Quang Nhat Le-VQC (EfficientSU2).ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "id": "c246cd34-872b-4f5f-ba92-f889df475e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris #import the iris dataset\n",
    "iris_data = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7844867c-2d31-49c4-a13f-5f163a16ffe4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      ":Number of Instances: 150 (50 in each of three classes)\n",
      ":Number of Attributes: 4 numeric, predictive attributes and the class\n",
      ":Attribute Information:\n",
      "    - sepal length in cm\n",
      "    - sepal width in cm\n",
      "    - petal length in cm\n",
      "    - petal width in cm\n",
      "    - class:\n",
      "            - Iris-Setosa\n",
      "            - Iris-Versicolour\n",
      "            - Iris-Virginica\n",
      "\n",
      ":Summary Statistics:\n",
      "\n",
      "============== ==== ==== ======= ===== ====================\n",
      "                Min  Max   Mean    SD   Class Correlation\n",
      "============== ==== ==== ======= ===== ====================\n",
      "sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "============== ==== ==== ======= ===== ====================\n",
      "\n",
      ":Missing Attribute Values: None\n",
      ":Class Distribution: 33.3% for each of 3 classes.\n",
      ":Creator: R.A. Fisher\n",
      ":Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      ":Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      "|details-start|\n",
      "**References**\n",
      "|details-split|\n",
      "\n",
      "- Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "  Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "  Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "- Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "  (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "- Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "  Structure and Classification Rule for Recognition in Partially Exposed\n",
      "  Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "  Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "- Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "  on Information Theory, May 1972, 431-433.\n",
      "- See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "  conceptual clustering system finds 3 classes in the data.\n",
      "- Many, many more ...\n",
      "\n",
      "|details-end|\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(iris_data.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3388321d-47b2-4dc5-af0b-7f6235256c51",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = iris_data.data #get the features of the dataset\n",
    "labels = iris_data.target #get the labels of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "06c33bc9-bb6e-48e7-bf9d-81ea75a6dcd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler #import the MinMaxScaler for scaling the features\n",
    "features = MinMaxScaler().fit_transform(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "729390ff-74e8-4106-b166-9cf1b03b17f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_17332\\282151373.py:3: DeprecationWarning: The property ``qiskit.utils.algorithm_globals.QiskitAlgorithmGlobals.random_seed`` is deprecated as of qiskit 0.45.0. It will be removed no earlier than 3 months after the release date. This algorithm utility has been migrated to an independent package: https://github.com/qiskit-community/qiskit-algorithms. You can run ``pip install qiskit_algorithms`` and import ``from qiskit_algorithms.utils`` instead. \n",
      "  algorithm_globals.random_seed = 123\n",
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_17332\\282151373.py:5: DeprecationWarning: The property ``qiskit.utils.algorithm_globals.QiskitAlgorithmGlobals.random_seed`` is deprecated as of qiskit 0.45.0. It will be removed no earlier than 3 months after the release date. This algorithm utility has been migrated to an independent package: https://github.com/qiskit-community/qiskit-algorithms. You can run ``pip install qiskit_algorithms`` and import ``from qiskit_algorithms.utils`` instead. \n",
      "  features, labels, train_size=0.8, random_state=algorithm_globals.random_seed\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from qiskit.utils import algorithm_globals #import the algorithm_globals for setting the random seed\n",
    "algorithm_globals.random_seed = 123\n",
    "train_features, test_features, train_labels, test_labels = train_test_split(\n",
    "    features, labels, train_size=0.8, random_state=algorithm_globals.random_seed\n",
    ") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9ecf0c1c-43ff-44d4-bdd5-fcd9ad0f85d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to \"iqp\" in a following release. To silence this warning, specify the current default explicitly as style=\"clifford\", or the new default as style=\"iqp\".\n",
      "  self._style, def_font_ratio = load_style(self._style)\n"
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mqf9KkiRJalrKBryrVq3iiSeeoHv37vzoRz9q9JwzzzwTgNNOO+3Q1xYsWMAFF1xAnz59yMrKoqCggKuuuopVq1YlpG5Jx7fuw+SMu/cglO9JztgNJWv+AGuTOLakQF09bGjB3rutYetu9+OWJEmS2pqUDXgff/xx6uvr+fKXv0ynTp0aPadjx+DpIg0D3srKSoYPH87MmTOZN28e99xzDytXrmTMmDGUlZUlpHZJTdtzILF7zx5p087kjd0WaihrA/OX2rutu6A2ie8maAt9UJIkSdInUnYP3vnz5wMwbty4Js/5OLBtGPBOmjSJSZMmHXbeWWedxeDBg3nqqae4+eab41CtpOZKdsC4aSeMHJjcGpL5PSjbGexHHIkkrwapvSurTO74m3bAp/sktwZJkiRJn4hEo9FosouIh8LCQsrKynjnnXcYMWLEUcdra2vp06cPFRUVrF+/noEDm05sduzYQffu3Zk9ezZTpkxpdg1VVVWHVg/36dOHtLT4LZjOzOjIIze5MZ5az42zB1Fdm8Slsk3of8ZljLzqoUaP3XoR5HU89vV52ZCWBvX1x35A0Z4D8MALR3/9g3eeZvHvb4qh4tZ32Y82EklLb/TY8b4HJzp/gD/++0Dq66pjqDj+7IGKh7baBwef/02GX/LvjR5LRB9c85c5/G3u92OoWJIkSdLx9O7dm6VLl7bo2pRdwVtVVQXAgQON/2L2xBNPUFFRQefOnRkwYMBRx+vq6qivr2fjxo3ccccd9O7dmyuvvLLF9WzdurXF1zZHdoecuN5f7c/WLVs4WLM/2WUcJb9oX5PH8jpCfjP/KaSlNf/chj6qqWXz5s2xX9haIpEmw11o/vegpfMH2Fa+nZqDTf93SAZ7oOKhrfbBvlVN15SIPrj/YHVy+6AkSZKkw6RswNu7d28qKyt5