02_probability_distribution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# State preparation example\n",
    "\n",
    "In this example we will use the generator to find quantum circuit which prepares given probability distribution. \n",
    "\n",
    "We compared with Qiskit's \"isometry\" method, which works faster but outputs deeper circuit with a more CNOT gates. Output of Quantastica's circuit looks less precise, but precision is actually lost during conversion to Qiskit (it is converted to Qiskit and executed for display purposes).\n",
    "\n",
    "\n",
    "## Prerequisites:\n",
    "\n",
    "### Install and import Quantastica QPS API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "try:\n",
    "  from quantastica.qps_api import QPS\n",
    "except:\n",
    "  !{sys.executable} -m pip install quantastica-qps-api\n",
    "  from quantastica.qps_api import QPS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fore details about quantastica-qps-api package, see: https://pypi.org/project/quantastica-qps-api/\n",
    "\n",
    "\n",
    "### Register your QPS API key\n",
    "\n",
    "Uncomment following line, add your API key and run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# QPS.save_account(\"YOUR_API_TOKEN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**WARNING:** Run this only once on your machine and then remove this cell. Don't keep your API key in the notebook for security reasons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper/utility functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import SVG\n",
    "\n",
    "from quantastica.qps_api import QPS\n",
    "from qiskit import QuantumCircuit, QuantumRegister, execute, Aer\n",
    "\n",
    "import scipy.interpolate as interpolate\n",
    "import numpy as np\n",
    "\n",
    "#\n",
    "# Function converts probability distribution to state vector\n",
    "# Given vector (of any size and any value range) will be normalized and interpolated to unit vector of size 2**num_qubits\n",
    "#\n",
    "def pd_to_sv(pd, num_qubits):\n",
    "    len_sv = 2**num_qubits\n",
    "\n",
    "    # get rid of negative values\n",
    "    min_val = np.min(pd)\n",
    "    if(min_val < 0):\n",
    "        pd = [i + np.abs(min_val) for i in pd]\n",
    "    \n",
    "    # interpolate to 2**num_qubits if needed\n",
    "    if(len_sv != len(pd)):\n",
    "        x = np.arange(0, len(pd))\n",
    "        f = interpolate.interp1d(x, pd)\n",
    "        xnew = np.linspace(0, len(pd) - 1, num = int(len_sv))\n",
    "        pd = f(xnew)\n",
    "\n",
    "    # normalize sum to 1\n",
    "    pd = pd / np.sum(pd)\n",
    "    \n",
    "    # Make statevector\n",
    "    pd = np.sqrt(pd).tolist()\n",
    "    \n",
    "    # convert to complex\n",
    "    sv = [complex(i) for i in pd]\n",
    "\n",
    "    return sv\n",
    "\n",
    "#\n",
    "# Function converts back from state vector to probability distribution\n",
    "# If pd_min and pd_max are given then pdf will be normalized into pd_min...pd_max range\n",
    "# If pd_len is given then pdf will be interpolated to that length\n",
    "#\n",
    "def sv_to_pd(sv, pd_min, pd_max, pd_len):\n",
    "    pd = np.abs(sv)**2\n",
    "    \n",
    "    min_val = np.min(pd)\n",
    "    max_val = np.max(pd)\n",
    "    \n",
    "    pd_min = 0 if pd_min is None else pd_min\n",
    "    pd_max = 1 if pd_max is None else pd_max\n",
    "    \n",
    "    pd = [ (pd_max - pd_min) * ((a - min_val) / (max_val - min_val)) + pd_min for a in pd ]\n",
    "\n",
    "    if(pd_len):\n",
    "        x = np.linspace(0, pd_len, pd_len)\n",
    "        f = interpolate.interp1d(np.linspace(0, pd_len, len(pd)), pd, kind=\"cubic\")\n",
    "        pd = f(x)\n",
    "    \n",
    "    return pd\n",
    "\n",
    "\n",
    "def plot(arrays):\n",
    "    fig = plt.figure(figsize=(12, 12))\n",
    "    ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "    for a in arrays:\n",
    "        plt.plot(a)\n",
    "\n",
    "    # show the plot\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def display_circuit(qasm):\n",
    "    circuit_svg = QPS.converter.convert(qasm, \"qasm\", \"svg-inline\")\n",
    "    display(SVG(circuit_svg))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Define probability distribution\n",
    "\n",
    "It has to be array of numbers with any length and range. Can be output of any function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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W7NBf91fJ5bTribtmaHRqrOlY6Ed2u02/XJiri0Ynq6XDra89tVkHKxtNxwIABDFKNQAgaD381h69srVUDrtNv7s9T3lDB5mOhAEQHmbX726fptzsBJ1o7tCSJzaqvK7VdCwAQJCiVAMAgtLT6wv1+Efe9Uq/XDBZl45NMZwIAyk6Ikx/+uoMDU+OVlldq5Y+tVmNbZ2mYwEAghClGgAQdNbuqdBP/rxbkvTP88ZoQV6W4UQwITE6XE8vnamkmHDtOVavb79QoE63x3QsAECQoVQDAILKzpI6ffuFrfJY0i0zsvV3l4wwHQkGZSdG6Y93zZDLadcH+6r00Ou7ZVmW6VgAgCBCqQYABI2y2hZ97enNaulw68JRSfq3GybKZrOZjgXDpmQn6De3TJXNJj2/sUiPf3TYdCQAQBChVAMAgkJze6fufWaLqhraNCY1Vo/ePk1OB7/m4DVvQpp+dM14SdLDb+3V2j0VhhMBAIIFzzYAAAHP47H0/17ert1l9RocHa4nvjpdcS6n6VjwM187P0e3zRoiy5L+4cWt2lfeYDoSACAIUKoBAAHvN2sP6K1d5XI6bHrszjxlDYoyHQl+yGaz6SfzJ+i84Ylqanfrnmc2q6ap3XQsAECAo1QDAALamzuP6TdrD0iS/v2GSZqRk2g4EfyZ02HX727P05DEKBXXtOi+5/LVwURwAMA5oFQDAALWwcoG/dPy7ZKkuy8YpptnZBtOhEAwKDpcf7xrumIiwrTxSI1+8dZe05EAAAGMUg0ACEiNbZ36xrP5am53a/bwwXrgK2NNR0IAGZ0aq/9aNFmS9Md1R/TGjmOGEwEAAhWlGgAQcCzL0vdX7NChqialxkXot7dNVRiTvtFDV01M1zcuGi5J+t6K7TpYyeAyAEDP8QwEABBwnlh3RG/sPKYwu03/d/s0JcVEmI6EAPXP88Zo1jDv4LJvPlegprZO05EAAAGGUg0ACCibjtTo4a73wP7omnHKG8pgMvRemMOu3942VSmxETpY2ajvrdwhy7JMxwIABBBKNQAgYFTWt+pbLxTI7bE0PzdDd83JMR0JQSAl1qX/u32awuw2vbHjmJ78uNB0JABAAKFUAwACQofbo2+/sFVVDW0anRqj/1gwSTabzXQsBInpOYn6l6vHSZIefnOPNhfWGE4EAAgUlGoAQED45eq92lRYo5iIMD12R56iwsNMR0KQWXp+jq6dnK5Oj6VvPV+gyoZW05EAAAGAUg0A8HtrPq3Q4x8dkST916LJGp4cYzgRgpHNZtMvFkzWqJQYVTa06R9f2iaPh/dXAwC+GKUaAODXyuta9b0V2yVJd18wTFdNTDecCMEsOiJMv7sjT5FOh9YfOq7ff3jYdCQAgJ+jVAMA/JbHY+n+l7fpRHOHJmTE6XtXjTEdCSFgZEqMHrpuvCTpV+/s0/biWrOBAAB+jVINAPBbf/josNYfOq5Ip0P/c+tURYQ5TEdCiFg8I1tXT0pTp8fSd17aqkb2VwMAzoBSDQDwS9uLa/Vfb++TJP3r/PEawfuoMYBsNpsevnGyMuJdKjzerH99fbfpSAAAP0WpBgD4nca2Tn3npa3q9Fi6elKabp6ebToSQlB8lFP/vXiK7DZpRX6JXt9eZjoSAMAPUaoBAH7nX1/frcLjzcqId+nhGyezjxrGzBo+WN++dKQk6Yev7FRxTbPhRAAAf0OpBgD4lde3l2lFfonsNunXt0xVfJTTdCSEuH+4fJSmDUlQQ2unvrtsmzrdHtORAAB+hFINAPAbxTXN+uGqnZKkb186UjOHJRpOBEhhDrt+c8tUxUaEacvRE/rtewdNRwIA+BFKNQDAL7i71mc1tHVq2pAE/cPlo0xHArplJ0bpZzdOlCT99r0Dyj9aYzgRAMBfUKoBAH7hTx8f0ebCE4oOd+g3t0xVmINfUfAv10/J1E1TM+WxpH9avkMt7W7TkQAAfoBnLAAA4w5WNuo/u9Zn/eja8cpOjDKcCDi9h+ZPUGpchI5UN+m/3tlnOg4AwA9QqgEARrk9lv5p+Xa1dXp04agk3TKD9VnwX/GRTv3HgsmSpCc/PqJNR7gGDgChjlINADDq8Y8Oa1txrWIjwvSLBazPgv+7dEyKFk/PlmVJ/7xiu5rbO01HAgAYRKkGABhzoKJBj7yzX5L04+vGKyMh0nAi4Oz88Npxyoh36ejxZv1yNdfAASCUUaoBAEZ0uj36f8u3q93t0aVjkrUoL8t0JOCsxbmc+sVC7zXwp9YXasOh44YTAQBMoVQDAIz4/YeHtaOkTnGuMD18E9e+EXguHJWs22YNkeS9Bt7UxjVwAAhFlGoAwIDbW16vX7/rvfb9r/MnKC3eZTgR0Dv/cvU4ZSZEquREix5+a4/pOAAAAyjVAIAB1eH26P+9vF0dbktzx6XqxqmZpiMBvRYTEab/7LoG/twnRVp3oNpwIgDAQKNUAwAG1O//eki7y+qVEOXUz2+ayLVvBLw5I5O0ZPZQSdL3V+7gGjgAhBhKNQBgwByuatT/vHdQkvTQdeOVEsu1bwSH7181VlmDIlVa26JH1uw3HQcAMIAo1QCAAWFZln74yi61d3p04agk3TCFa98IHtERYfrZDRMlSX/6+Ih2ltQZTgQAGCiUagDAgFiRX6INh4/L5bTr32+YxLVvBJ1LxqToutwMeSzpB6t2qNPtMR0JADAAKNUAgH53vLFN//6mdzLyP84drSGDowwnAvrHg9eOV5wrTLvL6vXU+kLTcQAAA4BSDQDodz97Y49qmzs0Ni1Wd18wzHQcoN8kx0boX64eJ0n61Tv7VVzTbDgRAKC/UaoBAP3qowNVemVrqWw26T8WTJbTwa8eBLebp2dr5rBEtXS49eBru2RZlulIAIB+xDMbAEC/aWl364ev7JIk3TU7R1OyE8wGAgaA3W7Tz2+cpHCHXe/vq9IbO4+ZjgQA6EeUagBAv/mf9w6oqKZZ6fEu/dO8MabjAANmZEqM/u7SEZKkf339U9U1dxhOBADoL5RqAEC/2HOsXo9/eFiS9JP5ExQTEWY4ETCw7rtkhEYkR6u6sU3/sXqv6TgAgH5CqQYA9DmPx9IDq3aq02Np3oRUXTkhzXQkYMBFhDn08xsnSZJe3FSkTUdqDCcCAPQHSjUAoM8t21KsbcW1iokI00/mTzQdBzBm1vDBumVGtiTpwdd2sbsaAIIQpRoA0Kdqm9v1y66rrt+9YrTS4l2GEwFmff+qsUqIcmpveYOe2XDUdBwAQB+jVAMA+tR/vbNPJ5o7NDo1RktmDzUdBzBuUHS4/rlrUN9/r9mvqoY2w4kAAH2JUg0A6DO7Suv0/MYiSdJPr5/ITmqgyy0zhmhSZrwa2jr1H28xtAwAggnPdgAAfcLjsfTga7tkWdL83AydN3yw6UiA33DYbfrp9RMkSSsLSpR/lKFlABAsKNUAgD6xsqBEBUW1ig536F+uHmc6DuB3pg4ZpJunZ0mSHnxtt9wey3AiAEBfoFQDAM5ZXUuHftE1nOwfLh/FcDLgDL5/1VjFucK0u6xeL2wqMh0HANAHKNUAgHP232v2q7qxXSOSo7X0/GGm4wB+a3BMhP6pa2jZf729TzVN7YYTAQDOFaUaAHBO9hyr1zMbCiVJP5k/UeFh/GoBvshtM4doXHqc6lo69J9vM7QMAAIdz3wAAL1mWZYeem23PJZ09aQ0XTAqyXQkwO+FOez6t66hZS9tLtb24lqzgQAA54RSDQDotde3l2lTYY0inQ798JrxpuMAAWN6TqJumpopy5IefG2XPAwtA4CARakGAPRKc3unfv7mHknSty8bqcyESMOJgMDyg6vHKjYiTNtL6rSyoMR0HABAL1GqAQC98vu/HlZFfZuyEyN19wUMJwN6KiXWpb+/fKQk6T/f3qemtk7DiQAAvUGpBgD0WHldq37/4SFJ0g+uGieX02E4ERCY7pqToyGJUapsaNPvPzxsOg4AoBco1QCAHvvl23vV2uHRjJxBunpSmuk4QMCKCHPoga+MlST94cNDOlbXYjgRAKCnKNUAgB7ZUVKrVQWlkqQfXTNeNpvNcCIgsF01MU0zhyWqtcOjX67eZzoOAKCHKNUAgLNmWZb+7S+fSpJumpqp3OwEs4GAIGCz2fTjrun5r2wt1TZWbAFAQKFUAwDO2lu7yrW58IRcTrv++aoxpuMAQWNSVrxumpYpSfrZXz6VZbFiCwACBaUaAHBW2jrdevgt7wqtr180QunxrNAC+tL35o1VpNOhLUdP6M2d5abjAADOEqUaAHBWnvq4UMU1LUqNi9A3Lx5uOg4QdNLiXfpG17+th9/ao9YOt+FEAICzQakGAHyp6sY2/e97ByVJ/zxvrKLCwwwnAoLT1y8arrQ4l0pOtOhPHxeajgMAOAuUagDAl/rvNfvV0NapiZlxumlqpuk4QNCKCg/TP8/zzit49P2DqmpoM5wIAPBlKNUAgC+0v6JBL24qkiT9+JrxsttZoQX0pxunZmpyVrwa2zr1yJr9puMAAL4EpRoA8IV+8dZeeSxp3oRUzRo+2HQcIOjZ7Tb9qGvF1rLNRTpY2Wg4EQDgi1CqAQBntPHwca3dWymH3abvXzXWdBwgZMwclqgrxqfKY0n/+fZe03EAAF+AUg0AOC3LsvQfq71P5m+Zka3hyTGGEwGh5Xvzxshuk97eXaH8oydMxwEAnAGlGgBwWm/vrtDWolpFOh36zuWjTMcBQs6o1FgtysuW5H0bhmVZhhMBAE6HUg0A+JxOt0e/7Lpyes+Fw5QS5zKcCAhN/3jFKEWE2bWpsEbv7a00HQcAcBqUagDA5yzPL9HhqiYlRofr6xcNNx0HCFnp8ZFaev4wSdIvVu+V28NpNQD4G0o1AOAULe1u/XfXGp+/v2ykYl1Ow4mA0HbfxSMUH+nU/opGrSooMR0HAPA3KNUAgFM8+fERVTa0KTsxUrfNGmI6DhDy4qOc+talIyRJj6zZr9YOt+FEAIDPolQDALrVNLXrsQ8OSZL+6coxighzGE4EQJKWzM5RRrxLx+pa9fT6QtNxAACfQakGAHR79P2Damjr1Pj0OF03OcN0HABdXE6HvnvFaEnef6d1zR2GEwEAfCjVAABJUnFNs57dcFSS9IOvjJXdbjOcCMBn3TQtS2NSY1Xf2qn/++tB03EAAF0o1QAASdJ/r9mvdrdH548crAtHJZmOA+BvOOw2ff8rYyRJf/q4UGW1LYYTAQAkSjUAQNK+8ga9sq1UkvSDq8bJZuOUGvBHl45J0cxhiWrv9Oi37x0wHQcAIEo1AEDSI2v2ybKkayala1JWvOk4AM7AZrPpe/O8p9UvbylRYXWT4UQAAEo1AIS4nSV1ent3hew26btXjDIdB8CXmJ6TqEvGJMvtsfSbtZxWA4BplGoACHG/WrNPknTDlEyNTIk1nAbA2fh/V3hPq1/dVqoDFQ2G0wBAaKNUA0AI21JYow/2Vclht+k7czmlBgLFpKx4XTUhTZYl/fe7+03HAYCQRqkGgBBlWZb+6x3vKfXN07M0dHC04UQAeuK7V4yWzSa9ubNcu0rrTMcBgJBFqQaAELX+0HF9crhG4Q67/v4yTqmBQDMmLVbzczMkSY+s4bQaAEyhVANACPrsKfVts4YoIyHScCIAvfGPc0fLYbfpvb2Vyj96wnQcAAhJlGoACEHv7a3U1qJauZx2/d2lI0zHAdBLw5KitXBaliTpV10vlAEABhalGgBCjMdj6VfveK+K3jUnRymxLsOJAJyLv798pJwOm9YfOq71B6tNxwGAkNOrUv3oo48qJydHLpdLs2bN0qZNm77w63/9619rzJgxioyMVHZ2tr773e+qtbW1V4EBAOdm9e5yfXqsXjERYfrmRZxSA4Eua1CUbp05RJL0qzX7ZVmW4UQAEFp6XKqXLVum+++/Xw899JAKCgqUm5urefPmqbKy8rRf/8ILL+gHP/iBHnroIe3Zs0dPPPGEli1bpn/5l3855/AAgJ5xe6zugUZ3XzBMg6LDDScC0Be+felIRYTZlX/0hD7YX2U6DgCElB6X6kceeUT33nuvli5dqvHjx+uxxx5TVFSUnnzyydN+/fr163X++efrtttuU05Ojq688krdeuutX3q6DQDoe69tK9XBykbFRzp194XDTMcB0EdS4ly6a06OJO97qzmtBoCB06NS3d7ervz8fM2dO/fkH2C3a+7cudqwYcNpHzNnzhzl5+d3l+jDhw/rzTff1NVXX33G79PW1qb6+vpTPgAA56bT7dH/rD0gSfr6RcMV53IaTgSgL33z4hGKDndoV2m91nxaYToOAISMHpXq6upqud1upaamnvL51NRUlZeXn/Yxt912m37605/qggsukNPp1IgRI3TJJZd84fXvhx9+WPHx8d0f2dnZPYkJADiN17eXqfB4swZFOfXVrhMtAMEjMTpcS7r+bf9m7QFOqwFggPT79O8PPvhAP//5z/V///d/Kigo0KpVq/TGG2/o3/7t3874mAceeEB1dXXdH8XFxf0dEwCCmttj6X/fOyhJuvei4YqOCDOcCEB/uPfC4YoKd2h3Wb3W7jn9vBsAQN/q0bOqpKQkORwOVVSceqWooqJCaWlpp33Mj3/8Y91555265557JEmTJk1SU1OTvv71r+uHP/yh7PbP9/qIiAhFRET0JBoA4Av8eXuZDlc3KSHKqSWzc0zHAdBPEqPDtWR2jh776yH9Zu0BXT4uRTabzXQsAAhqPTqpDg8PV15entauXdv9OY/Ho7Vr12r27NmnfUxzc/PnirPD4ZAkriUBwABweyz9z3ve91Lfe+FwxXBKDQS1ey8cpkinQztL6/T+Pk6rAaC/9fj69/3336/HH39cTz/9tPbs2aP77rtPTU1NWrp0qSRpyZIleuCBB7q//rrrrtPvfvc7vfTSSzpy5IjWrFmjH//4x7ruuuu6yzUAoP/8ZUeZDlc1KT7SqSWzh5qOA6CfDY6J6P63/pt3eW81APS3Hh9XLF68WFVVVXrwwQdVXl6uKVOmaPXq1d3Dy4qKik45mf7Rj34km82mH/3oRyotLVVycrKuu+46/fu//3vf/S0AAKfl9lj6bdd7qe+5YJhimfgNhIR7LxquZzYc1faSOn2wv0qXjkkxHQkAgpbNCoCXL+vr6xUfH6+6ujrFxcWZjgMAAePP28v09y9uVZwrTOt+cBlrtIAQ8u9vfKrHPzqiKdkJeuXv5vDeagDoobPtof0+/RsAYIbHY+m3Xe+lvvsC9lIDoebrF42Qy2nXtuJafXig2nQcAAhalGoACFJv7SrX/opGxbrC9NXzc0zHATDAkmMjdPss33ur9/PeagDoJ5RqAAhCHo+l36zdL0n62vnDFB/JKTUQir5x8XBFhNlVUFSrjzitBoB+QakGgCC0enfXKXVEmL52/jDTcQAYkhLr0m2zhkiSfrOWSeAA0B8o1QAQZDweS/+z1vte6qUXDFN8FKfUQCi77+IRigizK//oCX188LjpOAAQdCjVABBk1uyp0N7yBsVGhOluTqmBkJcS59KtM72n1b4X3AAAfYdSDQBBxLIsPfq+dy/1kjlDOaUGIEn65sUjFO6wa1NhjTYX1piOAwBBhVINAEFk3cFq7Sipk8tp573UALqlxbu0IC9LkrpfeAMA9A1KNQAEkf99z/tk+daZQzQ4JsJwGgD+5JsXD5fdJn2wr0q7SutMxwGAoEGpBoAgsaWwRhuP1MjpsOnrFw03HQeAnxk6OFrzczMkcVoNAH2JUg0AQcL3JHlhXpbS4yMNpwHgj/7u0pGSvGv3DlY2GE4DAMGBUg0AQWBXaZ3e31clu036xkUjTMcB4KdGp8bqyvGpsizp/z44ZDoOAAQFSjUABIHfdT05vi43QzlJ0YbTAPBn377Me1r92rYyFdc0G04DAIGPUg0AAe5gZaPe3HVMkvR3l4w0nAaAv5uclaALRyXJ7bH0+w85rQaAc0WpBoAA97sPDsmypCvGp2pMWqzpOAACwLe63lv98pYSVda3Gk4DAIGNUg0AAay4plmvbiuVdPJJMgB8mVnDEjV96CC1d3r0x3VHTMcBgIBGqQaAAPaHDw/L7bF0wcgkTclOMB0HQICw2WzdL8Q998lRnWhqN5wIAAIXpRoAAlRlQ6uWbSmWxCk1gJ67ZEyyJmTEqbndrafWF5qOAwABi1INAAHqiY+OqL3To7yhg3Te8ETTcQAEmM+eVj+1vlCNbZ2GEwFAYKJUA0AAqmvu0HOfHJUkfevSEbLZbIYTAQhE8yakaXhytOpaOvTCxqOm4wBAQKJUA0AAem7jUTW1uzU2LVaXjkkxHQdAgHLYbfrmxSMkSU+s895+AQD0DKUaAAJMa4dbf/q4UJL0jYuHc0oN4JxcPyVDqXERqqhv694mAAA4e5RqAAgwKwtKVN3YpsyESF07OcN0HAABLiLMobsvGCZJ+v1fD8njsQwnAoDAQqkGgADi9lh6/MPDkqS7Lxgmp4Mf4wDO3a0zhyjWFaZDVU1au7fSdBwACCg8GwOAAPL27nIVHm9WQpRTt8zMNh0HQJCIdTl1x3lDJUmP/fWQ4TQAEFgo1QAQICzL6n6yu2R2jqLCwwwnAhBMlp6fo3CHXflHT2hzYY3pOAAQMCjVABAgNhw+rh0ldXI57bpr9lDTcQAEmZRYlxbkZUryvrcaAHB2KNUAECAe+6v3vdQ3T8/W4JgIw2kABKN7Lxwum016d0+l9lc0mI4DAAGBUg0AAeDTsnp9uL9Kdpt0zwXDTccBEKSGJ8do3vg0SdIfuoYiAgC+GKUaAALA7z/0XsW8ZnKGhgyOMpwGQDD75iUjJEmvbSvVsboWw2kAwP9RqgHAzxXXNOsvO45Jkr5xEafUAPrXlOwEnTc8UR1uS098dMR0HADwe5RqAPBzT6w7IrfH0oWjkjQxM950HAAh4BsXe0+rX9xUpLrmDsNpAMC/UaoBwI/VNLXrpc1FkqRvdj3JBYD+dsnoZI1Ni1VTu1vPbTxqOg4A+DVKNQD4sWc2FKq1w6OJmXGaM2Kw6TgAQoTNZtM3Lva+3eRPHx9Ra4fbcCIA8F+UagDwU60dbj27wXtC9I2LRshmsxlOBCCUXDs5QxnxLlU3tuu1baWm4wCA36JUA4CfemVrqY43tSszIVJfmZhmOg6AEON02LX0/GGSpMc/OiKPxzKcCAD8E6UaAPyQx2Pp8Y+8O2KXnp+jMAc/rgEMvMUzsxUTEaaDlY366/4q03EAwC/xLA0A/ND7+yp1uKpJsRFhWjwj23QcACEqzuXULV0/g3wv9AEATkWpBgA/5HvyeuusIYp1OQ2nARDKll4wTA67TesPHdfusjrTcQDA71CqAcDP7Cqt0yeHaxRmt+mrc3JMxwEQ4jITInX1pHRJ0h8/OmI4DQD4H0o1APgZ3yn1NZPTlZEQaTgNAEj3XugdWPbn7WU6VtdiOA0A+BdKNQD4kbLaFv1lxzFJ0r0XDjecBgC8JmclaNawRHV6LD21vtB0HADwK5RqAPAjT60vlNtjafbwwZqYGW86DgB0873Q98LGIjW2dRpOAwD+g1INAH6iobVDL24skiTde9Eww2kA4FSXjU3R8ORoNbR26uXNxabjAIDfoFQDgJ9YtrlYDW2dGpEcrUtGp5iOAwCnsNttuvsC7wt+T6w7ok63x3AiAPAPlGoA8AOdbo/+9HGhJOmeC4fLbreZDQQAp7FgWpYSo8NVWtui1bvLTccBAL9AqQYAP/DmrnKV1rZocHS4bpyaaToOAJyWy+nQHecNlSQ9/tERWZZlOBEAmEepBgDDLMvSH7vWaN05e6hcTofhRABwZktmD1V4mF3bi2u15egJ03EAwDhKNQAYtrnwhHaU1CkizK47u06AAMBfJcVE6KauGzW+FwQBIJRRqgHAsCfXHZEk3Tg1U4NjIgynAYAv5xtY9s6nFSo63mw4DQCYRakGAIOKa5r1zqfeYT9fu4A1WgACw6jUWF04KkmWJT29odB0HAAwilINAAY9vb5QHku6cFSSRqfGmo4DAGfN90Lgy5uL1djWaTgNAJhDqQYAQxrbOrVsc7Ek6Wvnc0oNILBcPCpZw5Oj1dDWqRVbik3HAQBjKNUAYMiKLcVqaOvU8KRoXTw62XQcAOgRu92mpV0vCP5pfaE8HtZrAQhNlGoAMMDjsfSn9YWSpKXn58hut5kNBAC9sGBapuJcYTp6vFnv7a00HQcAjKBUA4AB7+2t1NHjzYpzhemmaVmm4wBAr0SFh+nWWUMkSU9+fMRwGgAwg1INAAb4nnzeOnOIoiPCDKcBgN5bMjtHDrtN6w8d155j9abjAMCAo1QDwADbc6xe6w8dl8Nu05I5OabjAMA5yUyI1FUT0yRJf+K0GkAIolQDwADzPem8amKaMhMiDacBgHN3d9d6rVe3lam6sc1wGgAYWJRqABhA1Y1tenVbmSTWaAEIHtOGDNKU7AS1d3r0wsYi03EAYEBRqgFgAL2wsUjtnR7lZido2pAE03EAoM98reu0+tlPjqqt0204DQAMHEo1AAyQtk63nv3kqCTpa+fnyGZjjRaA4PGViWlKi3OpqqFNb+w4ZjoOAAwYSjUADJA3dhxTVUObUuMidPWkdNNxAKBPOR12LZkzVJL0xLojsizLcCIAGBiUagAYAJZlda/RWjI7R04HP34BBJ9bZwyRy2nX7rJ6bS48YToOAAwIntUBwAAoKDqhXaX1Cg+z69aZQ0zHAYB+MSg6XDdOzZQkPb2+0GwYABgglGoAGABPr/e+l/r63AwlRocbTgMA/eeuOTmSpNW7y1Ve12o2DAAMAEo1APSzyvpWvbnTO7TH92QTAILV2LQ4zRqWKLfH0vMbj5qOAwD9jlINAP3shU1F6vRYyhs6SBMz403HAYB+53sB8cVNRazXAhD0KNUA0I/aOz16fmORJE6pAYSOK8enKj3eperG9u6bOgAQrCjVANCP3trlXaOVEhuhqyakmY4DAAMizGHXHed512s9tZ4r4ACCG6UaAPrRMxu8TyZvmzVE4WH8yAUQOhbPyFa4w67txbXaVlxrOg4A9Bue4QFAP9lVWqf8oyfkdNh02yzWaAEILUkxEbo2N12S9AzrtQAEMUo1APQT347WqyelKyXWZTYMABjw1a5ZEn/ZcUzVjW1mwwBAP6FUA0A/qGlq12vbyyRJS2bnmA0DAIZMzkrQlOwEtbs9erFraCMABBtKNQD0g5c2F6m906NJmfGaNiTBdBwAMMZ3Wv38xiJ1uD1mwwBAP6BUA0Af63R79Pwn3hOZJbOHymazGU4EAOZ8ZVKakmLCVV7fqnd2V5iOAwB9jlINAH3s3T2VKq1tUWJ0uK7LzTAdBwCMighz6LaZ3mGNTzOwDEAQolQDQB/zPWm8ZUa2XE6H2TAA4AduP2+owuw2bSqs0adl9abjAECfolQDQB/aX9GgDYePy27zPokEAEipcS7Nm5gmSXpmQ6HZMADQxyjVANCHfKfUV45PU2ZCpNkwAOBHfAPLXt1WqtrmdrNhAKAPUaoBoI/UtXRoVUGpJOmuriePAACv6UMHaXx6nFo7PHp5S7HpOADQZyjVANBHVuSXqKXDrTGpsTpveKLpOADgV2w2m+6a431bzDMbjsrtsQwnAoC+QakGgD7g8Vh6tut9gkvmsEYLAE7n+imZSohyquREi97fW2k6DgD0CUo1APSBvx6oUuHxZsW6wnTj1EzTcQDAL7mcDi2ekS1JepqBZQCCBKUaAPqAb0DZzdOzFRUeZjYMAPixO2YNld0mfXSgWgcrG03HAYBzRqkGgHNUWN2kD/ZVyWaT7mSNFgB8oezEKF0+LlWSut82AwCBjFINAOfomQ1HJUmXjE5WTlK04TQA4P/ump0jyTvgsaG1w2wYADhHlGoAOAfN7Z1anu9dDbOENVoAcFbOHzlYI1Ni1NTu1qtbS03HAYBzQqkGgHPw5+1lamjt1JDEKF08Ktl0HAAICDabTXfMGiJJevaTo7Is1msBCFyUagA4B899UiRJun3WENntrNECgLN1U16WIp0O7a9o1ObCE6bjAECvUaoBoJe2F9dqZ2mdwsPsWjQ923QcAAgocS6nrp+SIUl67pOjhtMAQO9RqgGgl3xPAq+ZlK7E6HDDaQAg8NzRtTHhrV3HVN3YZjgNAPQOpRoAeqG2uV2vby+TJN1x3hDDaQAgME3MjFdudoI63JaWbS42HQcAeoVSDQC9sCK/RG2dHo1Lj9O0IYNMxwGAgHVn12n1CxuL5PYwsAxA4KFUA0APWZalFzZ6B5Tdcd4Q2WwMKAOA3rp2crriI50qrW3RX/dXmo4DAD1GqQaAHlp/6LgOVzcpJiJMN0zJNB0HAAKay+nQorwsSSc3KgBAIKFUA0APPbvBO6DsxqmZio4IM5wGAALf7V1XwN/fV6nimmbDaQCgZyjVANAD5XWtWrOnQtLJqbUAgHMzLClaF45KkmVJL2zitBpAYKFUA0APvLTZO0hnZk6ixqTFmo4DAEHj9lneFypf3lystk634TQAcPYo1QBwljrcHr3YdYJyO2u0AKBPzR2XorQ4l443tWv1rnLTcQDgrFGqAeAsrd1ToYr6Ng2ODtdVE9NMxwGAoBLmsOuWmdmSpOc+OWo4DQCcPUo1AJwl31TaxTOyFRHmMJwGAILPrTOHyGG3aXPhCe0trzcdBwDOCqUaAM7CkeomrTtYLZvN+6QPAND3UuNcunJ8qiTpedZrAQgQlGoAOAvPd11FvHRMirITowynAYDg5dus8MrWUjW2dRpOAwBfjlINAF+itcOt5fklkqQ7GFAGAP1qzojBGp4Urca2Tr26tdR0HAD4UpRqAPgSf95eprqWDmUNitTFo1NMxwGAoGaz2XR712n1c58clWVZhhMBwBejVAPAl3huo/d9fbfN8g7QAQD0r4XTsuRy2rW3vEEFRSdMxwGAL0SpBoAvsLOkTtuLa+V02HTz9GzTcQAgJMRHOXXd5AxJJzcvAIC/6lWpfvTRR5WTkyOXy6VZs2Zp06ZNZ/zaSy65RDab7XMf11xzTa9DA8BA8e1K/crEdCXFRBhOAwChwzew7I0dx3S8sc1wGgA4sx6X6mXLlun+++/XQw89pIKCAuXm5mrevHmqrKw87devWrVKx44d6/7YtWuXHA6HFi1adM7hAaA/1bV06LXt3iE5d84eajgNAISW3OwETc6KV7vb0z0sEgD8UY9L9SOPPKJ7771XS5cu1fjx4/XYY48pKipKTz755Gm/PjExUWlpad0fa9asUVRUFKUagN9bVVCi1g6PxqTGavrQQabjAEDIuWOW9wXN5zcelcfDwDIA/qlHpbq9vV35+fmaO3fuyT/AbtfcuXO1YcOGs/oznnjiCd1yyy2Kjo4+49e0tbWpvr7+lA8AGEiWZen5rgFld5w3RDYbA8oAYKBdl5uhOFeYimta9NHBatNxAOC0elSqq6ur5Xa