QHack_Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dPvQS5jf-lpD"
   },
   "source": [
    "<h1 align=\"center\" > Event Classification with Layerwise Learning for Data Re-uploading Classifier in High Energy Physics\n",
    "</h1>\n",
    "\n",
    "\n",
    "<h1 align=\"center\" style='font-family:font_family name'> Entangled_Nets </h1>\n",
    "\n",
    "$\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;$ [@eraraya-ricardo](https://github.com/eraraya-ricardo) $\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;$ [@VoicuTomut](https://github.com/VoicuTomut)      $\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;$ [@T0gan](https://github.com/T0gan)\n",
    "\n",
    "\n",
    "------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s5Jf4W4aBAr5"
   },
   "source": [
    "### Abstract\n",
    "\n",
    "\n",
    "This project aims to use the method of modified layerwise learning[8] on data-reuploading classifier[6], where parametrized quantum circuit will be used as quantum classifiers to classify the SUSY dataset[1]. We managed to produce a better result using this approach compared to the previous related research[2] with less number of qubit. We obtained AUC of 0.8488 on testing dataset with 5000 training and testing samples, trained and tested using a simulator. We also tested to run the circuit on Rigetti's Aspen-9 QPU provided by AWS using the already optimized parameter to predict 2000 samples of test dataset and we obtained AUC of 0.8298."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mI3vx09W-SlP"
   },
   "source": [
    "### Introduction\n",
    "\n",
    "The large experiments conducted in the field of particle physics require the detection and analysis of data produced in particle collisions that occurred using high-energy accelerators such as the LHC [2]. In these experiments, particles that are created by collisions are observed by layers of high-precision detectors surrounding the collision points, which produces large amounts of data about the collision. This motivated the use of \"classical\" machine learning techniques in different aspects to improve the performance and analysis of the data. \n",
    "\n",
    "Accordingly, these developed techniques are also adapted to quantum computing, e.g, the unfolding measurement distributions via quantum annealing [3]. Intending to take advantage of both fields, the techniques in quantum machine learning, which are considered as one of the quantum computing algorithms that could bring quantum advantages over classical methods [4][5], will be used where QML algorithms developed for gate based quantum hardware, in particular the algorithms based on variational quantum circuits. In variational quantum circuits, the classical data input is encoded into quantum states and a QPU is used to obtain and measure the quantum states which vary with some parameters. Exploiting a complex Hilbert space that grows exponentially with the number of qubits [2]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KckdK0olBAr7"
   },
   "source": [
    "![Screenshot 2021-02-26 031428.png](data:image/png;base64,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)\n",
    "\n",
    "**Figure 1:** A Variational quantum circuit used for the event classification in [2]. Where {x, y} is a set of pairs composed of an             input data x and an input label y that is the desired output value. <Z\\> is the output from the quantum circuit. </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kHsWi5kwACNR"
   },
   "source": [
    "Furthermore, since the development of quantum hardware with a sufficient number of qubits is still in progress, circuits that make use of fewer qubits are more plausible to consider. Besides, such circuits may prove relevant even if they do not provide any quantum advantage, since they may be useful parts of larger circuits as a hybrid quantum-classical system. The classical components can be used to construct the encoding circuit or as an optimization method to extract the optimal parameters of the quantum circuit, such as the angles of the rotational gates. \n",
    "\n",
    "The quantum circuit will be responsible for computing the harder part of the classical classification algorithm such as the inner products required to obtain a kernel matrix. Next, the data will be classified directly by using a parametrized quantum circuit, whose variables are used to construct a cost function that should be minimized classically using optimizers in PennyLane [7]. This looks more convenient for the Near Intermediate Scale Quantum computation (NISQ) since it requires short-depth circuits, and its variational core makes it more resistant to experimental errors as expressed in [6]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dkNGl8C5BAr8"
   },
   "source": [
    "![Screenshot 2021-02-26 034226.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAisAAAFqCAYAAADbQhp8AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAEnQAABJ0Ad5mH3gAAJRWSURBVHhe7d0FfBRH+wfw393l4u7uAglJgCDB3R1KS0uNurv3rfu/brSUogXa4u5WXBMkEAgQJe6e0/3vLAdEzhICHMnz/XzufeneZXPZ3Zl9ZnbmGRHHAyGEEEKIiRJr/p8QQgghxCRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEJaQI38Y7twPF8GpVqzyRhVSfjvQBZqFM35IUKIQeoCHP8vAXm1Cr50tqYqnNl7CJlVcqg4zSZCbgMRx9P8m7QbalTmpCG/ho9WzaSQSkRQqRRQKpRQW7kj2NsBZmKR5rON8YHKzu/x44loPPHEIPjbSo2PeNWlODbve2xxvh8vjAmFnTnFyoQI1JXITS9AtVoEM3MpJCIVVHx5VPCtASu3IHg5sm2azzamzsfun39BYoeH8MjAYDhY1C9XfFnPTUVumRwKNQexxA4ewT5wNJc0KLeK0mxklcggMjODhFNBoeBg6+UPNxsxKhIW4qdt9rjrqdGIcLSERPMzhNxKkg95mn+TdqMK53esxKada/DrR5/ix7lrcSyvAqW5GciqcUbHMHdYSMTQVjfKz/2FD+ZWY8SjYxDlZgOpqNGnFPk4MPczLMqOQNcge5jXr2FFFnAOcEbm4pk44tQTsV7WeoIiQtqR6hTsXr0ZO9fMxKdf/Ig5q44ip6wEuZmXUeMUgVB3Kx1lRY6UJZ9gXsUgPDAyGl525mhYJKuRsnsttu/digVff4Wf5i5FssNADOjgAivp1Q+qUXRqI1b8+ye++eRb/LUzGbkltbAJikKAkyWsXfzglLUUfx6xQ5coL9jygQ6VWnKrUbDSLklh7xWCDv5F2PzrciQ4TMCXXz6D0b27o3O4NxyspRA3DkIYdQaWvvcLSke/gKldPWFjpglo1EU4zFd0fy1fiw2rF2DmrDW4HDAFd8V7wMqs/n74VqO1CwIcz+PP384hZFAsPKzM6FkkIWYO8AoOh3/JdsxacRw2oz/CZ8+MQ7+enRHu4whrFiBoK5KZK/DRb0UY8sRkdPO1h7RJQGMGB68ghPkV49iJQpScPoqjBW4YNj4OXjbmmrIngqWzH8Ki+IbEkXIMfu01TB8Zjw5eDkL5FUut4RLgiAvzZ+NcQB908rSBOTUyyC1G94l2SQwrBxfY56YitRbwiR+MuAAfeLq7wdXBChJttSKvYvcszC3picnxXrCT1ut5EdvAN3YAxt//JJ6ZFgub2jrIFCpA6xNGKZy63I1h0lX4c0sOauQ0foUQiC1h72KPvLQ01MITPQbFIdDPC+5urnBgAb3WIlmBPXMWoKjLWPTgAxoLrR8Sw9LeBXZ5ebAZ/BLuG+wL5anlWJFQhlr59fJpZu0IN9tamHUYg5HRoQjwcoWdBV/GNbuUOsZiylALbFi4DVkVda08LoYQwyhYabcUOH/wCHIU9oiNj4Gt1EDXrroYO/7dAft+IxFqb9Ho+bklPEMjERHsBx9PB1jylabefZn7YNSwQBxftQUZrT4gkJA7lCIFh4/lQGYTjR7RdjBv0CvZlLp4F5bvtEH8kBA48gGN7k8rcCGhAr5dIzHxoVEIlqRj7T+7kV8ja1D2ZKeToY6Igb3l1R6X+szhM3wIAk6ux/b0KtTybRFCbiUKVtordQ4OH7mAWmkk4ns4QWqoYizfg437HdCtjzcspY0vGxEkUmkzxp9I4NKvLwJPb8N/l2tBnSuE8GUs9yiOXayGJKInujmZQ//4czXK92/BAbtY9PS2hoW+Ua/qXCTmOSDKxwoeA+/DuChLFG7/B5syqlF3LehQ4nxiJXxjHCHV8YvFLn3QO+Acdu+7jJrrP0jILUHBSjulLjuGQ6erwIX2RC8PFmho3tBBfuoAEszC0cnbApJWuGrErrGIcj2LIycqoFTShDTS3vHBR8IRnK5QIbB7T3haWBionOVIOpQISXBHeFlJ9c7QUZcn4oJZB4RbSmFuG4O7p8TDqeYQlq46i8papeZDOUgscESUJ/8ZXTsTuyAmygUpx0+hTC4HlVpyK1Gw0k7JThzE8RI1vLv1Qghfiem/ENQoSD6PQo9ABFlKdE+hbA5pIIJ96pCakgmVirpWSHsnwyk++ChReqJrz2BYWxh6LFuIcylFcPULhJWZvkdA7PHOKchDOsFWag6xyBKhE+/BYG/g7MqlOFxSAzZ0RV1+AqnSjnxdYI5rk4SakCIg0BuyjIvIkimhCXMIuSUoWGmXFEg5dBS5Cnt0jo+GjaHxKny1lHs5DyInFzhenQF0o0Q2cHW1RkF2DtQq6lIm7ZzyAo4cz4HMNho9O9nBwsBjWSjzkJ3HwdGZ5UTSV40rhfEqPtHO1x7vmHkNx7RRwTC7vAH/7MxBlUzNBzRJUIZ2hDUf0Oj+zSJYu7rCuigXuTIVJYkjtxQFK+2ROgeHDl9ArXkU4rtrH6+irkpHWp4CVzo9OFRU1UBqZQlDdajxRLCxtkBNZRUoLyFp79Q5R3GEjVfp0APdnM3RZFgY1KjKSEeeXAkhtOcqUVUrhaWlrplCGnxZT8hzRJQvv8+rhVfsiF7TJiLWuhS7/9mA1MpKJCdUwjda93iVq8TWVrCorUQ1XzFQqSW3EgUr7ZC6/BgOJfFBQkhP9PLUNl5FjczVi7Eltw4KTY0k4qsmpZKvJluthuIrzmtBSqtFQITcgdQoP34YZypUCOoeL4xXaTJsRJ2Fdf9sQ061ZgYPKzKcki+T+oske7xzQcLGq9R/vMM3FKKmYmofF8iOL8WqE8nYl+OETh7muser1MNReSW3AQUr7ZDsxAEkCONV4hGsbbxK3SmsTpAi2tdME8iIYGdnA3l1NVpvWB2HmloZrO3sINKR14WQ9kGGU4dPoFgYrxIEKy1Te2RJ65DIBx1eZnzjQthiBztrBWpq+BKpZ8iX/PRJKEI6wY6NV9FsE1gGYtw9Q+GDi1g5bzYSlKEI0jte5QquphYyK1vY6shwTcjNQsFKu8Pyq2jGq/SKho1Z4/EqdTi35Gccc++HcFtzzWBaM/j4eEBcUYpSJdc64Yq6GoWFNXDz8oa4NaYXEXKnUqbgsDBeJQY9tI1XkZ3HPzOPwyU+BLZ84+JKkfSGt4cYleWlUKp1RStKpCRUwDvGGWZNHu+YwX3wvRgbbo68zVtQFhgBK73jVRgO1cVFqHHxhJd5Kw20J8RIdJdob9RZOHDwImqkUejVeLxK7WXsm/0anv0uGx0HhsGWr+CuvCuGS1QkvPIykSbXP7COkyug5DgoFAr9QY0qDanZNgiPCoCEghXSjqmzDuPw1fEqThYNxqvUZR/A3DdfwHcZQegdbH99+QqxCyI7eqHwciZkKqXWsqYuO4K1B+rg4SqFmZbBZmKH7pg2qSvsJYGI6eYCCwPjVVjwk5GaDeuQDvDng6YrPTyE3Bq06nI7oS7Zj3k/rcGpjBPYsno3LipCMXL6UISxVZM5JWrL85GWfAYpaVmoiHwHm5e+gM4u11dY5UpX4Yl+P8F/zkq81s0JVg16qmtxfPF3WJdchqKzu7Hmv4uQ+/bFhMGRcLEJw9hXHkBPvhKuX1+qs/7E1JGbMWzFXMwIt0eDhWIJafM4lBxcgJlrT+BS4las230BdYFDMW1oBBwsJOCUtSgvSMf5MylIzapAh9fXYPGL3eFpc3WaMoeytc9iyM+e+GHRK+jlYXsteOCK92HOj8vx39H9OHiyCC49B2PQpGfw5tRYOFk1DDFkaQvw9KuXMP3n19DP2x7mTWOa69SXMe++cdjUj//eM6Lgam3EABdCWgkFK+2FsgqFBaWoldWhukYOlUgKaxvLKwuf8ZeAWqWArK4ObEkfsZ0Xgv2cGq41oi7B+udG4reQWVj8bAwcLetXVGpUF+ehrE4FZV0tauUqcGJzWFuzBHIWcPRwub7ooUCN3CUPYcKGAVg48wGEOxhKgEVI26OsKkJBWQ1ktTVCmYHUCjbC7B4RXyTVUCtkqJOxnkox7DyD4OvENx7qFRR1ySa8POF3+H47G890defLmOYNZSUK80tRWcPvW8lBbM7v19EdXk5W/M83ikZUFcjNlcPB0xlWBtIScHn/4tG71iN+5ve4P8oVFKuQW4mCFWIkDhUHPsI9n5njvYWvoIeLZcu7gVWX8Of9T+PMtD/w8egA2Bka1UcIaYqrxKHP7scXolfw+4u94WmrGc9yU6iQOm8Gnj89Ad+/NxahTtTAILcWXW/ESCLYxT2Gpzsex9/bL/MtwZZmnVWjZPd8bHG4Bw/38YS1lmfphBAjiGzRZcbj6Hh6JXZnVUF2E3MrcqV78dc2O0yc3hs+dtoWOiTk5qJrjhhNZOGNoc/NgOuu+diWWdOiBQjVxXvxxwo5JjwxBuF864xmFBDSUiJYeA3B0w+5Y//fO5FeWXclYVxr44qxb85KyEY9jFERbtcH+RJyC1GwQppBDGv/IXjq8U44v+gfJBTK0Kw1COvOYcUfe+A29XGMjnKHJUUqhNwYsRX8Bj6OR6MvYenyBOTXGJiF12x1OL9yLvY6TcCDIzvBy8aMbhrktqAxK6T5FJUoyCqA0i0QnrYS/em+65MXICNLCXsfD2GALoUqhLQORVUhLhco4OLvAbsmuZNuhByFmZehsPOCu4MlzIwu7IS0LgpWSMuoVVBB0mB2gkEcy9EibjojgRByw4QFQSV840Hz362Dg0rFCYkbqdSS24mCFUIIIYSYNHr8SAghhBCTRsEKIYQQQkwaBSuEEEIIMWkUrBBCCCHEpFGwQgghhBCTRsEKIYQQQkwaBSuEEEIIMWkUrBBCCCHEpFGwQgghhBCTRsEKIYQQQkwaBSuEEEIIMWkUrBBCCCHEpFGwQgghhBCTRsEKIYQQQkwaBSuEEEIIMWkUrBBCCCHEpFGwQgghhBCTJuJ4mn+3SaWlpZg0aRLKyso0W3QzMzPDP//8g+DgYIjFuuO4nJwc3HvvvSgvL9ds0c3V1RUrV66Evb29Zot2+/fvx2uvvYba2lrNFt3i4+Px3XffwdraWrNFu3nz5uH333+HTCbTbNHtwQcfxDPPPANLS0vNFu3eeecdbN26FUqlUrNFt08++QQjR46EVCrVbGmKXX733HMPLl68CLVardmqHTs/8+fPR2RkpN7zU1RUhGnTpgn/b4ijo6NwfpydnTVbtDt69CheeeUVVFZWarbo1qVLF/z000+ws7PTbNFuyZIl+Pnnn40650FBQcLf7uDgoNnSdpw5cwbPPfecUFaN8dZbbwll2sLCQrNFu7///ls4vjU1NZotujk5OWHp0qVwc3PTbNGOXa/s2rpw4YLB65V5+OGH8cQTTxgsq7t27cIHH3yAiooKzRbdzM3NhWvHUD3FvPDCCzhw4IBR5ZWV1XfffdfgdXv27FnhfJWUlGi26Ma+38yZM9GtWzeh/OrzxRdfYM2aNairq9Ns0Y2VMVYHsvOmT2FhoVC35ebmarbo99FHHwnHwdC1NXfuXMyZMwfV1dWaLbqx87VixQr4+vpCJBJptpLmavPBCgssBg4cKNy4bWxsNFu1YwUrPDzc4A1bLpcLlZVCodBs0Y1dqBEREZBIJJot2rGbYHp6OlQqlWaLbizwCQwMNFhRFRQUIC8vz6hK1d3dHZ6engb3mZmZKdxUjLlsAgIChGDAUAFNSUkRbtiG9sm+W2hoKKysrPTuk52Xq+fH0D5ZIMXOj6GKtKqqSjg/xlT6tra2QnBh6JyzYIpdn4bOT0JCAv79918hkDZUOd+JWCD46quvCsGtMcEYq/RZcGnoWmXHl92kjClT7DpgZV9fYH2Vsdcr4+HhIbwMfVfW8MnKyjLq+mLXVVhYmMF6iklLSxMCIGO+K7u22LE1dN2yvz01NdWo+o/93awssDJhqB7Izs5GcXGxUfUV2x+rAw2VW/YdWUOI1dmGjgH7fn5+fkKdZeh8sbo1Pz/fqGtr6tSpWLdunXB9Gdov0a3NByusAAwdOhSHDx822LtBiCnau3cv/u///g9//fWXUJG2NSxY+d///if0Fri4uGi2EtI2REVFYfny5UKjiIKVlqMjRwghhBCTRsEKIYQQQkwaBSuEEEIIMWkUrBBCCCHEpNEAW3LbZGRk4L333jM4u6c9YDM2hg8fLkyJbzxtkgbYktvl2LFj+PXXX4VZUjQ4VDs244jVYWw2m7bZejTAtnVQsEJum+PHj+Ppp58W8lCwwt6erV+/Xpja+OabbzbJyUHBCrld1q5dKwQrU6ZMMZj6ob1iU843bNiAZcuWCekfGqNgpXW0+2Bl+/btRiVNaytYEqOYmBghT4OhfAo3G7tJvfHGG1i0aFG7DyS//vprofXK8o1QsNLQpk2bhHwW7aX3jeVc6du3r5Dz5Hbf3FiwwnL8sIRtbTHHT2s4ffo03n//faEeo2Dl5mn3wUr//v3h7+/fbm6WLFkayybJCtft7s1gN6m3335byO7YFjOzNsdnn30m/P/LL79MwUojPXv2FLIWt5fet6SkJKEn4/HHHzeY+fZmY8nM2I32+++/N5jlub06efIkXn/9dSxcuJCClZuozQcrrNeERbz333+/1hTKXbt2FZ41Gkrh3lb89ttvQtZHllabghXToS9YYZlYd+/ejbFjxxqVtfROw5bC2LJlC8aPH6/1mmSZShcvXgwfH5920bvC6qPY2Fg8+uijFKzcAQwFK2yZjIkTJ96yhkZiYqJRS0zcSizrMbvHsmzOLe3Rb/PBCsMGL7L1LrRVdN27d8esWbPQuXPndhGsfPnll0IAxx6/ULBiOvQFKyz9OKt82JiBtnizZlUQezyp6+9j6drZ2jls+Yb2EKywNajY+CW2pg0FK6bPULDCllJh1/atur+wgfrsScHtvnbqY5Mp2Pd6/vnnW/wUo10EK/pQsHL7ULBynb5gpb2jYOX2oWDFMEPByq3GxiSyep6NSzSVexpb2NXLy0tYLLelwUrbvzsT0hqUp7H4p1U4XyWD4aXLCCG3nPIM/v51Nc6V18LwcpBtFwtQ2IKMISEhJvNig7ONWSRUHwpWCLlKVYHsC8k4m5QkDHJs8Dqbg9Rdv+HHlWdRKlOhXXdHEnK78GU059I5HWU0G2m7Z+HnladRVKOkMnoLsdXCb/ZDmhYHK+riY9jwH1sy3/By3g2pUZqwETsu8tFvc3+0DVIXJOC/xAIoVS040VVnsPfQZdTSgWwV6oIjWDFnFn6b+auQW6LB66eZWHngOLav3Y+0GgV/FZP2QY3CxL04kV8HRQtOelXyfhy5XA05dce1CnXhMayeNxu/6yijK/Yfw7Z1B3CJekBvGTYm9IEHHkBubq5my83R4mBFlb4Nc1afhLzZN0oxRNnb8cv8/SiVm370W1eciUuXUpGWnoXLly8jMz0Nly5eQk6FEmrNl1dX5iL1UhrSs/jPZGUg7dIlZJfxNzQDf5w6fzd++XkPZI7WgNHP4hU4vegHrLpQA7mFJyzOzsHPG1NR1ZKalDQgcumGqU+/hFdee114Bn3t9drjiBeXwnHCh/jx/YnoYGdOXZImRFaSxZc/VkYzG5XR62WQq8pDWmoa0jLrl1G5gTKqRsGe3zBzTw1sLUWQGH3SlUj6+xesPl8BkasFkhf8hk0XyyGju+cNEznHYfITz+NlLWW0t3kFnMa+i+/+NxmdnKxwe7NItR8spQIbW3Wjj3kMaXGdy8kqUVxe27TrR5WP48tm4vufZuGP33/ENz8uwcHsOtTvOLDr3guuh9fgQKmiwXbTo0ZR0m5sWjkHHzw6EWPG3IXnv56P5Wu24PhlPnLXfPeatENYv+grPDd1PCbNeBs/LliFg+nVUOmrCeUp+PvrZZD3HYM4b+tGFaEKFdkXcPaslq7OpGTkpO7Cr9+vQHKlDSLHjIHrnp/xV0IJXxma9ME0Cawna09iARRagmyRuSO8AgIRFByM4PqvkEj0m/EJfnj/fgzq5AM7Mz7g1vyMVlVnse/wZdRQAHkL8GX0zB5sXj0XHz4+BWP5MvrcV3OxdPVmHMuogUITIFSnHcbGxV/jxWkTMfnht/D9vJXYn1oFuZ4yI7+wFN+tqEWP4V3gY9s4QOXLaM5FJGsto2eFMvr7TyuRrArGsBGuOPD7EiQU8t+HiqhB6sIT2HciHzK5tjLqAE9/7WW070Mf4tv3HsCQWD84SA2UUV2qk3HgSBaq6FGvUVgeM5Y0kKUGudlTs1u3gajOwaaPXsT3yd4YPH4Mxk24C+MjL2PWS+9j9SW+oGquPYn7YEzseA7rdhdApjTlS0IE9y6jcNfdPeFQeBEXCrwwfMb9uPfeyegbZAkzTWmwCh2AqTMeQryLG4a+8BqefvQ+DAqzhZlYV3FRI3P1T1hrNhQTegTAybxRwVLn4/DyPzFr5symXZ2//oSZKw8iYfta7E+Xw9w1BhMm+eDArGU4WyFvY12fMhRnXsIlvkWcnnWZbzVnIj3tEi5eykEFH2xcuXI4VOalIjUtDZn8Z7Iy+Fb1pWyU8Rdb4ytLXfAffv1lD+ocrPgr3/Clr8i6iLQqFR+UWsCvay908nGEpc5zWo8V6/Gah183XUSllgqXtCYR3GJHYPLUeDiXpuNSoQcGP3Afpt07Bf1CbCHVNK+tQvphykMPIt7dA0OffQlPPXYfhkTYw1yi43yqs7Du1/UQ9x+DbkEusGz8OXUBjq6ch9m/6SijKw7g2LZ1fBnl4Bg1DhN9j2DuyjMobXNjKWQoyUq9UkYzG5bRcj5SvFpGq/LSm5TRUrmKrwkbUhfsxazf9qDK2py/URgqawpcvpSOSjZeAhbw7RyPTr7OsDL4c3pYesDi3CLM2pyCslrqCjOEzaTt0aMHOnXqZLo9K02pUbz9J3yxToL+k/siwt8HXl4+CO1zNwbbbsf/fbcWWXWKKxev2Bn9JnVF1vrtyJaZ8vN/Eczt3eCmykJqoRKW0QP4Ci4Avt6ecLKSXHtyI7F2hpe7HLXew3F330iE+nvDxcZM95Odij34c04B4ib1hq+9FE3ufyJXdL/7Gbz06msNuzpffw2P9QSKHCfhk5/fx4RwG0jF5nDuNhWDsQJztuWgVt6GqkJ1Ic78txmr5nyAxyaNxZi7nsP/zVuGNZuPIatWqbluqpF+aAMWffU87pkwGQ+/9QPmrzrABxnXH9MJ5Cn45+tlkPUeja7eNnwgqdmukwJnNm3BuVoWAIogtTDn604jK0GJIzqNGQXXvTOxKKEYtSYdkN/pWBl15ctoNtILFTCP7IdBfBn19+HLqLXkWtliZdTTXQGZ91BM6ROF8AAfoYzqij0r983D/PxojOnpBycLvqxrtl8jckG3KU/ihVealtHHe5mhzGkc3v/+PUyOdIClpTO6ThkE8bqF2JFdCVlbil/VRTi7dwvWzP0IT9w1DmPvehZfzvkXqzcfRWb11Z5zvowe3YQl37yEeyfyZfTN7zB3xT6kssZVgzJ6Act+WIHqbkPR2VdPIHmVIhlbtp1DNV8XsOugWWVUF77sRo4cAbdDf+KfhHxUUVeYTqwXcc+ePcLiq7diKYbWC1b4lsa2xSuQ7NwZ/QP5C01TC4it/TE03hcZ6//F5kyZpndFBLvuU9CrbCO2pNXd1IG2bPAPy13BMui1jBqlRw/hTJUIofG94ME31bQdNMWFYygJiEeAle4K8Ao+qNv1L7ba9MHICAdYaCuQInM4egUgKKhRV2dwCCL7PYLPfnwP0wdGwdv2SsAksvDHqKG+fEtvKzLr5CYc/DWTyB2dR07B1HgnFKdeQIHnUDw0fRrundIPQdZ8kCd8yAoh/afi4Yd7wdV9CJ5/9Wk8et9ghNvVDwLVyFzzM9aZDcb4HgFwtjCii1iVieOFUgRYiAw38JoQw8otBuMn+eDQ7GVILq+7oR4vtkTCV199JSROa4tYwiiWZ6aqqkqzpbk4lB0/jLOVagT36AlPS/6mpXmnPuXFBJT4doOvtYX+c8qVYPfy7bDsMRRhTlbXelAb4Muog5c/ArWV0b4P4+Pv3sP9Q6L5xgirD0Sw8BuBwb6JWLcjA1V1bajFLnJD7PDJuKuXC8oyLqHAfRDuv/dKGQ2xZcED+5AVgvtOwkMP8vWn11A8/eJTeGz6EHRwtKh3bNV84/U3rEdfjOoWBDfWGNS8o4vqcgKKpD4wF/ENQ822G8fKbjTGTvDF8QWrcaa4Gjfa1mDrfeXl5d302TK3EvtbWO4UlugtNDRUyIp+s7VesFJ3AnsPFUDi5gVn/gq9fvGI4eTnA9vyY9ifUAmVJpQW2URj8gAVtm5OQe3VB8s3AavgWapytox3y8hw4mACytQ+6N47CJZam+Rq5B5Ng11MKP++gUKmLse+jftgF9cXvpZXb7hNKSvzkZlVhBq+pFw5YgpU5GaizDEG8VE+cGxww5XAvX8f+J7ahv+yWzZrwSTxNwR7Nze+UkpDodICUf0HIyLAH96eTrC+do1JYO3sCXd5HbyHTUXfyDAEeLvAhq8Frx2fyr2YO68Ancf34m8e5gaCySu4kkTkWUbCky+ELSok/Hd3jrsLg0SrMW97DqpvoDldWFgoJNBr+TVs2oqLi4W1u+RyuWZLc9Xh1OETKFF6IS4+EFZXn/00wJfR42mw7RQMK3P9Nzeu/AC27LdGbLwPrPlypouyqgBZfBmtvvZIki+jeVkotY9C9yhfOFnWqwskbujbyxdnd+1Hdk0bmqnCX+d2rq5Q5WSgSG6Ojn0HIiIwAD6ezrC+NraLL6NOfBlVyOA9eBL6RIUj0MeVL6P16rDKA1iwMA+dRvWAn5OlEQ0EDqUn8mER7gEz81YeSiuSwqnLJAyQbMSSnZdRUXdjFSq7/7DFKdtSsPLff/8JjYzJkycL2eFvhVYLVtQFF3GpWAUrO3v+QqsfrPCXqoMD7ETlSL2YC/nVvnmRFSImDoPVzvU4VaO4aYWXpSqvq6vT/FcLKJJx8Fg+FI5d0CvKBmbaShEfgCSc5hAVY6v9/frkp3HguBjh0T6w1Da9QF2C40t/xe//7sCBbTPxxss/YfeFo1g6cy7W7tuBP954Fl9uSucDvIYFSOzeGZ2czuDwiUoo29JjB64Mxw4loZILQc94T5hLtV2ySlw4XgK/Hv78jahxrwmHkl3LsNWqF0aEO8JCazO5qZqTF8CFh/KfN9zC04X1eI0c4otjq7cjs7blPV5sxeG2vDL41b+vxZW58jwOH8+D3C4GPaPs+GtEyxnjypGYxKFjlC2k2t6vR5Z0EAmiEET6WEGqrTxzpUhc8Tv++Gcb9m37HW+/8hN2JB/GslnzsXbvdsx++wV8tf4CqhoEqGK4xUbB6dwxnCqXt62BtnwZTTh8BhXqQHTv4QVLrQGeEhcTS+ET5wtry8bBIl9G96zAdvNuGBTGghxjSlwNTl3kEBpsAXMjy3RziCz8MHyQD05s2In0ytobuj+x+w+7D7UVrFHBelXuuusuIfncrcqS22q/RVVagnL+JmkmlfIXYqOLh99mxlfV5cUlUNWrkCyCx2GU6wGsO1oJhYneYNWXD+PIpVpYRMajm6NUe8QvO4mjNeHo6mRmcCyEujAZyQUeCAy2gqTJzlTIWDcPe8zjMWrkUAwdMxEhl2fjvRfm4HLkYPSxS8HmTRuxbl960+MlDUKwTw0unc/kK/+2UzBYj92hxDKovbuhdxB/89B2fNW5OJZqi5gQy6Y3F74i3bdxL2y79oGvtfbHA41xdfk4sCcP1m5qqG6ohEjg3o//vae3Y8/lWj5Q12y+bVQoST2Fc7k1UOqZqaYqTcXpc7moudZj0JQxn7lV1JeP4mhqDcw69ODLoDnMtZbR0zheE4JYvgxrvYauUaPo3HkUuPoh0Iovz032pULmhoXYK4nD0OFDMWzMBITmLcTHr8xBekg/9LK7hO1bN/FBSxpkjZKrSAOD4FOXhgtZsmuzlNqEulM4fLIECs+uiA+y1v5oW52H42k2iAyybNpg4APJg1v2wyq6J3xtjOtVqSs4jH15FnDm6zrJTbkCJXDr0wu+yf/hwOVqtKUndzdqzZo1YMteDB069JYuB9FqwQqnUOBKJ7WWK00z6EkhZwNs611YZj4YNS4Aiev2o+TayHFTokbZ8UNIMjRe5fxhFPp0hbeB7mVGmZOFPDjB1fHKeJMGag9j1VEHDIjvgEAfd7i62UFSW4BL6iAMjPaDX9cxeOL1z/DRA7FCD0IDIhu4u1mj4HJOm4rilecO4VieAvax8Yiy5YNeLQeYK09AEiIRre19WRIOJIgRFuWj4xGehiIfiSt/wVffzMTcpf9gZSp/DI8vwbfvvI9ft11CZQufrYndY9HJ6SyOnqqA4jY3p9UZ/+LdRx7GtIe/xt5iHSnJ1Rn4991H8PC9D+PrPUWo0fadjfnMLcOhPOEIzlSw8Srx8LTQngNHmXIURV6x8DQ3FLAqkXs5D5yjCxz466XJvmqPYu1xW/TqFoEgXw+hjJrVFSJVFYB+nfzg33U0Hnv1U3z8UFfY8cFOfSIbN7haFyEnV3btcXhboEw5goQ8GWz5YCPSzlx7Ga1IxFkuApE2Fk2DRfkZvkECBHf05us13T2ZioITWP3b1/h25hz8+/cKXOLL5PF/f8T/PvgVW1LKWj11g9gtBpHO53E8id83zeoTsDGgbAYQm6rM1vq5lWt1tdqoGLHZlRuFWsWCjkYXDX/zZNvEZuxmXv+P41ueQ8YjYtYGHCgdgQmWZjDQQ3vNqVOn8McffxjsHmdjVlhmPbbaozlfUTXGnruxbi3tXdAynDyYiFK1L0brGa+SfTQNDp0fMjxehcdVVKJaag0r/g9t8lk+eBtyTyjCXK2vtC6qzuJsqgr+U/siwIY/vubdMGVGLMxttT1uEsPG2gI1RZV6u9PZQK8DBw4gPT39hgZFFRQUCAub3cg+2FiMrKwsPd9XjcuHjyC1VoqO8d3gKNUeDMpOHUV16N3C+43PkLooGecK3NE7mG+x6WiyqQsP4o+Pf0Giz2TMmNwdfvJNuJg5DhPHdICo7AB+/fAt/CT6AS8P8Ia1sRfoVdJABPvUYsf5LChVPvwG7ceLnRc2LuXixYtNjim7ftn1/sILL8DCwkKzVTv2/qeffqo954GlPWz4a1ptZVdvzE9jlrC3EfEVvxXshNk02j5lzGeAffv2CTkYDD2GZdfBmTNn8NJLL8HS0lKz9bqioiI914gMpw4nokTlhYF6xqtkH0uHXfQ9sDTYoOBQWVUDqZUlXxdp+aSZNwZOCUKIh+2Vm25VMpLTFPAb3RsBthYws+6KSQ9GQ8qX0SaPJ8Q2sLaoQV4lX0fqufexa+H48ePCOJ4bKV/suLFZGi1dkp9h9SNb8Zs9qtOO1X/HkFpthg49usJJR7AoO52A6uDxcNASLKqLzuF8gQu6BFhDqi3S4X9H4eG5+HzmMbiPuR+TegRCuT0dOWPGYXSEFFVHfscn7/0C7qsXMTjATnvPWktIAxDkLcOOi5f5hkYQYKXtL+Pj19paIRkhG0irrachJycH7733nlAm9d3c2erM7H22jxt5tMK+S3MbrCxdPrsnJCcn4/z588K1czWFPnuxe2dgYCBSUlKEep9NVzZUF7W2lpeERsROjnDgj2+OtuCB3ybnRLB35AtOk5MlQ41cAgv+mzTnGmOrWw4cOFA4oPqUlJQIg4EGDBig9UJiq7nqrBAUqTiakAul/UD00DlepQAHT0oQ8zpfeWktaE1xKqX2Vi0frHToKLm2H8WZQ0go9UBczxBYsUBJzN8kHJtW5s1hy1ei7KIbNGiQ1uDNWOy4ssJ3I4WK3ZhZUiGduDIcP3wGlQhFz3gPneNVzh8pgk+8l9b3lTmXNT1ZOmZp1SZh3lvvYJXlA/jwnkF8hekExY4S2HSeAA9vD9j4DceQgB/wxsItmNp9OkIdLK5Uxop8HF21FWXdp2AAX8k27ui6RmTDt76tUZidwwfyuisQdl7Y6sLsmm5cCbAKhFV47BpmS83rw/ajazVtscsAvPLncjwmcYX/1b+jMbELBrw8G8selcDV3xEW2u5zxnyG5+/vL/w9hgYGX7p0CWlpacLfx75/Y1e7nbVSpuL4iVzIbeLRjY1X0VYGuUIcPi1B1HN8AGEw2GTvc1Apddyc+WAlooOErzOu7Edx9ggSS1wR3T0E1qxXQGwGO31llMVcBr4COwZs1eW+ffve0A2hNcooC5rYzUvnPrhyJBw9gwoVG6/iqXO8yoVjRfDq4sn/PU0vFmVuNvLVDnBhZVTLj9ee+Qvv/W8FJHe/g7sGxyHYRYXdpbaIHukBH28HSO0HI/Dnt7Bk60T0uD8SztZsJ2rkHPgXKzftwLZjBfCf9hk+upt/z4r9fjXyD/6DBcs243ixIyK6j8L9Dw5GiL1Fw0CKlV1XKxTm5GquB+15RNg5YuesX79+WhsJmzZtQnx8PLy9vfWeCxassFwlbH830mOxbds2o36eNfTZ4N+1a9cK05DZQFk2BoWtbs5mtrHvevX7ss+y62DlypXCd2Q9K+weMmHCBERGRt70HCsCPmpqEdn+t7n+M/7mqmrkVzZUbeSeCrXlnCbM5rL5beorWwXVq2ZwfnYe3F3zLnPV8vrvqLjcfx/hhj6/msuqVjT4GUP4IIXjT67BF3+AOb7gc/xNUev7Xbp04fgCyfEtB82e66nbzr3YwZ6z6fE+d7Sslv+2TSkvzeWeeXkJd7FSpvX9xmSH3uXivCZxszIrOZneP1jJnf1uGOfpdzc3N+36Z+UVFVyNWq3lWNVxm56N4MIeWcqVVmnOiRaff/45984773AFBQVaj4exr7KyMq6C/y7a3jP2xRcUjr/ghX1pVbOLezXWibMNf5pbl1PFNbh0rlKlc7Offp1bk639eNZte4mLDn2YW6z1fSV3cf6DXOeoKdwPR3O5aiX7gJw7+t2H3KL0Sq5OOKEKLuHTfpxn15e5LflVnKw8kVv52y/czz++yo2P7se9tbWA03O4eXXcf2905fzv/pMrLK/TbGvqk08+4d5//30uPz+/yXHavHkzN2rUKC4rK6vJe41f1dXVHN+q0uz19uKDFK3fsfGLXQdDhgzh+Fa81vf5ypPjgxntf1fdTu61WBfOvvs73H7+/Cg0m+tTpi7gXnxtMXe+VHsZbkjGHf6gN+c/+VfuQkmNgTpJySX/OIbzD5rKzTpfwtVqds7KaK22Mlq3hXspJpJ7ZHEGV1qre88vvvgi98MPP2i9Fprzao0y+u+//3L8jYkrLi7WfLtGav/j3urhwTmEP8GtTC/TXqepMrh5L7zFrU6/fozqq9vxOtet48Pc/NQSvm7TbLxKeYn769HuXMyUb7gDmRWcgr0vP8799Oli7lJpNX8GeIoT3BdD/LluL67n8spkbAtPzZfVXO7Q109zk4eFc9593uQ2ZJRrvh97L4c7/PVT3Kt/HeROpxXxZV+l5VzLuH3vxHMh9/zOXS6q1mxr6sSJE8L1yze+tB7DDh06cEePHuXKy8u1vn/1xc6Xoc8Y84qOjuZOnjyp/Z7Gy8zM5H755Rdu5MiR3PTp07mZM2dye/fu5fiAhUtNTeXy8vI4PtDlSktLr73Y+X/iiSe45557jtu/fz/HB0Tcp59+KtRL9913H7d48WLhu+uqez744APum2++ET7TUrrDvOay7oz+PV2hLsxGnqL+oDs18jKzUWvdBX16ODTsnVBfxuY12eg8rgdc9Dyr1IZ1bbJo1tCLtURZdKjtPfbS20UqdoazkwRiG/6z2iJVZQbWzjuJ8EkD4aVnGnJ9Ej669hBVoLRU1WRdEq4yHccPnUOhnHUT5+DAvmQow+PQ3VkzFkOdgRW/L0VKjZaZJVw1igpr4WYgemcRN+tR0XYsmvNyYDO8+Ehc23vGvgx1dypTjyExVwG76O6IstE22JH/swsP4bQkWvt4FQ3Wk6V1RJTqAjYv/w8VUaMwOszpSpZSVToSKjwQ68i3noWvVouc7EKorO1gLxZBbB2KvuMnYtKYSFhXVqBGbmiAKXvcov8TjL7zwo6Tsdc7++yNtMpaE+ux1PYdG7+u/n2srGp7X+/fI3GGk5MZJHwZtWHnR7P5GmUm1i88heAxfeFlY8yaThK+BewOcWUZypTqJuWMq8pA4uFzKKhTQq3OxeGDyZAHd0acs/mVx0LqTKyevQLJFU1nkHA1xSiqdYGnlzn/9+r+m1iZuNpav5FXa5RR1kunt4ymJeBErgw2UXG6x6sUHkGSpCP/voXWHkj2I7rKqOrSVqzaU46IYSMQ6moj7F+VmYgKt05wMNM8UqrLQU6hEla2dhBd+658eeJ/X2GtF+5+bgo65G/A4k1pqBCy0rJEgrYok3uhT78IhPi7wFpiRO4lPdgx0nX96nuv/oudL3t7e63vNefFfp+2MsOGO8yZMwd80CE88nnmmWfwv//9D9OmTUOvXr0QFRUl9O56eHgIjw9ZL9HVF3sUzXp4H3roIeERUP/+/fHYY48J+Z/4gAXbt28X/psPZG5aigXDZddYIncMe/AuROQcxO40+fVEb+os7N5zCb7jH8YYf4sGs2VUlzZgfXlPTIh1Mpyt8HYwC8eIkdGwyTyOw3ly1O/F5yrPY823v+BE6D2Y0sUNVrrulI1IXKMQ6ZmPzHQ+4GhQm9Vi349P44kn38WSc9WoPr8J28/KYOnlD1ehILFF1VbhvHMsfPibQJMTp0xHarY1wiL9+Yqw9U7r7aTKz0G+XAS/jlGw1zpeRYX0zfthxhc0FwvtwaKIr6yt5TWoVjQZSSUEm+cuVMMnMpYPlq8cU64oEQU20fCSXtkfV3oA2w5VI7zfAARY8ZWjmS0fEPrAx5vlejGmguNQUyvjK1IDN13SMpJQDBsWA7vsEziaV9dgxhVXmYL1P87EiYCJGN/FS8jrYZgELh07wpNvdGXIlI0SgtXiwC8v4Kmn38GipDJUnt+CnWdrYe7uB1czNvhejcJ9a5HiGAkvM2mTsRnK9FRkW4WgA18P8pdzm8DKaEEd4BPREQ6aMtOQChlbD0DSrQecLXU8erTjgyJFLR/4Ny2jqswUXKz2RIdOrrAUokEOxScKYd3RE1IhvwqHsoM7cLgyGL36BcKy/mMm2RmcUYagW48H8dggcxz+ZxkSSqohJPmWn8MZeRA62ZvrfoTLym4dXwfzgYZIpPNDdwQWaDz55JPCkAg2fpO9hg0bhoiICIPjmjjuSgK4kSNHIiwsTGiEsIYVC2qio6OFR0Es6GGzg9h4uS+//FIY08h+rjW14hngC3nfF/DJU+7Y9cPP2HImHxUlF7Hzl8+xyuw+vPvyMARY17/JKpC0ZhPU/caho72OKcGtgN0gWnyTEFkj9vFP8Xa/fMx770vMW/8fDu3fhpVzv8b/PluBoph7MGNsF3iz1PqaHzHIMhrxndVIPp0OeYNBa2KYi+RQOHnDLHkV5qyWY9LbjyG2aA9W79iJdQt/xV/ngzFxdCSctPRCqfMTcbK0I3p2drj2PP1OJ3HmW818QGBja6N1EKcyYx3mnwzBhP5esNZR47CeLHdRBcq09GRB4gQnRwkfgFz/2eqTlyDqGKIZLF2FhAW/YbfVODx9f2fheXezjyzr8SqqgauXF8Q3EES25UDnahlt0d/Il9HoRz7AGwNKsOSTrzFvzS4c5Mvoqvnf4P3PlyMvYjIeGBsHP23LWuhgGdUDsaILOJvReIrxlTKqdPSC2Xn+2ltbh9GvzECXikPYsHMn1v41E4uS/TB6eCRcrRrXCXxj4+RplIbFIcaRD1bayOmUODnDkS8/QhnVcoCVmRuw8FQgRvb2gp2l9huihC8b7uJKVJSx3irNRg2xgxMcJPVTQlTjdKoI4Vfzq1SdwKI/dsNi5OO4j2802tQ7sMqLJ1Dl3QkOtsEY9chkhGSvw5KtmaisU0OVnohKj05wMtPT28bVoJgvuy4ennwdoftmbsjtLLssYFi4cKGQxb0jH4S/+eabGDJkiDCejA1mN+a7sZT6rCdGVwI4to2Ncbnnnnvw4YcfCoNzWe9NYmKiwTGlzdHy2lMLkbUv+j/+CT5/vCuUSVuxat0hFAdPx0efPotR4U6Q1r+Y647zN2ErDBodBlshQr45XFxchFHaLRuoJoKlZ088+PGv+OKRbrAvS8HpCyUwDxqAex65H+P7xyLA2dLoSlAgcsSA8X1RdWif0HK7XjbNEf3oTMz+aBKiA7tg5L1TMWryS/jpqxmIdrKBV5eRmDpxICI9rLQEdhwK9v2HrJiRGOitZWrgHUoSNhwjYmyQlXgEeQ3WkOJQmbIO3888gaApk9BF6zG5QuIaKfRkZaVrmS4qicSEqd1RfvQ/pAqJCRVITpIjqBObbVWOM39/hC93e+ORD1/GqFDHa0tINIsyA6mXWY9XAL/Pll3nrJJ58MEHtc6UaQtYuu4ZM2boHBisH19GPbpj+gc/44tHe8K58hKS+DJq5t8Pd82YjgkDuyDQRff1oY3IsS/G9q7B8UMZqJXVj1bMEfXwj/j9wynoHNoVI+++iy+jL+L7L2ags7s9vDqPwOQJgxDlxdaeavQLuQLs35uFyKH94GPVaCDnHUwSNhTDYuyRc+oY8oR1tK7iy+iFDfjpt0T4jR+HLt52OusliUtHdPAsQnaWrElCS0nHsZjSrQqJB1JRVacEpziPM3J/dGSzIyvPYuln/4ddbtPxzgujEOFSP0eLGjmJhXCM9IDUXAqHLvfhwX4i7F+ygm/UVSL3RAGsI7349/Q0NPmym55jheAO/pBqnWVmnNdee02Y5qvvcdrNwGYEsR4RNij2ueeew8MPPyzUJc25F7JHRz/++COmTJliMAEce4zVvXt34e9lvSzvvvuuMPO0tR4LtfLR4ysO5wBEde+HQcNGYOTIERg6MB6xIR6w4a+i+hdF1YFV2O82HMMDdST6aiXsxEydOrXlM1/YOj1+HdFjwDCMHDMBE8YMQ/+enREV5g83vsA0P2gWw3ngdIxQ7MDm5GpczxslgrVnOLrFxyMupgNCAzxgZ+uO4E6d0bVrF3TqEAI/D/uGAd9VqjRsXJeHXlOHwvfamjl3PpF1DB796C30K1yID7+ah3W7D2L/tlWY9817+Hx5ASLvegjj4nxgy19AOk+DZSfwpwvnkzIgv/ZsUkNkg04Pfop3emVhwZc/YMGalVhzvhyVB5Zh3nc/YnlONB59/w1M7xfUdGVsI6kLEnG6tAO6xzrwFV5L9gBhqiDrgm1ZwG36WCU3evTolgdjrIz6RqBb/6HXyugA/qR3YmXU1vgelWvELuh37zCo9+7Auaraetlm+TLqEYauPePRLaYjQgPdYW/njqCoWKGMRncIhr+n9gX4VOlbsCG3OyYO8oetjh6GO5HIKhoPv/cGBpb9i8++mYc1Ow9g37bVmP/tB/hiWS7CJ96Psd38Ya+3jEahR4wIF5MzUNcokZ7IJgrT+Tqgb96/+O7n+Vi9Yg2SS8txYOVC/PDjcmRFPIC3X3sAA8JcGiaj48pxMk2KiCALIQkdyyY9esZE+KevxuJtF7D/LIfwYEu9s8PUBSdxuiQUXaMdYaH7WZFB7EbPxqLcamy2JZvlw3pVWG+Ku7t7swMmNhOPYY+MtM2mbYw9TmJBzfTp03HfffcJY11avoxGQzflviaW2sDR1R0eHm5wuraYVT3qUvy3KgEBo4bAx8iBqTeCXSg31hUngsTSFo4u7nB3c4Y9y4R6A7sT23fHY0+E4ejfO5FTV7/HgP89FtawYWMjru5fLIWVjQ2sdBZ2DqV7F2CzzV14uJ+3sB5HmyGyhGfPB/DxL1/gke5OKL+YhAslZgjodzdm3D8eAzoHwrn++ivaiBzRf1wfVB3Zj4y6+i0/RgQLt04Y/ehLePmxsYiWnEe+ZTQ6xQ7C2LsfwozpEzAwNsDw79CJ9XjtQWb0cAzw5ivGFp4aVsGwgXO3szv5ZmJ/F+tKbo0y6uDidqWM2rDFClu6PxHs42bgkfCTWLk7u9G6TlfKqLVQRjX758uopTUrozquE64U+xZthfWE6ejjawc9yw3deUQW8Oh+H9778XM8Gu+C6tQzuFgihl+fKXho+ngM7BIEVyuWi0fzeW1EDug7pjdqEw4jo6auYVoHPhB1jRyBh154EY+Pi4X5pQJYRkYiZuAYTH3wYUyfOAidA11g1Xj8mCwJZ5TB6Mg33q40hiVwjLsf9/dSYe9f32JVoTvC+MamvvEqhQf3I6vjYPT1scGNxJfs2r7VvSqMr6+v0LvBBs+2pNeS5ShjucxY4NHcBHBsHMzEiROFIKdlPaZN3ZZioy7YjlXnO2JsX3dYtpHxFc0isoLf8Gdxv+MOLNie1WSdn+bgivfhz+V1GP34WEQ4G1hN9g4kMneEb4fuGDBsBMZMmIAxwwYgvksUwvzdYMu3igz/uWI4D7gPw5W7sYX1ZDV5hCqBtas/gsM7IJTfmfeAwegY5A+fgEC+lewgzBDS/jtYsiTNP3VRpWPT+jz0nDIEvkbNRCGmQmTpi2FP3gfnff9gV2YlWr4OJYeSA/OwqmYYHhrdEW5NxrLc+UTmDvCN6IYBQ0didL0yGu7vDlujeiT5Mtr3Hgzj9mPXuQq+Pmz8uNYarn7BCOsYxgc9Pug3sAOCA3wREOgPT0erpo/cePLkoyj3ZAPzrz9yE1kFYdwj4+B9aT8KXDrAUaqnTKoysHVjLuLGD4S/vdUd+diO9YSwnFotfarAxruwcSgs/1FLenVZkMZ6hVsrUGv5XiQSSIVn8M0tenxrc+dG5HQehx6uN9ZDcScT2wZh2BMPIfzsYixNLIa8Jami61Kwcs5/cJwwA2OjPfjWRRs9mCIJLG0d4eLmDjdn+yvJtzRvGUNs3w2PPhaGhKW7kK1rQUERh/RLYkTEucCGb/rq3L/qErYv+AO/fLcKZ2qLcGzF7/h95nzsTK1ptNo1h9J9C7HFehIe7OstPKoidxIxbIIG49EHInBh2Uok5td/HGS8ugtrMH+PHUbdPxoxXjZtZjxZE3wZtbB10JRRB74M8UFZMwqpyD4OD80Ix+k1e3G5sq5RD+gVInUG0sSh6Oxsq7uuU2Viz8Jv8e4nS7Bz21/490BmvQUlJXDp+QAeGNAV8b2D9Ky+zaHswBJstRyDe3r7wqFNdYUZjwU7bAgFCzhMoVe3xWdB2vE+fPxMP1g2e3CsCI49n8GHz/SCm0VLu9fbAglsvLti7EN3o7e3MYt3aSFyQOcJD2BSnzC4tOtjaYDQk/U0pjvtxqKdmUJulCb4Su54hTtiDS1GKfZG1+FjMHH6e5izcS1mvf0Q7p48Al28LBrkmOBK9mPuihqMfGQsOjRzgCcxEWIbeHUehQem9oGvTcseV4vtYzB2+iT0jXA1Or1BuySyhO/QJzDN+QCW/peBCi0rB6qyElHhWi+/ijZid8QMn4onP52LP794BpO6ewlLm1wlsg7FlE++x/N9vWCnIy8/V3IQC1ZVYfD9oxDpcSW3S3vEekTYeLLb8QhLmxZ/C5FdGLp3cteb3EgXi8Au6Opvq7X7rl0RmcPBOwwhnjaQtORYmLsiMNQfrtZ8oNLOD6UhYttgDH3sQYSf+wfLEgtR13jVapE9eo4djiBDA5T5wMfZywe+geHo1JkNtA6Ev58XnNi05qvnoC4Fq+fugf3YhzEmxtPIJe+JKWKPOLxCg+Fpa9aigFPqGoAQP1chxwtdBfqJbYIw+JHp6HBpJVYn5gu5keoT2XfHqKHBOqdAC/igx9HTH6GRMcKAZx9nvqHQoG41g6NfELxsdaTLqLuAdQv3wHr4fRgVe2XwPjENLT8TIjNIW3giRQ3mzbd3Ypi19GCIJC0KFtsnMWy8u2DMA1PRx0fLDDSxMyIi/fjWbyv0UIkdEDvufkzqGwaXFg/OJSZD3PL6SiThyyhdAEbiy6hXZ4ycfhf6+Nk0GYgsdgpDRz9dix22Er7sRo++DxP7RcC9QV4wcrvRuSDth6YnK9jTVktPlgTmzRwLo5PUFQGhfnBllR3dqAgxHiujXqF8GbVrunCsxFyYFn5Ti5TUBf4hfkJaiju17LL8KmzRwZMnT5rMKz8//4anMIu41s6Je4dhSWxmzZqFzp07m8yzuZuJpUJmK2i+8cYbrTalrKWOHj2Kt99+GytWrBCejbZnn332mfD/LCeCMfkM2hO2XglbHZ2tBttWp2/X98orrwirLrNEgLf7Wli3bh2WL1+O77//XhhoSZpiN+PXX39dyBTr6emp2Xr7sJwqbC0iVr+bSnlJTU3F3XffLaT815YF1xhtPlgpLy/Hxx9/LKxZoO3m3LVrV+F9tohTewhWZs6cKUxle+eddyhYMSH6gpULFy5g1apVePbZZ4VKqK3JyMjA4sWL8cILLwj5ZBpj0y8XLVok5I1oD8HKJ598gi5dughZfSlYMX2GghWWQZ29z9bSuRXXb0pKClQNlnK5/dj3cXV1hZubm951iPRp88EKy6DHsmOyBZy0ZRGMj48XbtrsJtAeKkJ2Y2CrZLI1HChYMR36ghWWsvq7777Dn3/+KayA2tYkJCQI1+P8+fO13hBjY2OF7Jss10N7KKMs8+jTTz8trGJLwYrpMxSsxMXFYcmSJcIigO2hQXyztPlgJTs7W1in4PDhw1qDlbNnz7bqYkumjj3PZOsl+fj43PaCQ8HKdfqClb179+L//u//8Ndff7XJYIVdB2zVVlahs2uzsTNnzphcS/FmYn+rt7e30Aq93WWUghXDDAUrrNeeHUO2wjEFKy3X7oMVcvscPHhQeC7PerekUqlma/t0+vRpdOvWDV9//XWTRyHtPVght8/ff/+Nr776SrjhttW1qW5UaWmpEFDv3r1bCDIbo2CldVCwQm6bqqoqLFu2THgc1d4LMVuHg3UXs4qt8TNdClbI7ZKXl4etW7cK49zoRqsd661mgRxbbFTbo3UKVloHBSvktqqsrGwX4xAMYcWQPf7RNviMghVyu7DrkjUqqIzqx8otWzVc23GiYKV1ULBCiImjYIWQOxcFK62DjhwhhBBCTBoFK4QQQggxaRSsEEIIIcSktfkxK1lZWejXrx9efPFFg0nQ2OCoqVOnwsnJSe+AMjYolOUGqaur02zRjQ26uu+++4TR9Pqw77llyxajcr6wHCls5Lmh6b4nTpwQxgMYk6MiJiYGPXr0gJmZmWaLdjt37hSSVrER8IYMHDhQSISkL2Mhu/zYsSwuLhb+rQ87J5MmTRLyT+g7P2xmDdtnTU2NZotu7Lyw88POkz45OTnC+WFLFRjCMlWOGTPG4Dln05XZWCpD55xlsGWJ4TZs2NAmc13s379fyF7LzoMxGXr79OmDjh07GrxWk5KShOOrUCg0W3RjszmmTZtmsI5g1+jKlStRVFRk8HplWEI7NiXdUFlNT08XlhQw5vpi4x5YPcXGL+krB8ymTZuEusWY8hoSEoL+/fsbnKLM/nZ2LdbW1mq26Dd27FhhSq+h8Rr79u3DuXPnjKoDWRnTNfumPlYXrF69WqizjcHqrNDQUIPXVmJiopDM0Jhri2Uk3r59O41ZuUGSD1nqyDaM3YROnTolzLRghYvdwHS9WEXB0lyzz+qrBFiQwgoWS+WvbT/1XwxL6W+osiosLMTx48eFQqVtP/VfrEKPjIw0mLaYrceQnJwsjObXtp/6Ly8vLyGtuaHCxBIgsSy4xuyTBSos86i+fbLjzI4l+/tZxaJtP1df7LizoIqtLaHv/LAFs9ig1LKyMq37qf9iNxx2fgwFFiyYOnbsGCoqKrTup/6LVaDs/Biq8NhxZPkZDJ1zFmyyG8mgQYMMfs87Ebs5suPA/p+dY23HoP4rODhYSL5l6Fo19viyF7uZs/XBDN2om3O9shcrV/7+/gbLakFBgXADNOb6Ytc3+66G6imG1SlskoEx5ZU10oy5UbN678iRI0aVL1bnsgGmxgRW7FylpaUZdb5Yvc7KmKF6lR0rFuiXlJRo3U/9F/uu7O83Jhkfq1vZYoHGnC82sWPChAktXhOHXNHme1YYVmmxAmiosLD32YVqqLCymwerXIw5dKySMubiZ5U0Sy5kzD5Zhcpa2Ib+HlZBscJkDJaIzFAQwLDvaGyLilVQ7MZtaJ/sWBqbRZgdS0MVFLvxsH0a05pk58VQQMWwQJZVeMacHxZQsPNjaJ/sZsfOj6F9sr+DXZPGfM87EStP7IbKyoqha4Vh2Y6NuVEbe3wZdlzZtWUoqGCac70aW66aU/7ZvoyppxgWZBvTW8OwssqOraFrjPUmsP0aU74YNsPLUBDIsOCH1S2tWcbYd2SBpTG9y4yxdRarW1lQZcx3ZceL9YYbc76Ibu0iWCGEEELInavtNdMIIYQQ0qZQsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiHEKFx5Ck6eL4VSxWm26KNGbtJJZNUooTbm44QQogcFK4QQg7iyY1j422Zki8RQi0SarfqIYMudxeJf1yC5XAaj4htCCNGBghVCiH7qbGz6cSGywgeis48tpEbVGiLYhAzCcK/jmL3gMArrlKB4hRDSUhSsEEL04FC0/XcsKe6KMX3C4GEt4cMQ44itPRE9cjz8T8zD0lPl4OMVQghpEQpWCCG6qS5h7YIEeAwdgBAnC0iMjVQ0pC5dMLGfCFtWHkRRnZx6VwghLULBCiFEJ1XqdqzPCkKfWFdYNnj+w6Eu7wz2b12PdZt2IzGzAkptI2lFFggY0gt2+7fgaKkMCopWCCEtQMEKIUQHDsXHDuCCWySiHKQwu9arwqHs4M945cNVyLLwgJdNMbb/3/N4Y+5hFGgZmyLx7IIOZok4cq4GcnoURAhpAQpWCCE