuvJEmOPvroXe7fvn171qxZk2THYrxHj4/3vYOPe9yeGDNmTJqbm/fpGm1tbR2UBjrGkCFDOuXvCwAAsPfvAU899dRUq9Xd7h82bFjee++9vY31oTryPULZx9+Zmpqa2r82NDQUnKbrGX+5x5+Yg/11/AMGDMhTTz21V+eWphhvbW1Nkrz99tu73L906dK0tLSkV69e+dSnPtWV0Xarubk5W7ZsKToGdKgP/iEFAABIvEc40Mbf1tZW6q7C+Ms9/sQcdKfxl6YYHzBgQN58882sW7cuJ5100g77mpqacs011yRJRo4cud98cOaAAQP2+RptbW0H3H+ydG8DBw48IO6GAACAA9GB+B6wI98jlH38nampqSltbW3p0aNHBg4cWHScLmf85R5/Yg721/HvS39ammJ84sSJ2bhxYxYsWJDTTz89Q4cOTZKsXbs2F1xwQVpaWpIko0ePLjDljvb2xwB+Xmtr624/bBSK8NJLL6Wurq7oGAAA0C119XvAVatW7fM1OvI9QtnH35kaGhqyZcuWDBw4sP0z2srE+Ms9/sQcdMfx7//fkuwg8+bNS9++ffPqq69m+PDhGTFiRIYMGZKxY8dm0KBBOe2005LsuL44AAAAAADdT2mK8YaGhqxevTpTpkxJTU1NNm/enD59+uTOO+/M8uXL86Mf/SiJYhwAAAAAoLsrzVIqyfufIv3QQw/ttH3r1q3ZvHlzevTokc985jMFJAMAAAAAoKuUqhjfnQ0bNqRarWbo0KGpra3daf+yZcuSJC+88MIOj4855piMGTNmj48DAAAAAKA4ivEkzz//fJLdL6Nyzjnn7PLxhRdemG9961t7fBwAAAAAAMVRjOeji/FqtfqxrvNxjwMAAAAAoDil+fDND/NRxTgAAACU3bJly3LZZZflnXfeyfTp0zN06NCMGjUqp59+ejZt2lR0vE5X9vEDdDeK8SQrVqxItVrNlClTio4CAAAA+6X7778/06dPT5JccsklefHFF/Pss89m2rRpufjii4sN1wXKPn6A7kYxDgAAAOStt95KQ0ND+vbtm9GjR2fYsGHp2bNnLr744rz77rtZs2ZNTjvttNTU1GTy5MmpVCpJknHjxmXz5s3Fhu8AZR8/QNkoxgEAAID07t07559/fq666qqsX78+t9xyS8aNG5fFixdn5cqVGT9+fA455JCdzrv11lszbdq0AhJ3rLKPH6BsFOMAAABAkmT9+vU5/vjjkyRPP/10++8feOCBnHXWWTsdf9NNN2XTpk2ZP39+l+bsLGUfP0CZKMYBAACAJLsuhqvVah555JFMmjRph2MXLlyY++67Lw8//HBqa2uLiNvhyj5+gDJRjAMAAADZsmVLKpVKjjzyyCTJc889lxEjRuTJJ5/MsGHDUl9f337s17/+9SxZsiSPPvpoevfuXVDijlX28QOUzcFFBwAAAACK98wzz7TfLZ28v+b2okWL0q9fv0yfPr19e2NjY+bOnZtBgwZlwoQJSZKePXvmiSee6OrIHars4wcoG8U4AAAAkKlTp2bq1Kntj9euXZskGT58eFauXNm+vaGhIdVqtcvzdbayjx+gbBTjAAAAwG5t2LCh6AiFKvv4AborxTgAAAB0Q62trUVH2K2uyFb28QPw4RTjAAAA0A0dccQRRUcoVNnHD8CH61F0AAAAAAAA6EruGAcAAIBuora2Nlu3bi06xh6pra3t0GuVefwAfHyKcQAAAOgmKpVK6urqio5RmLKPH4CPz1IqAAAAAACUimIcAAAAAIBSUYwDAAAAAFAqinEAAAAAAEpFMQ4AAAAAQKkoxgEAAAAAKBXFOAAAAAAApaIYBwAAAACgVBTjAAAAAACUimIcAAAAAIBSUYwDAAAAAFAqinEAAAAAAEpFMQ4AAAAAQKkoxgEAAAAAKBXFOAAAAAAApaIYBwAAAACgVBTjAAAAAACUysFFBwAAAACgY1Sr1Wzbtq3Dr9vW1tb+tbW1tUOvXVtbm0ql0iHXKvv4gY9PMQ4AAADQTWzbti319fWddv2mpqYOv/7WrVtTV1fXIdcq+/iBj89SKgAAAAAAlIo7xgEAAAC6oddff32/vRO5tbU1RxxxRKc+R9nHD3w4xTgAAABAN1RXV7ffFsNdoezjBz6cpVQAAAAAACgVxTgAAAAAAKWiGAcAAAAAoFQU4wAAAAAAlIpiHAAAAACAUlGMAwAAAJTYqlWrUqlUdvhVX1+fE044ITfffHO2b99edMROZw6gfA4uOgAAAAAAxTvvvPMyefLkVKvVNDc359vf/nauvvrqbNy4MXfddVfR8bqEOYDyUIwDAAAAkBNOOCGzZs1qfzx79uwce+yxWbx4cW688cb079+/wHRdwxxAeVhKBQAAAICd1NXVZdy4calWq3n55ZeLjlMIcwDdl2