++23GTNmzGHHtm7dym233QZASUkJkUbea3z++eezcOFCAIqKipg/fz49evSIqYacnBz27dvHueeeS3l5eaPjtJbMjOMs15Fi1Kdv3za5ci2vc26Tx/Y0o9xYVq41JjMjjX79+h1/oDiqr60mLSOz0WPH+x6c6PwBevXoRrT+pGZUmjj2QMVDW+2DnXKymzyWiD7YMatD0vugJEmSlGp69+7d4mtTdouGqVOnMmvWLAoLC3n55ZcpLi4GYMmSJVx33XVs2LCBmpoapkyZwuzZs4+6fvXq1ezatYv333+f++67j/LychYuXEj//v0TPZVmqauGV2cmuwqlknFTIb3xDDGp1n8Is15u+fV3fTFYsbZrP9z1p9ivnzAMJo5o+fit4Qf/DRUtXEB7ovPPy4bvXdaysePJHqh4aKt98J2N8KvXW379ifaByz4DYwe3fHxJkiRJrSt+m8Im2fTp0+nWrRubNm1i2LBhDB8+nEGDBjFy5EgGDhzI+PHjgcMfsNbQ4MGDGTVqFFdffTWvvPIKe/fu5d57703kFCQ1ol9XSObzvQq6JnHwNlBDW5i/1N4VJvnfoX1AkiRJaltSNuAtKChgwYIFTJw4kezsbEpLS+natStz5sxh7ty5rFmzBmg64G0oPz+foqIi1q1bF++yJR1HdgfokZe88ZMdrCS7hsJuyRtbUqBbJ+iYpJXFkQj065KcsSVJkiQ1LmX34AUYMmQIzz777FFf37dvH6WlpaSlpXHqqace9z7l5eWsXr2aUaNGxaNMSTEa1g/K9yR+3D750KXpLYATZlgB/M+yJI3ttptS0kUiMKwvLC1N/NjFvSAzpX96lCRJksKnXf6IvnLlSqLRKMXFxeTkHP746GuvvZaioiJGjBhBfn4+a9eu5cEHHyQjI4NbbrklSRVLauicQfDqquSMG8dnJTZb75OgqBes+zCx4/bvFnxISr5zipMT8J5TnPgxJUmSJB1bym7RcCwrVqwAGt+eYfTo0Tz33HPccMMNXHzxxdx3332MHTuWZcuWUVRUlOhSJTWie2f4dJ/EjpmVAZ8ZkNgxj+XcQYkf85wkjCmpcSd3h74J3iohP8dV/JIkSVJbZMB7hJtuuonFixdTWVnJgQMHWLNmDXPmzOFTn/pUostsNdU1B7nzl//C9fcUM/mB05jxyAQ2VzS+n3DZ9rXcPPtsrr+nmCkPnUXptpUARKNRAH497y627Sw99PePfe83V/D30jfjO5HjmHBbhK/9eDhvrXoOgP96eirX/vBkJtwWYd3mZYed+52Hx3Hp/+7KHxf85NDXbvnpWLbufB849nzb4lwBZjxyITf+uITJD4zglp+OZd3mdw4dO3K+DecaVp8/HdISuJr24pJg/9+2oqQQBvRI3HgFXdtWwB2r9tgHjzdn+2C4+2AkAv9yRmLHnHQ6pLfLnxwlSZKktq1d/ph+rIA3VV0y6kYem76aObcuZ8ywL/DAk//W6HkPPTWZS0bdyC9nrOGqcTO474nrAViw4il+PncG+w7sYvWmxdzz+HXsqdoBwHsfLGbv/p0MPXlMoqbTpAe/uYBRQy4BYGzJ5Tz4zdfp1eXocP7+r7/K6KGTDvva5edN49cv3gk0Pd+2OleA/7ju//HItL8x59ZlXDb21kP/7eDo+Taca1j16wIXDk/MWAN6wHmDEzNWc6WlwTWjoUN6/MdKT4MvjQ5/sNMe++Cx5mwfDH8fLO4NZydoZX1JIZwe3te6JUmSpJQW8l/XW2b+/PlEo1EmTpyY7FISIrNDNqOGXELkH5uHDuk/mg8rS486r3JfOWvKlnLBGdcCMHb4ZWzftYnNFes4r+Ryziu5nBeX/IL/efNn3HrFo+TlBptxzl00h/GnfwmAfQd2cc0PCrj0zm5MfmAE/3rfEC65PYsfNxGkxGJT+Wqu+UEBW3dsAODJ1+7njp9fRH19faPnlww8jx75Bc2+/6ghE1my+nmqDuxucr4N5wpta76dOuYf+rzq4G6g6eWtDecaZhOGwae6x3bNngOwa3/wZ3N0zAzCzbQ22C175sEXYlzBF+v8AS4pSfxbwVtbe+yDzZ1zQ/bB8Jl0etALYhFrHzipI1xxVtvYg1ySJEnS0drlQ9bauz+9/hBjhn3hqK9v37WJrnl9SE8P/reIRCL07NKf8l0fsGHr33jvg7e48KwbOPXkc3jwDzfyjUkPkpfbjeXrX+PS84IH0HXqmM/4EV+iY1Znrp3wHyxZ/SKPz/8h06549ITrLuw5mK9NvI/v//ZKJn/ufp5547+YNXUxaa2UvGWkd2BA7+GseH8BNXXVjc634Vyh7c33nse/wvL1rwLwn199rsnzGs519NDPnXCtyZKeBjf+E8x+Gbbuat41D7zQ/PtnZgT37xFjeJJI5xYHIc28d5t3fizzBzj/0zB+aOx1tXXtsQ82NeeG7IPhk90BvjEeZr4ElVXNuyaWPtApC77xz9C5Y8vqkyRJkhR/BrztzO9e+SFbKtZx7+RXYrru3FO/yNjhl/LreXcxuHAk55VccWhVWMXuMrp06nXo3HVblvHFc6cCsLbsrxT1Pb3Re06dNYbNFWsbPfazW96hZ37hUV8ff/o1LF//Knf8/LPcO/kV8ju17iakXTr3ZvvuMj43enKj8z1yrtC25jvjml8DMG/pr/j5czP44THCjY/nGna5WXDTBfDIa7CxonXv+7V/Ch5k1NZdXBJs1TB3eeve98JTg3un2qq99tgHY5mzfTB8uuTC1Anw8Hz4cE8r3jcHvj4eep3UeveUJEmS1PoMeNuRJ1+7n9ff/SP33vgy2Zk5Rx3vkV/Izj1bqaurJT09g2g0SnnlB/TM738oxPjKhXcddV1WZg7VtQcP/X3DlmUU9Qt+uV9b9lfGDJt01DUAM78V+wN66upqKd32Lp1zulKxe3PM1x9Pde1Bsjp0bHK+R84V2uZ8L/zM/+Khp77Onqodh95CfqSP55oKcrOCcOOllTBvBdRHj3/NsZQUBm9HDsuKtUgEJpwa7BX8+CLYse/E7pefE+zvO7hP69TXlrTHPni8OR/JPhhOXXJh2sXBCz1/eQ9OsA0y6pRgC5iczFYpT5IkSVIctcFdJRUPf/jzA7y67HHu+dpLh+1R2FCXTj0p6ncGL7/9WyB4wE73/AL6dS865r0H9CmhrHw1QPDLdyRC95P6AbBh298Y0Lv1noT16HO3U9BjMA98cwGPPPudw54I3xo+KF/FwD5NP3yv4Vyh7cx334FdVOzecujvC999mrzcbnTO6drkvY8317BJT4OLhgcBx6dbGEz2Pgm+cg7cMDY84W5DRb1g+sRgS4XsDrFfn5URbMlw++dSM9xtj32wOXM+kn0wvDIz4ItnwrcmwMAWvsGlsGuwNc01ow13JUmSpLBwBW87sH1XGXOenUafrgP5zsPjAMjMyGLW1LcA+PGT/8aYoZM4e9gkvn3ZHO574noen/9DcrLzuO3Kx457//OGX87SNS9yRvEFrNv8zmFvze2Unc8zb/60VfZiXPT3Z1m6+gVmTV1MdmYOkz//AD/47ZU8NOUNMjtkH3X+T/4wmbfem8vOvdu449HPkpPVmV/d3nQQsm1nKfX1dZzSt+lf9hvOFUjafI9UdXA33//NFXxUc4C0SBon5fbg+zc8e2gF3pGaM9ew6tcleEvx9r2wcA2s3Bx83pTO2UEwevYgKOoZ/u0IsjKChy59dji8UwqLN8CmnVBT1/j5HdKD79lZA+DMAS0LhsOgPfbB4825MfbB1DCwJ0y9ELZUwsK18N7WY6/sz8+B4t7Bnt79G1/sLEmSJKkNi0Sj0RN9F5/agLpqeHVmcsY+8NE+bp59Ng996006ZuYmpwhgwm0R/vS9ymavUrv399dT1G8El479NhCsEuvbrYhLRjX95PewzhUOn29z5jpuKqSnyOqtA9VQVhk8gKi2Lljtm5sFBV2Dp8OHPdQ9nrp6KN8TPIiuujZ463ZmOvTJD/bWTE+B93IkswdCeHuDfbD99MGqj6BsJ+w+8Ekf7JwNhd2CPyVJkiSFVwr8Wq9k65jVia9PepBtO99Pah1dOvVi2s/O561VTT9Q52PfeXgcKzb8mewG4US3vL5cdNa/HvO6MM4Vjp5vc+aaSjpmwqBeMHJgsFJ31ClwakGwai3Vw10Igpw++XDGyTC6CMYUBat1+3ZJjXC3LQhjb7APtq8+mJsVbL3SsA8O7We4K0mSJKUCV/CmiGSvXlPqSaWVa0p99kDFg31QkiRJUhi4dkuSJEmSJEmSQsqAV5IkSZIkSZJCyoBXkiRJkiRJkkLKPXhTRDQK9TXJrkKpJK1D+3gAmVKDPVDxYB+UJEmSFAYGvJIkSZIkSZIUUm7RIEmSJEmSJEkhZcArSZIkSZIkSSFlwCtJkiRJkiRJIWXAK0mSJEmSJEkhZcArSZIkSZIkSSFlwCtJkiRJkiRJIWXAK0mSJEmSJEkhZcArSZIkSZIkSSFlwCtJkiRJkiRJIWXAK0mSJEmSJEkhZcArSZIkSZIkSSFlwCtJkiRJkiRJIWXAK0mSJEmSJEkhZcArSZIkSZIkSSFlwCtJkiRJkiRJIWXAK0mSJEmSJEkhZcArSZIkSZIkSSFlwCtJkiRJkiRJIWXAK0mSJEmSJEkhZcArSZIkSZIkSSFlwCtJkiRJkiRJIWXAK0mSJEmSJEkhZcArSZIkSZIkSSFlwCtJkiRJkiRJIWXAK0mSJEmSJEkh9f8Bih/Uo0PVfjkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1792.5x785.944 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import ZZFeatureMap\n",
    "num_features = features.shape[1]\n",
    "feature_map = ZZFeatureMap(feature_dimension=num_features, reps=1) # create the feature map for the quantum model\n",
    "feature_map.decompose().draw(output=\"mpl\", fold=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "71740c97-b7db-4b20-b44f-f7864ac3041f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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djLnAp7+cngs8crXO2JTQoMZGGZo0acLcuXP9GZKIiE9VjjS+rZm/wZjMVApXU0PEThwOaFTL+LN0i9HYCA9TU0PETqIioHtT+Hr96bmAmhoSKtTYCCFpGet49pPbyDt5jLrVG/LQ8HfZtW8jD782kKTazXly7DfUqFKH/II8nv74VrbsWYnD4eSWgf+iT7vrAXh17gQWr5tJSmJHHhv9WWAT8pC7+Z+ya99m7n6uE1d2G8td1/wbgFk/PMsXy14gKrIKr9y/NjCJiIjX3K0D6Qe28u9ZYzmWl0NhUT5dW17F2Kum4nQ6LVcH3M153s9vMGvJs+zev5nbB01jaO97S9/j9a8fZumG2USEVyIsLIIxAx6nS/MrANVFCX5mjAGAL5a9yGdLZxDmDMfpcDLjnp+IjIgK+rmRu/mfb5yXlJTw4hd/5ufNX+FwOBjS+16u7TkOUA2Q4GfGGDglJ3c/Y59uS6uGPUrH++K1M3l3wWMcPJrBZ/887P8E3WDGMTice4CnP7qFfTm7KCoppEX9rvz5upepFBFtiWNgy8bGwoULAx2CT0ydOZoHb3iTpompzPv5DV6d+yBXdBlDUu3mZ/0i+vj7aUSEVeLtv2wj89AOxk/vRmqTfsTG1GTsoKk0rNuaZRs/C1ge3nI3f4Ci4kL+PWssPdsMOevx6/rcR9PEDrz4+b3+C1xETONuHfjfLyfQs80QhvQaT0FhPndP78LKppfSreWVlqsD7uacktSJv438iA8XPnHOe7RN7s3I/pOoFBFNWsY67n+pDx9OyiA6MsZyx0Psx4wxsOzXz/lu9f8xY9wKYqKrcTj3AGFhEQBBPzdyN//zjfPvVr/Hrn2bePOhLRzPP8Kdz3YgtUk/GsW3Vg2QoGfGGDjluU9up3vLQRzNO1j6WN/UG2nRoBt3PJvqx6w8Y8YxeP+7x0mslcI/b5lDcUkxf3v9KuavfJOrL7rLEsdAi0aEiG171xBdqQpNE1MBuKzzH1m+6QsKiwrOee3362YyqMcdACTEJdOuSV9+/PVTf4ZrOk/yB3hvwT/o024YibVS/BiliPiSJ3XAgYPjJ44AcLLwBMXFhdSMTfBnuKbwJOcm9drTsG5LHI5zf/V3bTGQShHRACTHtwWXiyO5B3wau4gZzBoDH30/lVGXTSYmuhoA1avUJsxZxqrLQcaT/M83zhevm8mV3W4jzBlGbOU4+ra/kUVrP/BbHiLeMmsMAHz98+vExyXTJrm3X2I3i1nHwOFwkHfyGCUlJRQVF3CyMI9a1ZL8lkdF2fKMjVCUeWgHOzI3cPszqaWPnSzII/vo3nNeu//wburWaFj69/gajdh/eLc/wvQZT/LfvPsnNu1azlNjF/Dugsf8GKWI+JIndeDOa/7NpDcGM2fFS+Tm5TCi/ySaJnbwY7Tm8CRnd81f9SbxcY3P+j0hEqzMGgO7921iS/oq3l3wGIXFJ7ms0x8Y0mu8ydGaz9v8fz/O9x/eTd3qp8d83bhGbN61wicxi5jJrDGQeWgHc5e/zDN3/cDitTN9GbLpzDoGI/pP4h/vXMeN/4jnZNEJLkm9mYtaX+3L0E2lxkYIadGgG0/eNr/079c/WjuA0fifO/nnF+QxY/ZdTPrDJzi0YppIyHG3Dn6x7EX6dRjO8Ev+Sk7ufia83I/m9bvQqdll/grVNGbW/tVbv+PdBY/x1G0LVCPFMswYA8UlRWQd2sEzd/1A7okcHnjpYhLiGtO91SAzQ/UJT/PXOJdQU9Ex4HK5ePqjWxg35PnSsxmsxow6sHjthzSo04qnxn7LyYI8/v7W1Xz102tc2e1PPo3dLGpshIiEuMZnnXVxPP8o+QXHqRWbeM5r61RvwL6cXaWnXWfl7KRTs8v9FqsvuJt/5sE09h/ezYSX+wGQe+IwLlcJuSdymHjT236NWUTM5Ukd/GLZC7w5cQsANarUoWuLK1mXtthyjQ1Pcr6QdWnfM+2jMfxzzBzq12luZpgiPmPWGKhTvQH9OgwnzBlGtZhadG1xJZt3rwj6xoan+Zc3zutUb8C+w7toRQ8A9h3aSZ0aDXwbvIgJzBgDeflH2Z65nsffuxGAEydzOVmYx4RXLmXq7d/5PokKMqsOzFn2Ivde/yphzjAqR1Wld9vrWZe2yDKNDa2xESKaJqYS7ozgly0LAOMH8+L2NxIRHnnOa/u0G8bc5S8DxqlL69MW07PNtf4M13Tu5p+c0JZPHj3Aew/v5L2HdzK0971c0eUWNTVEQoAndTAhrjGrfpsHwImC46xNW0Sj+DZ+jdcMnuR8Puu3/8BTH47iH6M/p0m90LpjmIQ2s8ZAvw43s+o/Rk04WXiCdWmLaZwQ/GPBk/zPN877tBvGVz/9L8UlxRzNO8TidTPp2/5Gv+QgUhFmjIGY6GrMfuxg6eeDsYOm0anZ5ZZoaoB5dSC+ZmNW/nduVFRcyKot8y01N9IZGyHkrzf/H1M/GsP02XdSr2ZT/nLze+zM+vWc1w3rO4GnP7qFPzzRBKczjHFDnqdaTK0ARGwud/MXkdDlbh2YeNPbzPh0HJ/++ByFxQX0aHU1/VJvCkDEFeduzvNXvsVb8/9Gbl4OyzZ+xsffT+OfY+bQNLEDT398K4VFJ5k6c0zp6/8y/F2SE9r6MxURr5gxBq7vcz//nnU7t05thcPhoFfb67i4/bAAZOM5d/M/3zjv32kUv+1ZyeinUnDg4Lo+92v8i2WYMQaszoxjcNc1z/HcrDu47em2lJQU06phD67rfZ8/06gQNTZCSHJCW17886oLvi46Moa/jbTWojjucDf/M/3h8kd9E4yIBIS7daBpYgeeG7fUDxH5nrs5X9FlNFd0GV3mc28/tNXkqET8x4wxEBkRZdmzN93N/3zjPMwZxvihL5gZlojfmDEGznS+WhGszDgGCXHJZ63TYTW6FCXEhYdFcizvILc/k0pO7v4Lvv7VuRP4cNETVImu4YfofM/T/Gf98CzTZ98VEmewiIjBjnXA05zPJxSOh9iPmWPAinMj1QCxOzPHwOK1M5n05mBqVK1rUnT+Ybdj4HC5XK5AByHnKi6ARdMDHYX7+o2HMM8uZz0vq+UP5h8DEV+YPBuOnIBq0fDY0EBHUz7VAB0D8R3VAd8xcwzYPX/xHavUALDeONBcIHB1QGdsiIiIiIiIiIhlqbEhIiIiIiIiIpalxUODlDPCOI3HKpwR5r+flfIH84+BiJ2pBugYiNh9DNg9fxGw3jjQXCBwdUCNjSDlcNj7GkW75y9id6oBOgYidh8Dds9fBDQO7J6/J3QpioiIiIiIiIhYlhobIiIiIiIiImJZamyIiIiIiIiIiGWpsSEiIiIiIiIilqXGhoiIiIiIiIhYlhobIiIiIiIiImJZamyIiIiIiIiIiGWpsSEiIiIiIiIilqXGhoiIiIiIiIhYlhobIiIiIiIiImJZamyIiIiIiIiIiGWpsSEiIiIiIiIilqXGhoiIiIiIiIhYlhobIiIiIiIiImJZamyIiIiIiIiIiGWpsSEiIiIiIiIilqXGhoiIiIiIiIhYlhobIiIiIiIiImJZ4YEOQMrmckFJYaCjcJ8zAhwO897PavmD+cdAxM5UA3QMROw+BuyevwhYbxxoLhC4OqDGRpAqKYRF0wMdhfv6jYewSPPez2r5g/nHQMTOVAN0DETsPgbsnr8IWG8caC4QuDqgS1FERERERERExLLU2BARERERERERy1JjQ0REREREREQsS40NEREREREREbEsLR4qIhLiiooh8whkHYaTRcZjBUWw+yDUqw7hYYGMTkT84dgJ2HMIjpw4XQcKi+FwHlSL1p0sREJdcQlkHYHMw2fPBXZlQ70aEKG5gFicGhsiIiEovxBW7YCV2yE9x5jQnOlEITwzD8KcRnOjczJ0aQyVtZq9SMjYcwiWboHNGUZD4/fyCuDRT6FqFDSLh54pkFxbTQ6RUHGyCFbvhJ/TjHpQVMZc4Nn54HQYc4FOydC1McRUCkS0IhWjxoaISAjJL4Sv18OKbae/kTmf4hJjsrPnEHy5Fro2gSvbq8EhYmVb98HcNbDroHuvP5YPv+w0/tSrbtSANkk+DFBEfOpkEXyzAZZuNeYFF1LiMr4ESc+Br9YZX3Zc1R6qRPk+VhGzqLERQtalLebBl/ud9VhUZAxJtZvRv+Moru15D2FhoftPbvf8RX7LhA9XQE6ed9sXFMOPW2DDHrixG7RKNDc+f7B7HbB7/nZ3shDmrIEft3r/HhmH4bXvoXMjGNLZmt/c2n0c2D1/u0vbDx8sh+xc77YvLIbl22BDOgzrAu0bmBufP9h9DNg1/9DLSOiXOpyuLa7EhYucY1ks+OUdXp5zP7v3b+a+618NdHg+Z/f8xX5cLpi/AeZtMOf9jpyAVxfDJa1gcKo1T0u3ex2we/52dCgXXloIB46Z836rdhpnftxxCSRUN+c9/c3u48Du+dvRwk1Gc9Nlwnvl5sObS6B3M6PJ6dRcwHLslr8aGyEoJbEj/TuNLP374Ivu4tYpLfj659cYM+BxqlepHcDofM/u+Yv9zF0L320y/30XboLCIhja2XrNDbvXAbvnbzcHc2HGAmMhUDMdOWG87z2XWbO5YfdxYPf87Wb+BuNSVLMt2WKc0XljN+s1N+w+BuyWv273agPRkTG0aNgdl8tFxsG0QIfjd3bPX0LbD7/5pqlxypIt8O1G372/v9i9Dtg9/1CWXwgvLzS/qXFKXoFxJsjRMhYftRq7jwO75x/KVmzzTVPjlJ/S4Ot1vnt/f7H7GAj1/HXGhk1k/veHN7ZyXIAjCQy75y+had8R+GK1Z9vcPwBio40PKc/Mc2+br9dDy3qQZPHhY/c6YPf8Q9Xnqz2//MTTOnD0BHz0M9zax3pnb/2e3ceB3fMPRQdzYfYvnm3jzVzg203QOgka1fI8xmBi9zEQyvnb4oyN7OxsJk6cSNOmTYmKiqJ+/fr8+c9/5vjx49x66604HA6ef/75QIdpmvzCPI4cz+Zw7gF2ZG5g+uy72bZ3DS3qdyWpdrNAh+dzds9f7KGkBN5fce6t2y4kNhqqVzb+6/a+XP/dV7Fn+woku9cBu+dvF//JNBb585Q3deDXdOO2kVZi93Fg9/ztoMRlLBpe4MZd0M7kTQ1wueD95Z7vK5DsPgbsln/In7Gxdu1aBg4cSFZWFjExMbRq1YqMjAymT59OWloahw4dAiA1NTWwgZronW8m8843k896rFebodwz5IUAReRfds9fzlVUDOv3wKa9xj3bI8Ohfpxxr3ar3sps7W7Yle2//WXkGIsJdm/iv31WhN3rgN3zL0v2MViRBgeOQrELqkZBh4aQUteaZyG4XJ6fsVVRX6yB1IYQZpGvxew+Duye/+8VlxgNul/TjUusIsIgsYbxe62qBx/wg8nmvcYiv/6y/6hRR/s0998+K8LuY8Bu+Yd0YyM7O5vBgweTlZXFAw88wOTJk6latSoAU6ZM4aGHHiI8PByHw0G7du0CHK15ruo2lj7thlFUUsiOzA3MXPwU2UfSiYw4/Qnu8fduosRVwqRRH5U+djTvELdNa83YQdO4tOOIQIRuCnfy37B9CQ+/PvCcbYuKCygpKWb+FAt9NS3lcrmMNSi+3QjH8s9+bs2u0/dqH9IJKkUEJkZvLa3A7Ry99eMW6NbYGh8CVQdVB085lAufrITNGefeKWD5NqgTC1d3gDZJAQnPazsOGLdm9acjJ2DjXmhX37/79ZbqgOrAKUu3wjcbjJ/hM63dbdxRrENDY6HsypGBic9bFbm1s9f73GLcKUVzgeBntxoQ0o2N8ePHk56ezrhx45g2bdpZz02cOJH333+fdevWkZycTGxsbICiNF9irRQ6NusPQNcWA2mT3Iv7XuzFc7Pu4JGRHwJwz9AXGft0Wxau+YBLOgwHYMand9M6uZelBzC4l3/bxr2Z8/jZN/jOPpLB3dM7c81F4/wes5jP5TKuOV3yW/mvKSoxvnnYmwN3XQrRFpnQZB427lPvb+mHYNdBa1xfqzqoOgjGt4vPL4Cj+ed/zevfGyv+d2/qv9gqKhDNTTA+1FilsaE6oDoAxu1Pz7fIdnEJrNoBew/BuMsgppL/YquI7GPwnwz/73f/UeMskWbx/t+3p1QD7FUDLHIyoec2b97MzJkzqVWrFk888USZr+nUqRMA7du3L33sVCOka9euVKpUCYcV2pEX0LrRRfTvOIrF62aycecywFgw5oFhr/P8Z+PIPpLBD+s/YX3aYu4d+nKAozVfWfn/XkHRSR57ZyhtGvXi5ksf9nOE4gs/bjl/U+NMew7Bu0t9G4+ZNu4N3L43BXDfFaE6aL86WFAEryw6f1PjFBfG4phpfjyluyJcrsDVga374KSFrrE/k+qA/erAijT37xyWeQTe+MEYX1ZQ1llo/qK5gDWFeg0I2cbGBx98QElJCSNGjKBKlSplviY62rig7szGxrZt25g1axbx8fF06dLFL7H6w4j+k3A6w3h7/t9LH+vSYgAXt7uBpz4YyYzZd3H/sNeIjakZwCh9p6z8z/TcrDsoKMxnwo1v+Tcw8YniEs9vUbopwzgjwQoCGadVjlFZVAftVQdX7zTuFuCuEpex6r8VZOcat3kNBJfLWHPHqlQH7FMHSlzw7a+ebZO237