6KJF1IR3mPn6wN6s/VkWNojP7se9YPqSBIYjoMgATeplj32+zsTmLD0jUmo9dZe4HP5d8XEqtgErV+E1CCDGMghVCiA5qFBeXw8reHmYiUb1gRQzv0R9g3h8voY91OVKTTiGlRIaajLNIyZejSTwisoWjPVDG70tNSVcIIS1AwQohRCeOBRdc4wBDBIkyD0eW/YhvZm/DhSor+IT7wclMBaWS0zKIVsz/BAexUflZCCGkKQpWCCE6iODgaIO66mqo+IDlWhAiT8LC997HirJojB03HP26xyDcxxEWYhaUqJGTnIxChZL/lwZXjcpqDjb2dhBRwEIIaQEKVgghOkgRGOyP2oJ81KqvP9vhKk5jz940WIT3RWyYPzwcrSHPykKRUg6Fsg5JO3Yjo1ZxPVhRXkZWvjP8AmwhuT5KlxBCjEbBCiFEBxFcenSHX2YyLvDBx9WJPCK7rhg9Lga1Bxfi77UbsHbpAizN6ohRcQocXfQb9td5wVUiuVa5qAtP4mxNJLp1sIZ5cxO1EEIIT8TxNP8mhJCGas/gx/tfReEzC/B2f3fYSFmwoUB51kWklyohVsmhtrSHvb0DzCvTkFFpDVdPXwT6OMJczD6rRu6/j+H+HcPw29eTEepg0eIWkkqlgkIug6WVtWYLIaS9oGCFEKKHHFmr38Qru/vgy4/HI8jevF6wwUEpq4NKYgFzsyuDaNnTIrEQpGjUncavT3yBsoe/wov9fGErBDuEENI89BiIEKKHOXyGPImpdrvxz/4c1DRIoiKCmYUVLIRA5cp/NwhUIEPK8kU4F/MA7u7qAWsar0IIaSEKVggheontwjDysfsRkboWG5NKIFMa0xmrRsHev7CyKA73390HgfZSNIhjCCGkGegxECHECAqUZ6ciX+SLEE9rSAxGHhzKM5ORL/ZGoLeDZvwKIYS0DAUrhBAjqaFUAWYS4zpk1SoVRJL6afoJIaRlKFghhBBCiEmjMSuEEEIIMWkUrBBCCCHEpFGwQgghhBCTRsEKIYQQQkwaBSuEEEIIMWkUrBBCCCHEpFGwQgghhBCTRsEKIYQQQkwaBSvkplIXncSB00VQGLWezFXVOHfoGHJqlai/bB4hpDWoUXz6EE4X1kHRygWsOuUIErJrIFdpNhDSSiiDLdGPq0J+ZhFqOTEkUjNIRGqolEoolWpYuvrDw85M5wJ16sL9+POPRAROexCDguwhNTo0VqLw8CLMOeSBux8bikAbKUXVhFzDobogC4U1aoglUphJRFCrWJnkg3tLF/h5sLKma5EDNYoOzsOfCT6YdN9AhDpa8mVa8xaPqy5ARn455KxxIRLDxs0fng7mDT4DZTnysssgF0kgkQAqhQKwcYePizW4kmP4e/4huIx/EENCHGDBv09Ia5B8yNP8m5Cmqi/gv3XbsWfTXHz7w2ws2ZyIvPIS5GZcRo1TBIJdLbQvaie/hBX/Nx+5cVMwItYbdubiRmvE8AHJsb/x08o8hHTyh620/hoyYlg6ukJyfikWn3FBXJQHrMwa/zwh7VUNLu7dgB17NmLedz9h9pKNOJ5TipLcTGRVOyAsyBWWOsqLInU1vluYjehxw9DZlwUTDT9Vc2kvNu46iJ1//4yfZy/B2gsO6NMrFE6WZtf2py4+g61rV+KvX7/D78v2ICW3GNXWAYj04/dn4whX8QWs/PcMHGI6wINvaDT6FYS0CDVYiX6W/ug+bBQG+1ThTGIiygNGY/KECRg3diR6BljDTGsLTo3LG3/DakU8xvAVnZvl1UCEQ0niGsz5+Vt89fmHePPNT/D7ukQUylX8Ow1JbH3QbdxIOO2dg1XnKiCjbmVCNCzg13UIRgzyQ+35kzhR6othE8Zh/LixGNUziG8Y6Fg8Up2NTX+sRW3ccPQM84A1H9A0ZuEbh6EjBsLH3Bo28vM4vGIh1l2oQl298idyCEXvUVMwJMAGvsPux/338A2Szp6wYPsT28K76xiMcDqMhWuTUVarbFK2CWkJClaIfmZ2cPf3Rk1BDmTwQs/h/dApPARBQQHwsJdqfwRUdRAL5mWi49i+CHI0r/cZESycAxDVcwjGjOsHT1kBSqvqoOQ4LRUa/1mPeEzuXYcVC3chr0ZOlR4hAjPYufvBu7YQeTLAo/sw9OvUAWHBQQjw1P0IqOrwIizMDMWIXkFwudaAaMjMzg2+njWocByC5+7pC8/a41i+5iQfdCiulT+RuQO8ArxgYd8J4yf1RZfocPi72lzrYRVZuKP7pHgo1v2N3dlVkNHAM9IKKFghhikv4tDhy1DYRSO+M18ZmmmvDK/gULpnGTaLemBkpDPf2mr4WSvvjujcORpRkaHwsNU93kUgskLI6EFwPbgKO7JqIadKjxANJS4dPoYsmTWiesTAwUKqvTflKq4U+1ZuBddlCDq4sh5RzXYtlKmJKPWKQ9dJD2CoP4eUNctxoKAG8vqtBVkykpUR6OxiCfMm+xLBKmgkBrgdxfrdGaiq3y1DSAtRsEIMUucdweGUakgi4tHDRQr9sUo5Dm78DxZd+yLA2hyNx9eJpRaw1NVNrYWZ1wD09TyBbftyUNvaUxcIuVOp83DsaAqqRKHo0c0VFk0jhga4isPYsleKmHh/2OroVblCjfzEbNhG+sPGbyjuHR0BafZmLN2eheq66+VPeSkBZV6d4CI1134TMfNEv16eSNp1EDk1MlC4Qm4UBSvEAA4VCYdwqoJDUI9e8DY3MDNHfhb7j6kRFu0nDPK7YRJPdO5kh6TDp1CpoOffhDBc5UkcOV0GpX839PTR1rvRkDz5II4rg9DR1xrm+ka8cpU4mSJCeDj/OQsndJs6EXH25di3dD3OV9ZCKXyIBTR5sIv0gbn0+sDbhiTwiI2CffIxnC6XQUEFl9wgClaIATKcPngMhSoPxPUK0zoorz510Vkk57kjMNhamFJ548wRHOyD6kvnkSWnvCuEMLKkQ0goVMKtS0+E2pjr7+3kS01x8nnku/oh0NpM/2dlZ3GqNhCdHKR8ACSCTcdJuKufB+SnVmBlQilq2bMgFtBcEiMi1ApSqe6dmQcGwbsmFRcu88HKlSiHkBajYIXox8arHMmGwi4G8bF2Bsar8B/PyUQO5wxXJ77F1RqxCt9us3V3h1X+ZeTI1VBRC420e0qkHjmGyzJrdDJmvApUyL2cC7WjC5zMJHorfWVqAko8o+F29fGOhT9G3TMMgUjFuqV7kFctg1p2BqdlQYi0M9fboyOydYObVSFycuRQUsElN4iCFaLX1fEqZjrHq3CovpyJAqVa6PXgyitRLbWCFd/iapVYhSe2sYZFTQUq+AqPqjzS7qnzcPToBVSJw3SOV+Gqs5FVqIBK6IpUo6KSL8OWVnqSxTFXxqvYRfrz+7z6eMcMbv2mYUwnSxTsWIotGVWoSklAuVc0nHWNV7lKYgtrixr+dyuhpi5RcoMoWCF61Buv0lPHeBX1ZWxathO5tVenFvMBhUrVqo9rRKyLhiVapkiFEHCVJwyMV1Eje+sK7MquvjZtmCUqV6nZMFc9hYirxCk2XiXCGub1Hu+I7WNx96QecKw6jKXrkpBy4DLso7z5gMbQQPkr71KxJa2BghWihwxJB46jiI1XiQ8Vssg2JkveiCMyf7hLrnQvi2xtYaWoQbWi9XpBuNpayK1sYCNpvd4aQu5UsjOHkcjGq3TuiRBrLeNVZOex+WgdfFzFMBOm44lga2sFRU0NFGo9pVJ+FidrAzTjVTTbGJENwsZPxQBPBZJX/IUFx+QIDuaDJAOPhMHVoE5uCRtrvm6gOw25QXQJEd2UF3HwaDYUtrHax6vILmLVrENw6BUJB023scTLG+6oQFmZSugMuXEcaoqKUOPiBS+pWH9eFkLavCv5VS7LrNCpZ7SW8SoyXFozG4dtuyHcwRJXOkgk8PR0g7iKL5eqK49rtRHGq3jUG69Sj7nvMEwbEQJJ5gZsKPZFhK25wbW+uNpiFNc4w8PTHBLKuU9uEAUrRCd19kEcZONVOsSje6PxKnW5R7Dk4zfx/QV/9Otgf206pMStIzp6FOByhgwqvYPqFMJKzJyC/3+9QY0SGWnZsArtCH8+IKJ10Ui7ps7BkcMXUCkOR4+4RuNVZHk49s9nePuHc/DqGQ5Hy6uBjASuHSLgXpSDLJkSWhdA5ypwYsNB1Hl68I0SLY93JK7oNW0cYmyUcOsQB3dzA+NVeKrMdORYBCPC3wJSKrjkBtGqy6QRDqXH/sG8LcnIOrkeS9aeRk3YBDwxIQr25hJwyjpUFmbiQnIyziZfhs+zq7Ds9Z5ws9JUcFwRlj0yCH+Ez8PSl7rAid9eX93plZi1la9sS5Kxdck6nJaHYOS9o9DJxQZBQ5/AlC4ODbPe8pXz/HuHY3XfvzH/8Si+Aqb4mrQ/XNlxLFuwFWcyTmDjP+twsjIYYx4dj2gnS5hxStRVFSHrQjKSzybjsteT+Pfv19HH2/ZaA4MrWoEnR/yJgJ/n48UeHrA1u7KdZbY98s8cbDiYgH27DiDHfSBGDRmLGc+ORUcHft+ajzFc7Tn88fhrSLv/N7w92BcO5k1CmnrUyP3rQYxZ1R1/znoMsW421NAgN4SCFdKEvCQDqbkVqK0sQ3mtEiJLezjZWlx5BMOpoJDVoEpY08cMLuFxiPSqn75bjbwVT2Diohj8Me8JdHK0bNACU5VdxqW8Sijk1aioqIVCLYGVgz2s+aaXrWcI/Jz4Flu9OpDLX47HJixF1z9+x6OdnEGxCmmX5CXITMtDeU0lysproBRZwt7JFhZXCiVUChlqqqpQp+QgcQ5D10hv2EjrrbyszsfqZ+/CoojvMfPxLnC3uRo6yFGckYqc4jJUVMuhkljB3tETgWHecGiwEjqjQNGFC6j2CIWvnbn+1ZS5Aqx6+i782+l7/DSjc73fR0jLULBCWp26eAfevf8XuH44B8924wOMFtdTKmT89SieODgKP30xCWEOhrueCSHaqFGy+wM8/IsD3vzlKfTwsNWMZ7k5VJl/46ln9mPQlx9hckeXG6gDCLmC6n7S6sTO8XjkQX8cXb4HObVynQP6DOHKDmLxZinGPtgffjYG0vwTQvQQw6n7Q7g/4ATW7s9G9c1cCpkrx+F/tkAyfBr6BtjDggIV0gqo/ietT2SD4NFPYJLVTvyzJ7tlCxBypTi0cAXKBjyECdFusDQ0TZIQopfIJggjH5sIm30rsTer8loOltbFofTIYqwu6YV7x8bAk29kUMklrYGCFXJTSBw6YNSMKfBNWorVp4oha1a67Tpc3LAQ/5kPwfTxXeFjbUYXKiE3TAL78OF4cLI/Ulavx6mCmlZfYFCWuglL9kjQ9+6xiPOzNbjAIiHGojEr5ObhZChOP49cSQA6+tpDYnSSFBlyz59HpX0wgj1sYEbJVQhpNZysGBkpeRD5hcPXQap/oGwzyfJSkFJph4BAD9jVH+BLyA2iYIXcZCooFIC0WYkWOCiVaki05XsghNw4lRJKSIRkba1ZxjilEmqJWasGQIQwFKwQQgghxKTRE0VCCCGEmDQKVgghhBBi0ihYIYQQQohJo2CFEEIIISaNghVCCCGEmDQKVgghhBBi0ihYIYQQQohJo2CFEEIIISaNghVCCCGEmDQKVgghhBBi0ihYIYQQQohJo2CFEEIIISaNghVCCCGEmDQKVgghhBBi0ihYIYQQQohJo2CFEEIIISaNghVCCCGEmDQKVgghhBBi0ihYIYQQQohJo2CFEEIIISaNghVCCCGEmDQKVgghhBBi0kQcT/NvcoulpKSgtLQUIpFIs0U3Pz8/eHh4QCzWH19mZmYiPz8fxpzW8PBwODg4GPz9586dQ2VlpcF9sv3ExMTAwsJCs0U7tVqNU6dOQS6Xa7boZmlpiaioKEgkEs0W7WpqapCcnAyVSqXZopuTkxOCg4MN7rO4uBhpaWnC99VHqVQK+2TH09A+yZ3h6NGjwnk3pmwaez0xxpYlxtnZGUFBQa2+XxcXFwQGBhq1X1amqqqqjNqvm5sb/P39jdrv2bNnUV1dbdR+3d3dhfrP0H7Zvq7u1xB2bm1sbNChQwdIpVLNVmLKKFi5jR555BHU1tbC3t7eYKU4cuRIjBkzxmDBmjlzJpKSkgzeYNnvY7+/a9eueisBtp8PPvgAhYWFmi26mZmZ4a233oKPj4/ev4ft66uvvhIqQUNYMPX+++8LFYs+J0+exLx581BXV6fZolvHjh3x5JNPCoGQPhs2bMDmzZuhUCg0W7QrKysTzgs79nZ2dpqt5E5VXl6OadOmwdvb26gbWUREBJ544gmD1ygLpD/99FPk5eUZdZNmQToro4b2y4LlTz75BAUFBUbtNzY2Fg8++KDB/bLGBPu+bL/GiIuLw/Tp02Ftba3Zoh0ro2y/RUVFmi369ejRQzgfhvbLgpTPP/9caGQYwgI79j1mzZoFV1dXzVZiyihYuY3GjRuHQYMGoW/fvsKNXh/Wq+Ll5WWwZ+XixYuoqKjQ/Jd+rHXFWoX6Agt2eZw5c8aoXhC2H1ZxG1NZnT9/3qheEHZcIiMjDR4f1kOVkZFhMEhjWEBhTEs4JyfHqF6qAwcOYMuWLVi0aJEQXJE7Gwum+/Xrh59//lnohTDE1tZWuJ4MXaPsOmItf5lMptmiH7tOWc+Kof2ya57t15gyyrBrNCAgoNX36+joKOzXULli5Z712Bi7X1ZHGdNjw4I2tl9DjQvm9OnTWLhwIZYsWSLUrcT0UbByG91111144IEHMHr0aOqKvIOx3pe5c+di9uzZFKy0ASxYYY2IHTt20I2sjTp8+LDQuzNnzhzhMRMxfTTAlhBCCCEmjYIVQgghhJg0ClYIIYQQYtIoWDFhbMAYGyxGr9v/YueCEIYN89N2jdDL9F40JLPtoAG2t5G+AbZspPqKFSvoJmki2MyIyZMnC+er8ZRnGmDbthgaYPvss88KM8+o6jRt7PwMHjwYTz31lDBjqz4aYHvnoWDlNtIXrHz77bdCYiqWt8BQkjVy882fPx/R0dF48cUXm0zNpmClbTEUrLBpzZMmTRLy9RiaTktuH9bYY9Op33nnnSblkoKVOw8FK7fRG2+8gfHjxyM+Pr5JzgOW4yErKwsfffQRrKysNFvJ7fLuu+8KOTeefvrpJucjISEB//zzj5C8rnELjtx5WLLCGTNmCAnDWBbZxlhrnZXLXr16GcxVQm6f7777Tkj+9vLLLwuJN+u7cOGCUMd++OGHWs8xMT00ZuU2YllUWWudWmd3NpZmn51LCirbBnYe2U2MshG3XSx9/3PPPUfn+A5CwcptFBISInRPGkq1T0wb601h55KCzraBnUeW6p4SNbZdbNwZa2TQOb5zULBCCCGEEJNGwQohhBBCTBoFK8RItbi44Wf8svESahSGFws0FVzRfsz96V8kFtRBRUPJSVtUdwmbZv6GjSmVkBleG9T0ccU4uPBXLD2ei2oFFVpyBQUrxAi1uLDmK3y5phzeAY4wk9xBY2xsgxFhfRS//t/fSCyUUcBC2hY+UFn/zddYU+IKHwcpzNpEjW6DwDBrJPzxHf7hA5YaClgIj4IVYgCH0kN/4LO5Weg4eQoGhDlBKr5zghWRpSfixk3HIPEmfD9rJzJrFLhz+oUI0YMrxZE5X2JeegjGThiACFcL3EntCJ1ElvDsMgbTBkmx49c/sTOtAjIqtO0e5VkxUaaSZ0WVswWfPPstMgb9Dx890ht+tmZobn2oPLcKC9NjMW1wIKzNb0d8rEL5+VX49I0FUE77Eh9N6Qj7Zn4PfXlWSPtiGnlWVMjd9gVe/D4VvV97FzP6BgnXdLPKpjIFaxanodPkAQi0s4SpzWVTVVzA2i/exoLaifjw/SmIdrZq1nfUl2elLdi/f7+QuNDc3NxkZpSyTN9smYMXXnhBSMjXmqhn5Tb666+/kJqaKpxgk8QVY8+fP2CLZChmTIqDt43uQEVVmYf0Cxdw/vz5Jq8LWWew8bffsfFCOeTq2xEbS+AQNgKPTfPHqT//wOb0av57aN5qBewcsqRwdXV1mi3kTsbO408//SQkhzNVXPF+zPtlC9B/OsbH+cNOV6CiqkJ++kVc0FIuz1/IxNnNszBr4zmU1Klgaq1WiX0Ihs24GwHnFmLe5lSU89+xteTm5gr1b3V1tWbLnefEiRM4efKk8G+W5dwUXmx5mNWrV9+U40o9K7fRww8/jPvuu09oqZliBtvqxJ/x0GNrEPzRLPxvBN9yk+qKbdXI3zMHs7ZcQqVM2aTSU+YewPJt5Rjw6WLMeigatha3pw2nKkvEz489gQ0RH2LOO8PhZyM1uiWqr2dl165dWLx4Mb7//ntKMtUGlJSUCJmlV61aBTc3N83W625/z0oNTv72KJ5c5Yu3fnobo8KdYaGjaKoL9mL+7K1IKWMDzLmGZVOZh0OrtqOs9/uY+8vD6OpiDZPLx6sqw4nfnsYz6wLx5q9vYWSwg86/tTF9PSuJiYn45ptv8OOPP8LV1VWz9c7CAmrWUGJrH5lKz1FeXh4ee+wxrFmzRki815qoZ+U2Yi232tpa01wQTZ2NzbMX4FzgRNwb7wMbvSP3RLCPGIiJU+/BvXzwxQKwq69pY0JQkSHGwBfexZMD/WGpM+C5+SQOUZj6QE9UrluIDRerIW+lhppMJkNFRYXp9pCRZlGpVCgrKzPZ86nO2Yq5fyXDZ/Rd6OlnD31PNEV24eg//i7cc++9DcvmtLEIq7kMSb+n8cYTgxBsa2Fyj4EEEgdETZ6OnrVb8PemFFTUtc7CrnK5/I4vsyx5oY2NDTw9PeHt7W0SL/ZdWAB/Mx5LUbBCtFJe2ogl25XoefdIhDiYGxi4J4KVRxhiOndB165dG7zieg7Gfa9/hv89PhF9wpxhdjsH54rM4TVgGoY6JeCfFcdQpqUXiBDTpkTq5n+xQ9YVk4aHwslK/xgykZUHQqNj0aVRuewa1wODpr2KT959ApP7RcDFQmJ0L+OtJYLUsx+mDnHBqZVrcKy4FnIqtLcN6zlhK46zxz23WouDFa70FHYeugxls+eCcihP2on96XVQUkPURMlwes0yHLPsibG9PGF1A/MhRZaB6D28DyLcrU1ipoLYoTMmDvVF2ob1OFIqg5IqPnInkSVh/cpjkHYdiXhvW729KnqJLBEQPxS9O3jCli/fphmoaIjtETNuMPyytmDTkSLUUrRy2/zyyy/CitVsEO2t1uK7kDJjO+auOcV/6eb3pasztuK3JUdQzv8sXXYtJCtFNh/hZmZdRk5uHnKzLyMzIx0Z+VV8EKg5qlw1CjIzkJWVg9y8HFzOykBGXsX193WRJWLdmjOw6TkMcc4WMGtuTabIQXpWNVTs94gtYM1af7e6NlSex6p5m5FaI284VVlkg8jh/eGfuxObj5ZCTtEKaWXyshy+LGYi8zJf7nJzkX05CxnpGcivVOJ60SxEVmYWsnJykZuTjSy+LOeVK669r4vs5EasS7JE18Fd4GolbXYFrszl64NqJVScGBbWVnzZNqEwRZmCtQu34GJlHRreVUSw6TAUff0LsXf7URTXyum+cRscO3YMR44cQVhYmDAD6VZrec9KTRGy8iv4wtXoslEV4fSGhfhz3mIsWTQXf8xdg4S8+sm4RLCLjoLZ3nU4UqagJF0tpC5Oxr6t6/DXVy9jxgPT8dg7P+Pv1RuwPfEyZFcPak0Gjm5eip/efhwPzngRn/3+N9YdzkC1Sq23sCvO7cbOi1JE9+kKJ/PmT1VWpGzD9nO1aEEc2zyqKuTpmelwZuNv+H1DCsrl9YNiEaw69kU3t3zs330K1fJb351J2jI1is8dwPYNi/D1q4/iwQcew9s/LsKq9duQkFVzrUzUZB7HtmW/4N0nH8ajL36M3xavxaH0Ssj1VohKpOz5DxclHRHf2QUWzR7/xf/8zp04Xy3j/3WbsNlJGXrK7KY/8MeGZJTUNXxEK7KKQO84dxQd3ofTVXWgPHG3FhvHNXPmTAwfPvy2Ldra4mBFK3U+dv/wHn5NtETHbnHo1qMX4lxTMOf9b7Alo/Zal7uZz1CMCziNDfsLDRROoovIMQw9Bw1CqPIcDh9IgrrDMAwZMhQDYrxhcfV5i4U3Og8ahSF+cshDRmPy+NEYFucHa4m+bl8Vsg4ewEVFEDrzFaK02d0qKuQcvwylu5S/um5uq01dlID1f83DnD//xJ+NX7MWYOOhg9iw+gDSa/kWreZnGJFtDLpFWSH3+BFc4ivFmx1TkfZEBMfQHhgwIAzchaM4eFqJsMGDMXjoQMT6WMFMU8dbeMeg/4gh8FcqETxiIsaOGYY4f1tI9T0rVWXh0KGLkPlHI8bVovmPgFQ5SLysgIuEu22DadXFJ7BxyULMnaOtzM7HhoP7sXHNAaRVyRuWS5EtortGwrogEUcv1UBGbYxbiuVzKSwsxJgxY4RV5m+HVgxWOJTum42vl5YgYlhfdImKQHh4R8QOGI3o6jX49rdtyL4aLUs8MHBCB1xavwe5jSJoYhyRtRsCg2xRmV8GlUVHDBzdHZ0iIxDqbX99EKuZI3zCwmBnHoAR949Bn7hoRPg6GshAW4MziedQ494RMV7S5j8C4sr4CtEcHXyl4GOim0pkH47+E+7C3dOmYVr91z2jEVyZDkn/5/HOEwPgb92ou1zszB8rP6gvncDJUjmNnSKtSAQrV38E2lWhsFwFiw79MbJ7J0R1CIW3A18mrhZNB2+EhtnBMnAY7h3VB91jOsDPycBA9tqzOHm+Cq7hneBl3vxHQFz5SWSbh8Fbev173GoiuzD0GzsZU+9pWmZDa7Ih6fMUXn98MILsGs9OEsMpkj9GyMDpUyWQ3UHrk93pampq8Ntvv2Hy5MkICAiAWHyTK3YdWu+3ckX4b8k/OGYVi6EdXWGpuSGaOXTAyF6uOLvib+zIrtP0rojh3GsSOudvwM6sOrTX6+5Gp3hx5Qk4mFQOLqgnevmY80GI5o36lKlIKPJDfIgtzI2poZQZOHO+FJxPCEKsJM3vHKk9jRR1KMIszZof6DSTyMoT4TFd0DUuDnENXj0x+N7X8en/HsfEvuFw4Q9Mw69ihoCwINjUpSI5TXbDwQo7j6wAm0oWSXJjWBf3jVXIHCoSDyOpTAX/7vHwtTDXWtEq00+gyLs7f2O2NKqsKDOSkVKihmdQMKxZ3aHZbqzaMxegCgqBJR+s3K4r9crspM5ay+ygaa/gY83sJFcts5PM/EMRaCND+vl01ClurJF74+e4/WBJ3ljCt0GDBsHS0lKz9dZrvbNVdwq7914GPHzhLhXVu9AkcA/0g1XhIexJrLo2e0jkEIdJ8dXYsu0Sattp0/bJJ59Et27dWvz8T5Z0kA9E+MorrjdCrfgbpmZ7fer8Y7hoHY1wazPjWlPyNKRdVsLO0wcOeh8XacHJUHRsL3L4IEKivhUVgRLVRTnIKakVEl4JFFUoyK2ES3c208EDNlr/aBGs/fzgKs5H+sVKqG7wUWRMTAyeeOKJ21qQSethif0+/vjjG0i0JcOZw4koUrqjS3wIrC20BRZqFBy/BMtI/n0jx4UpMtKRrbCBh49DsxcT5WRFSNiXAws3Ebib3YrQ60qZzS2puT7QX1mFwrxKOMcNRq8OXrBr0ri4QmTjCx9XCQozLqFCeWPBChsk+txzz1ESRwOKioqwYMEC3HvvvfDy8rqtAV6r/WZ1YQouFChhZccXJL6FWf9iM3N0gL2oBBdT8qG4emMQ2aDTxP7Ajs1IrlG0y3EDvXv3FpLotOwCUOLCwaPIUdghJj6aL+DaKjwOlSdOQ9khBg5GtsTUpTnIq+Tgwl+YFqy3QLNdNwWKkjZhwcxZ+GvpOqxcdQFK8Rms/OP/8O3CfcisbjhepLVwZUnY8NdirPnvEHbN/xwfzTuI1JPr+e+wBQd3L8KXH83C7swanb12Zh4ecJPIUZBTxFeaN/YNWZbTPn36QMq3WMmdj53HkSNHtjz4VF7C4eM5kNl0Qs9O9rDQFhxwlTiVpERYlD3MjRooq0Zpbj4q1c7w8LTgGzhGlGZlEc5s+Qu///EX/l27CqtS5EDSWsz55lss2JOGitZcc8IIXPkZbFq8BGt2H8TOhV/hk3n7cCFxA5bwZfbArsX4v49nYWdape5FCyXucHc1g6IoF8UK1Q3VKw4ODujfvz81MAxgSxKwRz89evS4LTOA6mu1YEVVUoxSBccXdHP+BteoIPF/pJQPR0oLi6G8NntIBKuI8RhisxcbT1ReD2LaEda11uJIVZ2LI0cuokbaAfHddQ2ElSHpaDmCu7jy7xv3e1SFhShWcrBzcILY0GMNdTESFn2MD+achnlEd8R1CYBCEohhw3tjwJA+cDk1Ex/NOYTC1h6XpMrG9n/3oC6QdSd3Q+94GyTN+hTvzk+Ba1QHmCWvw/J/lmJXWq3OYEXs7AQHCcdfk0X8Z26s0maPf9i5NMnHQKoyZJ67iIJafZW7CmWZ53CxoPbKdHOtVCg3+Jm2g93EWno+1XlHcexiFSRhPRDnaqH98azsLI5V+CPGxdzIgbIqvpXL6k8bODiyRxiazTqoSxLx96cfY+5JMUK6dkWXIBWkgYMxpPcADO3jhrOzP8e8AzmovlXTalQ52Ll8H2p8O6FLXHf07mmLc3O/wnvzz8I+IhxmKZuwailfZi9VXZ/N2JjYGU4OEqCsCEV8sHKjtwyTLbMmgqXyX79+vZDxmC1JcLuPVQvvlE1xChn4uJ3V3MJ/NyC68mvkchn/v9evMJF5EEaPdMfRDUdR1sycKyxFfXFxsTBC+UZfLBeCtu0tebXWvtiaFvpSQXMVCTh0+sp4lXhvXeNVLuFInie6+JnD2LxuXHUVqvkTYWFtbSBYqcP5pZ/hw38L0GHUGAzqFYuOPtWocuiKboGhiIztjVG97XF6wV/YmcMHDddOrgJFJ9dhxd4s1LVwsFLdiQ04yMWgW2wkwgP8EeBjB3nmSRQ49kCX8HB0HjAFj7z0HMZF2MBcxxM2kYUNrM1FqKm+Mv3e0LXHrje2OJe2c9XcF7tGCgoKtL7X3Bf7TrqXa1Dj8rov8eJzT+LF3w+jqlb7FAr15XX48sXn8OSLv+FQaZ3Waa3CZ/hjqu8zV7GFAFn3sbbv29xXax0rto/W2hdL1a77mLPezCNIKmfjVXrCR9d4ldRjyHePEd437okOf/1V1fA3aAtYWUu0VrPX1KVgxZef4J/cYAwZMQi9OneEb001HGK7IjA0EjG9RqK3YzKW/L0TWVWya0GsOv841sz/Bm889iAe+WAFUsqv5ztRFx7Hqt8+xkvPvoIPv1uA3WnNW66i7tRmHFZFonN0FCICWZm1hyL7NArt4xATFo4u/Sfh4RefxfhIB1jpOiAic9hY843h2mpU8pGKMWWWDQ5tjWuxNcssywLbGvtiy7XcaCZZ9vNsuRC27As7Vux19dqePXu2MEwhOjraJHqNW7yQofzAOxj2Zww2/joFNlZSKI9/hL5D/g+F05bh6A/D4Wx5fUks+eH/ofeI71H5wCoc/HoI/971O4gqdxmefmgfxi78EqM9WJIizRsGsIWo2CJUrbHSLTtBrCXVGs/j2L7YQnc3GoWmpKQIz1Xnzp0rrP/QWN2+t9Fn/M8on7IYe34YDS8ti/KpLy/AC9/b4q2PxsHHlvV4GSbb8RK63jUfQZ+fxj+P+MLWQvtPqTKW4YV7P0HWuF/wy/O94WdrBuWJn/D5yTF4dVoQ/3NiKBM+wcDRa9H77034sK8F0neswdF8BTK2/YH9IV9gyVt94WLV/KXT5BcPI0HaCXF+1sLMpuqtLyJ++n6M+GcDPh7gDos6Poits4SzMx+s6BohXLceT0U/gNVdvsfxOffB207/8ZkxYwaSkpIQGBh4wzkGWMVwIy33+vr164dHHnlEx2KXauSuehl3vbUVDo/Mwd8v9ICDluOtzl2Fl+96C1sdZuDPhS+gp2vTBe3UuavxytS3sMX+YZ2fuWrWrFnYt2/fDWe5ZFUTO1atUZ7Yd2HBf2ssp797924sXLhQGHDYtBKvw4H3BmPyTwUY/ed2fD8+AA5NyhAfRC56FT9bvoi3x/rD0dKYekeGXa/3wj1zPfG/w4vwWIgzrLX+GSpkrngFD3yahmHf/IDn+wfCQarGqZlf4eTQZzElxBHWEiVOfDEc41d3xexV72Owlz2k/L64mgKkHv4TX3y3FXtOS3H/3AV4pZ8XbPk3udpCXDr4Oz5b7opp9/dDp04R8LQzflaR/NJRnDDrgFhfWyG1QvWO19D/wT3o/+dyfDTEDzaKYhTxZdbJSU+Z5Y/tphe64eEVEfh072zcH+QMKz2///XXXxem3AYFBd3wzZZdhzfUE15Pa5X/CxcuCPeIX3/9VVhk1RAWZLP7Cvu5ixcv4tKlS8jPzxfKGfsuV78PKyNOTk7477//8PjjjwuzgHx9fY2q93JycjBx4kSsXLlS+JnW1GrBivrSjxjR/R2kTFiEhN/GwaV+sLL3DXQfOxPS53di+3vd4Fhv2UyudBUeH78VY5d/h7HuxgcrLFpmNw+WrOZGseiSnYgbvXgYtq/WWImVrfjKKle2Mqi1tbVm61VKnOcDlAH/O4X47w/gr4eCYGfe+LtzKFz5Kj4sewZfTg+GnZFLlcq2vYguUxcg9P/OYMnD3rBtsl9GhYt/3oMRn6vw2rp5eKSjIyzEKqTN/RQbu7yER2McwOLRqk3PoceDiZiydh3e6W6L2pw0FNTUYcf/xuEPn1nY9vkwuNs0/1hxchkUZqw3iT1wVCDh08EYsygcP237ARN87aD1KzdWuw5PRj+INXywkjD3PngZCFZeeuklobCPHTtWqLRuBLtmWaXXGtcbG/MUERGh45rjIC+6gBPJBZD6RyPar9609no4eREunEhGgdQf0Z38hNW1G3/KmM9cdfbsWaESvNFF4ljVxI5Va