IcAAAAgF36oAzu06dPwUmKYw6ge7KUCgAAAADZtm1bWlpa2tfXvuOOO/LMM89k7NixGTp0aNHxuoQ5gPIo5R3jLS0tmTdvXgYPHpyampocddRRufLKK9Pa2pqLLroolUolt99+e9ExAQAAALrMDTfckP79++fwww/PyJEjs2jRopx99tn53ve+V3S0LmMOoDxKV4yvX78+I0aMyNe+9rU0NzfnuOOOy7vvvpvbbrstX/ziF7Nx48YkyejRo4sNCgAAANCFLrnkkjz66KP5wQ9+kAULFqRPnz5pbGxMTU1N+zEzZszIueeeu8N5b7zxRgYOHJh77rmnqyN3OHMA5VGqYrylpSVnnnlmmpubM3fu3DQ1NWXdunVpbm7OggULsnz58qxduzaVSiUjR44sOi4AAABAlxkyZEgmTpyYSZMmZd68eXnwwQezdu3aXHrppe3HLFq0KGvWrMmSJUvat11++eU5+eSTM3PmzCJidyhzAOVRqmL8iiuuSGNjY+bMmZOFCxemV69e7fvmzZuXUaNGZfv27TnmmGNy2GGHFZgUAAAAoFjjx4/PBRdckKVLl+bxxx9P8v4HUN59992ZM2dOXnvttSxbtiyrVq3KHXfcUXDazmEOoPsqTTG+cePGLF26NP369cv8+fN3ecxnP/vZJMmoUaPat31QpI8dOzY9e/ZMpVLZ5bnLli3Lr//6r+foo49ObW1tjj322Fx77bXZunVrxw8GAAAAoAtcd911Oeigg3L99de3bzvjjDNy7rnnZtasWZk9e3YWL16cvn37Fpiyc5kD6J5KU4wvWbIkbW1tmTlzZurr63d5zKGHHppkx2J806ZNuffeezNgwICceOKJu73+woULc9BBB+Wmm27Kww8/nMsuuyzf+MY3csYZZ6Stra1jBwMAAADQBQYPHpwZM2bksccey+rVq9u3L1y4MJs2bcqkSZMyZcqUAhN2PnMA3dPBRQfoKitWrEiSTJgwYbfHNDY2JtmxGD/llFPS1NSUJPnKV76SNWvW7PLcBx98MP37929//PnPfz79+/fPzJkz84//+I855ZRT9jjzmDFj0tzcvMfn/TylPPubIUOGpEeP0nxPDgAAoEt1Rg9w7bXXZsmSJbn++uuzcuXKJEldXV0GDRqUESNG7PP1O/J9Ymf1IJ05BwfC++QPurGmpqY0NDQUnKYYZZ+D/XX8AwYMyFNPPbVX55amGH/llVeSJEcfffQu92/fvr299P75Yvzj/sP086X4B8aMGZMk2bJlyx5l/UBzc/Nenwv7qw/+IQUAAGD/cOqpp6Zare52/7Bhw/Lee+912vPvD+8Ti5yD/WH8H1dbW1vpu6qyz0F3Gn9pivHW1tYkydtvv73L/UuXLk1LS0t69eqVT33qUx3ynB98B3HYsGF7df6AAQP2OUNbW9sB9Q8s3d/AgQP3+++EAwAAHKgOxB6gI98nln38naWpqSltbW3p0aNHBg4cWHScQpR9DvbX8e9Lf1qaYnzAgAF58803s27dupx00kk77Gtqaso111yTJBk5cuRuP2BzT2zZsiXXXXddzjjjjIwePXqvrrG3Pwbw81pbW3e7pjoU4aWXXkpdXV3RMQAAALqlA7EH6Mj3iWUff2dpaGjIli1bMnDgwPaliMum7HPQHcdfmmJ84sSJ2bhxYxYsWJDTTz89Q4cOTZKsXbs2F1xwQVpaWpJkr0vsn7d169ZMmzYtn/jEJ/I//sf/2OfrAQAAAOxvVq1aVXSEwpkDOHDt3z+n0YHmzZuXvn375tVXX83w4cMzYsSIDBkyJGPHjs2gQYNy2mmnJdlxffG98fbbb+fMM8/Mj3/84/zwhz/cr360AAAAAACAEhXjDQ0NWb16daZMmZKampps3rw5ffr0yZ133pnly5fnRz/6UZJ9K8bffffdfOELX8hTTz2Vhx9+OMcdd1xHxQcAAAAAoIOUZimV5P0PwXzooYd22r5169Zs3rw5PXr0yGc+85m9unZbW1tmzpyZxx57LD/4wQ8yduzYfY0LAAAAAEAnKFUxvjsbNmxItVrN0KFDU1tbu9P+ZcuWJUleeOGFHR4fc8wxGTNmTJLk8ssvz3e/+938wR/8QWpra/PP//zP7ed/+tOfTv/+/Tt7GAAAAAAAfAyK8STPP/98kt0vo3LOOefs8vGFF16Yb33rW0mShx9+OEnyJ3/yJ/mTP/mTHY7/y7/8y/zGb/xGByYGAAAAAGBvKcbz0cV4tVr9yGts3ry5IyMBAAAA7FeWLVuWxx57LDfffHNmzJiRF154IYceemgOP/zwfOMb38jgwYOLjtipyj5+6G5K8+GbH+ajinEAAACAsrv//vszffr0JMkll1ySF198Mc8++2ymTZuWiy++uNhwXaDs44fuRjGeZMWKFalWq5kyZUrRUQAAAAAK8dZbb6WhoSF9+/bN6NGjM2zYsPTs2TMXX3xx3n333axZsyannXZaampqMnny5FQqlSTJuHHjusVP0pd9/FA2inEAAAAA0rt375x//vm56qqrsn79+txyyy0ZN25cFi9enJUrV2b8+PE55JBDdjrv1ltvzbRp0wpI3LHKPn4oG8U4AAAAAEmS9evX5/jjj0+SPP300+2/f+CBB3LWWWftdPxNN92UTZs2Zf78+V2as7OUffxQJopxAAAAAJLsuhiuVqt55JFHMmnSpB2OXbhwYe677748/PDDqa2tLSJuhyv7+KFMFOMAAAAAZMuWLalUKjnyyCOTJM8991xGjBiRJ598MsOGDUt9fX37sV//+tezZMmSPProo+ndu3dBiTtW2ccPZXNw0QEAAAAAKN4zzzzTfrd08v6a24sWLUq/fv0yffr09u2NjY2ZO3duBg0alAkTJiRJevbsmSeeeKKrI3eoso8fykYxDgAAAECmTp2aqVOntj9eu3ZtkmT48OFZuXJl+/aGhoZUq9Uuz9fZyj5+KBvFOAAAAAC7tWHDhqIjFKrs44fuSjEOAAAA0A21trYWHWG3uiJb2ccPfDjFOAAAAEA3dMQRRxQdoVBlHz/w4XoUHQAAAAAAALqSO8YBAAAAuona2tps3bq16Bh7pLa2tkOvVebxAx+fYhwAAACgm6hUKqmrqys6RmHKPn7g47OUCgAAAAAApaIYBwAAAACgVBTjAAAAAACUimIcAAAAAIBSUYwDAAAAAFAqinEAAAAAAEpFMQ4AAAAAQKkoxgEAAAAAKBXFOAAAAAAApaIYBwAAAACgVBTjAAAAAACUimIcAAAAAIBSUYwDAAAAAFAqinEAAAAAAEpFMQ4AAAAAQKkoxgEAAAAAKBXFOAAAAAAApaIYBwAAAACgVA4uOgAAAAAAsO+q1Wq2bdvW4ddta2tr/9ra2tqh166trU2lUumw65kDPi7FOAAAAAB0A9u2bUt9fX2nXb+pqanDr79169bU1dV12PXMAR+XpVQAAAAAACgVd4wDAAAAQDfz+uuv77d3Ibe2tuaII47o9OcxB3wYxTgAAAAAdDN1dXX7bSncVcwBH8ZSKgAAAAAAlIpiHAAAAACAUlGMAwAAAABQKopxAAAAAABKRTEOAAAAACW2atWqVCqVHX7V19fnhBNOyM0335zt27cXHbHTmYPyObjoAAAAAABA8c4777xMnjw51Wo1zc3N+fa3v52rr746GzduzF133VV0vC5hDspDMQ4AAAAA5IQTTsisWbPaH8+ePTvHHntsFi9enBtvvDH9+/cvMF3XMAflYSkVAAAAAGAndXV1GTduXKrVal5++eWi4xTCHHRfinEAAAAAYJc+KIP79OlTcJLimIPuyVIqAAAAAEC2bduWlpaW9vW177jjjjzzzDMZO3Zshg4dWnS8LmEOyqOUd4y3tLRk3rx5GTx4cGpqanLUUUflyiuvTGtray666KJUKpXcfvvtRccEAAAAgC5zww03pH///jn88MMzcuTILFq0KGeffXa+973vFR2ty5iD8ihdMb5+/fqMGDEiX/va19Lc3Jzjjjsu7777bm677bZ88YtfzMaNG5Mko0ePLjYoAAAAAHShSy65JI8++mh+8IMfZMGCBenTp08aGxtTU1PTfsyMGTNy7rnn7nDeG2+8kYEDB+aee+7p6sgdzhyUR6mK8ZaWlpx55plpbm7O3Llz09TUlHXr1qW5uTkLFizI8uXLs3bt2lQqlYwcObLouAAAAADQZYYMGZKJEydm0qRJmTdvXh588MGsXbs2l156afsxixYtypo1a7JkyZL2bZdffnlOPvnkzJw5s4jYHcoclEepivErrrgijY2NmTNnThYuXJhevXq175s3b15GjRqV7du355hjjslhhx1WYFIAAAAAKNb48eNzwQUXZOnSpXn88ceTvP8BlHfffXfmzJmT1157LcuWLcuqVatyxx13FJy2c5iD7qs0xfjGjRuzdOnS9OvXL/Pnz9/lMZ/97GeTJKNGjWrf9kGRPnbs2PTs2TOVSmWX565evToTJ07MwIED07NnzzQ0NOywNAsAAAAAHGiuu+66HHTQQbn++uvbt51xxhk599xzM2vWrMyePTuLFy9O3759C0zZucxB91SaYnzJkiVpa2vLzJkzU19fv8tjDj300CQ7FuObNm3KvffemwEDBuTEE0/c7fXffPPNjBgxIrfddlt++MMfZsGCBdmwYUNOOumkNDY2duxgAAAAAKALDB48ODNmzMhjjz2W1atXt29fuHBhNm3alEmTJmXKlCkFJux85qB7Kk0xvmLFiiTJhAkTdnvMBwX2zxfjp5xySpqamvL9738/EydO3O25v/Zrv5abb74555xzTj7/+c9n5syZue+++/LTn/