jMywr2BPD3cSD3XVGqAaFbA0K2sbFw4UIA+vXrV+5r0tPTgbMbG3369CEzM5MvvviC/v37+zZIP0qs1ZR+7W9izbbv2LB9SenjYwdPY+/BbXRpMZBuLa8KYIS+VV7+AJ/+OJ2fNs/lsdGfERVZOUARipl+TT/3Olp3BOrUbk8FejJjlW+zfk910D510OUyztry1H8yjNO7g92egwHev4U/1KgO2KcO/JZpNAE95U3tCIRAf8lRormAJYVyDQjZxsauXbsAaNiwYZnPFxUVsXSpce75mY0NpzNkDwnDL30Ep8PJ29+c7tBFR8aQENeY5Pi2AYzMP8rKf+22Rbz25UNMGvUx8XGNAhecmGqzl9ecerudv+UcD9y+j+VbdzIDqoN2qYPHT0K6F2cVuDBuoRrsDucFdv+BrEFmUB2wRx3wdixvtkANAMgJYB04WQT5BYHbf0WpBoRmDQjZxUOPHzd+6544UfbXtjNnziQ7O5uqVauSnJzs01g6d+5MVlaWR9tEhkfz6jjPvj5u36QvC6aW/4mjYd2WPlvZNqVZCgVFXnxFXg5/5J91aCf/894N3DZoKu2b9PU21FJmHwPxXrcRL1G/3WCPtzuQk0tSUgsfRGQih4Prn9xT7tP3Dzj/feljo07/99Eh5b/u6Al4Zl7ZzzVp2pyiAt9+svGmBoDqoOqgIaZmIwZO/NGrbSf/4wl+Wxzct8Jrccl42lwxscznLlQDoOJ14H9fe5O7v5jkZrTes3sd8Ff+ZtaBYKkBAJ2un0Zyl5s83i4vv4SkpAY+iMhc1/5zK+GRZQ92f8wF2rbvwMlc31+3o89E9poLxMfHs2rVKq+2DdnGRnx8PDk5OaxevZoePXqc9VxmZiYTJkwAoF27dj5fIDQrK4u9ez1bZScqwlqn/2RmZJBfaF7r2Nf55xfkMfmta+nR6mqu7WnOir9mHwPx3rHD3p2nXXDimMdjNRBKiotwhpVdvmOjobobw8fpdO91ZUnfs5PiIt9+VWO1GgiqgxA8dTDmeInX2x7cnxH0dSA+p/wa524NAO/rwNEjh/xyjOxeB/yRv9l1IFhqAEBKTrZX2xWePB70NQCguKig3MaGP+YCe9N3cfK47xfcsVod0FwgcHUgZBsb/fv3Z/PmzTz11FNcdtllNGvWDICVK1cyatQosrONYpeamurzWOLjPb8fUmT4Bb5uCTIJ9eqZ3p30pSUbZrE9cx17s7eweN3Mc55//cFN1KnhWbfe7GMg3ss/6N0Fskf2ricxMdHkaMxXkHeIqKp1ynzu6AV+BGOjjIlMScn57xZR3vsU5h8jvm5tNyP1ntVqIKgOQhDVQYeT4znpxNRIcnsTl8uFw+Gg5NjOoK8DUWHl35bkQjUAKl4HIhwFfjlGdq8D/sjf7DoQNDUAOJnj3cJZh/euC/oaAFCYl0OlytXKfM7Xc4HiopPUjovF5W1XxANWqwOaC1TsGHjzufkUh8tl1WXgzi89PZ3U1FQOHjxIeHg4LVq0ID8/n23btjFw4EBKSkqYP38+r776KrfddluZ7/Hoo4/y2GOPEYhDVFwAi6b7fbde6zcewiLNez+r5Q/mHwPxXn4hTJ7t+S0Jb+8HLev5JiYzvbrIuIuLNx4dYnw7czgPHv3U8+2b1IF7LvNu355QDdAxqKgFv8KX6zzbJikOHhgAwX6n9705MPUr77evaB248xJonuD9/t1l9zFg9/wrqrDYmAvkeXiC4Zje0D74r0ThrSWwdrd321a0BtSPgwcGerdvT1ltHGguELg6ELIrZSYlJbFkyRKuuuoqoqKi2LlzJ3Fxcbzyyit8+eWXbNlifKN75sKhIhIaoiKgWxPPtqkb65+JuhmS4gK37/oB3LeIJ7o3gUoenpd6cfPgb2oAxFeD8ADO4AJZg0TcFREGPVM82yYuBtq4f6JXQGkuIHK2kL0UBaBly5bMnTv3nMdzc3PZuXMnTqeTNm3aBCAyEfG1wR2MbzXT9l/4tTGV4NaLwWmBDzQAKXXhm18Dt28RK6gaDX/sBa99796dfHqmQGffriVumjAnNK4DWzxbl9wU9aobNVPECga0M25P7M4dUqIi4E8XG+PLCgL5+7ip5gIShCwydM21ceNGXC4XKSkpVK587rVhn3zyCZ988gmbNm066+/ertAqIv4XEWZcWtKh7Ds+l4qvBn++HOrE+icuMzStG5h4a1S2xqU6Iqe0SoQ7LoGqUeW/JswJl7eB67pY42yNUzz9JtosFwVovyLeCHMazYoujeF8w7t2VWMuUK+G30KrsAY1ISkA8VaJgnb1/b9fkQsJ6TM2yrNhwwag/MtQhg0bVubf//jHP/LWW2/5NDYRMU9kuPGN7YB2sGwrbNoL2cfAhXEa99h+xjceVvowA0a8PVPg01/8u9+LUozFxkSspFk8TL4W1u+B5dtg235wuYwztAa2My5ZqWqttekA43T5atFwxI/rNFYKt85ZLSKnhIfBiB5wRRtjLvDrXjhw9PRc4NaLjUtRrXLW5ikOB/RqBh/+5N/99mhiHFORYGPLKeqFGhsul6vMP2pqiFhT3VgY0gkeufr0fd1jKhkfeKzW1DilWxPvb9HmjapR+qZWrCs8DDo2grv7G3cDAONn+rI21mxqgPFN9GV+vpq2b0vjdH0RK6pVFa7uCA8PPnsu0LKe9Zoap3RsZOTlL5UjoXdz/+1PxBM6YyOEpGWs49lPbiPv5DHqVm/IQ8PfZde+jTz82kCSajfnybHfUKNKHeb9/AazljzL7v2buX3QNIb2vrf0PV6dO4HF62aSktiRx0Z/FrBcvOFu/lM+HM3qrQuoFmPcsrJTs8sYO2gqYO38xV6iIuCm7vDyQv/sb1hXa1xX724dSD+wlRmf3sXh3P0UlxQxsv/f6Zt6IwCzfniWL5a9QFRkFV65f21gE3KDGbX/ja8fYfmmL3A6jK/hbrrkL/RLvQlQXQxmF6UYd0XYts/3+6pXAy5r7fv9eMOMMfDoW0PIPLSj9O87stbz6B8/46LWVwd9TXA3/9e/fpilG2YTEV6JsLAIxgx4nC7NrwDgp81f8vb8v7Mz61cG9biTu675d+n7B3v+dhYZDjd3hxkLjDNQfG1o59NNoWBixhh455tH+WLZC9SMNW712yi+NX+9+f8Aa4wBM45BzrF9PDf7TjKyt1FUUsig7reX1snFa2fy7oLHOHg0g8/+eThwiZ6HLRsbCxf66ZOAn02dOZoHb3iTpompzPv5DV6d+yBXdBlDUu3mZw3ClKRO/G3kR3y48Ilz3mPsoKk0rNuaZRs/81/gJnE3f4Ab+k44a0JzipXzF/tpkQAXNYVl29zf5tQ96S90j/szdWpknetp3a0DU2eO5oouY7iy2584nHuAu5/rTJvkXtSqlsh1fe6jaWIHXvz83oDl4Qkzav8NfSdwy8DHAcg+spdbp7akY0p/qsXUUl0MYk4HDO8OU7707PbWntaBMKfx4SlYTz83Yww8Ovr0PS9/27OKh18bQJfmAwCCvia4m3/b5N6M7D+JShHRpGWs4/6X+vDhpAyiI2NIrJXCAze8wQ/rP+bEydyz3j/Y87e7xnXg4haw+D/ub+PNXKBtkjEfCEZmjAGASzqMOKupd4oVxoAZx+DlOffTsG4rHv3jbE4UHOfe53vSulFPmtfvQt/UG2nRoBt3PJsasBwvxJaXooSibXvXEF2pCk0TUwG4rPMfWb7pCwqLzr15d5N67WlYtyUOR+j883uSv0goGdrZaHC465l5xj3rn5nn3uub1IEbu3kXm795Uge2Z66ja4srAahepTaN67Vn8dqZ/gzXFGbV/irR1Uv//8TJXFy4KHGV+CpsMVHNKvCnvp7d/tWTOuBwGGsVBestXn0x/5n38+tc2nEkEeGRvgjZVJ7k37XFQCpFGF+3J8e3BZeLI7kHAEiq3Ywm9doT5rTld56WN7iDZ19AeDoXaFgTRl4UnJfvmjUGrMysY7A94/TcKDoyhnaN+/DtL+/6JwkTqHqFiMxDO9iRuYHbn0ktfexkQR7ZR/cGLig/8jT/T5c8x7yf36BOjQaMvuJ/SguBiNWEh8EtfeDtH2GjycO9ebzx3pEW+U3hSR1ISerEd6vf48Z+E8k8uJ1NO5cRX6OR/4I1iZm1/9Mfp/PFshfIPpzOfcNeo0aVOiZGKr6UUtdYDPm176HAgzM3LiTMCX/oGdxnbJk9/zlZeIJFaz/g2buWmBShb3mb//xVbxIf15i6NS5w6zCxhFNj9b1lxuVpZmpcG27rC5WCdH0dM8fAD+s/Zl3aImIr12RE/0mkNu3nq7BNZdYxSEnqxMI179OyQXeO5h1k1Zb5JNW2zqIqFpmuijtaNOjGk7fNL/379Y/WDmA0/udu/rcMfJy4qgk4nU5+3PApj7w+kLce2kp0pSr+ClXEVJHhRgPiu00wfwMUV/CLdqcD+rc2boEZrKeel8fdOjDxxrd5Zc4D3P5MKnVrNKRDyqWW/abSrNo/pNd4hvQaT1rGOp78YCSdm11ObExNs8IUH2sWD/cPgPeXw+6DFX+/+Gpwcw/jlpLBzsz5zw/rPyGpdjOSE9qaEZpfeJr/6q3f8e6Cx3jqtgU4gvErePFKeJjR3GhYC75aB4XFFXs/hwP6tYCB7SEiyOcCZoyBQd3v4OZLHyE8LIJfdyzlsbeH8PyfV1qm+WfGMbh98NO8MudB7vx3B6pXqUP7xn05fNw6Z7RYcxYn50iIa8z+w6dbtMfzj5JfcJxa/10AJ9R5kn+taqcf69V2CK9//Rf2HPiNZkmd/BKriC+EOY1GRJtEmPkz7Mr27n2S4oxLT+oH6Wnn5+NJHYiPa8TkP84q/ftf/3cAnZpd7pc4zeSL2t+kXntqxSayLm0xvdtdZ0aY4ifx1eDPl8OizbDgV8/W3TglIsy4Xn9AW2s0Ns0eA/N+fp0BXW41Kzyf8zT/dWnfM+2jMfxzzBzq17HON7HiHqcT+rWEVonw0U+Qtt+790mobswFGtUyNTyfMGsMxMXGl/5/m+SeNEnswJY9qyzR2DDrGFSLqcXEm94q/fu/Z91Bo7pBump0GUJnkQWba5qYSrgzgl+2LABgzrIXubj9jZa4PtQMnuR/4HB66f9v2rWCo8cPklizqd9iFfGlejXg3svh3iugS7J7192HOY0FwcZfBg8MsGZTAzyrAznH9lFSYpzasvK3+ezav4lLOtzs13jNYFbt37VvU+n/Z2SnsS1jDQ3qtjI1VvGPMKdxxtVjQ+H6LpBQzb3talWBazrCo0NgUKo1mhpg7vxnb/Y2tqSvol+H4WaH6TOe5L9++w889eEo/jH6c5rUC607A8rZ6sbCPf/9nd69iXtnXDgdkNrAuC32xCut0dQA88bAmZ8P0g9sJS1jrWXO3DLrGBw9fpCi4kLAWLdj2a+fMfiiu3yfgEl0xkYI+evN/8fUj8Ywffad1KvZlL/c/B47s34953XzV77FW/P/Rm5eDss2fsbH30/jn2Pm0DSxQwCiNo+7+U+dOZqc3H04HWFUiohm0qiPiYl2c+YnYgEOhzEhaVQLbugGGTmw5xBkHTa+wXVhXL6SUA3q14R61a2zjsaFuFsHlm+aw8xFT+J0hlEzth6P3/pV6WJaVmNG7f/fLyeSdWgHYc4IwsLCGXft8zSs2zIA2YhZoiKgVzPomQJHTsCeg0YdOHICioqNBkhstNHITIqDuJjgXBjQHWbNf+atfIPeba8jJirW3ylUiLv5P/3xrRQWnWTqzDGlj/1l+LskJ7Rl9dbvmDrzj+TlH8WFiyUbPuGeIS9yUeur/ZmKmKx+TbipptHkzDhs1IDMw3Cy8PRcoG6scclZvRpQyaJzATPGwJvzHmFr+i84neGEOcO4Z8gLJNVu5s80KsSMY/CfPT/zwufjCXOGU7lSVf426iNqxnqwQn2AWfTHV8qSnNCWF/+86oKvu6LLaK7oMtr3AfmZu/lPuf1bP0QjEhwiwozrbRta5JuXinK3DlzZ7U9c2e1PfojI98yo/f9zy1yTo5Jg4XBA9crGn7ZBvAhoRZg1/7l14L9MjMp/3M3/7Ye2lvtcx5RL+eBv6eU+L9YWHmY0L6ywZo43zBgDE29628yQ/M6MY9C1xUC6tij/+WCnS1FCXHhYJMfyDnL7M6nk5F74QrtX507gw0VPUCW6hh+i8z275y8inteBWT88y/TZd1EtxrrdIE9zPh/VRbEiM8eAFWuC3fMX0Rgw9xgsXjuTSW8OpkbVuiZFZz6Hy+VyBToIOVdxASyaHugo3NdvPISZuJyH1fIH84+B+Mbk2cap2NWijWvQJTipBugY+IpqgHXYfQzYPX9fUh2wDquNA80FAlcHdMaGiIiIiIiIiFiWGhsiIiIiIiIiYllqbIiIiIiIiIiIZemuKEHKGWFcn2QVzgjz389K+YP5x0DEzlQDdAxE7D4G7J6/CFhvHGguELg6oMZGkHI4rLH4kq/YPX8Ru1MN0DEQsfsYsHv+IqBxYPf8PaFLUURERERERETEstTYEBERERERERHLUmNDRERERERERCxLjQ0RERERERERsSw1NkRERERERETEstTYEBERERERERHLUmNDRERERERERCxLjQ0RERERERERsSw1NkRERERERETEstTYEBERERERERHLUmNDRERERERERCxLjQ0RERERERERsSw1NkRERERERETEstTYEBERERERERHLUmNDRERERERERCxLjQ0RERERERERsSw1NkRERERERETEssIDHYCUzeWCksJAR+E+ZwQ4HOa9n9XyB/OPgdib3ceA3fMXAeuNA80FVAfEXBoD1jsGqgGBo8ZGkCophEXTAx2F+/qNh7BI897PavmD+cdA7M3uY8Du+YuA9caB5gKqA2IujQHrHQPVgMDRpSgiIiIiIiIiYllqbIiIiIiIiIiIZamxISIiIiIiIiKWpcaGiIiIiIiIiFiWGhsiYhslJcbq2nD6vyJiHy6XaoCI3WkuIBKadFcUEQlZ6Ydg417Ycwj2HIQjJ04/dzQfnv8W6sdB0zrQsh441eoVCSkFRbB+D+w4YNSBzMNQWGw8dzQfHv/CqAH1a0JqA6gRE9BwRcQHMg/DhnRjTrDnIOTknX7uaD7MWABJ/50LtEqEMM0FRCxJjQ0RCSnFJbBmF/y4BXZmn/+12/YZfxZthhqV4aIU409MJf/EKiK+kX0MlmyBn7fDiYLyX3fgmPFn9S74Yg20qge9m0PzeHA4/BeviJirpATW7oalWyFt//lfm7bf+PP9f6BaNPRoCj2bQdUo/8QqIuZQYyOErEtbzIMv9zvrsajIGJJqN6N/x1Fc2/MewsJC95/c7vkLZB2B95fD7oOeb5uTB1+uMyY2w7pC+wbmx+cPdh8Hds/f7kpKYPF/4Kt1UFTi2bYul3GG18a90L4+XN/Vmh9sNAZ0DOzuwFF4f4VxppanjpyAeRvgh9/gus7QsZE1m5x2HwN2z9+u9C8agvqlDqdriytx4SLnWBYLfnmHl+fcz+79m7nv+lcDHZ7P2T1/u/r+P8Y3rsUefpj5vdyT8OYS6NgQbuoOkRatknYfB3bP344O58FbSy58ppY71u2BbfthRA/j1HQr0hjQMbCjZVvh019OX3LmrbwCeHeZUQtu7gFREebE5292HwN2z99uLDpll/NJSexI/04jS/8++KK7uHVKC77++TXGDHic6lVqBzA637N7/nbjcsHctfDdJnPfd/Uu44PS2H7WnNDYfRzYPX+7yT4GL3wHOcfNe8/jJ+G1743mRqdk897XXzQGdAzs5psN8NV6c99z/R6jrtx5CVS24GWqdh8Dds/fbrQ8jg1ER8bQomF3XC4XGQfTAh2O39k9/1A3/1fzmxqnbD9gfLCp6Dc/wcDu48Du+YeyI3nwoslNjVNKXPDecuPDjdVpDOgYhLJFm81vapyy5xC8shhOFvnm/f3J7mPA7vmHOjU2bCLzv4M3tnJcgCMJDLvnH6q2ZME8H01kTtm2z/f78Be7jwO75x+KXC5jXZ1DPmhq/H4fvmic+JvGgI5BKNqZbVyK6ku7smGuj/fhL3YfA3bPP5TpUpQQlF+Yx5Hj2bhcxvVkc5a/zLa9a2hRvytJtZsFOjyfs3v+dnGyED5c4fl29w+A2Gg4egKemefeNgs3Q9v60KiW5/sLFLuPA7vnbxcr0uC3LM+28aYG5BfCzJ/g9n7WWUhQY0DHwA4Ki43Go8vl2Xbe1IElW6BdA0ip63mcgWL3MWD3/O3GFo2N7OxspkyZwuzZs0lPT6d27doMHTqUf/3rX4wfP5433niDGTNmMG7cuECHaop3vpnMO99MPuuxXm2Gcs+QFwIUkX/ZPf/y5Bw37hpSWGzczrRRLWvfq33eBu++pY2NhuqVPdvG5TI+1Ey80jofauw+Duyef1mKS4xvHY+fNMZ+3WpQs0qgo/Le8ZPw2S+eb+dNDQD4T6ZxK+mOjTzfNhA0BnQMynI4DzIPG3OBypHGXCA8LNBRee+7jbD/qOfbeVsHZv4EDw8Cp0XmT3YfA3bP325CvrGxdu1aBg4cSFZWFjExMbRq1YqMjAymT59OWloahw4dAiA1NTWwgZroqm5j6dNuGEUlhezI3MDMxU+RfSSdyIjT9617/L2bKHGVMGnUR6WPHc07xG3TWjN20DQu7TgiEKGbwp38N2xfwsOvDzxn26LiAkpKipk/JQQWVfivzRmw5Dfjv2d+oRH733u190qBqtEBC88rJ4tg+Tb/7jPzMGzdB83i/btfb6kOqA6ckpsPS7caY+Zw3tnPNU+A3s2gdaJ1mnan/JTm/2vev//NOo0Nu9cAUB0405YsYy7w696zz26oUgm6N4Vezbz7oB9IRcXw41b/7jP7GGzKgDZJ/t2vt+xeB1QD7CWkGxvZ2dkMHjyYrKwsHnjgASZPnkzVqlUBmDJlCg899BDh4eE4HA7atWsX4GjNk1grhY7N+gPQtcVA2iT34r4Xe/HcrDt4ZOSHANwz9EXGPt2WhWs+4JIOwwGY8endtE7uZekCBu7l37Zxb+Y8nnvWdtlHMrh7emeuuSg0ztxxueCz1cZtUMty9ATM3wArthl3/kis4d/4KmL1TuPUcH9busU6jQ3VAdUBMM7SemUh5OSV/fxvmcafi1Lg+s7W+RayxGU0a/xtV7axkGB9C1yabfcaAKoDYMwFvl4P3/xa9vO5J+HbjcZc4La+0NBCl1yu22M0bv3txy3WaWzYvQ6oBtiLRaYw3hk/fjzp6emMGzeOadOmlTY1ACZOnEj79u0pKiqiUaNGxMbGBjBS32rd6CL6dxzF4nUz2bhzGWAsmPPAsNd5/rNxZB/J4If1n7A+bTH3Dn05wNGar6z8f6+g6CSPvTOUNo16cfOlD/s5Qt/4al35TY0zHTkBLy2Eg7kXfm2wWLk9MPvdkB6YhooZVAfsVwcO58FL35Xf1DjTsq1GI9Qqdh4IXM0KVP2pKLvXALBnHfh2Y/lNjTPlnoSXF8G+I76PySyrdgRmv//JhGMnArPvirJ7HbBjDbCTkG1sbN68mZkzZ1KrVi2eeOKJMl/TqVMnANq3b1/62CeffMJ1111Hw4YNqVy5Mi1atOCRRx4hN9dCn/rKMKL/JJzOMN6e//fSx7q0GMDF7W7gqQ9GMmP2Xdw/7DViY2oGMErfKSv/Mz036w4KCvOZcONb/g3MR7KPwYKN7r8+Nx++XOuzcExVUgLphwK0b1fg9m0G1QF71YF5643Gpbt++A0ycnwXj5l2HQzcvvcEcN8VZfcaAPaqA4fzjLM13HWiwPd3FzGLywW7AzgWd2suYFl2qgF2E7KNjQ8++ICSkhJGjBhBlSplr44WHW0sLHBmY2PatGmEhYXxr3/9i6+//po777yTl156iQEDBlBSUuKX2H0hsVZT+rW/iTXbvmPD9iWlj48dPI29B7fRpcVAurW8KoAR+lZ5+QN8+uN0fto8l8dGf0ZUpMUuMC2HN6dor9tjjW8g9h2FggBe7rjHwpMZ1QH71IG8k/DLTs+3C8TlHd4IZIMxPcdYiNWK7F4DwF51YPk2oyHviU174ZAFvss7dNxYQDhQNBewLjvVALsJ2cbGwoULAejXr1+5r0lPTwfObmzMmTOHjz76iBEjRnDxxRfz5z//meeff56lS5fy448/+jZoHxt+6SM4HU7e/uZ0hzI6MoaEuMYkx7cNYGT+UVb+a7ct4rUvH2LSqI+Jj2sUuOBM9rMXp0oXl8Avu8yPxWxZAT5NNvNwYPdfUaoD9qgD6/YYdz3w1KodxllRwS4zgHWgsNi7OzIFC7vXALBPHfBmLuACVu00OxLzBXoukHU4sPuvKLvXAbvUALtxuFye3vnZGurXr096ejpr1qwp844nRUVFJCQkkJ2dTVpaGo0bNy73vbZs2ULz5s15//33GT58uMexdO7cmaysLI+2iQyP5tVx/vnq7IGX+tK95SCG9X3Q6/cY+3wKBUXmfd3vj/yzDu1k3PQujLxsMtf2rPjiQGYfA285wysx9PE0r7bd8sMrrP/ynyZHZK5GnW+k87Cny3zu1H3pzyc2ylggsaQEjp5n0bHy7m2/Z/0cfvq/Oz2I2Dv+rAEQfHXAX/mbWQeCpQYAtLrsQVr1v9erbb94rA0FeYdNjcdsAyb8SJVajcp87kJ1wN0aAOXXgQX/vpwjmZvcD9hLmguoDlTE0H/txBnm+X0Ctv/0Hqtn/8UHEZknqd1guo94qczn/DEXyNz8HUvf+qMHEXvH7nMBsN5ngmCqAVYUHx/PqlWrvNo2ZO+Kcvy48XXKiRNl/2DNnDmT7OxsqlatSnJy8nnfa9GiRQC0bNnSq1iysrLYu3evR9tERVjr9KfMjAzyC91Yoc5Nvs4/vyCPyW9dS49WV5vS1ADzj4G3wsIjvd722LFjHv+s+lu1JuWf/+nJfemdTu9ubXciL88vx8hqNQDMHQP+yN/sOhAsNQCg/rGjXm+bkZHByePBvdhGUVH5q/i6Wwe8rQEA+/dlka06cA6rzQUgtOuAt47n5gb9XKByUvkLbPhjLpCff0JzgXJYrQ6oBoSOkG1sxMfHk5OTw+rVq+nRo8dZz2VmZjJhwgQA2rVrh8PhKPd99u7dy6RJkxgwYECZZ364G4unIsMv0GoOMgn16pnenfWlJRtmsT1zHXuzt7B43cxznn/9wU3UqdHAo/c0+xhURH5uNlFVPL9nW1jxMRITE30QkXmqVC6/cXPUjcPvybc0ZYkIc/nlGFmtBoC5Y8Af+ZtdB4KpBoQVH/Nqu8L8Y9SqUcX7T/x+4iou/+L6C9UBT8/YKEtcjVgqlagO/J7V5gIQ2nXgxJEMYuI8m8sAOAqPBP1cIDYmqtzn/DEXCHeWaC5QDqvVgVCuAVbkzefmU0L2UpTx48czY8YM6tevz7fffkuzZs0AWLlyJaNGjWL79u0UFhZy99138/zzz5f5Hrm5ufTt25esrCxWrlxJQkKC3+IvLoBF0/22uwrrNx7CvD9R4BxWyx/MPwYV8cUaWOjhWdJhTnh0CFQtf64QFLKOwJNzvd/+0SHGZ7bDefDop55vf01H6OfdyVsesfsYsHv+FZVXAI/O9nyh3V7N4PouvonJTO8u9W5xVKh4DYgMgydvMD4U+ZrVxoHmAsFVB+ZtMO6O5AmHA/5+DdSI8U1MZjmUC//43PvtK1oHBraDK/ywFIXGgPWOQTDVALsJ2cVDJ06cSM2aNdmzZw+tW7embdu2pKSk0LVrVxo3bswll1wCnL1w6JlOnDjB4MGD2bFjB998841fmxoiFXVRUyj/PKSypTYI/qYGQJ2qEBnAc83qxwVu3yLuqhwJnc5/lWWZeqaYH4svBHIcJtbwT1NDpKJ6NAGnh5OB1onB39QAI8aYSoHbv+YCIsEnZH81JyUlsWTJEq666iqioqLYuXMncXFxvPLKK3z55Zds2bIFKLuxUVhYyPXXX8