5Qn9l3Y/m400GRee+01YUXfvn37Nr0JKlPw88SheP94Z3y2cz5mRPA31MZFjyvCmjc/RfEj72NaGB90GPWVZNjxWjzuneeDD44uxCO6btSqS5h3/1h8XvcMlv3xKKLdrCFRpWPBl5sR/dT9iHa25QOTamx9uS9mHB2FJcv4Ro+XnSbw5FC++TN8nxeCsj/fx96O7+Gv/7sbHZz4hhz/bvXOL/B10T14cVwgnJr8Ufo1LLNKJH45EhP/8sPna77jAygnWBpVFOqw8bk4PLyyAz7bZzhY+eSTT4RHGZMmTdLa4GuO1iyzrXW/YSsbs2uaDQZnwYUurFdo27ZtQuDGyhIbY8ce67D/d3Z2FrbV/y7l5eX44osvhLqFJYBjvTjs/4cMGSKMs2x6P7ruZgYr7I9tEdn+t7n+M/7mqmrkwn+rK9ZyjwXbcs6T5nA5/Da1sPWKmjWPcQF27tyk2Zlclbz+O2quYM0z3Ign/+EuVTX8mfbup59+4viWAVdTU6PZUl8dt/PlKM7BNo5750AJV6PUbK5PmcH9/fJL3Lyz5VydSrPNCHW7XuE6Odpzo3/N4CrrdJ0RGbf1hY6ca+/3uEPFNZzw69UF3IpPv+J2FVZzwilWl3E7X+/O+fZ4lducXcUpru2qhls5I4jr/OJGLr9Kodl2A5QXuF/H+nJ+k2Zy58vquCt/qpyrrKzl+BuU8F9aVS/nHvZz5HzvW8zlVMoMXnvvvPMO9+233+o4H6Q9GTRoELdnzx5OodBy/dbt5t7o6so5dH2D251XyWm7wpWZS7k3XpvHnSqqvlJ2jCLj/nszjnN3GsX9kFLMVessmtu5V2I9uT7v7OFyK6/8dnXhau7Lb3ZyueV1wnWuLt/NvdMniOv50louraz+tV/HHfz6PW7B+Wwucfb9XEz4SO6z3blcpVCg5VzCj+9zc5NLuCrjv7R2yovc75NDuKCJP3Gn+PriSplVcFVV/PfTV2a5am7V42Gcm/893Ly0Eq5W30d5rLx++OGHHH/z1WxpW3755RehXiotLdVsaYhvNHB8wMbdfffdHB9gc/PmzROuW76Rz2VlZXEVFRUcH4RpPn3d+vXruZEjR3K7du3iEhISuM2bN3Pff/899/jjj3PTp0/n5s6dKxxTbecqOzub6969u7D/1ta88FgPkU1n9IlzhqowB/nK+s8T1SjIyka1VSfEd3NsmKFRnYMda1IRMaY3PLVO7yPaiWFvbwexyE4YbNc0QFcie9sSHPMaiYF+VtrHs+ggtrWDDf/5uurqpkspXCOCra0NxBaWQi+G8OtrknCBC0e41ZX8KtVJ/2L2TjGGPXYfujpbNiMJFYeaomwUVF1fR6UhDtWXk5B4qVjIfqzOP4T9Z+UI7toDbuZXpm+rszdj0fpLqNE320FRizoVYGXN/x38ATT66xGij9gOdrYS/v8c4MBf9E2uK2U2dv57HO6D+8Lfli8Xms2GiWFjy8aR1aGmWg1O16UtsoGttQQWlhb8T1z57TVnLoLjW8ZWUvaouBpnls3FTm4AHpoWB/f6j4+VqUgs80AnZ3t0GD8DY70vYtW/u5BdLYNalYnEUjd0cpVqGRDMobYoB4VVutY24sts9hmcuFSEOqUa6oIjOHi2Fv6x/O/XjOlR52zFkg0pqKjV11OuQB1bKd3Smq+jqMzqwh4zseEDfKAm/PcDDzwgPMa+6667hN7AqKgoodeDTdtu/FiLDWOYM2cOpk6dKswA6tKlC4YNGyZMXWaPhEaPHi0sZMgesR09evSWLmjY5LJrMbEXhk8fi+DcI9ifWS8rqDoX+/anwmv4/RgdZNlgoKcqYyvW5cdiXDdX4xKWtTGnT58WBtLyQaNmi7HMEDp4MDpa5yLpRAEU9fPUcFVI3TYbfx51xoiJPeFjrT3/ii4SJzZLBqgqL7+eu6QJM3QYNR6x5SdwKLNOWO9EkXIadf7RsDeToDJlDb77biusJ72Bl8ZHwbkZy8+rczbgi2cfx4wXZ+NYmezaAL9r6o5izlsv4+VPVyClqhqpO3Yiqcoc3oEeMGMpc9XFOLLhFODrBLGeFLrq8grwLUQ4urnywdWNFQO+hYLk5OQbHuxGTAPr8j9+/HjLKmKzUAwc2BG2Bck4WSBvMBuNq07DznnzcMR+EEb38INts/Lli+Hk5MgH/dUoL9cVyPPMOmDEuFhUJR1BZg1bk02Ji0m18Im0h1RajQvrf8KPW80x+sXnMSHaHZb1nrur8xORZx8Fb3NzWLj1xPR7u6N21xKsTSpHdcFJ5Fh1hK9506zW6tzN+PrFJ/HIC7NwqKim6bo9dccx/3+v4dVPlyG5rBKpu3YjqdIMngGekLL1B9QlOLb5FDhPR4j1zQRQV6CCb8TAwRWu/Od0Dm0xArsps2EEt/Jmeyuwe8qrr76KQ4cOCcEJC1TYNPzIyEghTb6hR08sARwbs1I/ARwLaDw8PNC9e3fhEc/TTz+NDh064OuvvxZWYWZDMpp/D2u+G6ulG+AvvoEv4J17bbHz93n4L6UIVWUZODD/e6xWjMbLL45CqG39G6cC5zdsQHWPcYgxlGa6jWLJdk6cONGCcTci2Mc9gnee74Lsv/4Pvy7djkMJx7B381LM+uYnrMl0R/+JY9E7WPsYBX1Erl7wsBKhJL8ASt3NNzh2fQjvPROFi0t+wZ/Lt2LzhnOokJ3CxqWzMXvVRbiPfAYvzRiBKLdmnltVBbKSEnHiv03Yk3YlEGpApEJ5Ti4U0lokb/4H68v64rF7olCZsAMHDmzHioXLkOQ8GCNi3K6vkaSFKj8fRSoJXDzc+WN0Y8WAjdGYP39+qwz2Jrcfa0B8+eWXwv83m8gOXR56A8/E5WPp979h6daDOH5sL7Ys+wPf/rQaqU69MHZ0b4Q2u84TwcXTHdaiMhQW8sGKrnm7Igd0uf8dPN0pE8t/n4tlWzdifXK5MBtnxZzZWJXihCGPP48Zo6Lh2SCRJIfKU6mQhAfDiiWpE9kgbPzDGO16Div+/Q+ph89AHRKq6Z1phC+zl8+ewIk9m7HnkpZZQnyZrcjNg1xSg3PblmFDSU88dFcn1J7ehYMHtmHlX8tw2q4fBsd4wFpfN7AqH/lFSoicPeAmldzQ+kZpaWnCekQtOscmau3atfjss8/g5+cn9IKw8XUsR4qxY76uJoBjyx/oSgDHxqvExsZi+vTpwsB+Nkj3gw8+QEZGxg2PUTPkxmrpRsQOYRj51Ft4YZQvKvgLcePWw8i2H4Cn3nweE2PdG948ZKexbpsIfUZ3hAN/4TWr3LYR6enpKCkpaVFUKrIJxuBH38NHzwyBH3Jx9mQycursERY/FMOGDUbvKC/YNDcDLU9kHYgADwnK8rJRpdS9OrPI2g/xU2Zgxt3D0dWrFiczJAgPDUCnbn0wdMxETBrTH5FeNpA0N1hyG4RXf/sLsx/rALVcxB8bzRtXSSMx/fNv8NbETvAK7IIhY8fjvpc/x2tjfGEOa/jF8pXewC7wt5PqbXkpcnJQwLnCP5AP6G6k1uOVlZUJlV/zg05iilhrm/WUtazVLYJN0EDMeOdDPDssEOKCZJxKzkGtbSh6DhmGYYP7oJOPnTDQtHlEsA7wh4dZOfJyrmcCb0oEK98emPTww7hnVDf4yJKQJQlBUEAndOszFGMmTMKYAZ3g06R8yJGcJENgJP/dNAGD1KMXHrinMyp3/IVf1+TDrYM9pFp6g0RuA/DizwvwxxPRMFOLm/b6SDti2if/h7cmd4ZfcBcMHsOX2Zc+w2vjgmAtsYFvTF8MGtgVgfYGZhcp8pBbqIaLrz/stSbBNB6b9stutG2lN3T58uXCDDW20OCDDz4oLGGgfYagbosWLRKCm549exqcRODu7i48HmK9LOxx0kcffXRtZtHN0qrBCtudrU8M+g0ehL7x3RDXrSf6DB6KAXFBcGo0e6D62BrssRmIkaFspVHNRtIMfKXkGYk+I8Zg1LAh/A26P/r16YVe/HGP8neCRbMrQw2zIHQMtYHy8iWkK9RNH8NcI4LU0R+d4rqjW5QZVO69MbhTBCI6RCO2UwjcWS+ajq+g74IWWXohuv9AhLi6w8+L30fjK1TsgOCeQzCkTxxiY2IRFegKl6Cu6Nu3J7p0jkFMdCSCXA2NkVHhcloGqiVBiAjjP9u8Ji4h+oms4NGxF4aNGcUHJ0MwsH8/9OkVj/huUQho1vithswCIxBiq0Yuf+3K9AXGIikc/CLRhb9hRUrVcI8fiKiIDugQFYOoEA/t6ewVF3Ck2B1RzvXGpIhs0WHSwxjueAbbMx3RwdlMawI7kYUnovr2R4ibB19mtSxgKrZHULeBGNS3G19m+e/AymxgZ/Tu3QNdr5ZZNyuDvcDqnDRkVYnhH8Z6eGiMY30s+HrooYcwYcIEeHt7a+0V0aclCeBYjw0b/8KWjWGPjfTNEmoNzfuLjCS2dISHXxBCggPg7axlOjJXjoOrj8Bt2FAE2jRvTAVpSCS1hbOHLwKD+GPt5sAX4ub3pjQgckJMbAikuedxtkQBlcGePf7s5aZDEtEDXvZWeipiFTL2LcfiBbOxOUWG0tMb+Eh+IZYdyIK80dpQ6sL92FkZhm585ai1V5ivjK0dHGHHurE1v09iYQtHRzvhGbzBv5+rxJmkNCh8OiLa1azBOCpCWgcfzNs4wcM3AEEB3nBzvJIT6EaIHDshOtgcBRfPoUSuuJbMTSf+9+VlShAe5wUHvp7Vfv9RIefwCsz+6mu+LB7E+nXHkVt7dd/83+DVDw9O647wzj0RbGWuM7WEuugQdlcGIdbJHBbaGp+szNo78GX2eo8OK7MOxpZZ9pjq7FmkyT0R0cnNyCUK2o/hw4cLPR2sl6MlU6JZzwwbk9KSBHAsjw2bHt7S322s23LGuaL/sPp0AEYN8OUvVLroTIsEgXyLJ0B5HieSavR0N1+lQvbxUrjEuvKRtr5zKYZzcGd06zEQj342H3+8/yCG9o1HlyCnho+KuFIc2ZwER74V5sMqNs3mVqVIwYkzFXDtHI9w/ndQxx65I0gCEd89AKqLp3CmStF0IGtjqmwkljgjig/IdY/lFcMhIBa9Rj6KDz58DuN6h8HJvN5jebEdou55H1883ReeuhJL8mX22LYk2PfsAl8b85tUnhS4cPIsyp2j0T3cWvt6S+0Y6025kURzgwcPFhZjZWsmtYS2mUWtreV757+YmZhdls09OBwK92xEWocx6O1poTNSJ7eLCDadBqGXdxkSDiajVmloHIYIlpFDMCiEtRw1m7QSwc47FBEdY9Bz4HAMH9ADMVGRCPWybTSuxQqBvUZiSLRng5kKrUlxYS8OZtmiS/+ufMVM3cnkDiGyRtSAnvCpPIUjydWQKw1EKyJLdBw0ECE2Fnp6PPny7hmKTt36Y+jg3uga4Q1bvtFx/eMimHtEoUuok54AwRoBPYZjcIw3rG9W41N5EfsPZ8E6ug+6OFvpCb5IS7ApyqyHpDUSMN4sLT7lZmET8dqDPWDRgu44mw534aXH+Ui9GVNaya0jcuiBsYN9kbd/lxErYovgEt2FrxDNWvwsvgG+gvUMDYG7peTm9KpAidRdu5Di2A/j+3rAkrqTyR1DBPtuYzDIrxCH9yajqs7A4FCRM6I6B8PuZgfkIgt4hATDvd5j2damTN2D/1LsED+qD7ysWQ4Z0ppaI7Pzzdbicy52jsXgnr4Guv61EcE6vB8GdHBqmCCOmA6xE3pOGY2g7G3YdKLSQAtOBKmlJaQ38Vllq5KdxcYN5+A+aAp6ezUvYR4ht5vYsRsmjghGwe6tOFFeB7neoimFpaVUx1iVO4kM57ZsQrJLP0zo7S2sO0ban5afdb4gWGgdSWWYSGqhczVcYgr4FlzXe3FfXDm2rtqPAr4FZ+jx+J2BQ8Xh5VidFogxk+PgbkmPgMgdRmSPLnffg7iq3Vh/IE9/luY2gqs4ipVrU+E7bDy6erKFDjVvkHalxQsZkhvHVo0eMGCAMAK7cTfczz//jKysLGH+enPny7cOBXI3vYW730rD1Dlz8GQXJ+2j/O8kyjT8/eTd+NnsRfzxxVREOlkY3W397rvvCiubsrwCjc/HuXPnsHnzZmGA2s2evkduPpaunCW6Yudc24BDNhiRlctevXrdnmf8ijxseW863rkwFj//9iR6uOteAfvOp0T60ucw/Rc1Hv/xE9wT4w4rI3vk2WKTLOnbyy+/DHt7e83WK1jd+vfffwvTbls6qPR2Y4kL9+/fL0wbvj33iKbYIogs2d6uXbuE5HStiWLU24itvxAeHn7TR1G3jBSe/Z7AUz0LsHTuZqTVyg1PlTRpahTumoO5Z0Nw74whCHEwb7Xn64GBgbj77ruvpacmdzZ2Hp955hkhPblJknqgz6OPo2fpavy15SIq2SJXbZS6aA8WzD8D/8kPYHCYMyxbaegAW1GY5RQx2XNsBJb4ja24zjJnsyU/TOHFstiy9YNuRqONelZM1O3vWWHUKDu9AG++/C/sn/0FH4wNhu0dOshDXbAbXzz1Kc73ew+fPtEHfizvhOY9Y+jrWSHty23vWWHU5Uha9C5e+9cSj37zP4yPcESbG8qhLsSer5/D52e747X3H0f/IIdmPQLS17PSFrC/raCgQGjs3sz8Js3FQgrWq9LaZYN6VogeYjhGTsRLT8YhfcGvWHu+HHfkI3JlFjb98iuO+k7D03d1g7c1jVUhdzixAzqOfw5PdMvGP3+sRXJxHQymRLqjKHF562/4/YgHJj02BT387WisSiMst0lISIgw5Zj17prK62ZNgabTT/STOCFi1BN4bpQEW375E3sya66vqH0n4EpxdO53WF4ej0cen4Cu3taU24e0CRLHcAx/9BmMstiNP+b9h7SqO/1R7VUcSo8vwI/LS9D1/kcwIc5PWCKAtG90BRCDxLb+6D31SczoY4m0SwVQGM7BbzLUJReQIovG1EfuxZCOLnpXYibkziKGrV88pjz+MPraXEZ6obzpKuV3InUJLl6QIXLyg5g2NErI30KlllCwQowggtQ5DH3HT8awKFeY30E3fJFNEPpNnIjBnbxgQ0s7kLZGJIVTSG+MmTQUndws20beIJENgvqMx4RBMfC21b96Omk/qPYmRjNz8EGgZ+P0+KZNZOkGf7/Wm0VAiOkxg4N3ADztbl4G2VtKZAlXP18438SMuOTOQ8HKbaRUGkiXTe4YdC7bFjqfbR+d4zsLBSu3EUsKd+rUKahUbTdPQnuQlJSEmTNnoqamRrOF3Mmqq6vx9ttvCwmuSNuUlpaG77//XsgNQu4MlGflNrrnnnvwwAMPYMSIEZBKpZqtV7BA5syZM3j++ecp2ZgJYDkb2LS8F154oUmelW3btmHevHn4/fff22Q+h/amuLgYQ4YMwdatW+Hu7q7Zel3//v3x1FNPCSvVmvIqte3d3Llzhfwjb775ZpMstUePHsXnn3+OWbNmaT3HxPRQsHIb3XXXXUKwwjL+NQ5WfvnlF/zzzz8ICwu7I1bEbOtY7wk7X88++2yTYIWl2mcV4+zZs+/Y1N3kusLCQiGF+Y4dO+Dh4aHZet2oUaOEa8DR0dFEs08TJiUlBX379sVbb73VpBFx+PBhfPrpp5gzZw4FK3cIClZuI33BCuumTE9PN7nshO2VQqG4lvSocfBIwUrbYihYYTc6tn4QBSqmjZVZb29vocHXuAeMgpU7DwUrt5G+YIXcOShYaVsMBSvkzkfByp2HmgaEEEIIMWkUrBBCCCHEpFGwQgghhBCTRsEKIYQQQkwaDbC9jYYOHSpkUfT09DQ4s2DcuHGYMmUKzM3NNVu0Y/lAEhMTjUo0x6bh9uzZU2+uCLVajddeew35+fkwdKmwv+Gzzz6Dn5+f3r+noKAA7733HiorKzVbdGMDVr/++mvY2tpqtmiXkJAgTPeuq6vTbNEtKioKr7zySpMpyI2tXr1aeMnlcs0W7XJzcyGTybBx40ZhOiu5s5WVlSEuLg6xsbFG5TiKjIzESy+9ZPAaZWWJJZu7fPmywbLExMTE4LnnnjO4X1aHsOm57Do0Zr9du3YV8sQY2i+77tl+8/LyNFv069GjBx5//HHY2NhotmjHZlK98847Qp1ijN69e2PGjBkG98uS+bH9sgHShrBcOuzv2rJli1D/EtNHwcpttHv3bqMKFsMqxA4dOhjMuXLo0CGjKkM2HbpXr17w8vLSG1iw/bCkZ8Zk82TfjSXTYjkN9E23Zple2UwLYwILVkGxoM5QkMYqvgMHDhiVQpv9zSxIMzQD6/z580JiPkOBHztGzs7OGDBgAM3qagPYlFd2E2M3VWOwmx27ngxdo+w6Ydc9C4aMqXbZtNvu3bsb3C8LgrZv3y5kYzVmv76+vkIwZmi/7Lq/ul9jBAQECInyDJUBVkbZfo1prDAsXUDnzp0N7pedN3Z8jdkvO052dnbCrC9KunlnoGDlNmKH3thU+ywQMCbfCqu42MsYxmbfZN/R2MvE2H0auy4H+5uNTYpn7D5ZcGaoJ4u5GeeH3Bmac80bez0xtN8r7rT9ktuPghVCCCGEmDQKKwkhhBBi0ihYIYQQQohJo2CFEEIIISaNghVCCCGEmDQKVgghhBBi0ihYIYQQQohJo2CFEEIIISaNghVCCCGEmDQKVgghhBBi0ihYIYQQQohJo2CFEGIUrjINyakVUKmMXaFDjYKUc8itU/L/IoSQlqNghRBiEFdxGisXbkOGgoNalo0Tu7Zg3Ypl2H6mDHKdwYsI5rWnsXzBNlyskMO4JSEJIaQpClYIIfqp87D7z8VIdoxGBy8bSEQqyCvOYO3Mb7HoQAHq+ABGOxHsg+IQIzmABcsSUVynBK2aSghpCQpWCCF6cCjZvwCLU/0xaEAsfO3MILbwQET3KDhWpCO7VAZ9T4XE9kGIHzUQNgcXYm1yJWTUvUIIaQEKVgghuqkzsGXhPlj1H4pO7pYwE/HbxJZwcPeEs7UEYvbfeolg4d0LE7tUYsOaYyipU2i2E0KI8ShYIYTopM7YhbXnvdC3myeszLRXF8qKy0hOPI6TF3JRIVM3fdQjskbY8B6Q/LcFqTUUrBBCmo+CFUKIDhxKju/DWedOiHY2R9NYRY2SE2uxdPMxpBZXoCBpPWZ+PRMbkooga/RsSOoXhw6q4ziaWqfZQgghxqNghRCigxKXz12CyCcATmYSNH3iw0Fm5oyITp0QGxONmC490cXiOH77fDb+y6lBg3G3FgHwd8nB4dOlUNMoW0JIM1GwQgjRQY3ColLYODrCTCzSEqyIYOMdidiOwfB1d4VHYDQGjImEYv8i/L0rB3XyetlVRPZwclChpKgcCqPztBBCyBUUrBBCdFIprwQc2sfRiiC1sOArkavvimDhFwhv5CPpTDaUqvqp4CSQsNqG41BbP4ghhBAjULBCCNFBBHt7a8hraqDmgwyjyOtQpzaDnYMdRKJ6IQ5Xi+oaDtZ2thAZnkJECCENULBCCNHBDP5BvqgpLECtWsssH6hRlpuNSpVK854C2f/txyWnvpgw2B+W5vWqF1U2Lhc4wsfPDhIKVgghzUTBCiFEBzHcunWFZ9Y5pNYpm6TLF9l3Rj//NGxYsQYbt2/Dmvk/4OdtEox65RVMinaCueR6UKIuPo3kqgh0jbCGmZCshRBCjCfieJp/E0JIA1z1cXx13wdQvD4Pr8a7wvpqoMGVIflACsReNqgpqUatTAmxWIFalRNCYzrCx05aL2GcGgWrn8F9q3rixx/vQ0dHNs6FEEKMR8EKIUQ3rg4XFr+C/yWNxlf/G4EAW2nTwbZqOWqqFRBbWcHC7Ppw22tk5zDv+Y9wafxneHN4IOzqPx4ihBAjUK1BCNFNZIngUY9iOHZg9dF81GpbtFBsDms7G1hqC1QgR9rGpTjhPxn3xPvAWkpVDiGk+ajmIIToJXGJxcQHRsLl3GbsOF8KudF5UtQoPrYSa9P9MW7qEEQ4maPeMBZCCDEaPQYihBjG1aEgJQm5lhGI8rMVksQZxqH04lFcUgUiKtQVVkKiFUIIaT4KVgghRlJBoRBB2oxHOUqFAmKplLpwCSE3hIIVQgghhJg0avAQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEtGH5ufmoU6o1/0XInYnWBiKEEEKISaOeFUIIIYSYNApWCCGEEGLSKFghhBBCiEmjYIUQQgghJo2CFUIIIYSYNApWCCGkHaBpn+RORsEKIYS0BKeGWm26+Us4tQpyuRIqTXYKkfC/hNyZKM8KIYQQQkwa9awQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFpFKwQQgghxKRRsEIIIYQQk0bBCiGEEEJMGgUrhBBCCDFhwP8DhxjWQbHV3QIAAAAASUVORK5CYII=)\n",
    "\n",
    "**Figure 2:** A Single-qubit classifier with data re-uploading. The quantum circuit is divided into layer gates L(i), which constitutes the classifier building blocks. In circuit (a), each of these layers is composed of a U(x) gate, which uploads the data plus a parametrized unitary gate U(φ). We apply this building block N times and finally compute a cost function that is related to the fidelity of the final state of the circuit with the corresponding target state of its class. This cost function may be minimized by tunning the φi parameters. Eventually, data and tunable parameters can be introduced with a single unitary gate, as illustrated in circuit (b) [6]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mqq-vYb6Cz9I"
   },
   "source": [
    "![4z7xba.jpg](data:image/jpeg;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HrQ24x_zPkB3"
   },
   "source": [
    "We will use the idea of data reuploading discussed by Pérez-Salinas et al.[6], where it is shown that it's possible to load a single qubit with arbitrary dimensional data and then use it as a universal quantum classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mBtVpgHsn-ud"
   },
   "source": [
    "We also use a modified version of the layerwise learning approach for parameterized quantum circuits, where individual components of a circuit are trained one by one, inspired by reference[8]. Only subsets of parameters are updated in each training step. This strategy can help avoid the problem of barren plateaus of the error surface due to the low depth of circuits, a low number of parameters trained in one step, and larger magnitude of gradients compared to training the full circuit as described in [8]. This approach also makes the model preferable for execution on noisy intermediate-scale quantum devices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dl48vES3PkB4"
   },
   "source": [
    "## Data visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I0kiJryoPkB4"
   },
   "source": [
    "![download.png](data:image/png;base64,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)\n",
    "\n",
    "**Figure 3:** Visualization of the used SUSY dataset's parameters that were produced using Monte Carlo simulations. The first 8 features are kinematic properties measured by the particle detectors in the accelerator, the remaining are functions of the first 8 features; these are high-level features derived by physicists to help discriminate between the two classes [1]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cOZJWsKs2BPr"
   },
   "source": [
    "# Code Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h-f7g2Ev0Qgw"
   },
   "source": [
    "## Load the Dataset\n",
    "\n",
    "In this implementation, we only used a subset of the dataset, 5000 samples to be exact, both for the training set and testing set. This is done because of the limitation of computational resources that we have right now, training a quantum circuit for 5000000 samples takes a lot of resources and time. We feel that 5000 is a good number considering the reference [2] also only used a subset of the dataset up to 10000 samples.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KaGW_Mf2PkB6"
   },
   "source": [
    "Since we used the PennyLane's Rot gate for the Data Re-uploading Classifier (DRC)[6] scheme, it is nice and easy if the number of features is in multiple of three since every Rot gate needs 3 parameters. We decided to select the first 6 features from the list of best features mentioned in reference [2].\n",
    "\n",
    "In the original paper of DRC, the author mentioned that we can embed the data with zeros if the number of features is not in multiple of three. It is definitely important to try to vary which (and how many) features being used in training to understand which features are more important. We decided that this is a work that we will do after the hackathon since we only have about 1 week and we need to give priority in finishing the model's training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "4cgKAli6BAr-"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "kqoqjT640ysI"
   },
   "outputs": [],
   "source": [
    "# Every feature's name in the dataset\n",
    "header_list = {\n",
    "0: 'label',\n",
    "1: 'lepton 1 pT',\n",
    "2: 'lepton 1 eta',\n",
    "3: 'lepton 1 phi',\n",
    "4: 'lepton 2 pT',\n",
    "5: 'lepton 2 eta',\n",
    "6: 'lepton 2 phi',\n",
    "7: 'missing energy magnitude',\n",