409957bweNAgAAAAC61rXXXpsePXrscMd0XV1dBg0alBEjRhSYrOuYg+7n4KIDdJVXXnklSXL00Ufvcv/27duzZs2aJDsW4z167P33Dj748YmDD967aR4zZkyam5v3+vmTpK2tbZ/Oh442ZMiQffp7BQAAAOza3vZAp556aqrV6m73Dxs2LO+9997exvpQHd0TmIPO0dTU1P61oaGh4DT/z4ABA/LUU0/t1bmlKcZbW1uTJG+//fYu9y9dujQtLS3p1atXPvWpT+3187z33ntpa2vLK6+8kj/8wz/MgAEDcu655+7VtZqbm7Nly5a9zgL7ow/+IQUAAADQExxYc9DW1tZt+srSFOMDBgzIm2++mXXr1uWkk07aYV9TU1OuueaaJMnIkSN3+wGbH8fnP//59jvPBw8enBUrVqR///57nXlftbW1HVB/uej+Bg4cuN9/FxQAAAAORAdiD9TRPYE56BxNTU1pa2tLjx49MnDgwKLjtNuX/rQ0xfjEiROzcePGLFiwIKeffnqGDh2aJFm7dm0uuOCCtLS0JElGjx69T89z991356233sqPf/zjfO1rX8uv/uqvZs2aNfnlX/7lPb7W3v4YwM9rbW3d7YeNQhFeeuml1NXVFR0DAAAAup2u7oFWrVq1z9fo6J7AHHSOhoaGbNmyJQMHDmz/nMYD3f79rYgONG/evPTt2zevvvpqhg8fnhEjRmTIkCEZO3ZsBg0alNNOOy3JjuuL743/8l/+Sz73uc+1f1Ltz372s/zpn/5pRwwBAAAAAIAOUJpivKGhIatXr86UKVNSU1OTzZs3p0+fPrnzzjuzfPny/OhHP0qy78X4z+vdu3cGDx6cTZs2ddg1AQAAAADYN6VZSiV5/9NjH3rooZ22b926NZs3b06PHj3ymc98psOe71/+5V/y4osv5nOf+1yHXRMAAAAAgH1TqmJ8dzZs2JBqtZqhQ4emtrZ2p/3Lli1Lkrzwwgs7PD7mmGMyZsyYJMmsWbMyePDgjB49Or17985LL72Um2++OQcffHB+93d/t4tGAgAAAADAR1GMJ3n++eeT7H4ZlXPOOWeXjy+88MJ861vfSpKMGzcu3/72t3PrrbfmnXfeyVFHHZUJEybky1/+co4++ujOCw8AAAAAwB5RjOeji/FqtfqR15gzZ07mzJnTobkAAAAAAOh4pfnwzQ/zUcU4AAAAAJTZsmXLctlll+Wdd97J9OnTM3To0IwaNSqnn356Nm3aVHS8LmEOuhfFeJIVK1akWq1mypQpRUcBAAAAgP3O/fffn+nTpydJLrnkkrz44ot59tlnM23atFx88cXFhusi5qB7UYwDAAAAQMm99dZbaWhoSN++fTN69OgMGzYsPXv2zMUXX5x33303a9asyWmnnZaamppMnjw5lUolyfufu7d58+Ziw3cQc1AuinEAAAAAKLnevXvn/PPPz1VXXZX169fnlltuybhx47J48eKsXLky48ePzyGHHLLTebfeemumTZtWQOKOZw7KRTEOAAAAAGT9+vU5/vjjkyRPP/10++8feOCBnHXWWTsdf9NNN2XTpk2ZP39+l+bsTOagPBTjAAAAAMAuS+FqtZpHHnkkkyZN2uHYhQsX5r777svDDz+c2traIuJ2CnNQHopxAAAAACi5LVu2pFKp5Mgjj0ySPPfccxkxYkSefPLJDBs2LPX19e3Hfv3rX8+SJUvy6KOPpnfv3gUl7njmoFwOLjoAAAAAAFCsZ555pv1O6eT99bYXLVqUfv36Zfr06e3bGxsbM3fu3AwaNCgTJkxIkvTs2TNPPPFEV0fucOagXCrVarVadAg6T2tra/t3s7Zu3Zq6urqCE1FG/hwCAABA5+uM99/Dhw/PypUrc/jhh+/ztT7QmT2BOegcDQ0N2bJlS4488sg0NjYWHadDuGMcAAAAANilDRs2FB2hcOage1KMAwAAAEA309raWnSE3eqqbOaAD6MYBwAAAIBu5ogjjig6QuHMAR+mR9EBAAAAAACgK7ljHAAAAAC6gdra2mzdurXoGHuktra2w69X9jng41GMAwAAAEA3UKlUUldXV3SMQpkDPi5LqQAAAAAAUCqKcQAAAAAASkUxDgAAAABAqSjGAQAAAAAoFcU4AAAAAAClohgHAAAAAKBUFOMAAAAAAJSKYhwAAAAAgFJRjAMAAAAAUCqKcQAAAAAASkUxDgAAAABAqSjGAQAAAAAoFcU4AAAAAAClohgHAAAAAKBUFOMAAAAAAJSKYhwAAAAAgFJRjAMAAAAAUCoHFx0AAAAAAIB9V61Ws23btg6/bltbW/vX1tbWDr12bW1tKpVKh17z41CMAwAAAAB0A9u2bUt9fX2nXb+pqanDr79169bU1dV16DU/DkupAAAAAABQKu4YBwAAAADoZl5//fVC7sT+OFpbW3PEEUcUmkExDgAAAADQzdTV1e23xfj+wFIqAAAAAACUimIcAAAAAIBSUYwDAAAAAFAqinEAAAAAAEpFMQ4AAAAAQKkoxgEAAAAASmzVqlWpVCo7/Kqvr88JJ5yQm2++Odu3by86Yoc7uOgAAAAAAAAU77zzzsvkyZNTrVbT3Nycb3/727n66quzcePG3HXXXUXH61CKcQAAAAAAcsIJJ2TWrFntj2fPnp1jjz02ixcvzo033pj+/fsXmK5jWUoFAAAAAICd1NXVZdy4calWq3n55ZeLjtOhFOMAAAAAAOzSB4V4nz59Ck7SsUpXjLe0tGTevHkZPHhwampqctRRR+XKK69Ma2trLrroolQqldx+++1FxwQAAAAA6FLbtm1LS0tL/vVf/zXPP/98Lr/88jzzzDMZO3Zshg4dWnS8DlWqNcbXr1+fSZMmpbm5OXV1dTnuuOPy2muv5bbbbsvLL7+cN954I0kyevToYoMCAAAAAHSxG264ITfccMMO284+++z8xV/8RUGJOk9p7hhvaWnJmWeemebm5sydOzdNTU1Zt25dmpubs2DBgixfvjxr165NpVLJyJEji44LAAAAANClLrnkkjz66KP5wQ9+kAULFqRPnz5pbGxMTU1N+zEzZszIueeeu8N5b7zxRgYOHJh77rmnqyPvtdIU41dccUUaGxszZ86cLFy4ML169WrfN2/evIwaNSrbt2/PMccck8MOO6zApAAAAAAAXW/IkCGZOHFiJk2alHnz5uXBBx/M2rVrc+mll7Yfs2jRoqxZsyZLlixp33b55Zfn5JNPzsyZM4uIvVdKUYxv3LgxS5cuTb9+/TJ//vxdHvPZz342STJq1Kj2bR8U6WPHjk3Pnj1TqVQ+1vNNmjQplUolX/nKV/Y5OwAAAABAEcaPH58LLrggS5cuzeOPP57k/Q/hvPvuuzNnzpy89tprWbZsWVatWpU77rij4LR7phTF+JIlS9LW1paZM2emvr5+l8cceuihSXYsxjdt2pR77703AwYMyIknnvixnutv//Zvs379+n3ODAAAAABQtOuuuy4HHXRQrr/++vZtZ5xxRs4999zMmjUrs2fPzuLFi9O3b98CU+65UhTjK1asSJJMmDBht8c0NjYm2bEYP+WUU9LU1JTvf//7mThx4kc+z7//+7/nqquuysKFC/cxMQAAAABA8QYPHpwZM2bksccey+rVq9u3L1y4MJs2bcqkSZMyZcqUAhPunYOLDtAVXnnllSTJ0Ucfvcv927dvz5o1a5LsWIz36LFn3ze49tprM3To0MycOTOzZs3ay7T/z5gxY9Lc3LxP12hra9vnHNCRhgwZssd/twAAAAD4aJ3VBV577bVZsmRJrr/++qxcuTJJUldXl0GDBmXEiBH7dO196YoGDBiQp556aq/OLUUx3tramiR5++23d7l/6dKlaWlpSa9evfKpT31qr57jqaeeyje/+c08/fTTe53zFzU3N2fLli0ddj3YHzQ1NRUdAQAAAICfc+qpp6Zare52/7Bhw/Lee+91ynMX1RWVohgfMGBA3nzzzaxbty4nnXTSDvuamppyzTXXJElGjhz5sT9g8+e99957+e3f/u3MmTMnw4cP75DMyfu591VbW5sikv3KwIED3TEOAAAA0AkOxC5wX7qifelPS1GMT5w4MRs3bsyCBQty+umnZ+jQoUmStWvX5oILLkhLS0uSZPTo0Xt1/dtvvz2vv/56vvKVr3RQ4vft7Y8B/LzW1tbdfuAoFOGll15KXV1d0TEAAAAAup0DsQssqisqRTE+b968fOc738mrr76a4cOH59hjj80777zTvjj8Mccck0ceeWSH9cU/rpaWllx33XVZuHBhtm/fnrfeeqt93zvvvJO33norhx12mDtkAQAAAIBuZdWqVUVH2GulaGsbGhqyevXqTJkyJTU1Ndm8eXP69OmTO++8M8uXL8+PfvSjJNmrYryxsTE/+9nP8tu//dv55Cc/2f4rSRYsWJBPfvKT+clPftKh4wEAAAAAYO+V4o7x5P0F4h966KGdtm/dujWbN29Ojx498pnPfGaPrzt48OD2T2L9eRMmTMiFF16Y3/iN3+iQtcIBAAAAAOgYpSnGd2fDhg2pVqsZOnRoamtrd9q/bNmyJMkLL7yww+NjjjkmY8aMSX19fU499dRdXvuYY47Z7T4AAAAAAIpR+mL8+eefT7L7ZVTOOeecXT6+8MIL861vfatTswEAAAAA0PEU4x9RjFer1b267t6eBwAAAABA5yrFh29+mI8qxgEAAAAAym7ZsmW57LLL8s4772T69OkZOnRoRo0aldNPPz2bNm0qOt4eK30xvmLFilSr1UyZMqXoKAAAAAAA+6X7778/06dPT5JccsklefHFF/Pss89m2