+qVav4+uuvadWqlb/DF6mQWlXhcg++SYiNgqvK7vEFHaczcBMKpwOSNJkRixjQ1rMrSvq1hITqPgvHVA1qBnDfnl/lJxIQ1SrDoFT3Xx9TCa7u4LNwTOVwQMMA1QEHUD+ANUhEyhaya2yAsdjn3LnnnrOem5vLzp07cTqdtGnT5qznSkpKGDFiBN999x1fffUVXbt29Ve4IqYa0BaKiuG7C1ySUr0y3N4P4qr4Jy4zdEmGtP3+32+7+hAV4f/9inijWmW461J4ZREczD3/a3s3h8EW+UAD0Kg21KoC2RfIyxe6eHEmjEig9GtpXJJ2oUtSqkbB2L5QJ9YvYZmiczJsyvD/flvUs8YZriJ2E9KNjfJs3LgRl8tFs2bNqFz57K+z7r77bj7++GP+8pe/ULlyZVasWFH6XJMmTahdu7a/wxXxisNhfFBpngBLfoNf98KZK+pUr2xcsnJRClSx2C/ojo3g8zVwosC/++3ZzL/7E6moOrHGrQ+Xb4OlWyHn+NnPt0401tVokWDUDKtwOozx+Plq/+63US2dtSXW4nAYX3Sk1IElW2D9Hig5Yy4QGw09mhqXoV3oFqnBpl19o8Fw7AKLAJutl0Uu2ROxG1s2NjZs2ACUfRnK119/DcCTTz7Jk08+edZzb775JqNHj/Z5fCJmahZv/DmcB1O/hOMFxummk64xFgy1oshwoylzobNRzFSvBjSt47/9iZglphL0bw2XtIQ9h4wzOPIKoGoluK1voKPzXtfGMH8D5Jd/51fTXdzCf/sSMVOTusafIydgytz/zgUiYfK11p0LhIdB72bwlYcLpFZE7arQsp7/9ici7lNj43d27tzp52hE/KN6ZWMSABDutO5E5pTL28La3Rc+xd4MTgcM72atb7RFfs/phIa1ICLs9N+tLKYSXNsJPlxx4deaoVU9Y5FlESurFn3GXCDM+nOBS1rB6l3GHdN8zQHc1N36tVMkVKmxEULSMtbx7Ce3kXfyGHWrN+Sh4e+ya99GHn5tIEm1m/Pk2G+oUaUOr3/9MEs3zCYivBJhYRGMGfA4XZpfAcCsH57li2UvEBVZhVfuXxvYhDzkbv4AXyx7kc+WziDMGY7T4WTGPT8RGRHFq3MnsHjdTFISO/LY6M8Cm5CcV6VwGN4dnv/Ws+1O3ZPenfvcn3Jpq+BdKMzdn/t5P7/BrCXPsnv/Zm4fNI2hve8tfY/8gjye/vhWtuxZicPh5JaB/6JPu+sBgn5MmJH/Kbv2bebu5zpxZbex3HXNvwFr10Q76NYY1u2GzR5cZ+9NDYiKgBuCtLlpxhhIP7CVf88ay7G8HAqL8una8irGXjUVp9NpiTFgxvzn0x+n89WKV8HhwIGDG/pOpH+nkQAsXjuTdxc8xsGjGXz2z8MBzFR+LzwMbu4B/55/9iU2F+JNHejdHJoE6ZmbZowBgCXrZ/HOgkdLr13+5y1ziY9rFPR1wN383/j6EZZv+gKnw+ju3XTJX+iXehNg7bmQGGzZ2Fi4cGGgQ/CJqTNH8+ANb9I0MZV5P7/Bq3Mf5IouY0iq3fysItQ2uTcj+0+iUkQ0aRnruP+lPnw4KYPoyBiu63MfTRM78OLn9wYsD2+5m/+yXz/nu9X/x4xxK4iJrsbh3AOEhRkrQo4dNJWGdVuzbONngUlCPNK0rnE3ly/Xub/NM/M820ezeP/cq95b7v7cpyR14m8jP+LDhU+c8x4ffz+NiLBKvP2XbWQe2sH46d1IbdKP2JiaQT8mzMgfoKi4kH/PGkvPNkPOetzKNdEOHA6jwTn9G/cXEvW0BjgdMOoiz+4w409mjIH//XICPdsMYUiv8RQU5nP39C6sbHop3VpeaYkxYMb8p2Hd1vz77qXERFdj/+E93PlsB1o17EG9Wk3om3ojLRp0445nUwOWo5SvQU24piN8+ov723haB5Jre3aHGX8zYwxs27uGN+c9wpTbF1KrWj3y8o/hdBoNgGCvA+7mf0PfCdwy8HEAso/s5dapLemY0p9qMbUsPRcSg06mChHb9q4hulIVmiamAnBZ5z+yfNMXFBadu7pi1xYDqRRhrBCVHN8WXC6O5B7wZ7im8yT/j76fyqjLJhMTXQ2A6lVqE/bfwi3W0781XN7mwq/zRpM6cGuf06ftBhtPfu6b1GtPw7otcTjOLfvfr5vJoB53AJAQl0y7Jn358ddPfRq7GczKH+C9Bf+gT7thJNbSqnBWExsNd14KNX1wZyenA0b1hNZJ5r+3GcwaAw4cHD9hnMt/svAExcWF1IxN8GnsZjFr/tMx5dLSeUGd6vWJqxrPgSN7/JOEVNjFLXzXeGhYy1iPKDJIvw42awx88v3TXNfnfmpVMxYRqRxVlajIIO3onsGT/KtEVy/9/xMnc3HhosRVAlh3LiSnBekQFU9lHtrBjswN3P5MauljJwvyyD6697zbzV/1JvFxjalbo6GPI/QtT/LfvW8TW9JX8e6CxygsPsllnf7AkF7j/RitmMnhgCvbGyujf7EGCovNed/OyXBD1+CdyID34/739h/efVYNiK/RiP2Hd5sVps+Ylf/m3T+xaddynhq7gHcXPGZylOIPNavA+Mvg7R9hu0l9+ipRMKJHcC8UaNYYuPOafzPpjcHMWfESuXk5jOg/iaaJ1rj/ry/mP6u3fMuxEzk0q9/F7HDFh/q3Nsbt7JXGLW7NkNrAOCusUhDf6t2sMbBr/ybqxjXi/pcuJi//KN1bDmLU5Y8G/Zd/nub/6Y/T+WLZC2QfTue+Ya+VXqZu1bmQnBbEU3bxVIsG3Xjytvmlf7/+0fPfmnb11u94d8FjPHXbAhzBeOGwh9zNv7ikiKxDO3jmrh/IPZHDAy9dTEJcY7q3GuSvUMUHejeHZgnwwXLYme39+8RGGdfStwnSb2h/z9NxH2oqmn9+QR4zZt/FpD98EhJ10M6qVYZxlxm3t567tmJNzo4N4bouxgKlwc6MGvDFshfp12E4wy/5Kzm5+5nwcj+a1+9Cp2aXmRmqz5g5/9mRuYFpH43hbyNnEh0Z45N4xXe6NzHuYPbBCkjb7/37xFSCYV0g1SLf+5kxBoqLi9i2dw1P/GkeJa4S/v7m1cxZ/hLX9hzn09jN4En+Q3qNZ0iv8aRlrOPJD0bSudnlxMYE6UJq4hE1NkJEQlzjs7qKx/OPkl9wnFqxiWW+fl3a90z7aAz/HDOH+nWa+ytMn/Ek/zrVG9Cvw3DCnGFUi6lF1xZXsnn3CjU2QkDdWONb27W7YelWzyY1NatAzxRjUlTZAh9mwPNxX5461RuwL2dX6annWTk76dTsclNj9QUz8s88mMb+w7uZ8HI/AHJPHMblKiH3RA4Tb3rb9JjFt5wO45T0tknw41b4KQ2On3R/27ZJ0Ks5pNT1bZxmMasGfLHsBd6cuAWAGlXq0LXFlaxLW2yJxoaZ859d+zbxtzcG8cANb9AmuZdP4xbfqVUV7u4PG/YYc4EtWe5vW6MyXJRi/LFCYxPMGwN1ajSgV5uhpZeq9GozlM27lkOQNza8rYNN6rWnVmwi69IW07vddZadC8lpWmMjRDRNTCXcGcEvWxYAMGfZi1zc/kYiwiPPee367T/w1Iej+Mfoz2lSLzTuDONJ/v063Myq/xirRp0sPMG6tMU0TgiN4yDGbdg6NoJ7LoOHrjKuuW3fwGhcnPnFXEwlaJ5g3PHk9n7wyNXGbeOs0tQAz37uz6dPu2HMXf4yYJzSuT5tMT3bXGt2uKYzI//khLZ88ugB3nt4J+89vJOhve/lii63qKlhcXFV4OoO8OgQ+ENP6NMcGtc27qZ0SpgT4qtBl2QY0gn+fi2M6WOdpgaYVwMS4hqz6jfj9+KJguOsTVtEo3gfLV5kMrPmP7v2beaR16/k3utftURDR87P6TB+9991Kfx1EAxONS4rqfW7uUDlSGOR8EtaGetoTLoGLmtjnaYGmDcGLulwM79s+YaSkhKKi4v4Zcs3NLbA5wRP8t+1b1Pp/2dkp7EtYw0N6rYCrDsXktN0xkYI+evN/8fUj8Ywffad1KvZlL/c/B47s34953VPf3wrhUUnmTpzTOljfxn+LskJQXzrBze4m//1fe7n37Nu59aprXA4HPRqex0Xtx8WgIjF1xKqG39OcbmM28E5HcF520ZvuPtzP3/lW7w1/2/k5uWwbONnfPz9NP45Zg5NEzswrO8Env7oFv7wRBOczjDGDXmeajG1ApCN58zIX0JXRJjR6OzY6PRjJS6jFoSFyFc7ZoyBiTe9zYxPx/Hpj89RWFxAj1ZXl94C0QrMmP+8+Pl4jucf4bUvH+K1Lx8C4E9XPXXWrTDFmupWM/6cYue5wPnGQN/2N7E1fTV/ero1YY4w2iT3ZkivP/szDa+5m///fjmRrEM7CHNGEBYWzrhrn6dh3ZYAlp4LiUGNjRCSnNCWF/+86oKve/uhrX6Ixv/czT8yIkrfxtqUwwFhITKJOcXdn/sruozmii6jy3wuOjKGv42caXJk/mFG/mf6w+WPVjwoCWpOBxBCdcCMMdA0sQPPjVtqcmT+Y8b856mxC8wMSYKYnecC5xsDTqeT2wdP4/bB08wMzS/czf9/bplb7nNWnguJIUS+r5DyhIdFcizvILc/k0pO7oUXHJj1w7NMn31XyHQoPc3/1bkT+HDRE1SJruGH6ER8w9Of+/Ox4pgwM/9Qq4liDxoD5h6DxWtnMunNwdSoaqHrlMT27F4H7D4XsiOHy+VyBToIOVdxASyaHugo3NdvPIR5dknveVktfzD/GPjC5Nlw5ARUi4bHhgY6Gjkfu48Bu+fvS6oD1mG1caC5gDXqgGqAdWgMWO8YWKEGhCqdsSEiIiIiIiIilqXGhoiIiIiIiIhYlhYPDVLOCONUJqtwRpj/flbKH8w/BmJvdh8Dds9fBKw3DjQXUB0Qc2kMWO8YqAYEjhobQcrhsPf1WXbPX8TuY8Du+YuAxoHd8xfRGNAxEPfpUhQRERERERERsSw1NkRERERERETEstTYEBERERERERHLUmNDRERERERERCxLjQ0RERERERERsSw1NkRERERERETEstTYEBERERERERHLUmNDRERERERERCxLjQ0RERERERERsSw1NkRERERERETEstTYEBERERERERHLUmNDRERERERERCxLjQ0RERERERERsSw1NkRERERERETEstTYEBERERERERHLUmNDRERERERERCxLjQ0RERERERERsazwQAcgZXO5oKQw0FG4zxkBDod572e1/MH8Y2B3dv8ZsHv+IhoD1jsGqgFiNquNAdBcQHVAAkWNjSBVUgiLpgc6Cvf1Gw9hkea9n9XyB/OPgd3Z/WfA7vmLaAxY7xioBojZrDYGQHMB1QEJFF2KIiIiIiIiIiKWpcaGiIiIiIiIiFiWGhsiIiIiIiIiYllqbIiIiIiIiIiIZamxISIiIiIiIiKWpbuiiISw4hLIOgJ7DsHeQ5BXYDyeVwDzN0D9OONP1ejAxikivpObb9SAPYcg+9jpOnCiAJZvg6Q4SKgG4WGBjVNEfKO4BPadmgvknD0XmLfemAck1YRqmguIiIWpsSESgg4chaVb4eftpycwZyoshq/Xn/57cm3olQLtG+jDjUgoKCqGDenw4xZI21/2awqKYeZPxv9HRUCXxtAzBeKr+S9OEfGdg7mwbCusSIPjJ899vrAY5m04/fcGNaFXM0htAJH6hCAiFqOyFULWpS3mwZf7nfVYVGQMSbWb0b/jKK7teQ9hYaH7T273/AGOnYBZq2Dtbs+223HA+PPpari2I3RqBA6HT0L0Kf0M6BgIrNsNs1fBkRPub5NfCEt+M/60SYJhXaBaZd/F6Et2HwN2z1+MJsanv8AvO8DlwXa7D8L7y+Hz1TC4A3RrrLmAVekYiB3pJzoE9UsdTtcWV+LCRc6xLBb88g4vz7mf3fs3c9/1rwY6PJ+za/6rdxpNjbK+lXFXbj68t8xojNzQFWItelqqXX8GzqRjYD+5+fDJSs8bm7/3a7pxlseQTtAl2ZofbEBjwO7529WGPfDRz3As3/v3OH4SPlwBa3fBTd2hukWbnBoDOgZiL2pshKCUxI707zSy9O+DL7qLW6e04OufX2PMgMepXqV2AKPzPbvl73LBV+tgwUbz3vPXdEg/BHdeCnVjzXtff7Hbz0BZdAzs5WAuvPid8V8znCgwvrndm2OcxWXF5obdx4Dd87cblwu+3QhfrjPvPf+TCU9/DXdeAvVqmPe+/qIxoGMg9qK7othAdGQMLRp2x+VykXEwLdDh+F2o5292U+OUw3nwwgI4cMz89/a3UP8ZcIeOQejKOQ4zFpjX1DjT9/8xTml3eXI+e5Cy+xiwe/6hzuymxinH8uGF74yFyK1OY0DHQEKbGhs2kfnf4hVbOS7AkQRGqOb/83bfNDVOOZoPry6CgiLf7cNfQvVnwBM6BqGnqBheXWw0In3lh9+MxYhDgd3HgN3zD1Vrd/umqXHK8ZPwykLjTC6r0xjQMZDQpUtRQlB+YR5HjmfjchnX081Z/jLb9q6hRf2uJNVuFujwfM4u+R/OMxYI9MT9A4x1M46egGfmubfNgWMwdy0M7exxiAFjl5+B89ExsIf5GyDzsGfbeFMHvlgNLRKgVlWPQwwYu48Bu+dvF8fy4eOfPdvGmxqQk2csKnpTd89jDBSNAR0DsRdbNDays7OZMmUKs2fPJj09ndq1azN06FD+9a9/MX78eN544w1mzJjBuHHjAh2qKd75ZjLvfDP5rMd6tRnKPUNeCFBE/mWX/D/6ybiTgSdio71bBGzJb8bt3xrX8XzbQLDLz8D56BiEvj0H4btNnm/nTR0oKIYPf4K7L7XOeht2HwN2z98uZq30fNFwb+cCK9IgtaHR5LQCjQEdA7GXkG9srF27loEDB5KVlUVMTAytWrUiIyOD6dOnk5aWxqFDhwBITU0NbKAmuqrbWPq0G0ZRSSE7Mjcwc/FTZB9JJzIiqvQ1j793EyWuEiaN+qj0saN5h7htWmvGDprGpR1HBCJ0U7iT/4btS3j49YHnbFtUXEBJSTHzpxT7M2SP7T4ImzL8tz8X8M2vcMcl/ttnRdh9DIA9xoHdLdgIJX5c+2LbPti+H5rU9d8+K8LudUA1IPRlHan4XZA8NX+DdRobdq8BoDog9hLSjY3s7GwGDx5MVlYWDzzwAJMnT6ZqVeM82ilTpvDQQw8RHh6Ow+GgXbt2AY7WPIm1UujYrD8AXVsMpE1yL+57sRfPzbqDR0Z+CMA9Q19k7NNtWbjmAy7pMByAGZ/eTevkXpYv4u7k37Zxb+Y8fvZKe9lHMrh7emeuuSj4z9wJxPXu/8k0LkupbYFT0e0+BsAe48DODufBhnT/7/fHrdZpbNi9DqgGhL5AzAV2HICMHGvcJcXuNQBUB8ReQnrx0PHjx5Oens64ceOYNm1aaVMDYOLEibRv356ioiIaNWpEbKwF72npptaNLqJ/x1EsXjeTjTuXAcaCQQ8Me53nPxtH9pEMflj/CevTFnPv0JcDHK35ysr/9wqKTvLYO0Np06gXN1/6sJ8j9MyJAli9MzD7XmbRBQTtPgYg9MaB3a3YFpg7lazbbVzTb0V2rwOqAaGloAhWbg/Mvq26mLDdawCoDkhoC9nGxubNm5k5cya1atXiiSeeKPM1nTp1AqB9+/aljy1ZsoT+/fuTkJBApUqVSEpK4sYbb2Tz5s1+idtXRvSfhNMZxtvz/176WJcWA7i43Q089cFIZsy+i/uHvUZsTM0ARuk7ZeV/pudm3UFBYT4TbnzLv4F5YddBKAzQWYHb9gVmv2aw+xiA0BoHdrc1QGOxxGV8Y2tVdq8DqgGhI/2Q5+tsmUVzAWtTHZBQFbKNjQ8++ICSkhJGjBhBlSpVynxNdHQ0cHZjIycnh7Zt2zJ9+nS++eYbnnrqKTZu3EiPHj1ITw/Aeb8mSazVlH7tb2LNtu/YsH1J6eNjB09j78FtdGkxkG4trwpghL5VXv4An/44nZ82z+Wx0Z8RFenFalp+tudg4Padcdi4vaQV2X0MQGiNAzsrcRkfagIlkDWoouxeB1QDQseeANaA/UfhZICaKhVl9xoAqgMSukK2sbFw4UIA+vXrV+5rTjUqzmxsXH311Tz77LMMGzaMiy++mBEjRjB79myOHDnCrFmzfBu0jw2/9BGcDidvf3O6QxsdGUNCXGOS49sGMDL/KCv/tdsW8dqXDzFp1MfExzUKXHAeSM8J3L6LS4zFyqzK7mMAQmcc2NnBY3CyKHD7D2QNMoPd64BqQGjYG8Bx6Arw/ivK7jUAVAckNDlcrkBcpet79evXJz09nTVr1pR5x5OioiISEhLIzs4mLS2Nxo0bl/teBw8epFatWjz//PPcfffdHsfSuXNnsrKyPNomMjyaV8f55yLGB17qS/eWgxjW90Gv32Ps8ykUFJ0wLSZ/5J91aCfjpndh5GWTubZnxRdHMvsYlOfisR9Tu0mPMp87dW/68sRGgdMJJSVw9ALXyZd3f/sfXhvO/q1Lzn3CZFYbA2Duz4C/8jdzHPhrDNhdzYad6HfX52U+d6EaAO7XgfJqwKE9a1n4/CAPIvaOP2sA6HehaoC19Bz9Fgkt+5f5nD/mAsvevpWMTfM9iNg7mgtoLiD2Eh8fz6pVq7zaNmTvinL8+HEATpwoe2DNnDmT7OxsqlatSnJy8jnPFxcXU1JSwq5du/jrX/9KfHw8N9xwg1exZGVlsXfvXo+2iYqw1ulfmRkZ5BfmmfZ+vs4/vyCPyW9dS49WV5vS1ADzj0F5CotLyn3O3XvTO53e3cMeICfniMc/z96w2hgAc38G/JG/2ePAX2PA9qo2Kfcpd2sAeF8HiktQDSiH3X8Xqgb4T8F5Ftvyx1zg8NFjqgPl0FxAdUACI2QbG/Hx8eTk5LB69Wp69Dj72+3MzEwmTJgAQLt27XA4HOdsf/HFF7N06VIAmjZtysKFC6ldu7bXsXgqMvwCX7kFmYR69Uz/lsqXlmyYxfbMdezN3sLidTPPef71BzdRp0YDj97T7GNQnnBn+SdZHb3A7j39lqYsNapVpTAx8QJRVpzVxgCY+zPgj/zNHgf+GgN2F1ejWrnPXagGgGdnbJTF6SghUTWgTHb/Xaga4D8R4efOXU/xx1ygWtUY1YFyaC6gOiDe8+Zz8ykheynK+PHjmTFjBvXr1+fbb7+lWbNmAKxcuZJRo0axfft2CgsLufvuu3n++efP2f63337j8OHD7Nixg6lTp7J//36WLl1Kgwaefdj1VnEBLJrul12Zot94CIs07/2slj+YfwzK8/5y+NnLW7w9OsT4duZwHjz6qXfv8cjVULvqhV9XUXb/GbB7/lK+I3kw2cvxCxWvA6kNYHRv7/fvLo0B6x0D1QD/+WQl/LjFu23NmAtMuBISa3i3rSesNgZAcwHVAQmUkF08dOLEidSsWZM9e/bQunVr2rZtS0pKCl27dqVx48ZccsklwNkLh56pefPmdOvWjZtuuonvvvuOY8eOMWXKFH+mIFKm+nGB23dUBNQq+yZDIuIn1SpfeB0NX0oKYA0SEUMg5wLhTogv/8QxEZGACNnGRlJSEkuWLOGqq64iKiqKnTt3EhcXxyuvvMKXX37Jli1Gm7u8xsaZqlevTtOmTdm2bZuvwxa5oEB+qKgfB2VcuSUifhbIDzWB3LeIGAI5DuvVgLCQ/QQhIlYVsmtsALRs2ZK5c+ee83hubi47d+7E6XTSpk2bC77P/v37+e233+jWrZsvwhTxSIOaxvWxF7ou1hda+f5yWhFxQ+tE2Oj7dfvOUTkSkr1bbkpETBRfDeJi4NBx/++7teYCIhKEQrqxUZ6NGzficrlo1qwZlSufvdrwyJEjadq0KampqVSvXp2tW7fy7LPPEh4ezn333RegiEVOC3NC96bwza/+3W9EGHQr/67IIuJHnRrB56vhZJF/99u1MUTacuYgElycTrgoBeau9fN+HdCjqX/3KSLiDlueSLZhwwag7MtQunfvzldffcWYMWMYOHAgU6dOpXfv3qxdu5amTVXJJThclGJMLvypQ0OoXMm/+xSRslWKMJoM/tYzxf/7FJGydW/i/0tC2tUP7Bo/IiLlseX3LudrbIwbN45x4yp+D2cRX6peGfq2gIWb/bO/SuEwsJ1/9iUi7rmsDfyyE/IK/LO/XilQO9Y/+xKRC6sSBZe1hnkb/LO/iDC46sJL04mIBIQaGyEkLWMdz35yG3knj1G3ekMeGv4uu/Zt5OHXBpJUuzlPjv2GGlXq8MbXj7B80xc4HWEA3HTJX+iXehMAr86dwOJ1M0lJ7Mhjoz8LYDaeczf/R98aQuahHaXb7chaz6N//IyLWl/NrB+e5YtlLxAVWYVX7l8buGTcMLA9/LoX9h/1/b6u7QQ1Yny/H0+5+28+7+c3mLXkWXbv38ztg6YxtPe9pe9xvuesMB7MOAZTPhzN6q0LqBZjLJ7QqdlljB00FbDGMbCr2Gi4rjO8u8z3+4qLgcEdfL8fb5gxBtIPbGXGp3dxOHc/xSVFjOz/d/qm3ggQ9L8X3M3/9a8fZumG2USEVyIsLIIxAx6nS/MrAPh86QvMXfEyTkcYJSVFXNl9LEN6jQeCP3+7u6wNbEiHvTm+39dVqcHZ3DRjDEyffTcbdy4tfc89B/7DbVdNYUiv8SxeO5N3FzzGwaMZfPbPwwHK8vzM+Axg9fmQiC0bGwsXLgx0CD4xdeZoHrzhTZompjLv5zd4de6DXNFlDEm1m581Gbmh7wRuGfg4ANlH9nLr1JZ0TOlPtZhajB00lYZ1W7Ns42eBSaIC3M3/0dGnb9r+255VPPzaALo0HwDAdX3uo2liB178/F4/R++5iDAY0QNmLICiEve2OXri7P+6o3WicbprMHL33zwlqRN/G/kRHy584pz3ON9zVhgPZhwDMOrCmZOYU6xwDOysYyOjwblml/vbeFoHwpxwcw/j8pdgZMYYmDpzNFd0GcOV3f7E4dwD3P1cZ9ok96JWtcSg/73gbv5tk3szsv8kKkVEk5axjvtf6sOHkzKIjoyhf8eRXNPzbgCO5x/ltqfb0Da5N00TOwR9/nYX5jTmAv/+BgrcXHPHm7lAs3jo09zz+PzBjDEwfugLpa87dDSLUU8kc3G7GwDom3ojLRp0445nU/2cmfvM+Axg9fmQiC3X2AhF2/auIbpSFZompgJwWec/snzTFxQWnXuOcpXo6qX/f+JkLi5clLjc/GQcpDzJ/0zzfn6dSzuOJCI80g9Rmq9hLRjT2/1rbJ+ZB49+avzXHY1rwx97BectXj35N29Srz0N67bE4Tj3QJ3vuWBn1jEQ63I4jA81zRPc38aTOuB0wKiLoGld72P0JbPGwPbMdXRtcSUA1avUpnG99ixeO9OnsZvBk/y7thhIpQhjcYTk+LbgcnEk9wAAMdHVSl+XX3Cc4uJC3wcvpqlXA27tY3zh4Q5P5wINasItffy/tpc7zBoDZ/rml7fp3PwK4mLjfRq7Wcz6DKB5glidLc/YCEWZh3awI3MDtz+TWvrYyYI8so+WfT/AT3+czhfLXiD7cDr3DXuNGlXq+ClS3/A0f4CThSdYtPYDnr1riR8i9J3WSfCni+HNJe5/W+OOlvWMpkmw3gHBm3/zUGPmMfh0yXPM+/kN6tRowOgr/qd0giTBLzzMqAHvLoX1e8x734gw+ENPaFvfvPc0m1ljICWpE9+tfo8b+00k8+B2Nu1cRnyNRuYG6wPe5j9/1ZvExzWmbo2GpY/9sP4T3vlmMhnZ2xgz8F80TQzSa4+kTM0T4PZ+8Nr3kG9iXyqlLtx6MUQF6RlbZo6B0udWvsHYQdPMDtVn7P4ZQOSUIP3IIt5o0aAbT942v/Tv1z9au9zXDuk1niG9xpOWsY4nPxhJ52aXExtT0x9h+own+YMxiUuq3YzkhLa+Ds3nWtaDh66CD1bAtn0Ve6/IMBjUAXo1C