    "8: 'missing energy phi',\n",
    "9: 'MET_rel',\n",
    "10: 'axial MET',\n",
    "11: 'M_R',\n",
    "12: 'M_TR_2',\n",
    "13: 'R',\n",
    "14: 'MT2',\n",
    "15: 'S_R',\n",
    "16: 'M_Delta_R',\n",
    "17: 'dPhi_r_b',\n",
    "18: 'cos(theta_r1)'\n",
    "}\n",
    "\n",
    "\n",
    "# Best features according to reference [2]\n",
    "important_features = [\n",
    "                      'lepton 1 pT',\n",
    "                      'lepton 2 pT',\n",
    "                      'missing energy magnitude',\n",
    "                      'M_TR_2',\n",
    "                      'M_Delta_R',\n",
    "                      'lepton 1 eta',\n",
    "                      'lepton 2 eta'\n",
    "]\n",
    "\n",
    "# The index of the best features in the header_list\n",
    "important_features_id = np.array([\n",
    "                         0,\n",
    "                         3,\n",
    "                         6,\n",
    "                         11,\n",
    "                         15,\n",
    "                         1,\n",
    "                         4\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "X5SA3PJD1MnP",
    "outputId": "539e7fea-ad1e-4a6a-e786-669646d78af0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/Braket/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3156: DtypeWarning: Columns (0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>lepton 1 pT</th>\n",
       "      <th>lepton 1 eta</th>\n",
       "      <th>lepton 1 phi</th>\n",
       "      <th>lepton 2 pT</th>\n",
       "      <th>lepton 2 eta</th>\n",
       "      <th>lepton 2 phi</th>\n",
       "      <th>missing energy magnitude</th>\n",
       "      <th>missing energy phi</th>\n",
       "      <th>MET_rel</th>\n",
       "      <th>axial MET</th>\n",
       "      <th>M_R</th>\n",
       "      <th>M_TR_2</th>\n",
       "      <th>R</th>\n",
       "      <th>MT2</th>\n",
       "      <th>S_R</th>\n",
       "      <th>M_Delta_R</th>\n",
       "      <th>dPhi_r_b</th>\n",
       "      <th>cos(theta_r1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>signal</td>\n",
       "      <td>lepton 1 pT</td>\n",
       "      <td>lepton 1 eta</td>\n",
       "      <td>lepton 1 phi</td>\n",
       "      <td>lepton 2 pT</td>\n",
       "      <td>lepton 2 eta</td>\n",
       "      <td>lepton 2 phi</td>\n",
       "      <td>missing energy magnitude</td>\n",
       "      <td>missing energy phi</td>\n",
       "      <td>MET_rel</td>\n",
       "      <td>axial MET</td>\n",
       "      <td>M_R</td>\n",
       "      <td>M_TR_2</td>\n",
       "      <td>R</td>\n",
       "      <td>MT2</td>\n",
       "      <td>S_R</td>\n",
       "      <td>M_Delta_R</td>\n",
       "      <td>dPhi_r_b</td>\n",
       "      <td>cos(theta_r1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9728614687919616</td>\n",
       "      <td>0.6538545489311217</td>\n",
       "      <td>1.1762245893478391</td>\n",
       "      <td>1.157156467437744</td>\n",
       "      <td>-1.7398731708526611</td>\n",
       "      <td>-0.8743090629577636</td>\n",
       "      <td>0.5677649974822998</td>\n",
       "      <td>-0.17500004172325134</td>\n",
       "      <td>0.8100607395172118</td>\n",
       "      <td>-0.2525521218776703</td>\n",
       "      <td>1.9218870401382449</td>\n",
       "      <td>0.8896374106407164</td>\n",
       "      <td>0.4107718467712402</td>\n",
       "      <td>1.145620822906494</td>\n",
       "      <td>1.9326320886611936</td>\n",
       "      <td>0.994464099407196</td>\n",
       "      <td>1.3678154945373535</td>\n",
       "      <td>0.04071449860930442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.6679730415344238</td>\n",
       "      <td>0.06419061869382858</td>\n",
       "      <td>-1.225171446800232</td>\n",
       "      <td>0.5061022043228148</td>\n",
       "      <td>-0.33893898129463196</td>\n",
       "      <td>1.6725428104400635</td>\n",
       "      <td>3.475464344024658</td>\n",
       "      <td>-1.219136357307434</td>\n",
       "      <td>0.01295456290245056</td>\n",
       "      <td>3.7751736640930176</td>\n",
       "      <td>1.0459771156311035</td>\n",
       "      <td>0.568051278591156</td>\n",
       "      <td>0.4819284379482269</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.44841027259826655</td>\n",
       "      <td>0.20535576343536377</td>\n",
       "      <td>1.3218934535980225</td>\n",
       "      <td>0.3775840103626251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4448399245738983</td>\n",
       "      <td>-0.13429801166057587</td>\n",
       "      <td>-0.7099716067314148</td>\n",
       "      <td>0.4517189264297485</td>\n",
       "      <td>-1.6138712167739868</td>\n",
       "      <td>-0.7686609029769897</td>\n",
       "      <td>1.219918131828308</td>\n",
       "      <td>0.5040258169174193</td>\n",
       "      <td>1.8312475681304927</td>\n",
       "      <td>-0.4313853085041046</td>\n",
       "      <td>0.5262832045555115</td>\n",
       "      <td>0.9415140151977539</td>\n",
       "      <td>1.58753502368927</td>\n",
       "      <td>2.024308204650879</td>\n",
       "      <td>0.6034975647926331</td>\n",
       "      <td>1.5623739957809448</td>\n",
       "      <td>1.1354544162750244</td>\n",
       "      <td>0.18091000616550443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.38125607371330256</td>\n",
       "      <td>-0.9761453866958617</td>\n",
       "      <td>0.6931523084640503</td>\n",
       "      <td>0.44895884394645685</td>\n",
       "      <td>0.8917528986930846</td>\n",
       "      <td>-0.6773284673690796</td>\n",
       "      <td>2.033060073852539</td>\n",
       "      <td>1.5330405235290527</td>\n",
       "      <td>3.0462598800659175</td>\n",
       "      <td>-1.0052847862243652</td>\n",
       "      <td>0.5693860650062561</td>\n",
       "      <td>1.0152113437652588</td>\n",
       "      <td>1.582216739654541</td>\n",
       "      <td>1.5519142150878906</td>\n",
       "      <td>0.7612152099609374</td>\n",
       "      <td>1.7154637575149536</td>\n",
       "      <td>1.492256760597229</td>\n",
       "      <td>0.09071890264749528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    label          lepton 1 pT          lepton 1 eta         lepton 1 phi  \\\n",
       "0  signal          lepton 1 pT          lepton 1 eta         lepton 1 phi   \n",
       "1     0.0   0.9728614687919616    0.6538545489311217   1.1762245893478391   \n",
       "2     1.0   1.6679730415344238   0.06419061869382858   -1.225171446800232   \n",
       "3     1.0   0.4448399245738983  -0.13429801166057587  -0.7099716067314148   \n",
       "4     1.0  0.38125607371330256   -0.9761453866958617   0.6931523084640503   \n",
       "\n",
       "           lepton 2 pT          lepton 2 eta         lepton 2 phi  \\\n",
       "0          lepton 2 pT          lepton 2 eta         lepton 2 phi   \n",
       "1    1.157156467437744   -1.7398731708526611  -0.8743090629577636   \n",
       "2   0.5061022043228148  -0.33893898129463196   1.6725428104400635   \n",
       "3   0.4517189264297485   -1.6138712167739868  -0.7686609029769897   \n",
       "4  0.44895884394645685    0.8917528986930846  -0.6773284673690796   \n",
       "\n",
       "   missing energy magnitude    missing energy phi              MET_rel  \\\n",
       "0  missing energy magnitude    missing energy phi              MET_rel   \n",
       "1        0.5677649974822998  -0.17500004172325134   0.8100607395172118   \n",
       "2         3.475464344024658    -1.219136357307434  0.01295456290245056   \n",
       "3         1.219918131828308    0.5040258169174193   1.8312475681304927   \n",
       "4         2.033060073852539    1.5330405235290527   3.0462598800659175   \n",
       "\n",
       "             axial MET                 M_R              M_TR_2  \\\n",
       "0            axial MET                 M_R              M_TR_2   \n",
       "1  -0.2525521218776703  1.9218870401382449  0.8896374106407164   \n",
       "2   3.7751736640930176  1.0459771156311035   0.568051278591156   \n",
       "3  -0.4313853085041046  0.5262832045555115  0.9415140151977539   \n",
       "4  -1.0052847862243652  0.5693860650062561  1.0152113437652588   \n",
       "\n",
       "                    R                 MT2                  S_R  \\\n",
       "0                   R                 MT2                  S_R   \n",
       "1  0.4107718467712402   1.145620822906494   1.9326320886611936   \n",
       "2  0.4819284379482269                 0.0  0.44841027259826655   \n",
       "3    1.58753502368927   2.024308204650879   0.6034975647926331   \n",
       "4   1.582216739654541  1.5519142150878906   0.7612152099609374   \n",
       "\n",
       "             M_Delta_R            dPhi_r_b        cos(theta_r1)  \n",
       "0            M_Delta_R            dPhi_r_b        cos(theta_r1)  \n",
       "1    0.994464099407196  1.3678154945373535  0.04071449860930442  \n",
       "2  0.20535576343536377  1.3218934535980225   0.3775840103626251  \n",
       "3   1.5623739957809448  1.1354544162750244  0.18091000616550443  \n",
       "4   1.7154637575149536   1.492256760597229  0.09071890264749528  "
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the dataset (can be obtained from reference [1])\n",
    "\n",
    "# Note: please change the directory to the correct directory of the dataset if you want to run this code\n",
    "dataset = pd.read_csv('~/SageMaker/SUSY_cut.csv', header=None)\n",
    "dataset = dataset.rename(columns=header_list)\n",
    "dataset.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SylVmeJlQYAl"
   },
   "outputs": [],
   "source": [
    "# Convert it to numpy\n",
    "dataset = dataset.to_numpy()\n",
    "\n",
    "# Sepparating the label (Y) from the input features (X)\n",
    "Y = dataset[:, 0]\n",
    "Y = np.array(Y, dtype=int)\n",
    "X = dataset[:, 1:]\n",
    "\n",
    "# Sanity check\n",
    "print(X.shape, Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7sPN1bXd12kP"
   },
   "outputs": [],
   "source": [
    "# Separate data with label 0 and label 1\n",
    "\n",
    "x_0 = X[Y == 0, :]\n",
    "x_1 = X[Y == 1, :]\n",
    "y_0 = Y[Y==0]\n",
    "y_1 = Y[Y==1]\n",
    "\n",
    "# Sanity check\n",
    "print(x_0.shape, y_0.shape)\n",
    "print(x_1.shape, y_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-LfSLtap9JWD"
   },
   "outputs": [],
   "source": [
    "# Split 50% of the data for training set and 50% for testing set\n",
    "\n",
    "x_train_0, x_test_0, y_train_0, y_test_0 = train_test_split(x_0, y_0, test_size=0.5, random_state=2021)\n",
    "x_train_1, x_test_1, y_train_1, y_test_1 = train_test_split(x_1, y_1, test_size=0.5, random_state=2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qolr2ebO9QY4"
   },
   "outputs": [],
   "source": [
    "num_sample = 2500 # sample per class, total = 5000\n",
    "\n",
    "# Take the first 5000 samples (2500 from each class) from training set for X_train and Y_train\n",
    "X_train = np.concatenate((x_train_0[:num_sample, :], x_train_1[:num_sample, :]), axis=0)\n",
    "Y_train = np.concatenate((y_train_0[:num_sample], y_train_1[:num_sample]), axis=0)\n",
    "\n",
    "# Take the first 5000 samples (2500 from each class) from testing set for X_test and Y_test\n",
    "X_test = np.concatenate((x_test_0[:num_sample, :], x_test_1[:num_sample, :]), axis=0)\n",
    "Y_test = np.concatenate((y_test_0[:num_sample], y_test_1[:num_sample]), axis=0)\n",
    "\n",
    "# Sanity Check\n",
    "print(X_train.shape, Y_train.shape)\n",
    "print(X_test.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JQF3Fexp9-rd"
   },
   "outputs": [],
   "source": [
    "# Save the 5000 subset as a txt file for future use so we don't need to load the whole dataset each time\n",
    "\n",
    "np.savetxt('./SUSY Dataset/X_train_5000.txt', X_train)\n",
    "np.savetxt('./SUSY Dataset/X_test_5000.txt', X_test)\n",
    "np.savetxt('./SUSY Dataset/Y_train_5000.txt', Y_train)\n",
    "np.savetxt('./SUSY Dataset/Y_test_5000.txt', Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "LVcAd6fL-PYK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 18) (5000,)\n",
      "(5000, 18) (5000,)\n"
     ]
    }
   ],
   "source": [
    "# Code to load the saved subset\n",
    "\n",
    "num_sample = 5000\n",
    "\n",
    "X_train = np.loadtxt('./SUSY Dataset/X_train_' + str(num_sample) + '.txt')\n",
    "Y_train = np.loadtxt('./SUSY Dataset/Y_train_' + str(num_sample) + '.txt')\n",
    "\n",
    "X_test = np.loadtxt('./SUSY Dataset/X_test_' + str(num_sample) + '.txt')\n",
    "Y_test = np.loadtxt('./SUSY Dataset/Y_test_' + str(num_sample) + '.txt')\n",
    "\n",
    "# Sanity Check\n",
    "print(X_train.shape, Y_train.shape)\n",
    "print(X_test.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "nicWapUS-dyJ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5000, 6), (5000, 6))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take only the best 6 features\n",
    "\n",
    "X_train = X_train[:, important_features_id[:6]]\n",
    "X_test = X_test[:, important_features_id[:6]]\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YlrjXzUcQf-8"
   },
   "source": [
    "## Training the Quantum Circuit in Simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "BkX2TelVQ1OK"
   },
   "outputs": [],
   "source": [
    "import pennylane as qml\n",
    "from pennylane import numpy as np\n",
    "from pennylane.optimize import AdamOptimizer\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "qml.enable_tape()\n",
    "\n",
    "\n",
    "# Set a random seed\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AR-l2rb2RCLj"
   },
   "source": [
    "### The Circuit Architecture\n",
    "\n",
    "In our implementation, we used DRC scheme with 1 qubit. Below is the schematic of one layer of the circuit that we trained.\n",
    "\n",
    "![simple_rot_gate___cnot_page-0001.jpg](data:image/jpeg;base64,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)\n",
    "\n",
    "$$\n",
    "\\begin{array}{l}\n",
    "\\vec{\\theta}_{l}^{n \\sim n+2}=\\left(\\theta_{l}^{n}, \\theta_{l}^{n+1}, \\theta_{l}^{n+2}\\right) \\\\\n",
    "\\vec{\\theta}_{l}=\\left(\\theta_{l}^{1}, \\theta_{l}^{2}, \\theta_{l}^{3}, \\ldots, \\theta_{l}^{N}\\right)\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\theta_{l}^{n}=w_{l}^{n} x^{n}+b_{l}^{n}\n",
    "$$\n",
    "\n",
    "where $x$ is one sample of data, $w$ and $b$ is the weight and bias parameter that will be optimized during the training, $n$ is the feature index (for example, $n=1$ means the value of the first feature of the sample $x$), $N$ is the total number of features of the sample $x$, and $l$ is the index of layer (for example, $l=1$ means the first layer).\n",
    "\n",