7lZqaesrnU1NTVV5e/qWP37Rpk3bt2qV77rnnC7/u4YcfVnx8fPdHdjbDgQAMrC1HT+hgZaMinQ5dPzXTdBwACEmR4Q7dNC1LkvTiRgaWAfBPAzr9+4knntCkSZM0c+bML/y6Bx54QHV1dd0fxcXFA5QQALxe6HryNj83Q3Eup+E0ABC6bp05RJK0Zk+FKutbDacBgM/rUalOSkqSw+FQRUXFKZ+vqKhQWlraFz62qalJL730ku6+++4v/T4RERGKi4s75QMABkptc7ve2HlMknTrrCGG0wBAaBuTFqu8oYPk9lgMLAPgl3pUqsPDw5WXl6e1a9d2f87j8Wjt2rWaPXv2Fz52+fLlamtr0x133NG7pAAwQFYWlKq906Px6XHKzYo3HQcAQt5tXafVL24qYmAZAL/T4+vf999/vx5//HE9/fTT2rNnj+677z41NTVp6dKlkqQlS5bogQce+NzjnnjiCd1www0aPHjwuacGgH5iWZZe3OS9+n3rLAaUAYA/uGZyuuJcYSo5wcAyAP4nrKcPWLx4saqqqvTggw+qvLxcU6ZM0erVq7uHlxUVFcluP7Wr79u3T+vWrdM777zTN6kBoJ9sLjw5oOyGKRmm4wAAJLmc3oFlT60v1Asbj+ri0cmmIwFAN5tlWX5/h6a+vl7x8fGqq6vj/dUA+tV3l23TK1tLtXh6tn6xcLLpOACALvsrGnTlf38oh92mDT+4TClxLtORAAS5s+2hAzr9GwD82YmmkwPKbmNAGQD4ldGpsZreNbDs5S1shgHgPyjVANBl1daTA8omM6AMAPzOrd0Dy4oZWAbAb1CqAUDeAWUvbDwqyXtKzYAyAPA/voFlpbUMLAPgPyjVACDvgLJDVU2KCnfoegaUAYBf8g0sk9T9QigAmEapBgCdfHI2PzdDsS6n4TQAgDPxzbx4d0+lKutbDacBAEo1AOhEU7ve3FUu6eT79QAA/omBZQD8DaUaQMhbWVCi9k6PJmQwoAwAAgEDywD4E0o1gJBmWZZe2FQkiQFlABAoPjuw7MMDVabjAAhxlGoAIW3TkRod7hpQNj+XAWUAEAg+O7Dsxa4XRgHAFEo1gJDmO6W+fgoDygAgkHx2YFkFA8sAGESpBhCyTjS1662dDCgDgED02YFlyxlYBsAgSjWAkLWyoETtbo8mZsZpclaC6TgAgB7ynVa/uKlYbgaWATCEUg0gJH12QBmn1AAQmK6elK74SKdKa1v0EQPLABhCqQYQkjZ+ZkDZ9VMyTccBAPSCd2CZ92f4CxsZWAbADEo1gJD04mcGlMVEhBlOAwDordu6bhut3cvAMgBmUKoBhJyazwwou23mUMNpAADnYlRqrGbkeAeWvbyZgWUABh6lGkDIWfWZAWWTsuJNxwEAnCPfbIyXNjOwDMDAo1QDCCmWZXVf/WZAGQAEh88OLFt3sNp0HAAhhlINIKTkHz2hQ1VNinQ6ND83w3QcAEAfcDkdunGqd2DZss0MLAMwsCjVAELKS13vt7t2crpiXU7DaQAAfWXxjGxJ0ppPK1Td2GY4DYBQQqkGEDLqWzv0lx1lkqRbuPoNAEFlXHqccrMT1OG2tKqgxHQcACGEUg0gZLy+rUytHR6NSonRtCEJpuMAAPrYLV2n1S9tLpZlMbAMwMCgVAMIGcu6rn4vnpEtm81mOA0AoK9dl5uhqHCHDlc1acvRE6bjAAgRlGoAIWFXaZ12ltYp3GHXTdOyTMcBAPSDmIgwXTfZO4TypU3srAYwMCjVAEKC75T6ygmpSowON5wGANBfFs/0XgF/Y2eZ6lo6DKcBEAoo1QCCXku7W69uK5Uk3TKDAWUAEMymZidodGqMWjs8en17mek4AEIApRpA0Htr1zE1tHYqa1Ck5owYbDoOAKAf2Ww2Le56AZWd1QAGAqUaQNDz7aZePD1bdjsDygAg2N00NVPhDrt2ldZrV2md6TgAghylGkBQO1TVqE1HamS3SQunM6AMAELBoOhwzZuYJkl6idNqAP2MUg0gqL3cdUp96ZgUpcdHGk4DABgovp3Vr20tU0u723AaAMGMUg0gaLV3erSyoESSdzc1ACB0zB4+WNmJkWpo69SbO4+ZjgMgiFGqAQSt9/ZWqLqxXcmxEbp0bIrpOACAAWS327o3PvjWKgJAf6BUAwhaL27yPolamJclp4MfdwAQahbmZclukzYV1uhgZaPpOACCFM8yAQSl0toWfXigSpJ36jcAIPSkxrl0WddNpZe3cFoNoH9QqgEEpeVbimVZ3vfU5SRFm44DADDEt7N6ZX6J2js9htMACEaUagBBx+2xtHyLd0DZLTM5pQaAUHbpmGSlxEboeFO71u6pMB0HQBCiVAMIOh8dqFJpbYviI52aNyHNdBwAgEFhDrsWTc+SJL3IwDIA/YBSDSDo+Ka83jg1Uy6nw3AaAIBpN3fN1vjoQJVKTjQbTgMg2FCqAQSV6sY2rfnUe72P3dQAAEkaOjhac0YMlmWp++1BANBXKNUAgsrK/BJ1eizlZidoXHqc6TgAAD9xy0zvwLLlW4rl9liG0wAIJpRqAEHDsqzuq9+3cEoNAPiMK8enKiHKqbK61u6ViwDQFyjVAILG5sITOlzdpKhwh67LzTAdBwDgR1xOh26cmilJWraJgWUA+g6lGkDQeGlzkSTp2snpiokIM5wGAOBvbunaWf3ungpVNbQZTgMgWFCqAQSFhtYOvbnzmCRpcdeTJgAAPmtMWqymZCeo02Pp1a2lpuMACBKUagBB4S87jqm1w6MRydGaNiTBdBwAgJ/yrdd6eUuxLIuBZQDOHaUaQFB4eYv3/XE3T8+WzWYznAYA4K+uzU2Xy2nXgcpGbSuuNR0HQBCgVAMIeAcqGrS1qFYOu003Tss0HQcA4MfiXE5dPTFd0skXZAHgXFCqAQQ835Oiy8amKCXWZTgNAMDf3dy1dvHP24+pub3TcBoAgY5SDSCgdbg9WlXgHTbje58cAABfZNawRA0dHKXGtk69tbPcdBwAAY5SDSCgvbe3Useb2pUUE6FLxiSbjgMACAA2m02L8rIkScu4Ag7gHFGqAQS05V1PhhZMy5TTwY80AMDZWZCXJbtN2nSkRkeqm0zHARDAeAYKIGBV1rfq/X1VkqRFXP0GAPRAenykLhrtveG0Ip/TagC9R6kGELBWFpTK7bGUN3SQRqbEmI4DAAgwvlkcK/JL5PawsxpA71CqAQQky7K6r37fPD3LcBoAQCC6fFyKBkU5VVHfpg/3V5mOAyBAUaoBBKT8oyd0uLpJUeEOXTM5w3QcAEAAighz6Map3hdm2VkNoLco1QAC0rLN3ic/10xKV0xEmOE0AIBAdfMMb6l+d0+Fjje2GU4DIBBRqgEEnMa2Tr2x85gk6eYZDCgDAPTe2LQ4Tc6KV4fb0qvbykzHARCAKNUAAs6bO46pud2t4UnRmj50kOk4AIAA59sg8fLmYlkWA8sA9AylGkDA8b3vbdH0bNlsNsNpAACBbn5uhiLC7NpX0aAdJXWm4wAIMJRqAAHlUFWjthw9IYfdpgXTMk3HAQAEgfhIp74yMU0SA8sA9BylGkBA8T3ZuWR0slLiXIbTAACChW9n9evbytTS7jacBkAgoVQDCBgdbo9W5pdKOvn+NwAA+sJ5wwcrOzFSDW2dWr37mOk4AAIIpRpAwPjrvipVN7YpKSZcl49LMR0HABBE7HabFuX5BpaVGE4DIJBQqgEEDN/V7xunZsrp4McXAKBvLcjLks0mbTh8XEXHm03HARAgeFYKICBUNbTpvb2Vkrj6DQDoH5kJkbpgZJIkaUU+A8sAnB1KNYCA8MrWEnV6LE3JTtDo1FjTcQAAQco3sGx5foncHnZWA/hylGoAfs+yLL28xfv+tsUzOKUGAPSfKyekKiHKqWN1rVp3sNp0HAABgFINwO9tLa7VwcpGuZx2XTs53XQcAEAQiwhz6IYpmZLYWQ3g7FCqAfi95V1Paq6elK5Yl9NwGgBAsFs0PUuStGZ3hU40tRtOA8DfUaoB+LXm9k79ebt3X+jNDCgDAAyACRnxmpARp3a3R69uKzUdB4Cfo1QD8Gtv7ixXY1uncgZHadawRNNxAAAhwjfDY9nmYlkWA8sAnBmlGoBf872fbdH0bNlsNsNpAAChYn5uhsLD7Npb3qDdZfWm4wDwY5RqAH7rSHWTNh2pkd0m3TQt03QcAEAISYgK17wJaZIYWAbgi1GqAfitFfneJzEXjU5Wenyk4TQAgFBzc9fAsle3lqq1w204DQB/RakG4Jc63R6tyO/aTc2AMgCAAeePSFJmQqTqWzv19u5y03EA+ClKNQC/9NGBalXUtykxOlyXj0s1HQcAEILsdpsW5nlPq5dvKTGcBoC/olQD8Eu+96/dMCVT4WH8qAIAmLEwL0s2m7TuYLWKa5pNxwHgh3imCsDvnGhq17t7KiRJN8/IMpwGABDKshOjNGfEYEnSygJOqwF8HqUagN95fXuZOtyWJmbGaWxanOk4AIAQtyjPO9tjZUGJPB52VgM4FaUagN/xDShbOI1TagCAefMmpCk2IkzFNS3aeKTGdBwAfoZSDcCv7C2v187SOjkdNs2fwm5qAIB5keEOXZubLunkC78A4EOpBuBXVnY9Wbl8bKoSo8MNpwEAwMs3BfzNncfU2NZpOA0Af0KpBuA3Ot0evbK1TNLJJy8AAPiDaUMGaXhStFo63Hpz5zHTcQD4EUo1AL/x4YEqVTe2aXB0uC4ek2w6DgAA3Ww2mxZ0veC7gp3VAD6DUg3Ab/jep3bD1Ew5Hfx4AgD4lwXTsmS3SZsKa1RY3WQ6DgA/wbNWAH7hRFO73v20UhJXvwEA/ikt3qULRnlvUq1iZzWALpRqAH7hzzvK1O72aEJGnMals5saAOCfFnW98LuyoJSd1QAkUaoB+Inu3dScUgMA/NgV41MV5wpTaW2LNhw+bjoOAD9AqQZg3P6KBu0o8e6mvp7d1AAAP+ZyOjR/SoYkafmWYsNpAPgDSjUA43y7qS8dk8JuagCA31uYly1JWr27XPWtHYbTADCNUg3AqE63R6u2lkri6jcAIDDkZsVrVEqMWjs8emMHO6uBUEepBmDURweqVdXg3U196dgU03EAAPhSNput+4Vg30wQAKGLUg3AKN+TkeunsJsaABA4bpyaKYfdpvyjJ3SoqtF0HAAG8QwWgDG1ze1a82mFJK5+AwACS0qcSxeP9u6sXslpNRDSKNUAjPnzjmNqd3s0Lj1O4zPYTQ0ACCy+ndWrCkrlZmc1ELIo1QCMYTc1ACCQXTYuRQlRTpXXt2rdwWrTcQAYQqkGYMSBigZtL65VmN2m67v2fQIAEEgiwhy6YUqmJHZWA6GMUg3AiBUFXbupx6YoKSbCcBoAAHrHd9vqnU8rVNfMzmogFFGqAQy4TrdHrxSwmxoAEPgmZMRpbFqs2js9en1Hmek4AAygVAMYcOsOVquyoU2Dopy6dAy7qQEAgYud1QAo1QAG3Gd3U4eH8WMIABDYbpiaqTC7TduLa3WgosF0HAADjGezAAZUXXOH3mE3NQAgiCTFROjSsd6bV5xWA6GHUg1gQP15R5naOz0amxarCeymBgAEie6d1VtL1en2GE4DYCBRqgEMqM/uprbZbIbTAADQNy4dm6LB0eGqamjThweqTMcBMIAo1QAGzMHKRm0rrpXDbtP1XXs9AQAIBk6HXTdM9e2s5go4EEoo1QAGzErfbuoxyUqOZTc1ACC4+GaFvLunQiea2g2nATBQKNUABoTbY2lVwcmr3wAABJtx6XGamBmnDrel17aVmo4DYIBQqgEMiHUHq1VR791NfdnYVNNxAADoFwunde2sLuAKOBAqelWqH330UeXk5MjlcmnWrFnatGnTF359bW2tvvWtbyk9PV0REREaPXq03nzzzV4FBhCY2E0NAAgF86dkyumwaVdpvfYcqzcdB8AA6PEz22XLlun+++/XQw89pIKCAuXm5mrevHmqrKw87de3t7friiuuUGFhoVasWKF9+/bp8ccfV2YmQ4qAUFHX0qF3dpdLkhZM4+o3ACB4JUaHa+44740sdlYDoaHHpfqRRx7Rvffeq6VLl2r8+PF67LHHFBUVpSeffPK0X//kk0+qpqZGr776qs4//3zl5OTo4osvVm5u7jmHBxAY3thxTG2dHo1JjdXETHZTAwCCm292yKtbS9XBzmog6PWoVLe3tys/P19z5849+QfY7Zo7d642bNhw2se8/vrrmj17tr71rW8pNTVVEydO1M9//nO53e4zfp+2tjbV19ef8gEgcK3IL5bEbmoAQGi4eHSykmIidLypXe/vPf1tTgDBo0elurq6Wm63W6mppw4ZSk1NVXl5+Wkfc/jwYa1YsUJut1tvvvmmfvzjH+tXv/qVfvazn53x+zz88MOKj4/v/sjOzu5JTAB+5FBVowqKunZTT80wHQcAgH4X5rDrpmldO6u5Ag4EvX6fFuTxeJSSkqI//OEPysvL0+LFi/XDH/5Qjz322Bkf88ADD6iurq77o7i4uL9jAugnK7ueTFwyOlkpsS7DaQAAGBi+K+Dv761UdWOb4TQA+lOPSnVSUpIcDocqKipO+XxFRYXS0tJO+5j09HSNHj1aDoej+3Pjxo1TeXm52tvbT/uYiIgIxcXFnfIBIPC4PZZe2erd07mA3dQAgBAyOjVWuVnx6vRYenUrO6uBYNajUh0eHq68vDytXbu2+3Mej0dr167V7NmzT/uY888/XwcPHpTHc3JIw/79+5Wenq7w8PBexgYQCNYfqtaxulbFRzp1+bgU03EAABhQC6d738K4Ir9ElmUZTgOgv/T4+vf999+vxx9/XE8//bT27Nmj++67T01NTVq6dKkkacmSJXrggQe6v/6+++5TTU2NvvOd72j//v1644039POf/1zf+ta3+u5vAcAvndxNnaGIMMeXfDUAAMFl/uQMhYfZtbe8QbvLGLwLBKuwnj5g8eLFqqqq0oMPPqjy8nJNmTJFq1ev7h5eVlRUJLv9ZFfPzs7W22+/re9+97uaPHmyMjMz9Z3vfEff//73++5vAcDv1Ld2aPUu7wDDhVz9BgCEoPgop64cn6q/7DimFfklmpgZbzoSgH5gswLgLkp9fb3i4+NVV1fH+6uBAPHipiI9sGqnRqfG6O1/vIhVWgCAkPTBvkp99U+blRDl1MZ/uZybW0AAOdse2u/TvwGEJt/Vb3ZTAwBC2YWjkpUaF6Ha5g69t4ed1UAwolQD6HNHqpuUf/SE7DbphimZpuMAAGCMw27TTdO8b4NiZzUQnCjVAPqcbzf1xaOTlRLHbmoAQGjzzRb56/4qVTa0Gk4DoK9RqgH0KbfH0soC39XvbMNpAAAwb0RyjKYNSZDbY+m1rWWm4wDoY5RqAH1qw6Hj7KYGAOBv+F5oZmc1EHwo1QD61Ir8YknS/NwMuZxMOAUAQJKumZyuiDC79lU0aFcpO6uBYEKpBtBnGlo7tHq3dzf1AnZTAwDQLT7SqSsnpEk6+QI0gOBAqQbQZ97ceUytHR6NTIlRbla86TgAAPgV38Cy17aXqa3TbTgNgL5CqQbQZ9hNDQDAmV0wMomd1UAQolQD6BOF1U3aXOjdTX3jVHZTAwDwtz67s3oFO6uBoEGpBtAnfGu0LhqdrFR2UwMAcFoLukr1B/urVNXQZjgNgL5AqQZwzjweS6sKSiWdfLIAAAA+b2RKjKb6dlZvKzUdB0AfoFQDOGefHD6u0toWxbrCdMX4VNNxAADwa76BZcu3sLMaCAaUagDnzPe+MHZTAwDw5a6dnKHwrp3Vu8vYWQ0EOko1gHPS0NqhN3cdk3TylXcAAHBm8ZFOzeveWc3AMiDQUaoBnJO3dpartcOjEcnRmpKdYDoOAAABYcE076aMV7eVsrMaCHCUagDnZEXX1O8F7KYGAOCsXTgquXtn9ft72VkNBDJKNYBeO3q8SZuO1Mhuk26aytVvAADOlsNu041T2VkNBANKNYBeW9m1RuuCUclKi2c3NQAAPbEwz3sF/P197KwGAhmlGkCveDyWVna9ss6AMgAAem5kSqymZLOzGgh0lGoAvfLJkZO7qa9kNzUAAL3ie2F6RT47q4FARakG0Csr872vqF87md3UAAD01nVdO6v3lrOzGghUlGoAPdbU1qm32E0NAMA5i49ydt/4YmAZEJgo1QB67M2dx9Tc7tbwpGhNG5JgOg4AAAHN9wL1a9tK1d7pMZwGQE9RqgH0mO+VdHZTAwBw7i4clayU2AidaO7Qe+ysBgIOpRpAjxQdb9bGIzWy2aSbpmWajgMAQMBz2G26set3KlfAgcBDqQbQIysLvL/sLxiZpPT4SMNpAAAIDgunea+Av7+vkp3VQIChVAM4ax6PpVVb2U0NAEBfG5Uaq1x2VgMBiVIN4KxtKqxRcU2LYiPCdOX4NNNxAAAIKp/dWQ0gcFCqAZw13y/5a3PTFRnObmoAAPrS/MkZCnf4dlbXmY4D4CxRqgGclaa2Tr25k93UAAD0l/gop66YwM5qINBQqgGclbd2lau53a1hSdGaNmSQ6TgAAASlkzury9hZDQQISjWAs7LSt5t6Wia7qQEA6CcXjkxSSmyEapra9f4+dlYDgYBSDeBLFdc0a8Ph47LZpBuncfUbAID+Euaw68ap7KwGAgmlGsCXWlXgXe1x/ogkZSawmxoAgP60oOsK+Pt7K1XdyM5qwN9RqgF8IY/H0oqCYkkMKAMAYCCMTo1Vbla8Oj2WXttWZjoOgC9BqQbwhTZ37aaOiQjTvAnspgYAYCCwsxoIHJRqAF9oZYH3l/k1k9hNDQDAQLku17uzes+xenZWA36OUg3gjJrbO/XGjq7d1NO5+g0AwEBJiArXFeO9O6tX5pcaTgPgi1CqAZzR6l3lamp3a+jgKE0fym5qAAAGku8K+KvbStlZDfgxSjWAM/K9j2vhtCx2UwMAMMAuHJWk5K6d1R+wsxrwW5RqAKdVcqJZ6w95d1PfxNRvAAAGXJjDrpvYWQ34PUo1gNN6pWs39ezhg9lNDQCAIb6d1e/trdRxdlYDfolSDeBzLMvSiq6p3+ymBgDAnNGpsZrMzmrAr1GqAXzOlqMndPR4s6LDHbpqIrupAQAwiZ3VgH+jVAP4nBVbunZTT05XVHiY4TQAAIS26yZ7d1Z/ys5qwC9RqgGcorm9U2/s7NpNnZdtOA0AABgUHa6541MksbMa8EeUagCneHt3uRrbOjUkMUozcthNDQCAP/BdAX9tW6k63OysBvwJpRrAKXyvgC9gNzUAAH7jolHJSoqJ0PGmdn2wr8p0HACfQakG0K20tkUfH6qWJN00LdNwGgAA4BPmsHf/bl6RX2w4DYDPolQD6PZKQYksy7ubOjsxynQcAADwGQumea+Ar93DzmrAn1CqAUjq2k2dz25qAAD81Zi0WE3K9O6sfn07O6sBf0GpBiBJyj96QoXHmxXFbmoAAPwWO6sB/0OpBiBJWlng/eV89aR0RUewmxoAAH80PzdDTodNu8vq9WlZvek4AESpBiCppd2tv2z37abm6jcAAP5qUHS45o5LlXTyBXEAZlGqAeidT8vV0Nap7MRIzcxJNB0HAAB8Ad8L4K9uZWc14A8o1QC635e1YFqW7HZ2UwMA4M8uGn1yZ/Vf2VkNGEepBkJcWW2L1h307qb2reoAAAD+y+mw68apGZIYWAb4A0o1EOJe2Voqy5JmDUtkNzUAAAFiQdcV8LV7K1TT1G44DRDaKNVACLMsSyvZTQ0AQMAZmxaniZlx6nBben1bqek4QEijVAMhrKCoVoermxQV7tDVk9JNxwEAAD2wsOttWyuYAg4YRakGQpjvfVhfmchuagAAAs38KZlyOmzaVVqvPcfYWQ2YQqkGQlRrh1t/2V4miavfAAAEosTocF0+tmtnNQPLAGMo1UCIeufTCjW0dSozIVKzhrGbGgCAQNS9s3obO6sBUyjVQIjq3k2dx25qAAAC1cVjkpUUE67qRnZWA6ZQqoEQVF7XqnUHvL94F0zLNJwGAAD0ltNh1w1TvL/LVzKwDDCCUg2EoFVbS+SxpJnDEjV0cLTpOAAA4Bz4dla/u6dCJ9hZDQw4SjUQYizL6r76zYAyAAAC37j0z+ys7hpCCmDgUKqBELOtuFaHq5oU6WQ3NQAAwWKBb2c1U8CBAUepBkLMyd3UaYphNzUAAEHh+q6d1TtL67S3nJ3VwECiVAMhpLXD3X0tjKvfAAAEj8TocF02NkUSO6uBgUapBkLImk8r1NDq3U193vDBpuMAAIA+tDAvW5L0ytYydlYDA4hSDYSQ7t3U0zLZTQ0AQJC5ZEyyBkeHq7qxTR/uZ2c1MFAo1UCIqKhv1Uddu6lvmsbVbwAAgo3TYdcNU707qxlYBgwcSjUQIl7ZWiqPJc3IGaScJHZTAwAQjHwzU9buqWRnNTBAKNVACGA3NQAAoWFcepwmZMSp3e3Rn3ewsxoYCJRqIARsL6nTwcpGuZx2dlMDABDkfC+gcwUcGBiUaiAErMgvliR9ZWK6Yl1Ow2kAAEB/mp+boTC7TTtK6rSvvMF0HCDoUaqBINfa4dbr27zXvxYwoAwAgKA3OCbi5M7qAk6rgf5GqQaC3No9lapv7VRGvEuzR7CbGgCAUOC7Ar6qoFSd7KwG+hWlGghyvqvfN03LkoPd1AAAhIRLx6ac3Fl9gJ3VQH+iVANBrLK+VX/d7/1FuoCp3wAAhAynw67rp7CzGhgIlGogiPl2U08fOkjD2E0NAEBI8V0Bf/fTStU2s7Ma6C+UaiBIfXY3NafUAACEnvEZcRqf3rWzejs7q4H+QqkGgtTO0jodqGxURJhd10xmNzUAAKGIndVA/6NUA0HK98vzqolpimM3NQAAIen6Kd6d1dtL6rS/gp3VQH+gVANBqK3Trde6dlMv5Oo3AAAha3BMhC717azmtBroF5RqIAit3VOpupYOpce7NGdEkuk4AADAoO6d1VvZWQ30B0o1EIR8V79vnJrJbmoAAELcpWNSlBgdrqqGNn10oNp0HCDoUKqBIFPZwG5qAABwUniYXddPyZDEwDKgP1CqgSDz2tYyuT2Wpg1J0IjkGNNxAACAH/BdAV/zaQU7q4E+RqkGggi7qQEAwOlMyIjXOHZWA/2CUg0EkZ2lddpX0aCIMLuunZxhOg4AAPAj3TurC0oNJwGCC6UaCCK+U+p5E9IUH8luagAAcFL3zuriWh1gZzXQZyjVQJD47G7qRdO5+g0AAE6VFBOhS8Z4d1avKGBgGdBXKNVAkGA3NQAA+DK+K+CvFLCzGugrlGogSCzfUixJumkau6kBAMDpXTY2RYOinKpsaNNHB9lZDfQFSjUQBCrrP7ObehpXvwEAwOl5d1ZnSmJnNdBXKNVAEHhla6k8lpQ3dJCGs5saAAB8ge6d1bsrVNfcYTgNEPgo1UCA++xu6kXspgYAAF9iQkacxqbFqt3t0es72FkNnCtKNRDgdpTU6UBlo1xOu66enG46DgAA8HM2m+3kzmqugAPnjFINBLjl+d4BZVdNSFOci93UAADgy90wNbN7Z/XBSnZWA+eCUg0EsNYOt17v2k29MC/bcBoAABAoTtlZnV9qOA0Q2CjVQAB7d0+F6ls7lRHv0uwRg03HAQAAAWRhnncK+CtbS+T2WIbTAIGLUg0EMN/7oBbkZbGbGgAA9MhlY1M1KMqpivo2fXSgynQcIGBRqoEAVV7Xqg/ZTQ0AAHqJndVA36BUAwHKt5t6Rs4g5SRFm44DAAACkG8K+DufsrMa6C1KNRCAvLupvVO/F7KbGgAA9FL3zupOj/7MzmqgVyjVQADaVlyrQ1VN3t3Uk9hNDQAAeoed1cC561WpfvTRR5WTkyOXy6VZs2Zp06ZNZ/zap556Sjab7ZQPl8vV68AATv7Su3piumLZTQ0AAM7B9VMy5bDbtK24VgcrG03HAQJOj0v1smXLdP/99+uhhx5SQUGBcnNzNW/ePFVWVp7xMXFxcTp27Fj3x9GjR88pNBDKWjvcen27bzc1V78BAMC5SY6N0KVjkiVJKws4rQZ6qsel+pFHHtG9996rpUuXavz48XrssccUFRWlJ5988oyPsdlsSktL6/5ITU39wu/R1tam+vr6Uz4AeL3zaYUaWjuVmRCp84azmxoAAJw73wv1qwrYWQ30VI9KdXt7u/Lz8zV37tyTf4Ddrrlz52rDhg1nfFxjY6OGDh2q7OxsXX/99dq9e/cXfp+HH35Y8fHx3R/Z2dk9iQkEte7d1NMyZWc3NQAA6AOXjk1RQtfO6nUHq03HAQJKj0p1dXW13G73506aU1NTVV5eftrHjBkzRk8++aRee+01Pffcc/J4PJozZ45KSs58teSBBx5QXV1d90dxcXFPYgJBq7yuVesOdO2m5uo3AADoIxFhDl2fmyGJgWVAT4X19zeYPXu2Zs+e3f2/58yZo3Hjxun3v/+9/u3f/u20j4mIiFBERER/RwMCzsqCEnksaeawRA0dzG5qAADQdxbmZevpDUf19u5y1bV0KD6SYajA2ejRSXVSUpIcDocqKipO+XxFRYXS0tLO6s9wOp2aOnWqDh482JNvDYQ8y7K0suuVYwaUAQCAvjYxM05jUr07q//CzmrgrPWoVIeHhysvL09r167t/pzH49HatWtPOY3+Im63Wzt37lR6Ort1gZ4oKKrV4eomRTod7KYGAAB9jp3VQO/0ePr3/fffr8cff1xPP/209uzZo/vuu09NTU1aunSpJGnJkiV64IEHur/+pz/9qd555x0dPnxYBQUFuuOOO3T06FHdc889ffe3AEKA75fbVyalKSai39+5AQAAQtD1UzPksNu0tYid1cDZ6vEz88WLF6uqqkoPPvigysvLNWXKFK1evbp7eFlRUZHs9pNd/cSJE7r33ntVXl6uQYMGKS8vT+vXr9f48eP77m8BBLnWDrf+wm5qAADQz1JiXbpkdLLW7q3UyoISff+qsaYjAX7PZlmW3y+iq6+vV3x8vOrq6hQXF2c6DjDgXttWqu+8tE1ZgyL14T9fyiotAADQb97aeUz3PV+gtDiXPv7BZXLwvAMh6mx7aI+vfwMYeCd3U2dRqAEAQL+6bJx3Z3V5fas+Zmc18KUo1YCfK6tt0bquX2gLpnH1GwAA9K+IMIfms7MaOGuUasDPvbK1VJYlzRqWqCGDo0zHAQAAIcA3w8W3sxrAmVGqAT9mWZaWbymWJC2anm04DQAACBWTMuM1OjVGbZ0evbHjmOk4gF+jVAN+LP/oCRUeb1ZUuENfmZhmOg4AAAgRp+6sLjacBvBvlGrAj/nex3T1pHRFs5saAAAMoBumZMpht6mgqFaHqthZDZwJpRrwUy3tbv2l67oVu6kBAMBAS4lz6eLRyZKklQwsA86IUg34qbd3l6uxrVPZiZGamZNoOg4AAAhBvhf2VxWUyu2xDKcB/BOlGvBTy7vev7RwWja7qQEAgBGXj0tRfKR3Z/X6Q+ysBk6HUg34oZITzVp/6Lgk6aZpmYbTAACAUMXOauDLUaoBP/RKgXc39ezhg5WdyG5qAABgju8K+Opd5apvZWc18Lco1YCfsSxLKwq8rwQzoAwAAJg2OSteo1LYWQ2cCaUa8DObC0/o6PFmRYc79JVJ7KYGAABmnbqzmivgwN+iVAN+ZkXXgLJrJqcrKpzd1AAAwLwbp2bKbpPyj57QYXZWA6egVAN+pLm9s/ta1cK8bMNpAAAAvE7ZWV3AaTXwWZRqwI+8tbNcTe1uDR0cpRk5g0zHAQAA6OZ7wZ+d1cCpKNWAH3l5i283dZZsNnZTAwAA/+HbWX2sjp3VwGdRqgE/cfR4kzYeqZHNJi1g6jcAAPAzLufJndUrGVgGdKNUA37CN03zwlHJykiINJwGAADg83wv/K/ezc5qwIdSDfgBt8fqLtWLOKUGAAB+KjcrXiNTYtTa4dGb7KwGJFGqAb/w8cFqHatrVXykU1eMTzUdBwAA4LTYWQ18HqUa8AO+AWU3TMmQy+kwnAYAAODMfDurtxw9oSPVTabjAMZRqgHDapvb9c6nFZKkRdPZTQ0AAPxbapxLF/l2VnNaDVCqAdNe316m9k6PxqXHaUJGnOk4AAAAX8p3BXxlQQk7qxHyKNWAYb6r34vy2E0NAAACw9xxqYpzhelYXas2HDpuOg5gFKUaMOjTsnrtKq2X02HTDVMzTccBAAA4Ky6nQ/OneHdWr8gvNpwGMItSDRi0vOuX0BXjU5UYHW44DQAAwNlbmOedBbN6d7ka2FmNEEapBgxp7/To1a2lkqRFeQwoAwAAgSU3K14jkqO9O6t3srMaoYtSDRiydk+FTjR3KDUuQheOSjIdBwAAoEe8O6u9BwPsrEYoo1QDhizv+uWzYFqWwhz8UwQAAIHHt7N6c+EJFbKzGiGKZ/KAARX1rfpgX6WkkyspAAAAAk1avEsXjuraWV3AaTVCE6UaMGBlQYk8ljQjZ5CGJ8eYjgMAANBr3Tur80vkYWc1QhClGhhglmVpxRbvK7kMKAMAAIHuivHendVlda1az85qhCBKNTDA8o+e0OHqJkWFO3T15HTTcQAAAM7JZ3dWv7yFndUIPZRqYIAt7zqlvmZSumIiwgynAQAAOHc3Tz+5s7qumZ3VCC2UamAANbV16i87yiRJi6Zz9RsAAASHSZnxGpsWq/ZOj17bXmo6DjCgKNXAAHpz5zE1tbuVMzhKM3IGmY4DAADQJ2w2W/dpNVfAEWoo1cAA8u2mXjQ9WzabzXAaAACAvnPj1EyFO+zaVVqv3WV1puMAA4ZSDQyQwuombTpSI7tNumlapuk4AAAAfWpQdLiuGJ8q6eQMGSAUUKqBAbKi65T6otHJSo+PNJwGAACg7y2a7t1Z/crWUrV2uA2nAQYGpRoYAG6P1V2q2U0NAACC1YWjkpUe71JdS4fWfFphOg4wICjVwABYd7Ba5fWtSohyau74FNNxAAAA+oXDbtPCPO9pNQPLECoo1cAA8P1SuWFKpiLCHIbTAAAA9B/frbx1B6tVcqLZcBqg/1GqgX5W29yuNbu915987zMCAAAIVkMGR2n28MGyLGllPjurEfwo1UA/e21bmdrdHk3IiNOEjHjTcQAAAPrd4hne0+rl+cXyeCzDaYD+RakG+pnv6veiPE6pAQBAaLhqYppiXWEqOdGiDYePm44D9CtKNdCPdpfVaXdZvcIddl0/hd3UAAAgNLicDl0/JUOStGwzA8sQ3CjVQD9avsW7RuuKCakaFB1uOA0AAMDAuXm69wr46t3lqmvuMJwG6D+UaqCftHW69eo273AOrn4DAIBQMykzXmPTYtXe6dHr2xlYhuBFqQb6ydo9lapt7lBanEsXjko2HQcAAGBA2Wy27tPqZeysRhCjVAP9xPf+oQV5mXLYbYbTAAAADLwbpmbK6bBpV2m9dpfVmY4D9AtKNdAPSmtb9OGBKknS4ulDDKcBAAAwIzE6XFeOT5N0ctYMEGwo1UA/WLGlRJYlzR4+WEMGR5mOAwAAYMyi6d7ZMq9sLVVrh9twGqDvUaqBPubxWN27qW+ZmW04DQAAgFkXjkpWerxLdS0dWvNphek4QJ+jVAN97OND1SqtbVGcK0zzJqSZjgMAAGCUw27Twq5NKC8zsAxBiFIN9LGXugaU3Tg1Uy6nw3AaAAAA8xbleW/vrTtYrZITzYbTAH2LUg30oRNN7Vqz23ut6eYZXP0GAACQpCGDozR7+GBZlrQyn53VCC6UaqAPvbK1VO1ujyZmxmlCRrzpOAAAAH7j5hneK+DL84vl8ViG0wB9h1IN9BHLsrp3Uy+ewRotAACAz/rKxHTFusJUcqJFGw4fNx0H6DOUaqCPbC+p076KBkWE2TU/N8N0HAAAAL/icjq6nyMxsAzBhFIN9JFlm4skSddMSld8pNNwGgAAAP+zuGvmzFu7ylXX3GE4DdA3KNVAH2hq69Tr28okMaAMAADgTCZlxmtsWqzaOz16fTsDyxAcKNVAH3hj5zE1tbuVMzhKs4Ylmo4DAADgl2w2mxZN9x5ALOMKOIIEpRroAy93DSi7eUa2bDab4TQAAAD+68apmXI6bNpVWq/dZXWm4wDnjFINnKODlY3acvSEHHabFk7LMh0HAADAryVGh+uK8amSpOVbSgynAc4dpRo4R77plZeOSVZKnMtwGgAAAP93c9cV8Fe3laqt0204DXBuKNXAOWjv9GhVgfcVVnZTAwAAnJ0LRyUrPd6l2uYOvbO7wnQc4JxQqoFz8N7eClU3tis5NkKXjkk2HQcAACAgOOw2Lczzvm2OndUIdJRq4Bws6xpQtjAvS2EO/jkBAACcLd8V8I8OVKu4ptlwGqD3aAFALx2ra9Ff91dJOvlLAQAAAGcnOzFKF45KksRpNQIbpRropRVbSuSxpJnDEjUsKdp0HAAAgICzeIb3YOLlLcXqdHsMpwF6h1IN9ILHY+nlfO8rqrfM4JQaAACgN64Yn6rE6HBV1Ld13wAEAg2lGuiFDYePq7imRbERYfrKxHTTcQAAAAJSRJhDN03NlCS9uIkr4AhMlGqgF3wDyq6fmqHIcIfhNAAAAIHrlpneW3/v76tURX2r4TRAz1GqgR6qbW7X6t3lkqTF09lNDQAAcC5GpsRq+tBBcnssrcgvMR0H6DFKNdBDr24tVXunR+PS4zQxM850HAAAgIB3y0zvQcVLm4vk8ViG0wA9Q6kGesCyLL20+eSAMpvNZjgRAABA4Lt6UppiI8JUXNOiDYePm44D9AilGuiBnaV12lveoPAwu26Ykmk6DgAAQFCICg/T9VMzJEkvbioynAboGUo10AO+AWVXTUhTfJTTcBoAAIDgccsM7xXwd3ZXqKap3XAa4OxRqoGz1NLu1uvbyiSxmxoAAKCvTcyM18TMOLW7PVpVwMAyBA5KNXCW3tx5TA1tncpOjNR5wwebjgMAABB0fKfVyzYXy7IYWIbAQKkGzpLv6vfi6dmy2xlQBgAA0NfmT8lQpNOhA5WNKig6YToOcFYo1cBZOFzVqE2FNbLbpIV5XP0GAADoD3Eup66ZnC5JemlTseE0wNmhVANnYdkW7w/1i0cnKy3eZTgNAABA8PLNrvnLjmNqaO0wnAb4cpRq4Eu0d3q0Mt87LGNx1/t8AAAA0D/yhg7SyJQYtXS49fr2MtNxgC9FqQa+xNo9FapubFdSTIQuH5diOg4AAEBQs9ls3afVXAFHIKBUA1/ixa4BZTdPz5LTwT8ZAACA/nbTtCw5HTbtLK3TrtI603GAL0RDAL5AcU2zPjpQJUlazG5qAACAAZEYHa4rJ6RJOrmBBfBXlGrgC7y8pViWJZ0/crCGDo42HQcAACBk3No1y+bVbaVqaXcbTgOcGaUaOINOt0cvd039vnUmA8oAAAAG0pwRg5WdGKmG1k69ufOY6TjAGVGqgTN4f1+VKurblBgdrivGp5qOAwAAEFLsdpsWT/e+/Y4r4PBnlGrgDF7aVCRJWpiXpYgwh+E0AAAAoWdhXrbsNmlTYY0OVjaajgOcFqUaOI1jdS16f1+lJAaUAQAAmJIW79JlY70rTX1vywP8DaUaOI2XN5fIY0mzhiVqRHKM6TgAAAAha3HXwLKV+SVq7/QYTgN8HqUa+Btuj6Vlm71XvxlQBgAAYNalY5KVEhuh403tendPhek4wOdQqoG/8eGBKpXVtSo+0qmrJqaZjgMAABDSwhx2LZqeJUl6sWvmDeBPKNXA3/ANKLtpWqZcTgaUAQAAmLZ4uvf24LqD1SquaTacBjgVpRr4jMr6Vr27xzugjKvfAAAA/mHI4ChdMDJJliUtZ2AZ/AylGviM5fklcnss5Q0dpNGpsabjAAAAoItvI8vLW0rU6WZgGfwHpRro4vFYWrbZ+8rnLazRAgAA8CtXTkjVoCinyutb9eGBKtNxgG6UaqDL+kPHVVTTrFhXmK6dnGE6DgAAAD4jIsyhm6b5BpZxBRz+g1INdHmxa43WDVMyFRnOgDIAAAB/c+tM723C9/ZWqryu1XAawItSDUg63timd3aXS5JumcnVbwAAAH80MiVWM3MS5fZYepmBZfATlGpA0sqCEnW4LeVmxWtCRrzpOAAAADiDW2d5D0Be2lQkt8cynAagVAOyLEsvdb0v5xbWaAEAAPi1r0xMV0KUU2V1rfrr/krTcQBKNbDxSI0OVzcpOtyh63IZUAYAAODPXE6HFnQNLHthI1fAYR6lGiHvpU3eAWXzp2QoJiLMcBoAAAB8mVu7bhe+t7dCx+paDKdBqKNUI6TVNrfrzV1dA8pmcPUbAAAgEIxMidHMYYnyWNKyzZxWw6xelepHH31UOTk5crlcmjVrljZt2nRWj3vppZdks9l0ww039ObbAn1uVUGp2js9Gp8ep8lZDCgDAAAIFLfP8h6ILNtcrE63x3AahLIel+ply5bp/vvv10MPPaSCggLl5uZq3rx5qqz84iEBhYWF+qd/+iddeOGFvQ4L9CXLsvRC19XvW2dmy2azGU4EAACAs3XVxDQNinLqWF2r/rq/ynQchLAel+pHHnlE9957r5YuXarx48frscceU1RUlJ588skzPsbtduv222/XT37yEw0fPvycAgN9ZXPhCR2sbFRUuEM3TM00HQcAAAA9EBHm0MI838CyIsNpEMp6VKrb29uVn5+vuXPnnvwD7HbNnTtXGzZsOOPjfvrTnyolJUV33333WX2ftrY21dfXn/IB9LXnNx6VJM3PzVCsy2k4DQAAAHrKtw71/X2VKqtlYBnM6FGprq6ultvtVmpq6imfT01NVXl5+Wkfs27dOj3xxBN6/PHHz/r7PPzww4qPj+/+yM7O7klM4EvVNLXrrZ3e/2ZvnzXUcBoAAAD0xojkGJ033Duw7CUGlsGQfp3+3dDQoDvvvFOPP/64kpKSzvpxDzzwgOrq6ro/iov5B4K+tSK/WO1ujyZlxmsSA8oAAAAC1m1dByQvM7AMhvRoKW9SUpIcDocqKipO+XxFRYXS0tI+9/WHDh1SYWGhrrvuuu7PeTze/9DDwsK0b98+jRgx4nOPi4iIUERERE+iAWfN47H04ibvCzW+qZEAAAAITPMmpCoxOlzl9a16f1+Vrhif+uUPAvpQj06qw8PDlZeXp7Vr13Z/zuPxaO3atZo9e/bnvn7s2LHauXOntm3b1v0xf/58XXrppdq2bRvXumHEhsPHdaS6STERYbouN8N0HAAAAJyDUweWHTWcBqGoRyfVknT//ffrrrvu0vTp0zVz5kz9+te/VlNTk5YuXSpJWrJkiTIzM/Xwww/L5XJp4sSJpzw+ISFBkj73eWCg+KZD3jA1Q9ERPf4nAAAAAD9z68wh+sOHh/XB/iqVnGhW1qAo05EQQnrcKBYvXqyqqio9+OCDKi8v15QpU7R69eru4WVFRUWy2/v1rdpAr1U2tOrt3d4BZbfNZEAZAABAMBiWFK05IwZr/aHjenlzse6/cozpSAghNsuyLNMhvkx9fb3i4+NVV1enuLg403EQwB59/6D+8+19mjokQa/83fmm4wAAAKCP/GVHmb79wlalxkXo4+9fpjAHB304N2fbQ/kvDSHDO6DMe/WbNVoAAADB5crxaRocHa6K+jat3VtpOg5CCKUaIePDA1UqOdGiOFeYrp2cbjoOAAAA+lB4mF0Lp/sGlhUZToNQQqlGyHi+64frgrwsuZwOw2kAAADQ126d4V2X+uGBKhXXNBtOg1BBqUZIOFbXove6rgGxmxoAACA45SRF64KRSbIsadnmYtNxECIo1QgJyzYXy+2xNHNYokamxJqOAwAAgH5y60zvAcqyLcXqcHsMp0EooFQj6HW6Pd2vVHJKDQAAENyuGJ+qpJhwVTW0ae2eCtNxEAIo1Qh67++r0rG6ViVGh+uqiWmm4wAAAKAfhYfZtWh6tqSTM3WA/kSpRtB7YeNRSdLCvCxFhDGgDAAAINjdNnOIbDbpowPVKqxuMh0HQY5SjaBWXNOsD/ZXSTr5/hoAAAAEt+zEKF08OlmS9MImTqvRvyjVCGrLNhfLsqTzRw7WsKRo03EAAAAwQO6YNVSStHxLsVo73IbTIJhRqhG0OtweLdviG1A21HAaAAAADKRLx6YoMyFSJ5o79ObOY6bjIIhRqhG03v20QlUNbUqKidAV41NNxwEAAMAActhtunWmd2DZc58cNZwGwYxSjaDlm/a4eEaWnA7+UwcAAAg1N8/IVpjdpoKiWn1aVm86DoIUTQNBqbC6SesOVstmk26ZwYAyAACAUJQS69K8rpWqz23ktBr9g1KNoPRi15THi0cnKzsxynAaAAAAmOIbWPbq1lI1tHYYToNgRKlG0GntcHcPKLuNNVoAAAAh7bzhiRqZEqPmdrde3VpqOg6CEKUaQeeNHcdU29yhzIRIXT6OAWUAAAChzGaz6fZZ3oOWZz85KsuyDCdCsKFUI+j43i9z26whcththtMAAADAtJumZSnS6dD+ikZtLjxhOg6CDKUaQWVXaZ22FtXK6bDp5unZpuMAAADAD8RHOjU/N0MS67XQ9yjVCCq+H5JfmZiu5NgIw2kAAADgL+44zzuw7K1dx1Td2GY4DYIJpRpBo66lQ69u8w6fuHP2UMNpAAAA4E8mZcUrNyteHW5LL3cNtQX6AqUaQWNlfolaOzwamxar6UMHmY4DAAAAP3N712n1CxuL5PYwsAx9g1KNoGBZVvfV79vPGyqbjQFlAAAAONV1kzMU5wpTyYkWfbi/ynQcBAlKNYLC+kPHdbi6SdHhDt04NdN0HAAAAPihyHCHFuZ5h9kysAx9hVKNoOD7oXjTtCzFRIQZTgMAAAB/dft53p3V7+2rVMmJZsNpEAwo1Qh45XWteufTCkknpzoCAAAApzMiOUZzRgyWZUkvbioyHQdBgFKNgPfiJu+giZnDEjUmLdZ0HAAAAPg530HMss3Fau/0GE6DQEepRkDrcHu6X2G8k1NqAAAAnIUrxqcqJTZC1Y3tent3uek4CHCUagS0NZ9WqLKhTUkxEZo3Ic10HAAAAAQAp8OuW2YwsAx9g1KNgPbsBu8PwVtnZis8jP+cAQAAcHZumTlEdpu08UiNDlQ0mI6DAEYLQcA6WNmgDYePy26Tbp05xHQcAAAABJCMhEhdPi5VEqfVODeUagSs5z7xvpd67rhUZSREGk4DAACAQOObybOyoFSNbZ2G0yBQUaoRkJrbO7Uyv0SSdOdsBpQBAACg5y4YmaThSdFqbOvUK1tLTcdBgKJUIyC9tq1MDW2dyhkcpfNHJJmOAwAAgABkt9u6D2ieWV8oy7IMJ0IgolQj4FiW1T2g7I7zhsputxlOBAAAgEC1IC9LUeEOHahs1IbDx03HQQCiVCPgFBTV6tNj9YoIs2thXpbpOAAAAAhgcS6nbpyaKenkZhmgJyjVCDi+6YzzczOUEBVuOA0AAAAC3ZLZOZKkdz6t0LG6FrNhEHAo1QgoNU3temPHMUkMKAMAAEDfGJMWq1nDEuX2WHphY5HpOAgwlGoElGWbi9Xu9ig3K16TsxJMxwEAAECQuGtOjiTpxU1Faut0mw2DgEKpRsBwe6zuq993nMcpNQAAAPrOFeNTlRbnUnVju97aWW46DgIIpRoB4909FSqtbdGgKKeuy80wHQcAAABBxOmw67ZZQyRJz2woNBsGAYVSjYDh++F2y8whcjkdZsMAAAAg6NwyM1tOh00FRbXaWVJnOg4CBKUaAeFARYM+PnhcdhtXvwEAANA/UmJdunpSuiROq3H2KNUICE93/VC7cnyaMhMizYYBAABA0FrStWHm9e1lOtHUbjgNAgGlGn6vvrVDqwpKJUlL5nBKDQAAgP4zbcggTciIU1unRy9vKTYdBwGAUg2/t2JLiZrb3RqdGqPZwwebjgMAAIAgZrPZdNfsHEnSs58cldtjmQ0Ev0ephl/zeKzu97MsmZ0jm81mNhAAAACC3nW5GYqPdKrkRIs+2FdpOg78HKUafu3DA1UqPN6sWFeYbpyaaToOAAAAQkBkuEOLZ2RLkp7ecNRwGvg7SjX82tPrCyVJi/KyFR0RZjYMAAAAQsYds4bKZpM+3F+lI9VNpuPAj1Gq4bcKq5v0wf4q2WwnpzACAAAAA2HI4ChdNiZFkvQsp9X4ApRq+K1nPzkqy5IuGZ2snKRo03EAAAAQYu7sOthZnl+s5vZOw2ngryjV8EtNbZ3dKwyWzMkxGwYAAAAh6aJRycoZHKWG1k69urXMdBz4KUo1/NKr20rV0NqpnMFRunhUsuk4AAAACEF2u013dq3XemZDoSyL9Vr4PEo1/I5lWd0Dyu6cnSO7nTVaAAAAMGNhXpYinQ7tLW/QpiM1puPAD1Gq4Xc+OVyj/RWNigp3aNH0LNNxAAAAEMLiI526oWu169MbCs2GgV+iVMPv+E6pb5qWqTiX02wYAAAAhLy75ngHlr29u0JltS2G08DfUKrhV0prW/TOp+WSpCVd718BAAAATBqbFqc5IwbL7bH0DOu18Dco1fArz39yVB5LmjNisEanxpqOAwAAAEiSvtq1kealzUVqaXebDQO/QqmG32jtcOulzV1rtDilBgAAgB+5fFyqshMjVdvcoVe3lZqOAz9CqYbf+MuOY6ppaldmQqTmjksxHQcAAADo5rDbdFfXwc9TH7NeCydRquEXPrtG6/bzhijMwX+aAAAA8C+LpmcrKtyhfRUN2nDouOk48BM0F/iFgqJa7SytU3iYXbfMGGI6DgAAAPA58ZFOLczzrnz9U9eBEECphl94quuH0vzcDCVGh5sNAwAAAJzBXV0Dy97dU6Gi481mw8AvUKph3LG6Fr2585gkaen5OWbDAAAAAF9gRHKMLh6dLMuSnt5QaDoO/AClGsY9u+Go3B5Ls4YlakJGvOk4AAAAwBf6atdB0Mubi9XU1mk2DIyjVMOolna3XthUJEn62gXDDKcBAAAAvtzFo5I1PClaDW2dWllQYjoODKNUw6hXtpaqtrlD2YmRmjsu1XQcAAAA4EvZ7bbu91Y/tb5QHg/rtUIZpRrGWJalP318RJJ01+wcOew2w4kAAACAs7MgL0uxEWE6XNWkDw9UmY4DgyjVMGbdwWodqGxUdLhDN8/INh0HAAAAOGsxEWHdz2GfYr1WSKNUw5gn13lPqRdNz1acy2k4DQAAANAzd83Okc0mfbCvSoeqGk3HgSGUahhxuKpR7++rks12ctcfAAAAEEiGDI7S5WO9c4Ge5rQ6ZFGqYYTviszlY1M0LCnabBgAAACgl5Z2rddakV+i+tYOs2FgBKUaA66upUMr8r2rB5aezxotAAAABK45IwZrdGqMmtvdenlzsek4MIBSjQH38uZiNbe7NSY1VnNGDDYdBwAAAOg1m82mr87xHhQ9s+Go3KzXCjmU6v/f3p2HR13e6x+/Z7IHSEIIkIUsEBCQfZE0LoASWUpbt/agpS5otSB6bLHWcvo70NNzerB2Of1Vqbhr3etRarWVikDYDDtIrRBJCIQtCVv2feY5fySMpoKSIZlnMvN+Xddcl8x8M95z8TDDzWe+3wc+1exye776ffvlGXI42EYLAAAAXdt1Y1IUGxWm4lO1Wr23zHYc+BilGj618uNSHSmvU8/oMF0zOsV2HAAAAOCCRYWH6MYJZ7bXKrKcBr5GqYZPPbvxgCRpdla6IsNC7IYBAAAAOsgt2RkKcTq0seCk8kuqbMeBD1Gq4TMfHanQlgOnFOp06ObsdNtxAAAAgA6TEhelacNattd6diPT6mBCqYbPPNP65jJzZJL6xkRaTgMAAAB0rNtbd7Z5c+cRnaxusJwGvkKphk+UVdXr7Q+PSmIbLQAAAASmcek9NSo1To3Nbr24qdh2HPgIpRo+8eKmYjW5jMamxWl0apztOAAAAECHczgc+u7lLQOkFzYdUH2Ty3Ii+AKlGp2uvsmllzcflCTdfjlTagAAAASuGcMTlRIXpRPVjfrzrqO248AHKNXodG9/eFQnqhuVHBup6cMSbccBAAAAOk1oiFO3XZohSXpqw34ZY+wGQqejVKNTGWP0TOs2WjdnZyg0hCUHAACAwDZrQqq6hYfok9Jqrd93wnYcdDIaDjrV5qJT2nOsUpFhTt00IdV2HAAAAKDTxUSG6V8uafm771Mb2F4r0FGq0ameWr9fknT92H6Kiw63nAYAAADwjTmX9pfTIa375LjyS6psx0EnolSj0xQer9b7e8okSXdwgTIAAAAEkbRe0ZrWej2hZ5hWBzRKNTrN061vHjlD+yizd3fLaQAAAADf+u4VLYOl5buO6ER1g+U06CyUanSKk9UNemP7YUnSd68YYDkNAAAA4Htj03pqdGqcGpvdenHTQdtx0Eko1egUL24qVkOzWyP7xSqrf7ztOAAAAIDPORwOz7T6hbyDqm9yWU6EzkCpRoerb3LpD3kHJLVMqR0Oh91AAAAAgCXThyUqJS5KJ2sa9dauI7bjoBNQqtHhlu88opM1jUqJi9JXhyfajgMAAABYExri1G2XZkiSnlpfJGOM3UDocJRqdCi323i20ZpzWYZCQ1hiAAAACG6zJqSqW3iI9pVVa92+E7bjoIPReNChcj8pU+HxGvWICNWs1g3vAQAAgGAWExmmWZekSZJnAIXAQalGh3pyXcs2WjdOSFWPyDDLaQAAAAD/MOeyDDkd0vp9J5RfUmU7DjoQpRod5qMjFcrbf1IhToduu6y/7TgAAACA30iNj9b01usNPb2BaXUgoVSjw5z5KsvMEUlKiYuynAYAAADwL3dcPkCS9KedR3W8qsFyGnQUSjU6xLGKOr2z+5gk6c4rBlhOAwAAAPifcek9NSYtTo0ut17YdNB2HHQQSjU6xHMbD6jZbZTVP14j+sXajgMAAAD4pTsubzlN8qVNB1Xf5LKcBh2BUo0LVt3QrJe3FEuS7prIlBoAAAA4l+nDEpUSF6WTNY1avvOI7TjoAJRqXLDXth5SVX2zBvTupisH97EdBwAAAPBboSFOzbksQ5L05Lr9cruN3UC4YJRqXJBml1vPbGjZRuu7lw+Q0+mwnAgAAADwbzdOSFOPyFDtP1GjlXtKbcfBBaJU44K8+1GJjpTXqVe3cF0/NsV2HAAAAMDvdY8I1c1fSZckPbGO7bW6Oko1vGaM8Wyj9Z2vpCsyLMRyIgAAAKBruO3SDIWHOLX94GltO3DKdhxcAEo1vLb1wGl9eLhC4aFO3ZydbjsOAAAA0GX0iYn0fNPzcabVXRqlGl57snVKfcPYFCV0j7CcBgAAAOha7pw4QA6HtPLjUhWUVduOAy9RquGVohM1er/1ogp3XM42WgAAAEB7ZfburpyhfSW1XAkcXROlGl55Yt1+GSNdNaSPBvbpbjsOAAAA0CXNndQyoFq+84jKKustp4E3vCrVS5cuVUZGhiIjI5WVlaUtW7ac89g333xT48ePV1xcnLp166bRo0frhRde8Dow7CurqtcbOw5LkuZOyrScBgAAAOi6xqXHa1x6TzW63Hr2gwO248AL7S7Vr732mhYsWKDFixdrx44dGjVqlKZNm6aysrKzHh8fH6+f/OQnysvL0+7duzVnzhzNmTNHf/vb3y44POx4duMBNTa7NTYtTpdk9LQdBwAAAOjSvjexZVr94qaDqm5otpwG7dXuUv2b3/xGd955p+bMmaOLL75Yy5YtU3R0tJ555pmzHj958mRdd911Gjp0qDIzM3Xfffdp5MiR2rBhwwWHh+9V1TfpxU0HJUnfm5Qph8NhOREAAADQteUM7asBvbupqr5Zr24pth0H7dSuUt3Y2Kjt27crJyfn0ydwOpWTk6O8vLwv/XljjFatWqX8/HxNnDjxnMc1NDSosrKyzQ3+4ZUtxaqqb1Zm7266uvWiCgAAAAC853Q6dNcVLdPqpzcUqcnltpwI7dGuUn3ixAm5XC717du2TPXt21clJSXn/LmKigp1795d4eHhmjlzph555BFdffXV5zx+yZIlio2N9dxSU1PbExOdpKHZpac3FEmSvjcxU04nU2oAAACgI1w7JkW9e0ToWEW93v7wqO04aAefXP27R48e2rVrl7Zu3aqf//znWrBggXJzc895/MKFC1VRUeG5HTp0yBcx8SXe2nlUpZUN6hsToWvGJNuOAwAAAASMyLAQzbksQ9KZnXaM3UA4b6HtOTghIUEhISEqLS1tc39paakSExPP+XNOp1MDBw6UJI0ePVp79uzRkiVLNHny5LMeHxERoYiIiPZEQydzu42WrSuUJN1xeX9FhIZYTgQAAAAEltlZ6Vq6ukB7S6q09pPjmjy4j+1IOA/tmlSHh4dr3LhxWrVqlec+t9utVatWKTs7+7yfx+12q6GhoT3/a1i2ck+p9h+vUY/IUN00Ic12HAAAACDgxEaFef6u/fja/ZbT4Hy1++vfCxYs0JNPPqnnn39ee/bs0bx581RTU6M5c+ZIkm655RYtXLjQc/ySJUu0cuVK7d+/X3v27NGvf/1rvfDCC/rOd77Tca8CncoYo2VrW6bUN38lXT0iwywnAgAAAALT7Zf3V6jTobz9J7X7cLntODgP7fr6tyTNmjVLx48f16JFi1RSUqLRo0drxYoVnouXFRcXy+n8tKvX1NTo7rvv1uHDhxUVFaUhQ4boxRdf1KxZszruVaBTbSk6pZ3F5QoPdWrOZf1txwEAAAACVnJclL4xKllv7jyix9ft19Jvj7UdCV/CYbrAGfCVlZWKjY1VRUWFYmJibMcJOnOe3aI1+cf17aw0/fd1I2zHAQAAAALa3pJKTf/tejkdUu4Pr1Rar2jbkYLS+fZQn1z9G13X3pJKrck/LqdDnr3zAAAAAHSeIYkxmnRRb7mN9NQGzq32d5RqfKEzF0iYMTxJGQndLKcBAAAAgsP3JrUMtP647ZBOVnORZ39GqcY5HT5dqz+3bjw/d1Km5TQAAABA8Mge0EsjUmJV3+TW8x8csB0HX4BSjXN6an2RXG6jywb20oh+sbbjAAAAAEHD4XBo3uSWwdZzHxxQdUOz5UQ4F0o1zup0TaNe23pIElNqAAAAwIZpwxI1oHc3VdY36+XNB23HwTlQqnFWz+cdUF2TS8OSY3T5wATbcQAAAICgE+J0eAZcT64vUn2Ty3IinA2lGp9T29jsOW9j7qRMORwOu4EAAACAIHXt6BQlx0bqeFWD/nf7YdtxcBaUanzO69sO63Rtk9LiozVjeKLtOAAAAEDQCg916q6JLVcCf3xdoZpdbsuJ8M8o1Wij2eXWk+tbttG6c+IAhYawRAAAAACbZl2Spl7dwnXoVJ3e2X3Mdhz8ExoT2nh791EdPl2nXt3C9a1x/WzHAQAAAIJeVHiIbr+8vyTp97kFcruN5UT4LEo1PNxuo9+vKZQk3X55f0WGhVhOBAAAAECSvvOVdPWICNUnpdV6f0+p7Tj4DEo1PN77uET7yqrVIzJUN2en244DAAAAoFVsVJjn7+hLcwtlDNNqf0GphiTJGKNH1xRIkm7NzlBMZJjlRAAAAAA+6/bL+ysi1KkPD5Urr/Ck7ThoRamGJGntJ8f10ZFKRYV9er4GAAAAAP+R0D1CN16SKklamltgOQ3OoFSjZUq9uuUP5eysNMV3C7ecCAAAAMDZ3DlxgEKdDm0sOKldh8ptx4Eo1ZC0ueiUth08rfAQp+5s3QMPAAAAgP/p1zNa145JkST9fg3Tan9AqYaWtv5h/Nb4fuobE2k5DQAAAIAvMndSphwO6b2PS/VJaZXtOEGPUh3kPjxUrvX7TijE6dDcSZm24wAAAAD4EgP7dNf0YYmSpMdyCy2nAaU6yJ254vc1o5OVGh9tOQ0AAACA83H35IGSpD9/eFSHTtVaThPcKNVBbG9JpVZ+XCqH49M/lAAAAAD834h+sZp4UW+53EaPr2NabROlOogtXdPyh++rw5M0sE93y2kAAAAAtMf8yS2nb/5x22GVVdZbThO8KNVBquhEjf6y+6gk6e4rOZcaAAAA6Gom9I/XuPSeamx26+kNRbbjBC1KdZB6LLdAbiNdNaSPhiXH2o4DAAAAoJ0cDofmtw7IXtx0UBW1TZYTBSdKdRA6Ul6nN3cckSTNv5JzqQEAAICu6srBfTQ0KUY1jS49+wHTahso1UHoibWFanYbZQ/opXHpPW3HAQAAAOClz06rn9lQpKp6ptW+RqkOMmVV9Xp16yFJ0j1XMaUGAAAAuroZrRcerqxv1h/yDtqOE3Qo1UHm6fVFamh2a3RqnC7N7GU7DgAAAIALFOJ06J7W0zqfWr9fNQ3NlhMFF0p1ECmvbdSLm1r+5ereqwbK4XBYTgQAAACgI3xtZJL6J3TT6domvbSZabUvUaqDyLMbD6im0aWhSTG6akgf23EAAAAAdJDQEKfubt23+ol1+1XX6LKcKHhQqoNEdUOznvvggCRp/pWZTKkBAACAAHPtmBSlxkfpRHWjXtlSbDtO0KBUB4nnPzigiromDUjophnDk2zHAQAAANDBwkKcuntyy7nVy9YWqr6JabUvUKqDQE1Ds55av19SyxW/Q5xMqQEAAIBAdMPYfkqOjVRZVYNe33bIdpygQKkOAn/IO6jTtU3K6BWtb4xKth0HAAAAQCcJD3VqXuu51Y/lFqqx2W05UeCjVAe4moZmPemZUg9SaAi/5QAAAEAg+9b4VPXpEaGjFfV6Y8dh23ECHg0rwL246aBO1TQqvVe0rh3NlBoAAAAIdJFhIZo7qWVavXRNgZpcTKs7E6U6gNU2NuuJdS1T6vlXDmRKDQAAAASJmyakKaF7uA6frtOfdh6xHSeg0bIC2EubinWyplGp8VG6bkyK7TgAAAAAfCQqPER3TRwgqWVa3cy0utNQqgNUXaNLj68rlCTdc+VAhTGlBgAAAILK7Kx0xXcL14GTtXpr11HbcQIWTStAvbT5oE5UN6pfzyhdP7af7TgAAAAAfKxbRKhnWv3I6n1MqzsJpToA1Te59PhnzqVmSg0AAAAEp1uyP51WL+fc6k5B2wpAL28u1vGqBqXERekGptQAAABA0IoOD9X3WqfVj3JudaegVAeY+iaXlq1tOZd6/pUDFR7KbzEAAAAQzG7OTldC93AdPFmrN5lWdzgaV4B5dUuxylqn1N8cx5QaAAAACHYt0+qWfasfWb2Pfas7GKU6gNQ3ufRY65R63uRMptQAAAAAJEmzv9Kyb/WhU3V6c8dh23ECCq0rgLy8uVillQ1Kjo3Ut8YzpQYAAADQIjo8VHMnnZlWFzCt7kCU6gBR1+jS73NbptT3ThmkiNAQy4kAAAAA+JPZWelK6B6hw6fr9MZ2ptUdhVIdIF7YdEAnqhuUGs+51AAAAAA+Lyo8RPMmfzqtbmxmWt0RKNUBoLqhWcvWtuxLfe9Vg9iXGgAAAMBZzc5KU+8eETpSXqf/ZVrdIWhfAeD5Dw7oVE2jMnpF6/oxKbbjAAAAAPBTkWEhmtd6bvXSNUyrOwKluourqm/SE+taptT35QxSKFNqAAAAAF/g21lp6tM6rX59+yHbcbo8GlgX98yGA6qoa1Jm7276xiim1AAAAAC+WGRYiO5uPbf60dUFqm9yWU7UtVGqu7CK2iY9taFlSv39nIsU4nRYTgQAAACgK7hxQpqSYiN1rKJer2wpth2nS6NUd2FPbdivqvpmDe7bQzNHJNmOAwAAAKCLiAwL0b1XDZIkLV1TqNrGZsuJui5KdRd1uqZRz2wokiT94OpBcjKlBgAAANAO3xrfT2nx0TpR3aDnPzhoO06XRanuoh5ft181jS5dnBSjqRcn2o4DAAAAoIsJC3Hq+zkt0+plawtVWd9kOVHXRKnuglr+JemAJGnB1RcxpQYAAADglWtGpyizdzdV1DV5vgmL9qFUd0HLcgtV1+TSqH6xmjK0j+04AAAAALqoEKdDC64eLEl6en2RTtc0Wk7U9VCqu5hjFXX6w6aW8x1+cPVFcjiYUgMAAADw3ozhiRqaFKOqhmY9sX6/7ThdDqW6i/ndqgI1Nrs1oX+8Jl3U23YcAAAAAF2c0+nQ/VdfJEl6buMBHa9qsJyoa6FUdyEHTtToj9sOSZIemDaYKTUAAACADjFlaB+NSo1TXZNLv88tsB2nS6FUdyH/8/4ncrmNrhzcW5dkxNuOAwAAACBAOBwO/XBqy7T6pU3FOlpeZzlR10Gp7iL2HKvUnz88Kkm6f+pgy2kAAAAABJrLByZoQv94NbrcenQN0+rzRanuIn793icyRpo5MknDU2JtxwEAAAAQYFqm1S0DvD9uPaSDJ2ssJ+oaKNVdwI7i03p/T6mcjpZ9qQEAAACgM0zoH6+JF/VWs9vof1Z+YjtOl0Cp7gJ+9bd8SdI3x/VTZu/ultMAAAAACGQPtE6r3/rwqPYcq7Scxv9Rqv3cxoIT+qDwpMJDnPrXKYNsxwEAAAAQ4Eb0i9XMkUky5tMBH86NUu3HjDF6uHURfzsrTf16RltOBAAAACAY3H/1RQpxOrRqb5m2HjhlO45fo1T7sZUfl+rDQ+WKCgvR/CsH2o4DAAAAIEgM6N1d/zI+VZL0i3f3yhhjOZH/olT7KZfb6NfvtVwY4PbLM9S7R4TlRAAAAACCyX1TBiki1KltB09rTX6Z7Th+i1Ltp97adUT5pVWKiQzVXVdk2o4DAAAAIMgkxkbqtkszJEkPr8iX2820+mwo1X6ovsnlmVLPmzxQsdFhlhMBAAAACEbzJmeqR2So9pZU6c8fHrUdxy9Rqv3Qi5sO6kh5nRJjIjXnsgzbcQAAAAAEqbjocM2d1PLN2d+s/ESNzW7LifwPpdrPVNY3aemaAknSD64epMiwEMuJAAAAAASzOZdlKKF7hIpP1eq1rcW24/gdSrWfeWLtfp2ubVJm7266YWw/23EAAAAABLno8FDdN6VlN6L/v6pAtY3NlhP5F0q1HymrrNdTG/ZLkn40fYhCQ/jtAQAAAGDfrEvSlBYfrRPVDXp24wHbcfwKrc2P/HbVPtU3uTU2LU5TL+5rOw4AAAAASJLCQ51acPVFkqRluYU6VdNoOZH/oFT7if3Hq/Xa1kOSpB/PGCqHw2E5EQAAAAB86hujknVxUoyqGpr16OoC23H8BqXaT/zqvXy53EY5Q/toQv9423EAAAAAoA2n06GFXx0iSXph0wEVn6y1nMg/UKr9wM7i0/rr30vkcEgPTBtiOw4AAAAAnNUVg3rrikEJanIZ/fK9fNtx/AKl2jJjjH6xYq8k6Yax/TQ4sYflRAAAAABwbj+eMUQOh/T2h0f14aFy23Gso1RbtvaT49q0/5TCQ536QeuJ/wAAAADgr4Ylx+q60SmSpCXv7pExxnIiuyjVFrncRg+92zKlvu3SDKXERVlOBAAAAABfbsHUixQe6tSm/aeUm3/cdhyrKNUWvbHjsPaWVCkmMlR3T860HQcAAAAAzku/ntGac2mGpJZptcsdvNNqSrUltY3N+nXrif33XjVIcdHhlhMBAAAAwPm7e/JAxUaF6ZPSar2x/bDtONZQqi15an2RSisb1K9nlG65NN12HAAAAABol9joMN171UBJ0q9X5quu0WU5kR2UagvKquq1bG2hJOnB6UMUERpiOREAAAAAtN/N2enq1zNKpZUNemZjke04VlCqLfiflftU2+jS6NQ4fW1kku04AAAAAOCViNAQ/XDqYEnSY7mFOlndYDmR71GqfeyT0iq9trVYkvSTmUPlcDgsJwIAAAAA731jVLKGJceouqFZv1u1z3Ycn6NU+9hD7+6V20jThyXqkox423EAAAAA4II4nQ795KtDJUkvbi5WQVm15US+Ran2oY0FJ7R6b5lCnQ49OGOI7TgAAAAA0CEuHZignKF95HIbPfTuHttxfIpS7SNut9HP/9KyuL7zlXT1T+hmOREAAAAAdJyFXx2qUKdD7+8p08aCE7bj+Ayl2keW7zyij49VqkdEqP51yiDbcQAAAACgQ2X27q7vfKVlu+D/+sseudzGciLfoFT7QF2jS796L1+SNP+qgYrvFm45EQAAAAB0vPumDFJMZKj2HKvUG9sP247jE5RqH3h6w34dq6hXSlyUbrs0w3YcAAAAAOgUPbuFe76Z+8v38lXT0Gw5UeejVHey0sp6/T63UJL0o+mDFRkWYjkRAAAAAHSem7PTld4rWserGvT42kLbcTodpbqTPbwiX7WNLo1Ji9M3RiXbjgMAAAAAnSoiNEQLW3c7emL9fh0tr7OcqHNRqjvR7sPlemNHy3kEi752sRwOh+VEAAAAAND5pg1L1ISMeNU3ufWrv+XbjtOpKNWdxBijn739sSTpujEpGpPW03IiAAAAAPANh8Oh//e1oZKkN3ce0e7D5XYDdSJKdSf5y9+PadvB04oKC9GPpg+2HQcAAAAAfGpkvzhdPyZFkvRf7+yRMYG5xRaluhPUN7m05K97JUlzJ2UqKTbKciIAAAAA8L0fThusyDCnthw4pXc/KrEdp1NQqjvBU+v360h5nZJiI3XXxAG24wAAAACAFclxUbprYqYk6b//ukf1TS7LiToepbqDfXYLrR/PGKKocLbQAgAAABC85k4aoMSYSB0+XaenNxTZjtPhKNUd7Jd/YwstAAAAADgjOjxUC7/assXW0jUFKq2st5yoY1GqO9DfD1fof7ezhRYAAAAAfNY3RiVrbFqcahtd+sWKvbbjdChKdQcxxuhn7/xDEltoAQAAAMBnORwOLf76MEnSmzuOaNehcruBOhCluoN8eLhCWw+cVmSYky20AAAAAOCfjEqN0w1j+0mSnt0YOOdWh9oOEChGp8bpz/dcpn2l1WyhBQAAAABn8aPpgzU0qYduyc6wHaXDOEwX2IG7srJSsbGxqqioUExMjO04AAAAAIAAd7491Kuvfy9dulQZGRmKjIxUVlaWtmzZcs5jn3zySV1xxRXq2bOnevbsqZycnC88HgAAAACArqLdpfq1117TggULtHjxYu3YsUOjRo3StGnTVFZWdtbjc3NzddNNN2nNmjXKy8tTamqqpk6dqiNHjlxweAAAAAAAbGr317+zsrJ0ySWX6NFHH5Ukud1upaam6t5779WPf/zjL/15l8ulnj176tFHH9Utt9xyXv9Pvv4NAAAAAPClTvn6d2Njo7Zv366cnJxPn8DpVE5OjvLy8s7rOWpra9XU1KT4+PhzHtPQ0KDKyso2NwAAAAAA/E27SvWJEyfkcrnUt2/fNvf37dtXJSUl5/UcDz74oJKTk9sU83+2ZMkSxcbGem6pqantiQkAAAAAgE/4dJ/qhx56SK+++qqWL1+uyMjIcx63cOFCVVRUeG6HDh3yYUoAAAAAAM5Pu/apTkhIUEhIiEpLS9vcX1paqsTExC/82V/96ld66KGH9P7772vkyJFfeGxERIQiIiLaEw0AAAAAAJ9r16Q6PDxc48aN06pVqzz3ud1urVq1StnZ2ef8uYcfflj/+Z//qRUrVmj8+PHepwUAAAAAwI+0a1ItSQsWLNCtt96q8ePHa8KECfrtb3+rmpoazZkzR5J0yy23KCUlRUuWLJEk/eIXv9CiRYv08ssvKyMjw3Pudffu3dW9e/cOfCkAAAAAAPhWu0v1rFmzdPz4cS1atEglJSUaPXq0VqxY4bl4WXFxsZzOTwfgjz32mBobG/XNb36zzfMsXrxYP/3pTy8sPQAAAAAAFrV7n2ob2KcaAAAAAOBLnbJPNQAAAAAA+BSlGgAAAAAAL1GqAQAAAADwEqUaAAAAAAAvUaoBAAAAAPASpRoAAAAAAC9RqgEAAAAA8BKlGgAAAAAAL1GqAQAAAADwEqUaAAAAAAAvUaoBAAAAAPASpRoAAAAAAC9RqgEAAAAA8BKlGgAAAAAAL1GqAQAAAADwEqUaAAAAAAAvUaoBAAAAAPASpRoAAAAAAC9RqgEAAAAA8BKlGgAAAAAAL1GqAQAAAADwEqUaAAAAAAAvUaoBAAAAAPASpRoAAAAAAC9RqgEAAAAA8BKlGgAAAAAAL1GqAQAAAADwUqjtAOfDGCNJqqystJwEAAAAABAMzvTPM330XLpEqa6qqpIkpaamWk4CAAAAAAgmVVVVio2NPefjDvNltdsPuN1uHT16VD169JDD4bAd55wqKyuVmpqqQ4cOKSYmxnYcQBLrEv6JdQl/w5qEP2Jdwh8F07o0xqiqqkrJyclyOs995nSXmFQ7nU7169fPdozzFhMTE/ALDF0P6xL+iHUJf8OahD9iXcIfBcu6/KIJ9RlcqAwAAAAAAC9RqgEAAAAA8BKlugNFRERo8eLFioiIsB0F8GBdwh+xLuFvWJPwR6xL+CPW5ed1iQuVAQAAAADgj5hUAwAAAADgJUo1AAAAAABeolQDAAAAAOAlSjUAAAAAAF6iVAMAAAAA4CVKdQdZunSpMjIyFBkZqaysLG3ZssV2JASRn/70p3I4HG1uQ4YM8TxeX1+v+fPnq1evXurevbtuuOEGlZaWWkyMQLRu3Tp9/etfV3JyshwOh/70pz+1edwYo0WLFikpKUlRUVHKycnRvn372hxz6tQpzZ49WzExMYqLi9Mdd9yh6upqH74KBJovW5e33Xbb594/p0+f3uYY1iU60pIlS3TJJZeoR48e6tOnj6699lrl5+e3OeZ8PreLi4s1c+ZMRUdHq0+fPnrggQfU3Nzsy5eCAHI+63Ly5Mmfe7+cO3dum2OCdV1SqjvAa6+9pgULFmjx4sXasWOHRo0apWnTpqmsrMx2NASRYcOG6dixY57bhg0bPI/94Ac/0Ntvv63XX39da9eu1dGjR3X99ddbTItAVFNTo1GjRmnp0qVnffzhhx/W7373Oy1btkybN29Wt27dNG3aNNXX13uOmT17tv7xj39o5cqVeuedd7Ru3TrdddddvnoJCEBfti4lafr06W3eP1955ZU2j7Mu0ZHWrl2r+fPna9OmTVq5cqWampo0depU1dTUeI75ss9tl8ulmTNnqrGxUR988IGef/55Pffcc1q0aJGNl4QAcD7rUpLuvPPONu+XDz/8sOexoF6XBhdswoQJZv78+Z5fu1wuk5ycbJYsWWIxFYLJ4sWLzahRo876WHl5uQkLCzOvv/665749e/YYSSYvL89HCRFsJJnly5d7fu12u01iYqL55S9/6bmvvLzcREREmFdeecUYY8zHH39sJJmtW7d6jnn33XeNw+EwR44c8Vl2BK5/XpfGGHPrrbeaa6655pw/w7pEZysrKzOSzNq1a40x5/e5/de//tU4nU5TUlLiOeaxxx4zMTExpqGhwbcvAAHpn9elMcZMmjTJ3Hfffef8mWBel0yqL1BjY6O2b9+unJwcz31Op1M5OTnKy8uzmAzBZt++fUpOTtaAAQM0e/ZsFRcXS5K2b9+upqamNmt0yJAhSktLY43CZ4qKilRSUtJmHcbGxiorK8uzDvPy8hQXF6fx48d7jsnJyZHT6dTmzZt9nhnBIzc3V3369NHgwYM1b948nTx50vMY6xKdraKiQpIUHx8v6fw+t/Py8jRixAj17dvXc8y0adNUWVmpf/zjHz5Mj0D1z+vyjJdeekkJCQkaPny4Fi5cqNraWs9jwbwuQ20H6OpOnDghl8vVZvFIUt++fbV3715LqRBssrKy9Nxzz2nw4ME6duyY/uM//kNXXHGFPvroI5WUlCg8PFxxcXFtfqZv374qKSmxExhB58xaO9t75ZnHSkpK1KdPnzaPh4aGKj4+nrWKTjN9+nRdf/316t+/vwoLC/Vv//ZvmjFjhvLy8hQSEsK6RKdyu936/ve/r8suu0zDhw+XpPP63C4pKTnr++mZx4ALcbZ1KUnf/va3lZ6eruTkZO3evVsPPvig8vPz9eabb0oK7nVJqQYCwIwZMzz/PXLkSGVlZSk9PV1//OMfFRUVZTEZAPi3G2+80fPfI0aM0MiRI5WZmanc3FxNmTLFYjIEg/nz5+ujjz5qcx0UwLZzrcvPXktixIgRSkpK0pQpU1RYWKjMzExfx/QrfP37AiUkJCgkJORzV2QsLS1VYmKipVQIdnFxcbroootUUFCgxMRENTY2qry8vM0xrFH40pm19kXvlYmJiZ+7wGNzc7NOnTrFWoXPDBgwQAkJCSooKJDEukTnueeee/TOO+9ozZo16tevn+f+8/ncTkxMPOv76ZnHAG+da12eTVZWliS1eb8M1nVJqb5A4eHhGjdunFatWuW5z+12a9WqVcrOzraYDMGsurpahYWFSkpK0rhx4xQWFtZmjebn56u4uJg1Cp/p37+/EhMT26zDyspKbd682bMOs7OzVV5eru3bt3uOWb16tdxut+eDG+hshw8f1smTJ5WUlCSJdYmOZ4zRPffco+XLl2v16tXq379/m8fP53M7Oztbf//739v8g8/KlSsVExOjiy++2DcvBAHly9bl2ezatUuS2rxfBu26tH2ltEDw6quvmoiICPPcc8+Zjz/+2Nx1110mLi6uzZXvgM50//33m9zcXFNUVGQ2btxocnJyTEJCgikrKzPGGDN37lyTlpZmVq9ebbZt22ays7NNdna25dQINFVVVWbnzp1m586dRpL5zW9+Y3bu3GkOHjxojDHmoYceMnFxceatt94yu3fvNtdcc43p37+/qaur8zzH9OnTzZgxY8zmzZvNhg0bzKBBg8xNN91k6yUhAHzRuqyqqjI//OEPTV5enikqKjLvv/++GTt2rBk0aJCpr6/3PAfrEh1p3rx5JjY21uTm5ppjx455brW1tZ5jvuxzu7m52QwfPtxMnTrV7Nq1y6xYscL07t3bLFy40MZLQgD4snVZUFBgfvazn5lt27aZoqIi89Zbb5kBAwaYiRMnep4jmNclpbqDPPLIIyYtLc2Eh4ebCRMmmE2bNtmOhCAya9Ysk5SUZMLDw01KSoqZNWuWKSgo8DxeV1dn7r77btOzZ08THR1trrvuOnPs2DGLiRGI1qxZYyR97nbrrbcaY1q21fr3f/9307dvXxMREWGmTJli8vPz2zzHyZMnzU033WS6d+9uYmJizJw5c0xVVZWFV4NA8UXrsra21kydOtX07t3bhIWFmfT0dHPnnXd+7h/FWZfoSGdbj5LMs88+6znmfD63Dxw4YGbMmGGioqJMQkKCuf/++01TU5OPXw0CxZety+LiYjNx4kQTHx9vIiIizMCBA80DDzxgKioq2jxPsK5LhzHG+G4uDgAAAABA4OCcagAAAAAAvESpBgAAAADAS5RqAAAAAAC8RKkGAAAAAMBLlGoAAAAAALxEqQYAAAAAwEuUagAAAAAAvESpBgAAAADAS5RqAAAAAAC8RKkGAAAAAMBLlGoAAAAAALz0f4vftcmr29ELAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def normal_dist(x):\n",