rRpufjii4sNtxdKX4wDAAAAAJTdW2+9lYaGhvTt2zejR4/OsGHD0rNnz1x88cV59913s2bNmpx22mmpqanJ5MmTU6lUkiTjxo3L5s2biw2/FxTjAAAAAAAl17t375x//vm56qqrsn79+txyyy0ZN25cFi9enJUrV2b8+PE55JBDdjrv1ltvzbRp0wpIvG8U4wAAAAAAZP369Tn++OOTJE8//XT77x944IGcddZZOx1/0003ZdOmTZk/f36X5uwIinEAAAAAAHZZjFer1TzyyCOZNGnSDscuXLgw9913Xx5++OHU1tYWEXefKMYBAAAAAEpuy5YtqVQqOfLII5Mkzz33XEaMGJEnn3wyw4YNS319ffuxX//617NkyZI8+uij6d27d0GJ983BRQcAAAAAAKBYzzzzTPvd4sn7a44vWrQo/fr1y/Tp09u3NzY2Zu7cuRk0aFAmTJiQJOnZs2eeeOKJro68TyrVarVadAg6T2tra/t3c7Zu3Zq6urqCE1FG/hwCAAAAdL7O6GCGDx+elStX5vDDD9/na31gf+iK3DEOAAAAAMAubdiwoegInUIxDgAAAADQzbS2thYdYbf2h2yKcQAAAACAbuaII44oOsJ+rUfRAQAAAAAAoCu5YxwAAAAAoBuora3N1q1bi46xR2prawt5XsU4AAAAAEA3UKlUUldXV3SMA4KlVAAAAAAAKBXFOAAAAAAApaIYBwAAAACgVBTjAAAAAACUimIcAAAAAIBSUYwDAAAAAFAqinEAAAAAAEpFMQ4AAAAAQKkoxgEAAAAAKBXFOAAAAAAApaIYBwAAAACgVBTjAAAAAACUimIcAAAAAIBSUYwDAAAAAFAqinEAAAAAAEpFMQ4AAAAAQKkoxgEAAAAAKBXFOAAAAAAApaIYBwAAAACgVBTjAAAAAACUimIcAAAAAIBSUYwDAAAAAFAqinEAAAAAAEpFMQ4AAAAAQKkoxgEAAAAAKBXFOAAAAAAApaIYBwAAAACgVBTjAAAAAACUimIcAAAAAIBSUYwDAAAAAFAqinEAAAAAAEpFMQ4AAAAAQKkoxgEAAAAAKBXFeMm1tbXl5ptvzrHHHpuampocddRRmTt3blpbW4uOBgAAAADQKRTjJfe7v/u7ufrqq3Pcccflz//8z3POOefktttuy5lnnpm2trai4wEAAAAAdLiDiw7QXbS1teW+++7LX//1X+fJJ5/Mv/3bv6Vv37459thjM23atFx22WX5xCc+UXTMHWzYsCF//ud/nrPPPjv33ntv+/ZPfepTueKKK/I3f/M3Of/88wtMCAAAAADQ8dwx3gH+5V/+JaeddlrOOeecfO9738shhxyS0aNH55BDDsnKlSvzh3/4hznooIOKjrmTJUuWpFqt5qqrrtph+2/91m+ltrY2f/3Xf11MMAAAAACATuSO8X3U2tqayZMn5+mnn87nP//53HbbbRk5cmT7/g0bNuTee+/dq2L8z//qvvxs69v7lO8//+Od3e5bu3ZtevTokbFjx+6wvaamJqNHj87atWv36blhV75259/kEz1rio4BAAAAwAGuV/2h+Z0Lz96rcxXj+2ju3Ll5+umnc/LJJ+eHP/zhTsulDB8+PMOHD9+ra/9s69v596379iGY//mf/7Hbfa+99lr69euXnj177rTvyCOPzOOPP57//M//3O+WgOHA9u+t2/KJd98rOgYAAAAAJaYY3wf/5//8nyxevDg9e/bMPffc0+EFcq/6Q/f5Gv/5H7u/U33btm27LMWT9+8a/+AYxTgd6bC6WneMAwAAALDP9qU/VYzvg+985zt577338qUvfSm//Mu/3OHX39sfA/h5ra2t+aO5F+9yX21tbf7lX/5ll/veeeed9mOgI13z2zNSV1dXdAwAAAAASkwxvg/+/u//PkkyefLkTrl+Z68x/ku/9Et54YUX8h//8R873Tm+ZcuW9OvXz93idDhrjAMAAADQEawxXpBXX301STJo0KBOuX5nrzF+4okn5oc//GGefPLJ/H//3//Xvv2dd97J+vXrc8opp+zTc8OuWGMcAAAAgKIpxvdBa+v7pfXbb+/bXd2709lrjH/xi1/MTTfdlFtuuWWHYvyb3/xmtm3blpkzZ+7z88MvssY4AAAAAB1hX/rTSrVarXZgllIZNWpUnnvuufzpn/5prrnmmqLj7FJra2vq6+uTJFu3bt1pbeff+Z3fye23356zzjorkydPzsaNG3Pbbbflv/7X/5oVK1akR48eRcSmm/moP4cAAAAA0JW0nvvgrLPOSpLceOON+eEPf7jDvi1btuSmm27KSy+9VES0j+2WW27JwoULs2HDhlx++eX5m7/5m/zO7/xOHnroIaU4AAAAANAtuWN8H2zdujWf//zns27duiTJgAEDctRRR+WNN97I//