    "The R gate here is the PennyLane's [Rot gate](https://pennylane.readthedocs.io/en/stable/code/api/pennylane.Rot.html) where\n",
    "$$\n",
    "R(\\phi, \\theta, \\omega)=R Z(\\omega) R Y(\\theta) R Z(\\phi)=\\left[\\begin{array}{ll}\n",
    "e^{-i(\\phi+\\omega) / 2} \\cos (\\theta / 2) & -e^{i(\\phi-\\omega) / 2} \\sin (\\theta / 2) \\\\\n",
    "e^{-i(\\phi-\\omega) / 2} \\sin (\\theta / 2) & e^{i(\\phi+\\omega) / 2} \\cos (\\theta / 2)\n",
    "\\end{array}\\right].\n",
    "$$\n",
    "\n",
    "This circuit can then be repeated for several layers. Below is the schematic of the circuit with $L$ number of layers.\n",
    "\n",
    "![simple_rot_gate___cnot(1)_page-0001.jpg](data:image/jpeg;base64,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)\n",
    "\n",
    "In this implementation, we set $L=5$.\n",
    "\n",
    "How we measured the circuit will be explained in the next part (*The Cost Function*)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "irg2LXiwiemO"
   },
   "source": [
    "### The Cost Function\n",
    "\n",
    "We followed the weighted fidelity cost function proposed by the authors of the DRC[6]. Since there are two class of labels (0 and 1) in the dataset, we represented the label 0 as $|0\\rangle$ and the label 1 as $|1\\rangle$ as they are maximally orthogonal points in the Bloch sphere for two class case.\n",
    "\n",
    "Then, the cost function for one batch of training can be written as\n",
    "$$\n",
    "J\\left(\\vec{\\alpha}, \\vec{\\theta}\\right)=\\frac{1}{2M} \\sum_{m=1}^{M} \\operatorname{sum}\\left\\{\\left(\\vec{y}_{\\text {pred }_{m}}(\\vec{\\alpha}, \\vec{\\theta})-\\vec{y}_{\\text {true }_{m}}\\right)^{2}\\right\\}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\vec{y}_{\\text {true }_{m}} = \\left[\\begin{array}{c}\n",
    "1 \\\\\n",
    "0\n",
    "\\end{array}\\right] \\text{if sample $m$ has label 0,}\n",
    "\\left[\\begin{array}{c}\n",
    "0 \\\\\n",
    "1\n",
    "\\end{array}\\right] \\text{if sample $m$ has label 1}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\vec{y}_{\\text{pred}_{m}}(\\vec{\\alpha}, \\vec{\\theta}) =\n",
    "\\vec{\\alpha}\n",
    "\\odot\n",
    "\\left[\\begin{array}{c}\n",
    "\\left\\langle O_{0}(\\vec{\\theta})\\right\\rangle \\\\\n",
    "\\left\\langle O_{1}(\\vec{\\theta})\\right\\rangle\n",
    "\\end{array}\\right]\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\langle O_{0}(\\vec{\\theta})\\rangle=\\langle\\Psi_{D R C}(\\vec{\\theta})|O_{0}| \\Psi_{D R C}(\\vec{\\theta})\\rangle=\\langle\\Psi_{D R C}(\\vec{\\theta})|0\\rangle\\langle 0| \\Psi_{D R C}(\\vec{\\theta})\\rangle=|\\langle 0|\\Psi_{D R C}(\\vec{\\theta})\\rangle|^2\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\langle O_{1}(\\vec{\\theta})\\rangle=\\langle\\Psi_{D R C}(\\vec{\\theta})|O_{1}| \\Psi_{D R C}(\\vec{\\theta})\\rangle=\\langle\\Psi_{D R C}(\\vec{\\theta})|1\\rangle\\langle 1| \\Psi_{D R C}(\\vec{\\theta})\\rangle=|\\langle 1|\\Psi_{D R C}(\\vec{\\theta})\\rangle|^2\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\vec{\\alpha} = \\left[\\begin{array}{c}\n",
    "\\alpha_{0} \\\\\n",
    "\\alpha_{1}\n",
    "\\end{array}\\right]\n",
    "$$\n",
    "\n",
    "$$\n",
    "|\\Psi_{D R C}(\\vec{\\theta})\\rangle=U(\\vec{\\theta}_{L})U(\\vec{\\theta}_{L-1})\\ldots U(\\vec{\\theta}_{1})|0\\rangle\n",
    "$$\n",
    "where $m$ is the index of data sample (for example, $m=1$ means the first sample from the batch), $M$ is the total number of samples in one batch, $\\vec{\\theta_{l}}$ is the same as discussed in the previous section (*The Circuit Architecture*), $\\vec{\\alpha}$ is the weight vector that will also be optimized during the training.\n",
    "\n",
    "In this case, we need to measure the circuit twice for every sample of data, once using observable $|0\\rangle\\langle 0|$ and once using observable $|1\\rangle\\langle 1|$. To make the code cleaner and easier, we decided to use two qubits instead. Both qubits will be operated by the same gates (with the same parameters and sample), but the first qubit is measured by the observable $|0\\rangle\\langle 0|$ and the second qubit is measured by the observable $|1\\rangle\\langle 1|$. The final result will be exactly the same as measuring one qubit two times with two different observables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KnnUZr5_4k8o"
   },
   "source": [
    "This code below will set up the observables needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "3zSUb94JQ4VC"
   },
   "outputs": [],
   "source": [
    "# Define output labels as quantum state vectors\n",
    "label_0 = [[1], [0]]\n",
    "label_1 = [[0], [1]]\n",
    "\n",
    "\n",
    "def density_matrix(state):\n",
    "    \"\"\"Calculates the density matrix representation of a state.\n",
    "\n",
    "    Args:\n",
    "        state (array[complex]): array representing a quantum state vector\n",
    "\n",
    "    Returns:\n",
    "        dm: (array[complex]): array representing the density matrix\n",
    "    \"\"\"\n",
    "    return np.outer(state,  np.conj(state))\n",
    "\n",
    "state_labels = [label_0, label_1]\n",
    "dm_labels = [density_matrix(state_labels[i]) for i in range(2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N6yw98TdzKwD"
   },
   "source": [
    "### The Training Process\n",
    "\n",
    "We used Keras to optimized the parameters by turning PennyLane's QNode as a Keras layer. The training is carried out layer by layer as inspired by reference [8].\n",
    "\n",
    "We first initialize all the weights randomly. We then trained the $\\vec{\\theta_{l}}$ parameter layer by layer, from the first layer until the last layer $L=5$.\n",
    "\n",
    "When we trained the $k$ layer, we kept all parameters in the other layer ($\\vec{\\theta_{l\\neq k}}$) as constant and only optimized the $\\vec{\\theta_{k}}$. The weight vector $\\vec{\\alpha}$ is still being optimized. The value of the parameters after each epoch is saved. After the training is done, we kept the parameter $\\vec{\\theta_{k}}$ from the saved files that gives the best balance between train and test/validation accuracy to avoid overfitting as well as underfitting, and discard the rest. We also discard the weight vector $\\vec{\\alpha}$. We re-initialized the weight vector $\\vec{\\alpha}$ randomly, and continued training the next layer. This process is repeated until all parameters in every layer is trained.\n",
    "\n",
    "After that, we again re-initialized the weight vector $\\vec{\\alpha}$ randomly (but not the $\\vec{\\theta}$ parameters) and then trained all parameters at once for the last time. The value of the parameters after each epoch is saved. Just like before, we then kept the the parameters from the saved files that gives the best balance between train and test/validation accuracy. This final parameters will be called as the best parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yES07Ls-4u31"
   },
   "source": [
    "#### The Hyperparameters\n",
    "\n",
    "In this implementation, we used these hyperparameters:\n",
    "1. Optimizer: Adam Optimizer with learning rate = 0.05, $\\beta_{1}=0.9$, $\\beta_{2}=0.999$, $\\epsilon=10^{-7}$\n",
    "2. Batch size: 128 samples per batch\n",
    "3. Maximum epoch: 10 epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sQDtPoKc5cvZ"
   },
   "source": [
    "Let's start the training process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NoXUfiQn5ktS"
   },
   "source": [
    "Below is the code for the custom layer for the weight vector $\\vec{\\alpha}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "02h_zRZJxOuc"
   },
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "\n",
    "\n",
    "# Alpha Custom Layer\n",
    "class class_weights(tf.keras.layers.Layer):\n",
    "    def __init__(self):\n",
    "        super(class_weights, self).__init__()\n",
    "        w_init = tf.random_normal_initializer()\n",
    "        self.w = tf.Variable(\n",
    "            initial_value=w_init(shape=(1, 2), dtype=\"float32\"),\n",
    "            trainable=True,\n",
    "        )\n",
    "\n",
    "    def call(self, inputs):\n",
    "        return (inputs * self.w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TMrJ2qw67fI8"
   },
   "source": [
    "Initializes the initial parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "tgsMKLT67kbS"
   },
   "outputs": [],
   "source": [
    "num_fc_layer = 5 # number of layer\n",
    "params_fix = np.random.uniform(size=(2, num_fc_layer, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R-KYsOQm6Dyx"
   },
   "source": [
    "#### Training the 1st Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NGK6aA4p5_0o"
   },
   "outputs": [],
   "source": [
    "n_qubits = 2  # number of class\n",
    "dev_fc = qml.device(\"default.qubit\", wires=n_qubits)\n",
    "layer_id = 0 # the layer index to be trained, it starts from zero because of Numpy convention\n",
    "\n",
    "@qml.qnode(dev_fc)\n",
    "def q_fc(params, inputs):\n",
    "    \"\"\"A variational quantum circuit representing the DRC.\n",
    "\n",
    "    Args:\n",
    "        params (array[float]): array of parameters\n",
    "        inputs (array[float]): 1-d input vector (data sample)\n",
    "\n",
    "    Returns:\n",
    "        array[float]: 1-d output vector in the form of [alpha0*<O0>, alpha1*<O1>]\n",
    "    \"\"\"\n",
    "    \n",
    "    # layer iteration\n",
    "    for l in range(len(params_fix[0])):\n",
    "        # qubit iteration\n",
    "        for q in range(n_qubits):\n",
    "            # gate iteration\n",
    "            for g in range(int(len(inputs)/3)):\n",
    "              if l == layer_id: # train only the specified layer\n",
    "                qml.Rot(*(params[0][3*g:3*(g+1)] * inputs[3*g:3*(g+1)] + params[1][3*g:3*(g+1)]), wires=q)\n",
    "              else:\n",
    "                qml.Rot(*(params_fix[0][l][3*g:3*(g+1)] * inputs[3*g:3*(g+1)] + params_fix[1][l][3*g:3*(g+1)]), wires=q)\n",
    "    \n",
    "    return [qml.expval(qml.Hermitian(dm_labels[i], wires=[i])) for i in range(n_qubits)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3gSW4ntd8JrL"
   },
   "outputs": [],
   "source": [
    "n_component = 6 # number of features used\n",
    "\n",
    "X = tf.keras.Input(shape=(n_component,), name='Input_Layer')\n",
    "\n",
    "\n",
    "# Quantum Layer\n",
    "q_fc_layer = qml.qnn.KerasLayer(q_fc, {\"params\": (2, n_component)}, output_dim=2)(X)\n",
    "\n",
    "# Alpha Layer\n",
    "alpha_layer = class_weights()(q_fc_layer)\n",
    "\n",
    "model = tf.keras.Model(inputs=X, outputs=alpha_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yE3ivqkg8eSv"
   },
   "outputs": [],
   "source": [
    "opt = tf.keras.optimizers.Adam(learning_rate=0.05)\n",
    "model.compile(opt, loss='mse', metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XuKzoMPF8jvy"
   },
   "outputs": [],
   "source": [
    "# Note: please put the correct directory to save the weights accordingly if you want to run the code\n",
    "filepath = \"./Model/Best6_layer5(layer_id=0)_set5000_10epoch_saved-model-{epoch:02d}.hdf5\"\n",
    "checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath, monitor='val_acc', verbose=1,\n",
    "                                                save_weights_only=True, save_best_only=False, mode='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IyFttDn89W9H"
   },
   "outputs": [],
   "source": [
    "H = model.fit(X_train, to_categorical(Y_train), epochs=10, batch_size=128, initial_epoch=0,\n",
    "              validation_data=(X_test, to_categorical(Y_test)), verbose=1,\n",
    "              callbacks=[checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fbKHKoRb9g7f"
   },
   "outputs": [],
   "source": [
    "# load the parameters that is considered giving the best balance between train and test accuracy\n",
    "model.load_weights('./Model/Best6_layer5(layer_id=0)_set5000_10epoch_saved-model-08.hdf5')\n",
    "\n",
    "\n",
    "# keep that parameters and replace the initial one\n",
    "params_layer_0 = model.get_weights()[0]\n",
    "params_fix[:, 0, :] = params_layer_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bSo8KQnQ-Rz_"
   },
   "source": [
    "#### Training the 2nd Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ux2oZBAJ9a-O"
   },
   "outputs": [],
   "source": [
    "n_qubits = 2  # number of class\n",
    "dev_fc = qml.device(\"default.qubit\", wires=n_qubits)\n",
    "layer_id = 1 # the layer index to be trained, it starts from zero because of Numpy convention\n",
    "\n",
    "@qml.qnode(dev_fc)\n",
    "def q_fc(params, inputs):\n",
    "    \"\"\"A variational quantum circuit representing the DRC.\n",
    "\n",
    "    Args:\n",
    "        params (array[float]): array of parameters\n",
    "        inputs (array[float]): 1-d input vector (data sample)\n",
    "\n",
    "    Returns:\n",
    "        array[float]: 1-d output vector in the form of [alpha0*<O0>, alpha1*<O1>]\n",
    "    \"\"\"\n",
    "    \n",
    "    # layer iteration\n",
    "    for l in range(len(params_fix[0])):\n",
    "        # qubit iteration\n",
    "        for q in range(n_qubits):\n",
    "            # gate iteration\n",
    "            for g in range(int(len(inputs)/3)):\n",
    "              if l == layer_id: # train only the specified layer\n",
    "                qml.Rot(*(params[0][3*g:3*(g+1)] * inputs[3*g:3*(g+1)] + params[1][3*g:3*(g+1)]), wires=q)\n",
    "              else:\n",
    "                qml.Rot(*(params_fix[0][l][3*g:3*(g+1)] * inputs[3*g:3*(g+1)] + params_fix[1][l][3*g:3*(g+1)]), wires=q)\n",
    "    \n",
    "    return [qml.expval(qml.Hermitian(dm_labels[i], wires=[i])) for i in range(n_qubits)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9h_c4O709bAm"
   },
   "outputs": [],
   "source": [
    "n_component = 6 # number of features used\n",
    "\n",
    "X = tf.keras.Input(shape=(n_component,), name='Input_Layer')\n",
    "\n",
    "\n",
    "# Quantum Layer\n",
    "q_fc_layer = qml.qnn.KerasLayer(q_fc, {\"params\": (2, n_component)}, output_dim=2)(X)\n",
    "\n",
    "# Alpha Layer\n",
    "alpha_layer = class_weights()(q_fc_layer)\n",
    "\n",
    "model = tf.keras.Model(inputs=X, outputs=alpha_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "o2TaM4PO9bC8"
   },
   "outputs": [],
   "source": [
    "opt = tf.keras.optimizers.Adam(learning_rate=0.05)\n",
    "model.compile(opt, loss='mse', metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9jYY25Az-0zL"
   },
   "outputs": [],
   "source": [
    "# Note: please put the correct directory to save the weights accordingly if you want to run the code\n",
    "filepath = \"./Model/Best6_layer5(layer_id=1)_set5000_10epoch_saved-model-{epoch:02d}.hdf5\"\n",
    "checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath, monitor='val_acc', verbose=1,\n",