    "    mean = np.mean(x)\n",
    "    sd = np.std(x)\n",
    "    prob_density = (np.pi*sd) * np.exp(-0.5*((x-mean)/sd)**2)\n",
    "    return prob_density\n",
    " \n",
    "\n",
    "# Create probability distribution. Can be output of any function\n",
    "# We are using a normal distribution for example \n",
    "\n",
    "input_dist = normal_dist(np.linspace(0,1,256))\n",
    "\n",
    "\n",
    "num_qubits = 4\n",
    "\n",
    "target_state = pd_to_sv(input_dist, num_qubits)\n",
    "\n",
    "\n",
    "# Plot input probability distribution\n",
    "plot([input_dist])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Using Quantastica generator to prepare state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantastica generator:\n",
      "\n",
      "\n",
      "Depth: 9\n",
      "Ops: 16\n",
      "CNOTs: 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1541.66x367.889 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Quantastica generator:\")\n",
    "print(\"\")\n",
    "\n",
    "instruction_set = [\"u3\", \"cx\"]\n",
    "\n",
    "job_id = QPS.generator.state_preparation(target_state, settings = { \"instruction_set\": instruction_set, \"max_diff\": 0.0005, \"pre_processing\": \"experimental5\" })\n",
    "job = QPS.generator.get_job(job_id, wait=True)\n",
    "job_status = job[\"status\"]\n",
    "job_output = job[\"output\"]\n",
    "\n",
    "if(job_status == \"error\"):\n",
    "    raise Exception(job_output[\"message\"])\n",
    "else:\n",
    "    if \"circuits\" not in job_output or len(job_output[\"circuits\"]) == 0:\n",
    "        raise Exception(\"No solutions\")\n",
    "\n",
    "resulting_circuit = job_output[\"circuits\"][0]\n",
    "qasm = resulting_circuit[\"qasm\"]\n",
    "\n",
    "\n",
    "# To visualize result, we will make Qiskit circuit from returned QASM\n",
    "qc = QuantumCircuit.from_qasm_str(qasm)\n",
    "\n",
    "print(\"\")\n",
    "print(\"Depth:\", qc.depth())\n",
    "print(\"Ops:\", sum(j for i, j in qc.count_ops().items()))\n",
    "print(\"CNOTs:\", qc.count_ops()[\"cx\"] if \"cx\" in qc.count_ops() else 0)\n",
    "\n",
    "display(qc.draw(output=\"mpl\"))\n",
    "\n",
    "# Run circuit on state vector simulator\n",
    "backend = Aer.get_backend(\"statevector_simulator\")\n",
    "job = execute(qc, backend=backend)\n",
    "job_result = job.result()\n",
    "\n",
    "# Read state vector and convert it to pd\n",
    "output_vector = job_result.get_statevector(qc)\n",
    "output_dist = sv_to_pd(output_vector, np.min(input_dist), np.max(input_dist), len(input_dist))\n",
    "\n",
    "plot([input_dist, output_dist])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4 (Optional) - Transpile to IONQ native instruction set\n",
    "(this will take about 2-3 minutes...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" class=\"qc-circuit\" width=\"1020\" height=\"330\" version=\"1.1\"><text class=\"qc-wire-init\" x=\"0\" y=\"54\" dominant-baseline=\"middle\" text-anchor=\"start\">|0⟩</text><line class=\"qc-wire\" x1=\"20\" x2=\"1020\" y1=\"54\" y2=\"54\" stroke=\"black\" stroke-width=\"1\"/><text class=\"qc-wire-label\" x=\"20\" y=\"38\" dominant-baseline=\"hanging\" text-anchor=\"start\" font-size=\"75%\">q0</text><text class=\"qc-wire-init\" x=\"0\" y=\"128\" dominant-baseline=\"middle\" text-anchor=\"start\">|0⟩</text><line class=\"qc-wire\" x1=\"20\" x2=\"1020\" y1=\"128\" y2=\"128\" stroke=\"black\" stroke-width=\"1\"/><text class=\"qc-wire-label\" x=\"20\" y=\"112\" dominant-baseline=\"hanging\" text-anchor=\"start\" font-size=\"75%\">q1</text><text class=\"qc-wire-init\" x=\"0\" y=\"202\" dominant-baseline=\"middle\" text-anchor=\"start\">|0⟩</text><line class=\"qc-wire\" x1=\"20\" x2=\"1020\" y1=\"202\" y2=\"202\" stroke=\"black\" stroke-width=\"1\"/><text class=\"qc-wire-label\" x=\"20\" y=\"186\" dominant-baseline=\"hanging\" text-anchor=\"start\" font-size=\"75%\">q2</text><text class=\"qc-wire-init\" x=\"0\" y=\"276\" dominant-baseline=\"middle\" text-anchor=\"start\">|0⟩</text><line class=\"qc-wire\" x1=\"20\" x2=\"1020\" y1=\"276\" y2=\"276\" stroke=\"black\" stroke-width=\"1\"/><text class=\"qc-wire-label\" x=\"20\" y=\"260\" dominant-baseline=\"hanging\" text-anchor=\"start\" font-size=\"75%\">q3</text><g class=\"qc-gate-group\" data-id=\"etZJ6JK7nxxNh4rNJ2\" data-gate=\"gpi\"><rect class=\"qc-gate-box\" x=\"68\" y=\"34\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"88\" y=\"54\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi</text><text class=\"qc-gate-params\" x=\"88\" y=\"80\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">0.73</text></g><g class=\"qc-gate-group\" data-id=\"J8QBjAR5kbLG414AMU\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"68\" y=\"108\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"88\" y=\"128\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"88\" y=\"154\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">1.519</text></g><g class=\"qc-gate-group\" data-id=\"Vwy38Sy8b63Fmmgpik\" data-gate=\"vz\"><rect class=\"qc-gate-box\" x=\"68\" y=\"182\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"88\" y=\"202\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">VrtZ</text><text class=\"qc-gate-params\" x=\"88\" y=\"228\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">-2.168</text></g><g class=\"qc-gate-group\" data-id=\"xzxv7xhFyNfbPjKppy\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"68\" y=\"256\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"88\" y=\"276\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"88\" y=\"302\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">-0.759</text></g><g class=\"qc-gate-group\" data-id=\"Jx4YGQm3EjyglNA9AK\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"136\" y=\"34\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"156\" y=\"54\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"156\" y=\"80\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">4.809</text></g><g class=\"qc-gate-group\" data-id=\"DJMuClJWJ4yskMT5tf\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"136\" y=\"108\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"156\" y=\"128\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"156\" y=\"154\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">1.084</text></g><g class=\"qc-gate-group\" data-id=\"If6BgPpJJ1j5YD9jO5\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"136\" y=\"182\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"156\" y=\"202\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"156\" y=\"228\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">-0.772</text></g><g class=\"qc-gate-group\" data-id=\"U3jFDUwbZGyygNNnS5\" data-gate=\"gpi\"><rect class=\"qc-gate-box\" x=\"136\" y=\"256\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"156\" y=\"276\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi</text><text class=\"qc-gate-params\" x=\"156\" y=\"302\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">3.675</text></g><g class=\"qc-gate-group\" data-id=\"medn86FYRmKv89w2Vw\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"204\" y=\"108\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"224\" y=\"128\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"224\" y=\"154\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">2.305</text></g><g class=\"qc-gate-group\" data-id=\"K3GEBCpSmQ9cyFtpPx\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"204\" y=\"256\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"224\" y=\"276\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"224\" y=\"302\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">6.232</text></g><g