2//zeVSiX//u//Xujdse7UZX/gzyEAAAAA+xO3BO+D+vr6rF69On/8x3+cUaNG5ac//Wn+9//+32lra8v06dPzV3/1VwpAAAAAAID9jDvGuzl36rI/8OcQAAAAgP2JO8YBAAAAACgVxTgAAAAAAKWiGAcAAAAAoFQU4wAAAAAAlIpiHAAAAACAUlGMAwAAAABQKopxAAAAAABKRTEOAAAAAECpKMYBAAAAACgVxTgAAAAAAKVycNEB6Dqtra1FR6Ck/NkDAAAAYH+iGC+RI444ougIAAAAAACFs5QKAAAAAAClUqlWq9WiQ9B5qtVqtm3bVnQMaFdbW5tKpVJ0DAAAAABKTDEOAAAAAECpWEoFAAAAAIBSUYwDAAAAAFAqinEAAAAAAEpFMQ4AAAAAQKkoxgEAAAAAKBXFOAAAAAAApaIYBwAAAACgVBTjAAAAAACUimIcAAAAAIBSUYwDAAAAAFAqinEAAAAAAEpFMQ4AAAAAQKkoxgEAAAAAKBXFOAAAAAAApaIYBwAAAACgVBTjAAAAAACUimIcAAAAAIBSUYwDAAAAAFAqinEAAAAAAEpFMQ4AAAAAQKkoxgEAAAAAKBXFOAAAAAAApaIYBwAAAACgVBTjAAAAAACUimIcAAAAAIBS+f8B13Jr0OJNcR0AAAAASUVORK5CYII="
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Idea: Repeat the steps from before\n",
    "# Initialize  register\n",
    "cr = ClassicalRegister(1, name='c')\n",
    "qr = QuantumRegister(15, name='q')\n",
    "big_pac = QuantumCircuit(qr, cr, name='small_pachinko')\n",
    "# Appending the 'small_pac' circuit to the 'markov' circuit\n",
    "big_pac.append(small_pac, qr[:3], cr)\n",
    "# Angle for controlled-rotation gates\n",
    "rotation_angle = np.pi / 2\n",
    "# Specify control qubits\n",
    "# (First step skipped by integrated small_pachinko. Think about how this can code can be generalized for the first step too.)\n",
    "c_qubits=list(range(1,10,1))+list(range(1,10,1))\n",
    "c_qubits.sort()\n",
    "# Specify target qubits\n",
    "# (First step skipped by integrated small_pachinko. Think about how this code can be generalized for the first step too.)\n",
    "offset = 1\n",
    "t_qubits = list()\n",
    "for d in range(2,5,1):\n",
    "    offset += d\n",
    "    for i in range(d+1):\n",
    "        t_qubits.append(i+offset)\n",
    "        if (i != 0) and (i != d):\n",
    "            t_qubits.append(i+offset)\n",
    "\n",
    "# Apply controlled rotation gates with the specified angle\n",
    "for cq, tq in zip(c_qubits, t_qubits):\n",
    "    big_pac.cry(rotation_angle, control_qubit=qr[cq], target_qubit=qr[tq])\n",
    "\n",
    "'''\n",