    "                                                save_weights_only=True, save_best_only=False, mode='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hYMazkHT-01T"
   },
   "outputs": [],
   "source": [
    "H = model.fit(X_train, to_categorical(Y_train), epochs=10, batch_size=128, initial_epoch=0,\n",
    "              validation_data=(X_test, to_categorical(Y_test)), verbose=1,\n",
    "              callbacks=[checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fNg7jFyd-03e"
   },
   "outputs": [],
   "source": [
    "# load the parameters that is considered giving the best balance between train and test accuracy\n",
    "model.load_weights('./Model/Best6_layer5(layer_id=1)_set5000_10epoch_saved-model-08.hdf5')\n",
    "\n",
    "\n",
    "# keep that parameters and replace the initial one\n",
    "params_layer_1 = model.get_weights()[0]\n",
    "params_fix[:, 1, :] = params_layer_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kfbcccen_IZj"
   },
   "source": [
    "#### Training the 3rd Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "O_84hdH0-05n"
   },
   "outputs": [],
   "source": [
    "n_qubits = 2  # number of class\n",
    "dev_fc = qml.device(\"default.qubit\", wires=n_qubits)\n",
    "layer_id = 2 # the layer index to be trained, it starts from zero because of Numpy convention\n",
    "\n",
    "@qml.qnode(dev_fc)\n",
    "def q_fc(params, inputs):\n",
    "    \"\"\"A variational quantum circuit representing the DRC.\n",
    "\n",
    "    Args:\n",
    "        params (array[float]): array of parameters\n",
    "        inputs (array[float]): 1-d input vector (data sample)\n",
    "\n",
    "    Returns:\n",
    "        array[float]: 1-d output vector in the form of [alpha0*<O0>, alpha1*<O1>]\n",
    "    \"\"\"\n",
    "    \n",
    "    # layer iteration\n",
    "    for l in range(len(params_fix[0])):\n",
    "        # qubit iteration\n",
    "        for q in range(n_qubits):\n",
    "            # gate iteration\n",
    "            for g in range(int(len(inputs)/3)):\n",
    "              if l == layer_id: # train only the specified layer\n",
    "                qml.Rot(*(params[0][3*g:3*(g+1)] * inputs[3*g:3*(g+1)] + params[1][3*g:3*(g+1)]), wires=q)\n",
    "              else:\n",
    "                qml.Rot(*(params_fix[0][l][3*g:3*(g+1)] * inputs[3*g:3*(g+1)] + params_fix[1][l][3*g:3*(g+1)]), wires=q)\n",
    "    \n",
    "    return [qml.expval(qml.Hermitian(dm_labels[i], wires=[i])) for i in range(n_qubits)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5qcssaMp--xU"
   },
   "outputs": [],
   "source": [
    "n_component = 6 # number of features used\n",
    "\n",
    "X = tf.keras.Input(shape=(n_component,), name='Input_Layer')\n",
    "\n",
    "\n",
    "# Quantum Layer\n",
    "q_fc_layer = qml.qnn.KerasLayer(q_fc, {\"params\": (2, n_component)}, output_dim=2)(X)\n",
    "\n",
    "# Alpha Layer\n",
    "alpha_layer = class_weights()(q_fc_layer)\n",
    "\n",
    "model = tf.keras.Model(inputs=X, outputs=alpha_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "io0KOkOq--zf"
   },
   "outputs": [],
   "source": [
    "opt = tf.keras.optimizers.Adam(learning_rate=0.05)\n",
    "model.compile(opt, loss='mse', metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "X3ZAlTuf-07q"
   },
   "outputs": [],
   "source": [
    "# Note: please put the correct directory to save the weights accordingly if you want to run the code\n",
    "filepath = \"./Model/Best6_layer5(layer_id=2)_set5000_10epoch_saved-model-{epoch:02d}.hdf5\"\n",
    "checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath, monitor='val_acc', verbose=1,\n",
    "                                                save_weights_only=True, save_best_only=False, mode='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7j3pnznM_ZTE"
   },
   "outputs": [],
   "source": [
    "H = model.fit(X_train, to_categorical(Y_train), epochs=10, batch_size=128, initial_epoch=0,\n",
    "              validation_data=(X_test, to_categorical(Y_test)), verbose=1,\n",
    "              callbacks=[checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FpwKnuiK_ZVV"
   },
   "outputs": [],
   "source": [
    "# load the parameters that is considered giving the best balance between train and test accuracy\n",
    "model.load_weights('./Model/Best6_layer5(layer_id=2)_set5000_10epoch_saved-model-08.hdf5')\n",
    "\n",
    "\n",
    "# keep that parameters and replace the initial one\n",
    "params_layer_2 = model.get_weights()[0]\n",
    "params_fix[:, 2, :] = params_layer_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1Q2Bee60_kjN"
   },
   "source": [
    "#### Training the 4th Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iKm1ouZt_ZXf"
   },
   "outputs": [],
   "source": [
    "n_qubits = 2  # number of class\n",
    "dev_fc = qml.device(\"default.qubit\", wires=n_qubits)\n",
    "layer_id = 3 # the layer index to be trained, it starts from zero because of Numpy convention\n",
    "\n",
    "@qml.qnode(dev_fc)\n",
    "def q_fc(params, inputs):\n",
    "    \"\"\"A variational quantum circuit representing the DRC.\n",
    "\n",
    "    Args:\n",
    "        params (array[float]): array of parameters\n",
    "        inputs (array[float]): 1-d input vector (data sample)\n",
    "\n",
    "    Returns:\n",
    "        array[float]: 1-d output vector in the form of [alpha0*<O0>, alpha1*<O1>]\n",
    "    \"\"\"\n",
    "    \n",
    "    # layer iteration\n",
    "    for l in range(len(params_fix[0])):\n",
    "        # qubit iteration\n",
    "        for q in range(n_qubits):\n",
    "            # gate iteration\n",
    "            for g in range(int(len(inputs)/3)):\n",
    "              if l == layer_id: # train only the specified layer\n",
    "                qml.Rot(*(params[0][3*g:3*(g+1)] * inputs[3*g:3*(g+1)] + params[1][3*g:3*(g+1)]), wires=q)\n",
    "              else:\n",
    "                qml.Rot(*(params_fix[0][l][3*g:3*(g+1)] * inputs[3*g:3*(g+1)] + params_fix[1][l][3*g:3*(g+1)]), wires=q)\n",
    "    \n",
    "    return [qml.expval(qml.Hermitian(dm_labels[i], wires=[i])) for i in range(n_qubits)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bRI1Pkq2_jNe"
   },
   "outputs": [],
   "source": [
    "n_component = 6 # number of features used\n",
    "\n",
    "X = tf.keras.Input(shape=(n_component,), name='Input_Layer')\n",
    "\n",
    "\n",
    "# Quantum Layer\n",
    "q_fc_layer = qml.qnn.KerasLayer(q_fc, {\"params\": (2, n_component)}, output_dim=2)(X)\n",
    "\n",
    "# Alpha Layer\n",
    "alpha_layer = class_weights()(q_fc_layer)\n",
    "\n",
    "model = tf.keras.Model(inputs=X, outputs=alpha_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_md_Syil_jPw"
   },
   "outputs": [],
   "source": [
    "opt = tf.keras.optimizers.Adam(learning_rate=0.05)\n",
    "model.compile(opt, loss='mse', metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0SMG3un0_jR2"
   },
   "outputs": [],
   "source": [
    "# Note: please put the correct directory to save the weights accordingly if you want to run the code\n",
    "filepath = \"./Model/Best6_layer5(layer_id=3)_set5000_10epoch_saved-model-{epoch:02d}.hdf5\"\n",
    "checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath, monitor='val_acc', verbose=1,\n",
    "                                                save_weights_only=True, save_best_only=False, mode='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qn6uKUN2_ZZc"
   },
   "outputs": [],
   "source": [
    "H = model.fit(X_train, to_categorical(Y_train), epochs=10, batch_size=128, initial_epoch=0,\n",
    "              validation_data=(X_test, to_categorical(Y_test)), verbose=1,\n",
    "              callbacks=[checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nAaAG9ET_51c"
   },
   "outputs": [],
   "source": [
    "# load the parameters that is considered giving the best balance between train and test accuracy\n",
    "model.load_weights('./Model/Best6_layer5(layer_id=3)_set5000_10epoch_saved-model-08.hdf5')\n",
    "\n",
    "\n",
    "# keep that parameters and replace the initial one\n",
    "params_layer_3 = model.get_weights()[0]\n",
    "params_fix[:, 3, :] = params_layer_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7Ps_5j1lAF2f"
   },
   "source": [
    "#### Training the 5th Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RObR5ubX_530"
   },
   "outputs": [],
   "source": [
    "n_qubits = 2  # number of class\n",
    "dev_fc = qml.device(\"default.qubit\", wires=n_qubits)\n",
    "layer_id = 4 # the layer index to be trained, it starts from zero because of Numpy convention\n",
    "\n",
    "@qml.qnode(dev_fc)\n",
    "def q_fc(params, inputs):\n",
    "    \"\"\"A variational quantum circuit representing the DRC.\n",
    "\n",
    "    Args:\n",
    "        params (array[float]): array of parameters\n",
    "        inputs (array[float]): 1-d input vector (data sample)\n",
    "\n",
    "    Returns:\n",
    "        array[float]: 1-d output vector in the form of [alpha0*<O0>, alpha1*<O1>]\n",
    "    \"\"\"\n",
    "    \n",
    "    # layer iteration\n",
    "    for l in range(len(params_fix[0])):\n",
    "        # qubit iteration\n",
    "        for q in range(n_qubits):\n",
    "            # gate iteration\n",
    "            for g in range(int(len(inputs)/3)):\n",
    "              if l == layer_id: # train only the specified layer\n",
    "                qml.Rot(*(params[0][3*g:3*(g+1)] * inputs[3*g:3*(g+1)] + params[1][3*g:3*(g+1)]), wires=q)\n",
    "              else:\n",
    "                qml.Rot(*(params_fix[0][l][3*g:3*(g+1)] * inputs[3*g:3*(g+1)] + params_fix[1][l][3*g:3*(g+1)]), wires=q)\n",
    "    \n",
    "    return [qml.expval(qml.Hermitian(dm_labels[i], wires=[i])) for i in range(n_qubits)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Cy7ekzlA_56I"
   },
   "outputs": [],
   "source": [
    "n_component = 6 # number of features used\n",
    "\n",
    "X = tf.keras.Input(shape=(n_component,), name='Input_Layer')\n",
    "\n",
    "\n",
    "# Quantum Layer\n",
    "q_fc_layer = qml.qnn.KerasLayer(q_fc, {\"params\": (2, n_component)}, output_dim=2)(X)\n",
    "\n",
    "# Alpha Layer\n",
    "alpha_layer = class_weights()(q_fc_layer)\n",
    "\n",
    "model = tf.keras.Model(inputs=X, outputs=alpha_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wdX2A9UlAMZf"
   },
   "outputs": [],
   "source": [
    "opt = tf.keras.optimizers.Adam(learning_rate=0.05)\n",
    "model.compile(opt, loss='mse', metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "67jdqXT4AMbv"
   },
   "outputs": [],
   "source": [
    "# Note: please put the correct directory to save the weights accordingly if you want to run the code\n",
    "filepath = \"./Model/Best6_layer5(layer_id=4)_set5000_10epoch_saved-model-{epoch:02d}.hdf5\"\n",
    "checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath, monitor='val_acc', verbose=1,\n",
    "                                                save_weights_only=True, save_best_only=False, mode='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "h432tdhpAMd3"
   },
   "outputs": [],
   "source": [
    "H = model.fit(X_train, to_categorical(Y_train), epochs=10, batch_size=128, initial_epoch=0,\n",
    "              validation_data=(X_test, to_categorical(Y_test)), verbose=1,\n",
    "              callbacks=[checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hFrzeDsS_58T"
   },
   "outputs": [],
   "source": [
    "# load the parameters that is considered giving the best balance between train and test accuracy\n",
    "model.load_weights('./Model/Best6_layer5(layer_id=4)_set5000_10epoch_saved-model-08.hdf5')\n",
    "\n",
    "\n",
    "# keep that parameters and replace the initial one\n",
    "params_layer_4 = model.get_weights()[0]\n",
    "params_fix[:, 4, :] = params_layer_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wmobb2rBBWWC"
   },
   "source": [
    "#### Final Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "mXnmp6z-BYcc"
   },
   "outputs": [],
   "source": [
    "n_qubits = 2  # number of class\n",
    "dev_fc = qml.device(\"default.qubit\", wires=n_qubits)\n",
    "\n",
    "@qml.qnode(dev_fc)\n",
    "def q_fc(params, inputs):\n",
    "    \"\"\"A variational quantum circuit representing the DRC.\n",
    "\n",
    "    Args:\n",
    "        params (array[float]): array of parameters\n",
    "        inputs (array[float]): 1-d input vector (data sample)\n",
    "\n",
    "    Returns:\n",
    "        array[float]: 1-d output vector in the form of [alpha0*<O0>, alpha1*<O1>]\n",
    "    \"\"\"\n",
    "    \n",
    "    # layer iteration\n",
    "    for l in range(len(params[0])):\n",
    "        # qubit iteration\n",
    "        for q in range(n_qubits):\n",
    "            # gate iteration\n",
    "            for g in range(int(len(inputs)/3)):\n",
    "              qml.Rot(*(params[0][l][3*g:3*(g+1)] * inputs[3*g:3*(g+1)] + params[1][l][3*g:3*(g+1)]), wires=q)\n",
    "    \n",
    "    return [qml.expval(qml.Hermitian(dm_labels[i], wires=[i])) for i in range(n_qubits)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "9fi4j-8ZBYex"
   },
   "outputs": [],
   "source": [
    "n_component = 6\n",
    "\n",
    "X = tf.keras.Input(shape=(n_component,), name='Input_Layer')\n",
    "\n",
    "\n",
    "# Quantum Layer\n",
    "num_fc_layer = 5\n",
    "q_fc_layer = qml.qnn.KerasLayer(q_fc, {\"params\": (2, num_fc_layer, n_component)}, output_dim=2)(X)\n",
    "\n",
    "# Alpha Layer\n",
    "alpha_layer = class_weights()(q_fc_layer)\n",
    "\n",
    "model = tf.keras.Model(inputs=X, outputs=alpha_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "_5ibACVSCYYb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "Input_Layer (InputLayer)     [(None, 6)]               0         \n",
      "_________________________________________________________________\n",
      "keras_layer (KerasLayer)     (None, 2)                 60        \n",
      "_________________________________________________________________\n",
      "class_weights (class_weights (None, 2)                 2         \n",
      "=================================================================\n",
      "Total params: 62\n",
      "Trainable params: 62\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# view each of the layer's name and its specifications\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gt3JeQLqCJGM"
   },
   "outputs": [],
   "source": [
    "model(X_train[0:3])\n",
    "\n",
    "# set the initial weights to the params_fix that has been optimized layer by layer\n",
    "# Note: please change the name of the keras layer accordingly based on the model.summary()\n",
    "model.get_layer('keras_layer').set_weights([params_fix])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "C8TZo7JgBYg5"
   },
   "outputs": [],
   "source": [
    "opt = tf.keras.optimizers.Adam(learning_rate=0.05)\n",
    "model.compile(opt, loss='mse', metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8-Vv2lC2BYi9"
   },
   "outputs": [],
   "source": [
    "# Note: please put the correct directory to save the weights accordingly if you want to run the code\n",
    "filepath = \"./Model/Best6_layer5(layer_id=all)_set5000_10epoch_saved-model-{epoch:02d}.hdf5\"\n",
    "checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath, monitor='val_acc', verbose=1,\n",
    "                                                save_weights_only=True, save_best_only=False, mode='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "repWrVwfCvjT"
   },
   "outputs": [],
   "source": [
    "H = model.fit(X_train, to_categorical(Y_train), epochs=10, batch_size=128, initial_epoch=0,\n",
    "              validation_data=(X_test, to_categorical(Y_test)), verbose=1,\n",
    "              callbacks=[checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7leymaY3Cvlo"
   },
   "outputs": [],
   "source": [
    "# load the parameters that is considered giving the best balance between train and test accuracy\n",
    "model.load_weights('./Model/Best6_layer5(layer_id=all)_set5000_10epoch_saved-model-08.hdf5')\n",
    "\n",
    "\n",
    "# keep the parameters as the best params, also keep the weight vector alpha\n",
    "best_params = model.get_weights()[0]\n",
    "alpha = model.get_weights()[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zi4bx-y5Ahvj"
   },
   "source": [
    "### Testing the Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XKuc9Hd-BDyA"
   },
   "source": [
    "After training process, this is the list of parameters and the weight vector $\\vec{\\alpha}$ that we got"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "Hr8HLKOeAW6T"
   },
   "outputs": [],
   "source": [
    "best_params = np.array([[[ 0.7079311 ,  0.37632585, -0.33214605, -0.13929626,\n",
    "          -0.7721329 , -0.5372071 ],\n",
    "         [-0.29678243,  0.01558876, -0.24696895,  0.17431983,\n",
    "          -0.12074132,  0.37094465],\n",
    "         [-0.40574065, -0.04427808,  1.0555736 , -0.81905687,\n",
    "          -1.0446428 ,  0.05147649],\n",
    "         [-0.49339634, -0.57406455,  0.50634474, -0.4733748 ,\n",
    "           0.29814383,  0.3295614 ],\n",
    "         [ 1.2720922 ,  0.2524813 ,  0.7091267 , -0.12986897,\n",
    "           0.20881489, -0.7476898 ]],\n",
    " \n",
    "        [[ 0.5376858 , -1.1241286 ,  2.0380347 ,  2.3011663 ,\n",
    "          -0.61580783,  0.5203443 ],\n",
    "         [ 0.7615255 , -2.1535773 , -0.9438669 , -0.7446632 ,\n",
    "          -0.8937272 ,  0.5636517 ],\n",
    "         [-0.15026595,  1.9657563 , -0.31717998, -0.6812052 ,\n",
    "          -1.3080239 , -0.09160299],\n",
    "         [ 0.981377  ,  1.6538823 , -0.38690042,  0.7046689 ,\n",
    "           0.03279989, -0.15615936],\n",
    "         [-0.532189  , -0.27468985, -0.04790952, -1.2638885 ,\n",
    "          -0.93331784, -0.3911027 ]]])\n",
    "\n",
    "alpha = np.array([0.94681466, 1.0617169 ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "r_Glw1xCHudw"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "Input_Layer (InputLayer)     [(None, 6)]               0         \n",
      "_________________________________________________________________\n",
      "keras_layer (KerasLayer)     (None, 2)                 60        \n",
      "_________________________________________________________________\n",
      "class_weights (class_weights (None, 2)                 2         \n",
      "=================================================================\n",
      "Total params: 62\n",
      "Trainable params: 62\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# view each of the layer's name and its specifications\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "4RA4bXP4AW8t"
   },
   "outputs": [],
   "source": [
    "# load the parameters to the model\n",
    "# Note: please change the name of the keras layer accordingly based on the model.summary()\n",
    "model.get_layer('keras_layer').set_weights([best_params])\n",
    "model.get_layer('class_weights').set_weights([np.array([alpha])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HQ3El56SDekx"
   },
   "source": [
    "The metric we will be using is the Area Under the Receiver Operating Characteristics Curve (AUC), the same metric that is used in the reference [2].\n",
    "\n",
    "The output from the model is in the form of $[\\alpha_{0}\\langle O_{0}\\rangle, \\alpha_1\\langle O_{1}\\rangle]$ for every sample. To decide which label the model predicts into, we just consider the $\\alpha_1\\langle O_{1}\\rangle$ value during test/inference time since it will gives how confident the model is in predicting a positive label (label = 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "OYJPoFT2AW-v"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "luNMmuu6G2jd"
   },
   "source": [
    "AUC for training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "NTF-n58uGFe2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8532208"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred_train = model.predict(X_train)\n",
    "roc_auc_score(Y_train, Y_pred_train[:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e4XrVzMzG4xt"
   },
   "source": [
    "AUC for testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "favS4hZ_DXT2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8488144000000002"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred_test = model.predict(X_test)\n",
    "roc_auc_score(Y_test, Y_pred_test[:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bTOIzDNLHChP"
   },
   "source": [
    "### Testing the Optimized Parameters on Real Hardware\n",
    "\n",
    "After we obtained the optimized parameters, we tested the model to do inference on testing set. We used Rigetti — Aspen-9 QPU provided by AWS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "P-OdQNwpDXWV"
   },
   "outputs": [],
   "source": [
    "device_rigetti = \"arn:aws:braket:::device/qpu/rigetti/Aspen-9\"\n",
    "\n",
    "# Note: please change the s3 directory accordingly based on your account if you want to run the code\n",
    "my_bucket = f\"amazon-braket-edo\" # the name of the bucket\n",
    "my_prefix = \"edo-testing\" # the name of the folder in the bucket\n",
    "s3_folder = (my_bucket, my_prefix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cxa4aIWkJz2t"
   },
   "source": [
    "To save cost, we made the model predicts 10 data samples directly for every task/execution. To do this, we created a circuit of 10 qubits, where each qubit act as a single DRC.\n",
    "\n",
    "The parameters used is the same for every qubit, which is the best parameters, but the data sample is different for every qubit. At the end of the circuit, all qubit is measured with Pauli-Z gate since Rigetti device plugin in PennyLane doesn't support custom Hermitian observable (at the time of the hackathon). This is not a problem since we can still get the measurement result we wanted by applying this equation\n",
    "\n",
    "$$\n",
    "\\langle O_{1}\\rangle = \\langle (|1\\rangle \\langle 1|) \\rangle = \\frac{1 - \\langle Z\\rangle}{2}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2lUZSYi1Iac0"
   },
   "outputs": [],
   "source": [
    "n_samples = 10\n",
    "n_qubits = n_samples  # number of samples tested\n",
    "\n",
    "dev_rigetti_aws = qml.device('braket.aws.qubit', device_arn=device_rigetti, shots=10000, wires=n_qubits, s3_destination_folder=s3_folder)\n",
    "\n",
    "\n",
    "@qml.qnode(dev_rigetti_aws)\n",
    "def q_fc(params, inputs):\n",
    "    \"\"\"A variational quantum circuit representing the DRC.\n",
    "\n",
    "    Args:\n",
    "        params (array[float]): array of parameters\n",
    "        inputs (array[float]): array of inputs (data samples)\n",
    "\n",
    "    Returns:\n",
    "        array[float]: 1-d output vector in the form of <Z> for every qubit\n",
    "    \"\"\"\n",
    "    \n",
    "    #print(len(inputs))\n",
    "    #print(len(params[0]))\n",
    "    #print(int(len(inputs[0])/3))\n",
    "    # data sample iteration\n",
    "    for data in range(len(inputs)):\n",
    "        # layer iteration\n",
    "        for l in range(len(params[0])):\n",
    "            # qubit iteration\n",
    "            for q in range(1):\n",
    "                # gate iteration\n",
    "                for g in range(int(len(inputs[0])/3)):\n",
    "                  qml.Rot(*(params[0][l][3*g:3*(g+1)] * inputs[data][3*g:3*(g+1)] + params[1][l][3*g:3*(g+1)]), wires=data)\n",
    "\n",
    "    return [qml.expval(qml.PauliZ(wires=i)) for i in range(n_qubits)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g-fd4wG9L0q3"
   },
   "source": [
    "We ran the circuit 100 times for data samples with label 0 and 100 times for data samples with label 1. Since we used 10 samples in each execution, the total number of samples tested is $100*2*10=2000$ samples.\n",
    "\n",
    "Note that we used 10000 shots in every circuit execution to get good precission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VcQvC0ksAXBL"
   },
   "outputs": [],
   "source": [
    "# execute the circuit on Rigetti's QPU\n",
    "# we saved the result for every execution just in case\n",
    "# Note: please change the directory to save the result accordingly (or just delete/comment it) if you want to run the code\n",
    "\n",
    "for i in range(100):\n",
    "    result_0 = q_fc(best_params, X_test[Y_test==0][i*10:(i+1)*10])\n",
    "    np.savetxt('./QPU Probs/Testing_label0_sample' + str(i) + '.txt', result_0)\n",
    "    result_1 = q_fc(best_params, X_test[Y_test==1][i*10:(i+1)*10])\n",
    "    np.savetxt('./QPU Probs/Testing_label1_sample' + str(i) + '.txt', result_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kUoaEdszNCW2"
   },
   "source": [
    "After all executions finished, we post-processed the results to give us the $\\alpha_1\\langle O_{1}\\rangle$ value that we wanted for every samples. And then used it to calculate the AUC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "BLeQ-jIXJux7"
   },
   "outputs": [],
   "source": [
    "z_label_0 = np.zeros((1000,))\n",
    "z_label_1 = np.zeros((1000,))\n",
    "\n",
    "# load the results from saved files\n",
    "for i in range(100):\n",
    "    temp = np.loadtxt('./QPU Probs/Testing_label0_sample' + str(i) + '.txt')\n",
    "    z_label_0[i*10:(i+1)*10] = temp\n",
    "    \n",
    "    temp = np.loadtxt('./QPU Probs/Testing_label1_sample' + str(i) + '.txt')\n",
    "    z_label_1[i*10:(i+1)*10] = temp\n",
    "\n",
    "# calculate the α1⟨O1⟩\n",
    "fidel_1_label_0 = np.clip(alpha[1]*(1 - z_label_0)/2, 0, 1)\n",
    "fidel_1_label_1 = np.clip(alpha[1]*(1 - z_label_1)/2, 0, 1)\n",
    "\n",
    "# concatenate all into one array of prediction\n",
    "Y_pred_1 = np.concatenate((fidel_1_label_0, fidel_1_label_1), axis=0)\n",
    "Y_label = np.concatenate((np.zeros((1000,), dtype=int), np.ones((1000,), dtype=int)), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c1y-He9oOaQv"
   },
   "source": [
    "AUC for 2000 testing samples using the real QPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "Ct7gYF7HJu0H"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8297940000000001"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(Y_label, Y_pred_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "awFiNt82XX9J"
   },
   "source": [
    "### Conclusion\n",
    "\n",
    "In this project, we used the quantum machine learning technique of data re-uploading classifier and trained it using the modification of layerwise learning method to classify SUSY dataset.\n",
    "\n",
    "With just using 2 qubit, we obtained AUC of 0.8488 on testing dataset with 5000 training and testing samples, trained and tested using a simulator. We also tested (inferenced) to run the circuit on Rigetti's Aspen-9 QPU provided by AWS using the already optimized parameter to predict 2000 samples of test dataset and we obtained AUC of 0.8298.\n",
    "\n",
    "Comparing our project with the work done in [2], we may conclude that we were able to obtain better results. The AUC obtained from the simulators are also very close to the one obtained from running the test/inference on the real quantum hardware."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AP-ulDTpXbhk"
   },
   "source": [
    "### Acknowledgment \n",
    "\n",
    "This work is submitted to the QHack 2021 organized by Xanadu. During the hackathon, we had the opportunity to learn and implement ideas in the field of QML. It was a pleasure to be part of this hackathon, and as a team, we would like to thank all the speakers for their valuable presentations and the Xanadu team for organizing an amazing event."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "du6PWY8YeIuE"
   },
   "source": [
    "Source code\n",
    "\n",
    "This notebook is the clean version of the code that by itself should be enough to reproduce the results, but all the testing/temporary codes and other files used in this project are available at the\n",
    "[GitHub Repo](https://github.com/VoicuTomut/Event-Classification-with-data-reuploading-in-High-Energy-Physics)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4-KBrwqPBAr_"
   },
   "source": [
    "**References:**\n",
    "\n",
    "[1] [SUSY Dataset - UCI Machine Learning Repository.](https://archive.ics.uci.edu/ml/datasets/SUSY#)\n",
    "\n",
    "[2] [Event Classification with Quantum Machine Learning in High-Energy Physics.](https://arxiv.org/abs/2002.09935)\n",
    "\n",
    "[3] [Unfolding measurement distributions via quantum annealing.](https://link.springer.com/article/10.1007/JHEP11(2019)128)\n",
    "\n",
    "[4] [Quantum Computing in the NISQ era and beyond.](https://quantum-journal.org/papers/q-2018-08-06-79/#)\n",
    "\n",
    "[5] [Quantum Machine Learning in High Energy Physics.](https://arxiv.org/abs/2005.08582)\n",
    "\n",
    "[6] [Data re-uploading for a universal quantum classifier, Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, José I. Latorre.](https://arxiv.org/abs/1907.02085)\n",
    "\n",
    "[7] [PennyLane optimizers](https://pennylane.readthedocs.io/en/stable/introduction/optimizers.html)\n",
    "\n",
    "[8] [Layerwise learning for quantum neural networks, Andrea Skolik, Jarrod R. McClean, Masoud Mohseni, Patrick van der Smagt,  Martin Leib](https://arxiv.org/abs/2006.14904)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "QHack_Final (2).ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "conda_braket",
   "language": "python",
   "name": "conda_braket"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}