class=\"qc-gate-group\" data-id=\"L5RGaQ59v3GO1B70Q2\" data-gate=\"ms\"><line class=\"qc-gate-link\" x1=\"292\" x2=\"292\" y1=\"128\" y2=\"276\" stroke=\"black\" stroke-width=\"1\"/><rect class=\"qc-gate-box\" x=\"272\" y=\"108\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"292\" y=\"128\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">MS</text><rect class=\"qc-gate-box\" x=\"272\" y=\"256\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"292\" y=\"276\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">MS</text><text class=\"qc-gate-params\" x=\"292\" y=\"302\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">0, -0.036</text></g><g class=\"qc-gate-group\" data-id=\"rJObPyXgJojqhO6DGc\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"340\" y=\"108\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"360\" y=\"128\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"360\" y=\"154\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">-1.365</text></g><g class=\"qc-gate-group\" data-id=\"zmf4D5bz1BjxZfCw9y\" data-gate=\"vz\"><rect class=\"qc-gate-box\" x=\"340\" y=\"182\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"360\" y=\"202\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">VrtZ</text><text class=\"qc-gate-params\" x=\"360\" y=\"228\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">0.786</text></g><g class=\"qc-gate-group\" data-id=\"ISzXiI2Uax0f2sicBG\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"340\" y=\"256\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"360\" y=\"276\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"360\" y=\"302\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">3.302</text></g><g class=\"qc-gate-group\" data-id=\"fSZDUbhHravpxAiq96\" data-gate=\"gpi\"><rect class=\"qc-gate-box\" x=\"408\" y=\"108\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"428\" y=\"128\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi</text><text class=\"qc-gate-params\" x=\"428\" y=\"154\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">-1.764</text></g><g class=\"qc-gate-group\" data-id=\"sFaMFvzzZi8uGGEhCg\" data-gate=\"gpi\"><rect class=\"qc-gate-box\" x=\"408\" y=\"256\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"428\" y=\"276\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi</text><text class=\"qc-gate-params\" x=\"428\" y=\"302\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">3.275</text></g><g class=\"qc-gate-group\" data-id=\"yzkCnaFMfnAhkGGvKc\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"476\" y=\"108\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"496\" y=\"128\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"496\" y=\"154\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">2.132</text></g><g class=\"qc-gate-group\" data-id=\"Ir0NTX2EHpPiw7qrue\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"476\" y=\"256\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"496\" y=\"276\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"496\" y=\"302\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">-1.589</text></g><g class=\"qc-gate-group\" data-id=\"YyufyiKMDyKJjJDDYi\" data-gate=\"ms\"><line class=\"qc-gate-link\" x1=\"564\" x2=\"564\" y1=\"202\" y2=\"276\" stroke=\"black\" stroke-width=\"1\"/><rect class=\"qc-gate-box\" x=\"544\" y=\"256\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"564\" y=\"276\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">MS</text><text class=\"qc-gate-params\" x=\"564\" y=\"302\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">0.339, 0.014</text><rect class=\"qc-gate-box\" x=\"544\" y=\"182\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"564\" y=\"202\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">MS</text></g><g class=\"qc-gate-group\" data-id=\"rpM0qwJvoRMwF2WanR\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"612\" y=\"182\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"632\" y=\"202\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"632\" y=\"228\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">-2.975</text></g><g class=\"qc-gate-group\" data-id=\"X9sGU3YA2OFZvjffTo\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"612\" y=\"256\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"632\" y=\"276\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"632\" y=\"302\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">0.1</text></g><g class=\"qc-gate-group\" data-id=\"HuPt01SUIF3eMC7iyv\" data-gate=\"ms\"><line class=\"qc-gate-link\" x1=\"700\" x2=\"700\" y1=\"54\" y2=\"202\" stroke=\"black\" stroke-width=\"1\"/><rect class=\"qc-gate-box\" x=\"680\" y=\"34\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"700\" y=\"54\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">MS</text><rect class=\"qc-gate-box\" x=\"680\" y=\"182\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"700\" y=\"202\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">MS</text><text class=\"qc-gate-params\" x=\"700\" y=\"228\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">3.142, 1.74</text></g><g class=\"qc-gate-group\" data-id=\"gaQ3cviiptyG5CtPRe\" data-gate=\"gpi\"><rect class=\"qc-gate-box\" x=\"680\" y=\"256\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"700\" y=\"276\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi</text><text class=\"qc-gate-params\" x=\"700\" y=\"302\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">0.774</text></g><g class=\"qc-gate-group\" data-id=\"mVENhfl09IOlHjshZY\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"748\" y=\"34\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"768\" y=\"54\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"768\" y=\"80\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">3.142</text></g><g class=\"qc-gate-group\" data-id=\"RN3cUjMMOMTCiyVfNh\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"748\" y=\"182\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"768\" y=\"202\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"768\" y=\"228\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">4.354</text></g><g class=\"qc-gate-group\" data-id=\"M51O2Vs7poIUAgnHBp\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"748\" y=\"256\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"768\" y=\"276\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"768\" y=\"302\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">-4.953</text></g><g class=\"qc-gate-group\" data-id=\"BdSLt9TdTazadhPQ5l\" data-gate=\"gpi2\"><rect class=\"qc-gate-box\" x=\"816\" y=\"182\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"836\" y=\"202\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">GPi2</text><text class=\"qc-gate-params\" x=\"836\" y=\"228\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">2.788</text></g><g class=\"qc-gate-group\" data-id=\"yet4LUvpai7sFj15Fm\" data-gate=\"vz\"><rect class=\"qc-gate-box\" x=\"884\" y=\"182\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"904\" y=\"202\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">VrtZ</text><text class=\"qc-gate-params\" x=\"904\" y=\"228\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">0.354</text></g><g class=\"qc-gate-group\" data-id=\"e5flhXSwh3ao3AJ4uI\" data-gate=\"ms\"><line class=\"qc-gate-link\" x1=\"972\" x2=\"972\" y1=\"202\" y2=\"276\" stroke=\"black\" stroke-width=\"1\"/><rect class=\"qc-gate-box\" x=\"952\" y=\"182\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"972\" y=\"202\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">MS</text><rect class=\"qc-gate-box\" x=\"952\" y=\"256\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\"/><text class=\"qc-gate-label\" x=\"972\" y=\"276\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"black\">MS</text><text class=\"qc-gate-params\" x=\"972\" y=\"302\" dominant-baseline=\"hanging\" text-anchor=\"middle\" font-size=\"75%\">-1.571, -1.571</text></g></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "instruction_set = [\"gpi\", \"gpi2\", \"vz\", \"ms\"]\n",
    "\n",
    "job_id = QPS.generator.transpile(resulting_circuit[\"qasm\"], method=\"replace_blocks\", method_options={ \"block_size\": 2, \"two_pass\": True }, settings = { \"instruction_set\": instruction_set, \"pre_processing\": \"experimental1\", \"diff_method\": \"ignorephase\" })\n",
    "\n",
    "job = QPS.generator.get_job(job_id, wait=True)\n",
    "job_status = job[\"status\"]\n",
    "job_output = job[\"output\"]\n",
    "if(job_status == \"error\"):\n",
    "    raise Exception(job_output[\"message\"])\n",
    "\n",
    "if(len(job_output[\"circuits\"]) == 0):\n",
    "    raise Exception(\"No solutions\")\n",
    "\n",
    "transpiled_circuit = job_output[\"circuits\"][0]\n",
    "\n",
    "transpiled_qasm = transpiled_circuit[\"qasmExt\"]\n",
    "\n",
    "display_circuit(transpiled_qasm)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}