Quang Nhat Le-VQC (AQGD).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_17748\\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_17748\\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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",
      "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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AV/e4gxWbP6Ky6twZpT7fMJMhfX8JQLOE1nRtewVffvu+P8MVES9zpw0AeGvhH+jfdTjJTTTxiAQHd3LAho2y8qMAnKwsp7q6ksbxzfwZrohXuHPct22eSWrTDths557+92o/iHoR0QC0TuoCTidHSw/6NHZvCskuZ8Go4PAudhVs5O7nsk6/d7LiOEXH9p2z7IEje2jaKPX0z0mNWnHgyB5/hCkiPuJOG7Blz1ds3r2CSWMX8u+FT/oxShHfcScHfnXDX3nsteuZs/IflB4vZuSAx0hLvtiP0Yp4hzvHvavmr/kXSQltzjpXDHQqaIJI+5a9eeau+ad/vumJWsaqFZGg5EobcKLiONNm38NjP3sPmyYdkSDj6vfgR8tfJPviW7n1yt9QXHqACS9l065FT7pnXO2vUEW8xpvnf2u3f8a/Fz7JpLsWWuo7QgVNkGiW0OasuyxlJ45xoqKMJvHJ5yx7UcOW7C/effr2emFxLt0zrvFbrCLifa62AQWHcjhwZA8TXsoGoLT8CE6ng9LyYiaOeMOvMYt4kzvfgx8t/zv/mrgNgEZxF9Gr/XVsyFmigkYsx53j/kI25HzOlHfG8Mcxc2hxUTtvhulzeoYmSKQlZxFuj+DrbQsBmLP8RS7PvIWI8HNnSuvfdThzV7wEGLcqv8lZwqWdf+rPcEXEy1xtA1o368J7Txzkrd/m8tZvcxl22QNc2/PnKmbE8tz5HmyW0IY13w8CUF5RxvqcxbRK6uzXeEW8wZ3j/ny+2fkFk94exR9Gf0jb5tYbBVgFTRD5zW3/4dWPf8Mdz6SxIWcJY4dMrnG54VdMoKKqnJ893Zbf/PNaxg19gQaxTfwcrYh4m6ttgEiwcjUHJo54g49Xvcrdz2Vy7996cHHaVWRnjfBztCLe4epxP3/169z6pxSWfvMuby54glv/lMKOfesA+Mu7d1JZdZLJM8dw93NZ3P1cFrsKNvrz16gTdTkLIq2bdeHF+9dccLnoyFh+f/tMP0QkIv7kahtwpp9d84RvghExgas5kJZ8MX8bt8wPEYn4nqvH/bU9R3Ntz9E1fvbGI9u9HJV/6Q5NkAsPi6Tk+CHufi6L4tIDF1x++twJvL34aeKiG/khOhHxNXfbgFlfPM/U2fforq0EDeWAhCJ3j/vzsUJO2JxOp9PsIORc1RWweKrZUbguezyEudddU8QUj8+Go+XQIBqeHGZ2NLWzWhsAagesQjngO8oBa1AO+I5ZOaA7NCIiIiIiYlkqaERERERExLI0KECAskcYt+2swh5hdgQiwcVqbQCoHRDvUg5IqFMOuE4FTYCy2dQPVySUqQ2QUKcckFCnHHCdupyJiIiIiIhlqaARERERERHLUkEjIiIiIiKWpYJGREREREQsSwWNiIiIiIhYlgoaERERERGxLBU0IiIiIiJiWSpoRERERETEslTQiIiIiIiIZamgERERERERy1JBIyIiIiIilqWCRkRERERELEsFjYiIiIiIWJYKGhERERERsSwVNCIiIiIiYlkqaERERERExLJU0IiIiIiIiGWpoBEREREREcsKNzsAqZnTCY5Ks6NwnT0CbDazoxAJHlZrA0DtgHiXckBCnXLAdSpoApSjEhZPNTsK12WPh7BIs6MQCR5WawNA7YB4l3JAQp1ywHXqciYiIiIiIpalgkZERERERCxLBY2IiIiIiFiWChoREREREbEsDQogIkGv7CTkHYbDZXCyynivshoOlUJCrEYlkuB3ohL2HYaDpT/kQEUV7D8KifFgVw5IkKushn3FcODY2TmwrxiSGkCYLvFbmgoaEQlKhUdh2XbYlGcUMj92vAL++CHEREJaU7gkHTKSdGInwaO4DFbsgPV74OAxcP7o8/JKeHou1AuHVk2gbxp0aaETOwkeZSfhqxz4OhcKjoDjR0lQXgmT/wcRYZCSAL3aQLdWRk6ItehPJiJBZe8h+GgdbN/v2vLHK+CbvcYrsT5c0xl6tNZdG7GuohIjBzbmGfNYXMjJKthaaLzioyG7A/Rvp8JGrKv0BMxdD2t2QZXjwstXVsOug8brw7VwaTpc2wUidZZsGfpTBZENOUv49UvZZ70XFRlLSmIGA7qN4qeX3kdYmP7kEpyqqmH+Rvhs87lX4Vx1sAT+swLW7YZbekODGO/G6A9qB0KXwwnLtsGcdVBR7dk2jpUbJ3TrdsNtfY2uOFajHAhtG/bAu6ug9KRn65+oNL5HvtkLt/aBNhd5Nz5/CMUcCK7fRgDIzrqVXu2vw4mT4pJCFn79Ji/NeYg9B7bw4E3TzQ5PxOtKT8DLi2HvYe9sb3M+TJoHd10BrRO9s01/UzsQWiqq4I0vYdM+72xvzyGY8j+4/VLIaumdbfqbciC0OJwwezV8ud072ztYAtMWwk+6GXctrSiUckA3lINQenI3BnS/nau7j+LmKyYw9b6VJDZI4eNVr3Ck9KDZ4Yl4VekJeOFT7xUzpxyvgH98BjkHvLtdf1E7EDoqq+GfS7xXzJxS5TCKpK9zvbtdf1EOhA6HE95e6b1i5hQnxh3Lhd96d7v+Eko5oIImBERHxtI+tQ9Op5P8QzlmhyPiNVXVMH2JMQCAL1R8f6K4/5hvtu9PageCk9MJby13/ZkxT7b/n+WwvdA32/cn5UDw+t8GWLXTd9uft8EYXMDqgjkH1OUsRBR8f+DGxySYHImI9yzcZHSNccdDA40Hn4+Vw3OfXHj5E5UwYwWMvxrsFr8EpHYg+KzZZTwz4A53c8DhhP+uhEcGQ1SEZ3EGCuVA8Nl5AD7b5N467uYAwOw1kN4UEuLcjzGQBGsOWPzr2TVFRUVMnDiRtLQ0oqKiaNGiBffffz9lZWXceeed2Gw2XnjhBbPD9JoTlcc5WlbEkdKD7CrYyNTZ97Jj3zrat+hFSmKG2eGJeEXeYc+6AcRHQ8MY47+uyi2Cz7e6vy8zqR0IfkfLYfbX7q/nSQ4Ul8FHa93fl5mUA8GvogpmrDx3SPIL8SQHTlbB21+5NnJgoAilHAj6OzTr169n0KBBFBYWEhsbS8eOHcnPz2fq1Knk5ORw+LDR8T4rK8vcQL3ozQWP8+aCx896r1/nYdw39O8mRSRmO3IcVuZA4RGjv31sPejaAjo2t+5dhznrPR/NzBMff2PM02GVK9RqB852/KTRJWX3IeMkKDoC2jeHzJbGHBRWtPBbKK/w3/6W74DLO0DTeP/tsy6UA2erqDJGr9tWaNx5jgyHNonQs4112rUfW77DeHjfX7YVwpZ86Jjsv33WRSjlQFAXNEVFRVx//fUUFhby8MMP8/jjj1O/fn0Ann32WR555BHCw8Ox2Wx07drV5Gi9Z3DvsfTvOpwqRyW7CjYyc8kkio7mERkRdXqZp94agcPp4LFR75x+79jxw9w1pRNjh0zhqm4jzQhdvKz0BLy32hh+8scn/6t2QqNYGNTVmEzMSg4eg60F/t1nRZXRvaefRS5qqR0wVFQZdxa+2mkU82dakwvvf22MYHRVR2vNPXSiElb78JmB2izfDkO7+3+/nlAOGBwOmP8tLN1qDHZypnW7jfla+qTB9VkQbqHi/tQw5f62bLt1CppQygGLXpt1zfjx48nLy2PcuHFMmTLldDEDMHHiRDIzM6mqqqJVq1bEx1vkkpMLkpuk0y1jAL3aD+KW7In8ccwctuat5m+zfnl6mfuGvcim3GUsWjfj9HvT3r+XTq37WebglfM7Wg5/XWDMEl7bnYziMvjvCuuN4LLMyyPZuOrLbdbpbqB2wOgi8o/PjJGPflzMnFJ20jihm2mxriRf7zJ+P3/7Ksec/XpCOWAUM28uM+bo+nExc8rJKvj8O2OAlSoP5y8yw/ZC/96dOWXzPjhU6v/9eiKUciBoC5otW7Ywc+ZMmjRpwtNPP13jMt27G5eZMjMzT793qgDq1asX9erVw2alS3a16NTqEgZ0G8WSDTPZlLscMB4Ge3j4q7zwwTiKjubzxTfv8U3OEh4Y9pLJ0Yo3OJzwyhJjxnBXzNtgFD5W4e3haV1VeBQOl5mz77oKxXZgxgrYVeTasitzjMn0rMKsHDhRCbkWHe01FHPg429cb9u3FRp39K3CrBxwYnQ7s6JgzoGgLWhmzJiBw+Fg5MiRxMXVPCRFdLTxNNiZBc2OHTuYNWsWSUlJ9OzZ0y+x+sPIAY9ht4fxxvz/O/1ez/YDubzrzUyacTvTZt/DQ8NfIT62sYlRird8l+/+vCwLv7XGFeoTleZclTslz8vz3fhTKLUD+4+5X6Qv3lL7nZxAY+Zx6O05n/wplHLgRKX7g5ms2mk8c2kFZuaAvgcCT9AWNIsWLQIgOzu71mXy8vKAswua/v37U1BQwEcffcSAAQN8G6QfJTdJIztzBOt2fMbGnUtPvz/2+insO7SDnu0H0bvDYBMjFG/60oN+xfuKjdG8At0+k79IrHwyF0rtgCd968tOwvrd3o/F244eh2MnzNu/csAaVu80niFzh8MJK3b4Jh5vcjggr9i8/SsHAk/QFjS7dxvfSqmpqTV+XlVVxbJly4CzCxq7VYd8csGtV/0Ou83OGwt+qMqjI2NpltCG1kldTIxMvMnphO88fGDeCrfRi02+elhs0S5np4RKO6Ac8J0jygFL8PRYtkIOHK9wv1jzJqvcxapNMOZA0I5yVlZmtLjl5eU1fj5z5kyKioqoX78+rVu39mksPXr0oLDQvWmWI8OjmT7OvSefM9tewcLJtfcZSm3agfnP+qY/RXpGOhVVNf9bi3+FRUQz9E+ePTX/95deZeycxy+8oIla9RxBj5um1PjZqcnSzic+6of/PjG09uVqm3Dtwznz+M0td7sYrec8aQNA7cApg3/3NdHxTd1eb94nn/HbEXf4ICLvadK6D1f88r0aP/NHDmzYuImUX1zrYrSeUw7UzRW/nEWT1r3dXm/TdztJ+UV/H0TkPVHxSQz53ZpaP79QHtQ1B44eKyMlpZ2L0Xou1HIgKSmJNWtq/7ueT9AWNElJSRQXF7N27Vr69u171mcFBQVMmDABgK5du/r8wf/CwkL27XPv6bWoiBgfReMbBfn5nKi0+CWLIGGzeX6X8cjh/W4fq/5Wv/WBWj87NVmaK+x215c9U1nJUb/8G1mtDYDAagdOlpd6VNCUHDsU8DngiKn9Ero/cuBEeZlyoBaBlAOlJcU08WC98rIjAZ8D0cdOnvdzV/PA0xyoqjypHKiFWTkQtAXNgAED2LJlC5MmTeLqq68mI8OYPGL16tWMGjWKoiLjYQF/TKiZlJTk9jqR4W5MXxsAmjVvHjBXpQQO711PQosst9erLM4hOTmwB9iPjaz9qtMxFw7B+CjjS8zhOP9zCLVty+4o98u/kdXaAAisduBYwUYaNm3r9nonirYGfA7ERdc+WYg/csBZUaIcqEUg5cDxA98BA91er6RwU8DngC0sAkd1JfawmmcEvVAe1DUHKsuLlQO1qEsOeHK+fIrN6bTCuEbuy8vLIysri0OHDhEeHk779u05ceIEO3bsYNCgQTgcDubPn8/06dO56667atzGE088wZNPPokZ/0TVFbB4qt9367Hs8RAWaXYUcspXOTBjpXvrNIyBx26AsAB/jKykHB6b7fn6Tww1ftcjx+GJ991f/7a+/pmI1GptAARWO5BzAKYtdG+dcDs8OQxi6/kmJm9xOOG37xqjWHmirjlwTWe4LvPCy9WVcqBuisvgDx+6P3rlhOsguZFvYvKmKf/zfGCAuubAxalwRz/P9u0O5YDrAvzUxXMpKSksXbqUwYMHExUVRW5uLgkJCbz88svMmzePbduMIXDOHBBAJFhcnAr1oy683Jkuywj8YgagfjQ0MPGiVYsE8/YtrmuT6P7fqkfrwC9mAOw2SDHxODRz3+K6RrGQ2cK9ddKaWqOYAXOPQ30PBB4LnL54rkOHDsydO5eSkhJKSkr46quvGDt2LGVlZeTm5mK32+ncubPZYYp4XWQ4/OJy47+u6NoCsjv4NiZvSnf/0QiviI+CpvHm7FvcY7PB6Msu/ID8KS0bw9Aevo3Jm9JMyoEwu1EsijXc3BuaNXRt2UaxMOpSn4bjVWZ9D5i9b6lZUBc0tdm0aRNOp5P09HRiYs594Oq9997jvffeY/PmzWf97OnICyJmSG0C9w2AhNjal7HZ4NJ049a5lUYsvyTdnP32SbPWv1OoaxwH918DKRe44twlBe65CupZ6KnSPm2NOzX+1rUFxLl591fMExMJ4wZA+2bnX65VE3jgGnPvfrura0tz7qi2SIAW1ppzMiRYqPn2no0bNwK1dzcbPnx4jT/fcccdvP766z6NTcSbWjSG3/8ENufD8u2wpcDoT223wVUdjcKg0XkKnkDVOhGaN4J8P06sZreZV0iJ5xrHwcODYOcB+HI7rN/zQw5cmm78TV29gh1IGsYYhdiGvf7d72UZ/t2f1F1sPfjllcbs9su2w7ZCOFwKTiAiDO4dAKmNjQtcVhIRZhT2n2327