85vZ87k6b95KDLjGNwy8HHiqibgdDr5ccOnPPL6QN56aCvRlaqYGar4UESY0Yhcvs2c28Am14abuwfn9fS/Z8YYmHjj27wy5wFufyaVujUa0iHlUsKc1pgieZr/6q3f8e6Cx3jqtgU4zjgdr0+76+nT7nqyDu3k0beH0L3lIOrXCdJrD6RMTesac4GZP8F/Miv2XuFOuLK9sVC5M8i/wDdrDABs2L6EvJPHSs/gsgq7fwYQAV2KEjIS4hqz//Du0r8fzz9KfsFxasUmnne7JvXaUys2kXVpi30coW95k/+8n19nQJdb/RGeX9SsAnddCsO6Ggv9ecrpMG7jNvEq4zraYG9qePszH0rMOga1qiXi/O/MtVfbIVSOimXPgd9MjVV8z+EwbgX90FXG7Zm9GcPVK8PQznBPf2s0NcwaA/FxjZj8x1m8cv9a/jHmc46fOELD+NZmh2s6T/Nfl/Y90z4awz/HzCm3aREf14gWDbqxYvNcn8QsvlUjxjhzY3h3qFXV8+0dGGtrTbgSLmkV/E0Ns8fA1z+/zuWd/kiY083reoKA3T8DiJwS5OVK3NU0MZVwZwS/bFkAwJxlL3Jx+xvLXDti175Npf+fkZ3Gtow1NKjbym+x+oIn+QPszd7GlvRV9Osw3J9h+pzTAT1T4G9Xw9i+0CYJKp9n+RCHA+KrwRVt4e/XGtfQejMRCgRP/81DkVnH4MDh9NL/37RrBUePHySxZlNTYxX/iatirI0zeYhxm+aE6udvckRHQqt6xjX6k675b2PTIrMDs8ZAzrF9lJQY15mv/G0+u/Zv4pION5ser9k8yX/99h946sNR/GP05zSpd/Zd4c6cFxzOPcDabQtpnKB7fFuVwwHdmsDDg+HOS6B9fahS6TyvB+rEQv/W8Ldr4La+ULda+a8PJmaNATAaAks2fMIVXW7xedxmsvtnAJFTrHGepbjlrzf/H1M/GsP02XdSr2ZT/nLze+zM+vWc1/3vlxPJOrSDMGcEYWHhjLv2eRrWbRmAiM3lbv4A81a+Qe+21xETZYGvJL3gdEKrROOPywWHjhu3gjtRAMUlxjX5tapAYhxUsnAVcPfffP7Kt3hr/t/Izcth2cbP+Pj7afxzzByaJnY473NWYMYxmDpzNDm5+3A6wqgUEc2kUR+ftZigWFO1aKNpeUVbY/2dvTmQfQwKi40aER0BiTWMs72CcYFgd5kxBpZvmsPMRU/idIZRM7Yej9/6Vekig8HO3fyf/vhWCotOMnXmmNLH/jL8XZIT2vLpkufYsGMJ4WGRgIuhve+lU7PL/JiF+ILTYay90TzBmAsczoP0Q5B3xlygZhWjDgTrGhruMGMMACxe+yEpSZ1Iqp3it9jNYsZnAKvPh0Qs/JFGfi85oS0v/nnVBV/3P7eE5uml7uYPcOvAf/k4muDhcBgTl5ohuFyCu//mV3QZzRVdRnv8nBWYcQym3P6tyVFJsIkMN9bNSA7BZWjMGANXdvsTV3b7k8mR+Ye7+b/90NZyn7v3+lfMDEmCkMNhXKZSw4tLVYOdGWMA4KruY7mq+1izwvIrMz4DWH0+JGKRk03FW+FhkRzLO8jtz6SSk7v/gq9/de4EPlz0BFWia/ghOt/zNP9ZPzzL9Nl3US2mlh+iE1/w9N/8fKw6HnQMxO7MHANW/L1g9/xFzBwDi9fOZNKbg6lRNUjve10OzQXEbhwul8sV6CDkXMUFsGh6oKNwX7/xEGbi0gZWyx/MPwZ2Z/efAbvnL6IxYL1joBogZrPaGADNBVQHJFB0xoaIiIiIiIiIWJYaGyIiIiIiIiJiWboUJUi5XFBSGOgo3OeMMHdVfavlD+YfA7uz+8+A3fMX0Riw3jFQDRCzWW0MgOYCqgMSKGpsiIiIiIiIiIhl6VIUEREREREREbEsNTZERERERERExLLU2BARERERERERy1JjQ0REREREREQsS40NEREREREREbEsNTZERERERERExLLU2BARERERERERy1JjQ0REREREREQsS40NEREREREREbEsNTZERERERERExLLU2BARERERERERy1JjQ0REREREREQsS40NEREREREREbEsNTZERERERERExLLU2BARERERERERy1JjQ0REREREREQsS40NEREREREREbEsNTZERERERERExLLU2BARERERERERy1JjQ0REREREREQsS40NEREREREREbEsNTZERERERERExLLU2BARERERERERy1JjQ0REREREREQsS40NEREREREREbGs/wddL4eEiaTRsAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1374.44x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import EfficientSU2\n",
    "ansatz = EfficientSU2(num_qubits=num_features, reps=3) #create the ansatz for the quantum model (EfficientSU2 ansatz with 3 repetitions, the number of qubits equal to the number of features)\n",
    "ansatz.decompose().draw(output=\"mpl\", fold=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "55d29484-d178-40ca-969c-92137e404045",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_17332\\1894683844.py:1: DeprecationWarning: ``qiskit.algorithms`` has been migrated to an independent package: https://github.com/qiskit-community/qiskit-algorithms. The ``qiskit.algorithms`` import path is deprecated as of qiskit-terra 0.25.0 and will be removed no earlier than 3 months after the release date. Please run ``pip install qiskit_algorithms`` and use ``import qiskit_algorithms`` instead.\n",
      "  from qiskit.algorithms.optimizers import COBYLA\n"
     ]
    }
   ],
   "source": [
    "from qiskit.algorithms.optimizers import COBYLA \n",
    "optimizer = COBYLA(maxiter=100) #create the COBYLA optimizer for the quantum model (with 100 iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2b44c2dd-f69a-416f-9e4e-61f5263a3bc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.primitives import Sampler\n",
    "sampler = Sampler() #create the sampler (Qiskit Aer Statevector Simulator) for the quantum model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "993d75aa-7f37-4dbe-a992-85456b433933",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from IPython.display import clear_output\n",
    "objective_func_vals = [] #create a list to store the objective function values\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
    "def callback_graph(weights, obj_func_eval):  #define the callback function for the quantum model \n",
    "    clear_output(wait=True)\n",
    "    objective_func_vals.append(obj_func_eval)\n",
    "    plt.title(\"Objective function value against iteration\")\n",
    "    plt.xlabel(\"Iteration\")\n",
    "    plt.ylabel(\"Objective function value\")\n",
    "    plt.plot(range(len(objective_func_vals)), objective_func_vals)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bc75fbbd-5931-4fb6-ac23-8267799bd455",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 767 seconds\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from qiskit_machine_learning.algorithms.classifiers import VQC\n",
    "vqc = VQC( #use the VQC classifier\n",
    "    sampler=sampler,\n",
    "    feature_map=feature_map,\n",
    "    ansatz=ansatz,\n",
    "    optimizer=optimizer,\n",
    "    callback=callback_graph,\n",
    ")\n",
    "objective_func_vals = [] #clear objective value history\n",
    "start = time.time()\n",
    "vqc.fit(train_features, train_labels)\n",
    "elapsed = time.time() - start\n",
    "print(f\"Training time: {round(elapsed)} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0af53699-c32f-45bc-9180-fb3021344982",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantum VQC on the training dataset: 0.83\n",
      "Quantum VQC on the test dataset:     0.77\n"
     ]
    }
   ],
   "source": [
    "train_score_VQC = vqc.score(train_features, train_labels)\n",
    "test_score_VQC = vqc.score(test_features, test_labels)\n",
    "print(f\"Quantum VQC on the training dataset: {train_score_VQC:.2f}\") #print the scores of the quantum model\n",
    "print(f\"Quantum VQC on the test dataset:     {test_score_VQC:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1da06b0b-a9e2-40d3-9090-48e3e1bd28fe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}