    "(Explicit)\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[1], target_qubit=qr[3])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[1], target_qubit=qr[4])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[2], target_qubit=qr[4])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[2], target_qubit=qr[5])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[3], target_qubit=qr[6])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[3], target_qubit=qr[7])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[4], target_qubit=qr[7])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[4], target_qubit=qr[8])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[5], target_qubit=qr[8])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[5], target_qubit=qr[9])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[6], target_qubit=qr[10])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[6], target_qubit=qr[11])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[7], target_qubit=qr[11])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[7], target_qubit=qr[12])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[8], target_qubit=qr[12])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[8], target_qubit=qr[13])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[9], target_qubit=qr[13])\n",
    "big_pac.cry(rotation_angle, control_qubit=qr[9], target_qubit=qr[14])\n",
    "'''\n",
    "\n",
    "# Draw circuit\n",
    "big_pac.draw('mpl', style='bw', plot_barriers=False, justify='none', fold=-1)\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Simulate Big Circuit"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.9392\n",
      "1 0.0608\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 1 Axes>",
      "image/png": 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"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a copy of the 'small_pac' circuit\n",
    "markov_circuit = big_pac.copy()\n",
    "\n",
    "# Measure qubit at index 2 into classical bit at index 0 (Try it for other qubits as well.)\n",
    "markov_circuit.measure(10, 0)\n",
    "\n",
    "# Set the number of shots for the simulation\n",
    "nshots = 10_000\n",
    "\n",
    "# Simulate the circuit\n",
    "counts = simulate_circuit(markov_circuit, nshots)\n",
    "\n",
    "# Print the normalized results\n",
    "for key in sorted(counts.keys()):\n",
    "    print(key, counts[key] / nshots)\n",
    "\n",
    "# Visualize the results using a histogram\n",
    "plot_histogram(counts, title='Big Pachinko')\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
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 },
 "nbformat": 4,
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