377KQcCUkhea7xQQeN0Omt8qZgRK7LboXMKjM3+Yez9+lEwOMuaxQwYX7wD/Tz3V++2ng3vKeaz2aBtU+NO5Jk5cGNPaxYzp1zd2b93adKbGhcTxJpSEuCW3sbgL6e6YsZEGndnrFbMnNK/PUT78QH0xPrQrZX/9ieu0x2aIJKTv4Hn37uL4ydLaNowlUdu/Te792/it68MIiWxHc+MXUCjuIv4ZNVrzFr6PHsObOHuIVMYdtkDp7cxfe4ElmyYSXpyN54c/YFpv4vIhXRtYQx+sG637/fVKAZu6Ob7/dSVq23As2+PZu32hTSINc5Ou2dczdghkwG1AVaSkgADOsGCb32/r3rhMKJP4J/4upoDeQe3M+39ezhSeoBqRxW3D/g/rsi6BYBZXzzPR8v/TlRkHC8/tN7cX0jOq0E0DO0O/13h+33ZMEa5jKh91HTTeOP877WPf8eKzR9htxm/4IgrHyU7awRgje+FkCxoFi1aZHYIPjF55mh+ffO/SEvO4pNVrzF97q+5tucYUhLbndUop6d05/e3v8Pbi54+Zxtjh0wmtWknlm/6wH+Bi3joxh6Qs//88wj82Kl5BVyZrwOML7ERfSCq5ukOAoqrbQDAzVdMOOvL7BS1AdZyTWf4dp973S/dzQGAn3Qzuu8FOldzYPLM0VzbcwzX9f4FR0oPcu/fetC5dT+aNEjmxv4PkpZ8MS9++IBpv4e4rmdr+GYvfJvn+jqe5MAVHQL3DqU3zv9uvmICPx/0FABFR/dx5+QOdEsfQIPYJpb4XgjJLmfBaMe+dUTXiyMtOQuAq3vcwYrNH1FZVXHOsm2bZ5LatEOdZpQXCQRxUUZXOneKjec+MeYdeO4T15a/qRe0u8ADtYHAnTZAgkd4GIy94vyDf/yYuzmQ3QEuSfMoPL9yJwd2FmygV/vrAGgYl0ib5pksWT/Tn+GKl9hscPslxkiFrnI3BzJbwJAsj8LzOW+d/8VFNzz9/+UnS3HixOF0+CpsrwvJOzTBqODwLnYVbOTu57JOv3ey4jhFx/aZF5SIH6QkGA+1vrQIyk56b7s2jGLmUosMBOBuG/D+0r/xyarXuKhRS0Zf+6fTX4ZiPQ1jjJGs/rEIDpZ4d9tXdTRO5AK9qxm4lwPpKd35bO1b3JI9kYJDO9mcu5ykRq38F6x4VVSEMfDBP5fAroPe3fbFqTCyb+DO0+bN87/3v5zKR8v/TtGRPB4c/gqN4i7yYqS+pYImiLRv2Ztn7pp/+uebngjQe6MiXtYiAR4eCG9/ZYzgU1eNYuHWPpCRVPdt+ZOrbcDPBz1FQv1m2O12vtz4Pr97dRCvP7Kd6HoW6FMkNUqIgweuhffXwJrcum8vJtIYNKF7q7pvy59czYGJt7zBy3Me5u7nsmjaKJWL068izK5TIiuLiYRfXQkffwNLthijuNVFRBgMzoT+7QJ/uH5vnf8N7Teeof3Gk5O/gWdm3E6PjGuIj7XGGNUB/icSVzVLaMOBI3tO/1x24hgnKspoEp9sYlQi/pMQZ3yZ3dzL+GLzxKmhfB8ZbL1ixp02oEmDZOzff0P36zKUmKh49h7c6rdYxTdi68Htl8Kd/es2Il9mS3h0iPWKGXdyICmhFY/fMYuXH1rPH8Z8SFn5UVKTOvkzXPGByHBjAJfx10CzBp5vJ60pTLjOeG4m0IsZX5z/tW2eSZP4ZDbkLPFChP6hyxFBIi05i3B7BF9vW0j3jKuZs/xFLs+8hYhwP45nKGIy2/dzxfRobcw38uU22HPowus1iDbW65NmrYnlzuROG3DwSB6JDVMA2Lx7JcfKDpHc2AIPSYhLurSAjsmwaR8s2wZbXbhrGRNpDE1+SboxNK0VuZMDxSX7aRCbiN1uZ/XW+ew+sJkrL77NhKjFF1onwsTBsGO/Mf/Uxr3guMAtm4gwo4i/NMO4628V3jr/271/M6lNOwKQX5TDjvx1tPz+ZytQQRNEfnPbf5j8zhimzv4VzRun8ehtb5FbeO54nvNXv87r839P6fFilm/6gHc/n8Ifx8whLfliE6IW8b7IcOjVxniVlMPew8aruAwqq42+0HH1jOdvWiRA4/rmzLruba62AZNnjqa4dD92Wxj1IqJ5bNS7xEbX4XKmBJwwuzG0edcWcLzCmFRx7yHjGZvKauOqc0wkJDcycuCi+MB9RsAdrubAis1zmLn4Gez2MBrHN+epO/9HvQiLXs2QGtlskJ5kvE5Wwb7vvwf2H4WKauM5yXoRxlxULROM/4YH4JDMrvDG+d8/502k8PAuwuwRhIWFM+6nL5DatIMJv41nVNAEkdbNuvDi/WsuuNy1PUdzbc/Rvg9IJADUjzauVncMgd6XrrYBz979qR+ikUARE2l0obRaN0pPuJoD1/X+Bdf1/oUfIpJAUC8c2lxkvIKRN87//vTzuV6Oyr+C4HqMnE94WCQlxw9x93NZFJceuODy0+dO4O3FTxMX3cgP0YmIr6kNkFDnbg7M+uJ5ps6+hwaxTfwQnYhvuHvcn48VvhdsTqezrgNBiA9UV8DiqWZH4brs8RCmx3UC3uOz4Wi58ZzIk8PMjkbOx2ptAFijHVAOWIdywHeUB9agHHCd7tCIiIiIiIhlqaARERERERHLUkEjIiIiIiKWpVHOApQ9wuiHaBX2CLMjEAkuVmsDQO2AeJdyQEKdcsB1KmgClM1mjQcLRcQ31AZIqFMOSKhTDrhOXc5ERERERMSyVNCIiIiIiIhlqaARERERERHLUkEjIiIiIiKWpYJGREREREQsSwWNiIiIiIhYlgoaERERERGxLBU0IiIiIiJiWSpoRERERETEslTQiIiIiIiIZamgERERERERy1JBIyIiIiIilqWCRkRERERELEsFjYiIiIiIWJYKGhERERERsSwVNCIiIiIiYlkqaERERERExLLCzQ5AauZ0gqPS7ChcZ48Am83sKCSYWC0HQHkg3qUckFCnHBBXqaAJUI5KWDzV7Chclz0ewiLNjkKCidVyAJQH4l3KAQl1ygFxlbqciYiIiIiIZamgERERERERy1JBIyIiIiIilqWCRkRERERELEsFjUiIcDqN16n/FwlFygEJddUOHf8SfDTKmUiQqqqGb/fBzgOw9zDsK4aKKuOzYyfgDx9Ai8bQIgG6tICm8aaGK+J1DidsLYDt+2HvIcgrhvIK47NjJ+D/ZhvHf4sE6JgMLRubG6+ItzmdkFsEW/Ih7zDsOQylJ374/Fg5/OMzSEmAds0gvamGHBZrUkEjEmSOHocvt8GKnLO/uH7scJnx2rAH5q6HjCTolwFdUvSFJtZ2vAJW7oBl2+FQae3LHSuHTfuM1ycbjcKmXwZ0bwXhYX4LV8TrKqth1U5Ytg3yj9S+nBPYWmi8PtsMifXh0gzo0xaiIvwVrUjdqaAJIhtylvDrl7LPei8qMpaUxAwGdBvFTy+9j7Aw/cmDldMJK3Pgg6/hZJX7628rNF4ZSTCiNyTEeT9GX1MOyKZ98M5XcLTc/XX3HoYZK+HzrTCyLyQ38n58/qA8CG27i+C/K2D/MffXPVhifId8/h3c2sf4PrAi5UDo0V8zCGVn3Uqv9tfhxElxSSELv36Tl+Y8xJ4DW3jwpulmhyc+UHYS/r0Mviuo+7a2FcKkeTC8F/RoXfftmUE5EHoqq+HdVcZV6brKL4a/fAzXZcJVHa17x1J5EFocTvjkG1i4qe7PyBSXwYufGXcsh3aHMIs+ca0cCB0qaIJQenI3BnS//fTP119yD3c+256PV73CmIFP0TAu0cToxNtKyo0vnoKj3tvmySp4azmUnoQr2ntvu/6iHAgtFVXwyudGMe4tDqfRFfNoOQzrbs2iRnkQOhxOeHuldwr6M325DY4ch9H9rNkNUzkQOixac4s7oiNjaZ/aB6fTSf6hHLPDES8qr4B/LPZuMXOmD76GFTt8s21/Ug4Er2oHvPaFd4uZMy3dCnPW+2bb/qY8CE5OJ8xa7f1i5pRv8+Dfy42iyeqUA8FLBU2IKPg+ceNjEkyORLxp9hqje4wvvbfa9/vwB+VAcPp0k3e6Wp7Pos3GsznBQHkQfNbuNgbA8KUNe4ziPhgoB4KTupwFoROVxzlaVoTTafQZnbPiJXbsW0f7Fr1IScwwOzzxkm/zYPUu99Z5aCDERxujOz33iWvrVDvgvyvhwWut049aORAa8othwbfureNJDgDM/AoeHQwx9dzbn5mUB8HvWLlxd8ZdnuTB3PXQsTkkWmiIf+VA6AiJgqaoqIhnn32W2bNnk5eXR2JiIsOGDePPf/4z48eP57XXXmPatGmMGzfO7FC94s0Fj/PmgsfPeq9f52HcN/TvJkVkPofTmIei5ITRF75JHDRtYHZUnqv6/gFod8VHQ8MY99fLO2xcnbuig/vrmkE5cC6nEwqOGA/7OjGOg+RG1nw2BIzfZ+ZXRsHtDk9z4Fg5zNtgDJZhFcqDcx08Zozk5XBCXBS0TAC7RS7U1OTDtcYw5e7yJA8qq4079r+6yv39mUU5EDqCvqBZv349gwYNorCwkNjYWDp27Eh+fj5Tp04lJyeHw4cPA5CVlWVuoF40uPdY+ncdTpWjkl0FG5m5ZBJFR/OIjIg6vcxTb43A4XTw2Kh3Tr937Phh7prSibFDpnBVt5FmhO51JyqNZ0CWbYeikrM/a5NojLd/cSrYLXZSt36PZ8PS1sXSbdC/nTW+/JUDP6iqNu7kLdtuFKZnatYQ+qVDr7YQYbEHfncfMl7+tGonDM6CmEj/7tdTygOD0wkb9hpzsmzff/ZnCbFwSTpckmatu29gfAes2+3ffW4thMKjkGSRC4LKgdBhgVMTzxUVFXH99ddTWFjIww8/TEFBAWvXrqWwsJBJkyYxb948Vq9ejc1mo2vXrmaH6zXJTdLpljGAXu0HcUv2RP44Zg5b81bzt1m/PL3MfcNeZFPuMhatm3H6vWnv30un1v2CJnmLy+Cv840rWD8uZgB2HjSGOn5jqXHlyUq+3Ob/fR4qhS0+flbBW5QDhvIKeHmxcSfjx8UMGHds3l0NL35qDP1tJWbkQGU1rPbRg9e+oDww7uD9Zzm8vvTcYgaMyYXnrofn59f8PRHIVu4w50H9ZSbknqeUA6EjqAua8ePHk5eXx7hx45gyZQr169c//dnEiRPJzMykqqqKVq1aER9voU6hburU6hIGdBvFkg0z2ZS7HDAehnt4+Ku88ME4io7m88U37/FNzhIeGPaSydF6x/GT8NIi40rShWzYa0xCZpURXIpKILfInH1b6WTuTKGYA1XVxuhfNZ3E/diuInhliXUK+8pqWO/nK9OnuPvcWiAJtTxwOuGdVbAm98LLHiwxvjNKT/g8LK9ZY9KxuCbXOt+XPxZqORBKgrag2bJlCzNnzqRJkyY8/fTTNS7TvXt3ADIzM0+/995773HjjTeSmppKTEwM7du353e/+x2lpaV+idtXRg54DLs9jDfm/9/p93q2H8jlXW9m0ozbmTb7Hh4a/grxsY1NjNJ7lnzn3izJ63bDdh8N++pt/u5mc6a9NVzlt4pQy4G1u10rZk7ZVQRfWWQU04IjUOXmszPekl9sncKvJqGUB7luHtNFpcaoeVZw/KRRhJmhvMJ6d7POFEo5EEqCtqCZMWMGDoeDkSNHEhcXV+My0dHRwNkFzZQpUwgLC+PPf/4zH3/8Mb/61a/4xz/+wcCBA3E4TPoG9YLkJmlkZ45g3Y7P2Lhz6en3x14/hX2HdtCz/SB6dxhsYoTeU1Xt2dwpZnRh8cReEwuaQ6XW65p0SijlAHh2PC/bVvcZxv3BzBxwOK09jHko5YEnObBqpzFRa6Az++KSmTlYV6GUA6EkaAuaRYsWAZCdnV3rMnl5ecDZBc2cOXN45513GDlyJJdffjn3338/L7zwAsuWLePLL7/0bdA+dutVv8Nus/PGgh+uSkRHxtIsoQ2tk7qYGJl3bS0wRjNz17f7rHGy7ko3umDef12ESg7sPwp7PDjhKDha87M2gcbsY9Ds/ddVKOTBySpj8BR3Ha8whsQPdGYfg2bvv65CIQdCTdCOcrZ7t9HBOjU1tcbPq6qqWLZsGXB2QZOYmHjOsj169ABg3z7PZlbr0aMHhYXu9WeKDI9m+jj3ZsrKbHsFCyfXfnk1tWkH5j/rm74S6RnpVFT5editWrTpM4puQ2vuZng+Tif0vOQqju0P7NnDrvjlLJq07l3jZ6fmFqhNfNQP/31i6Pn3U9v8BLfcOorCrYtdjNZzVssBCJw8uCj9Mvr/YsaFF6zBjbfeSf7m+V6OyLt6DP8LrXrcUuNn/siBib95jJzl/3IxWs95kgOg7wKAmEYtuO7RFR6t++vf/pFtX7zs5Yi8q332fXQe+EiNn10oB8D1PKgtB158+VXunvP4uR94mXIgtCQlJbFmzRqP1g3agqasrAyA8vKaD6qZM2dSVFRE/fr1ad269Xm3tXixcfLWoYNnk3AUFha6XQxFRXgwUYKJCvLzOVF53OwwAEg4csTjdffvL+Swh4Wrv5w8WfttJFfnFrDbPZuLA+DgwQMeF/fusFoOQODkgb3hQY/XPXSoyC9/37ro8H37XhN/5MCRI8XKgVoESg7UP+n56c2RI0cCPgeaH6v9Fok7c8x4mgelpSXKgVoESg6EmqAtaJKSkiguLmbt2rX07dv3rM8KCgqYMGECAF27dsV2npnl9u3bx2OPPcbAgQM9nqsmKSnJ7XUiwy9weSXANGvePGCuSERS+8nO+Tiqq4iPthGdnOzliLwrzFb7VaVjF/gTxEcZX2AOBxy7QLe82rbVKD6Waj/8G1ktByBw8iAm3Jhpz+l0nrd9O9OpZaPDTpIc4DkQeZ5vLn/kQFxMpF/+jZQDnguLiKC66iRh4e5PLhNJWcDnQEy92pPgQjkArudBbduKirQrB2oRKDlgRZ6cL59iczqt8Aio+8aPH8+0adNo0aIFn376KRkZGQCsXr2aUaNGsXPnTiorK7n33nt54YUXatxGaWkpV1xxBYWFhaxevZpmzZr5Lf7qClg81W+7q7Ps8RAWIJPNVTvgyfcvfLLyY11bwM/7+yYmb/pwLSze4tm6Tww1rsYdOQ5PvO/ZNp66CWL9MAGd1XIAAisPnp8Pu90c3rt5Q5hwHbhYA5lm2XZ4d5Vn63ojBx4aCC39MACScqBu3lru/tDGMZHGMXK+ojkQbCuEFz/zfP265sGoS6F7K8/37yrlgLgqaAcFmDhxIo0bN2bv3r106tSJLl26kJ6eTq9evWjTpg1XXnklcPbzM2cqLy/n+uuvZ9euXSxYsMCvxYzUTZgd+qa7v16/DO/H4gspCebtu3Gcf4oZqbt+HuTApRmBX8wAtDAxB+w2aNbQvP2L6zxp03u3DfxiBiClkbn7NzMHRWoStAVNSkoKS5cuZfDgwURFRZGbm0tCQgIvv/wy8+bNY9s2YzzHmgqayspKbrrpJtasWcPHH39Mx44d/R2+1NEV7aFZA9eX794K0pv6LByvSjVxaHx/XJUW7+jWCtq5cR2m7UXQu43PwvGq5g0hIsycfSc3Mm/f4p7UxtA3zfXlE+vDgE6+i8ebYuoZ8Zqy70hoYtK+RWoTtAUNGA/xz507l5KSEkpKSvjqq68YO3YsZWVl5ObmYrfb6dy581nrnJq75rPPPuPDDz+kV69eJkUvdREdCb+80jjxuZCLU+HWPta4Mg3GF0nrcwfj84ue5x8/QwJImB3GXAbtXOiS3PYiuLM/hFvkRD08DLJqHsDS53papOgTo02/qSf0cuFvdlE8/OpKa92BNutY7NHauFMpEkgscGPV+zZt2oTT6SQjI4OYmLNH0Lj33nt59913efTRR4mJiWHlypWnP2vbtm2NwzpLYGoQA/dfA1/tNCZYO3Ds7M/TmxpdbLq2sF7j3C8ddnk+kJVHGsdB++b+3afUTVQEjM2Gr3ONSTN3/2humpQEo1tOj1bWKWZO6ZcOq3f6d5+RYSrqrSbMblyw6pICX2435ik7U5P6cGk69GlrXAizkj5tYf5G47lRf7rUg+6sIr4WkgXNxo0bgZq7m3388ccAPPPMMzzzzDNnffavf/2L0aNH+zw+8Z56EdC/HVyWAXnF8I/PjInT4urBvQPMjs5zmS1hznrjgU5/6d/OeoWfGCd0vdoYr8KjMHXBDznw8EDr3Jn8sdQm0LoJ7HJz4IO66G3Bk14xjvEuLYzXoVL4y8dGDsTWg99eb912LT4auqXCajcHPqiL9s2gqRvduUX8Jai7nNXmfAVNbm4uTqezxpeKGeuy2YyHGE/1fQ+z+JEfHgY3+7E3ZMvG1hk0QWqX1ODsHLBqMXPKzb39l8sNY+C6mseQEQtpHPdDDoTbrVvMnPKTbv7rJhcZbnThEwlEFj+t88z5Chory8nfwLipvfj55A785p8DOVJ6kA05Sxj8m2jufi6L4tIDALz68W/5+bPtufu5TO75Ww9Wb/1hVvBZXzzPHc+kcfdzWSb9FuKqjsmu9Q0/07Fy466OK/MUnBJmh9v6WqMIdDUHAD5a/iI/n9yBu/7Shbufy6Si0hjne/rcCdz2VEsef/2nJv0W4qpmDWFgF/fW8SQHAG7pHbh3Z1w97j9Z9Rp3/aUL1z4Szuylfz1rGycqjvPUf27ljmfSGD0pgy++ee/0Z8qJwFU/CoZ7UGR4kgfXZwXuYADeyIFTdu/fwpDfxvDihw+cfk/nRoEvJLucLVq0yOwQfGLyzNH8+uZ/kZacxSerXmP63F9zbc8xpCS24+WH1p9erkvry7h9wGPUi4gmJ38DD/2jP28/lk90ZCw39n+QtOSLz0pkCVzDekD+Ecg77Nryz33i/j5u7mVc2bcCV3Ng+bcf8tna/zBt3EpioxtwpPQgYWERAIwdMpnUpp1YvukDc34JccuVHSG3CDa5OGm5JzlwdSfoEMDPj7l63KendOf3t7/D24uePmcb734+hYiwerzx6A4KDu9i/NTeZLXNJj62sXIiwGWlwmUHYelW19dxNw8uTjWeOQ1U3sgBgKrqSv46ayyXdh561vs6Nwp8FrjmKq7YsW8d0fXiSEvOAuDqHnewYvNHVFZVnLNsr/aDqBdhzL7bOqkLOJ0cLfXzE+biFVER8MtsaO6jOQlu7GE8N2AF7uTAO59PZtTVjxMbbVRqDeMSCbNb7Kl4AYw7h3f0M/r2+8Ll7QO7q5k7x33b5pmkNu2AzXbuV//nG2YypO8vAWiW0Jquba/gy289nHlU/G5od/eGqHZH1xZw+yWB2z3PWzkA8NbCP9C/63CSm2jkA6sJyTs0wajg8C52FWw863boyYrjFB07/2XL+Wv+RVJCG5o2MmkMVKmzuCgYNwD+s9z1q9QXEhVh3Jnp1so72/MHd3Jgz/7NbMtbw78XPkll9Umu7v4zhvYb78doxZsiw+EXl8OsNbBih3e2GWaHwZmQ3SGwnzXytO3/sQNH9pz1PZDUqBUHjuzxVpjiY3ab0WY3iIEFG8Hh9M52+7eDG7oFdpdjb+XAlj1fsXn3CiaNXci/Fz7p5SjF11TQBJH2LXvzzF0/PA9z0xPnH2J67fbP+PfCJ5l010JsgfyNLRcUE2mc0K3eBe9/DeXnXphyWftmMKKP8RC01biaA9WOKgoP7+K5e76gtLyYh/9xOc0S2tCn4xB/hSpeFh5mPOfStQXM/KpuIwC2SDCeG2vW0Gvh+ZS7bb8EJ5vNeKasU3P47wooOOr5thrHGcNdp1lkwum65sCJiuNMm30Pj/3sPZ0PWZQKmiDRLKHNWVfTyk4c40RFGU3ik2tcfkPO50x5Zwx/HDOHFhe181eY4kM2mzFIQIdmsGy7caX6qIsPfNow5pjpl24MNmDF9tydHLioYUuyL76VMHsYDWKb0Kv9dWzZs1IFTRDo0BweHQIrc4y5dw6WuL5u6ybGcwIXpwb2Fekzudv21+aihi3ZX7ybxvFG373C4ly6Z1zj1VjFP1o0hocHGfNPfbkN9rr4jCUYz0v2yzAm7axnkTNEb+RAwaEcDhzZw4SXsgEoLT+C0+mgtLyYiSPe8HrM4n0WOVzlQtKSswi3R/D1toV0z7iaOctf5PLMW4gIP3dYnm92fsGkt0fxh9Ef0rZ5AHcOF4/Uj4aBXeHqzrB5H+w8aAwakFf8w50bu824Atciwfjy65ISuKPXuMqdHMi++DbWfPcJF6ddycnKcjbkLOHmKyaaELX4QlQEXNHe6C6zYz9sLzRO6vKKodQYzA4b0DD2+xxIMAqhlARTw/aIO8f9+fTvOpy5K16iY2ofCg7v4pucJYwf9qKPohZfCw8znn/s3Rb2HIIt+d/nwGE4ehxO9UiLjzKO+5QEaJcEbS6y3gUtb+RA62ZdeO+JH54lfnPBE5SWH+GeG/7qg4jFF1TQBJHf3PYfJr8zhqmzf0Xzxmk8ettb5BZ+e85yf3n3TiqrTjJ55pjT7z16679p3czN8U8loIXZf5hM7hSHE5xO61x9dperOXBT/4f466y7uXNyR2w2G/263MjlmcNNiFh8yW6DjCTjdYrTaeSB3Wa9E7fauHrcz1/9Oq/P/z2lx4tZvukD3v18Cn8cM4e05IsZfsUE/vLOz/nZ022x28MYN/QFGsQ2MeG3EW9r2dh4naIcqDkHxNpU0ASR1s268OL9ay643BuPbPdDNBKI7DaMS9NBytUciIyIUjeCEGWzQViQ5YCrx/21PUdzbc/RNX4WHRnL72+f6eXIJBApB0ZfcLmfXfNE3YMSvwrS67RySnhYJCXHD50zqWBtZn3xPFNn36MrcxI03M2B6XMn8Pbip4mL9tFY2CJ+4O5xfz7KCbEib+aAzo0Cn83pdHppcD/xpuoKWDzV7Chclz0ewgJ0Fu0zPT7beFC+QTQ8OczsaOR8rJYDYI08UA5Yh3LAN5QD1qEcEFfpDo2IiIiIiFiWChoREREREbEsDQoQoOwRxm1Lq7BHmB2BBBur5QAoD8S7lAMS6pQD4ioVNAHKZlMfTAltygEJdcoBCXXKAXGVupyJiIiIiIhlqaARERERERHLUkEjIiIiIiKWpYJGREREREQsSwWNiIiIiIhYlgoaERERERGxLBU0IiIiIiJiWSpoRERERETEslTQiIiIiIiIZamgERERERERy1JBIyIiIiIilqWCRkRERERELEsFjYiIiIiIWJYKGhERERERsSwVNCIiIiIiYlkqaERERERExLJU0IiIiIiIiGWFmx2A1MzpBEel2VG4zh4BNpvZUQQPq/39QceAeJdyQMR6eaAcELOooAlQjkpYPNXsKFyXPR7CIs2OInhY7e8POgbEu5QDItbLA+WAmEVdzkRERERExLJU0IiIiIiIiGWpoBEREREREctSQSMiIiIiIpalgkZERERERCxLo5xJUDt+EvKKYe8hOFACxyuM98sr4MttkJIAzRtCpDJBgtTJSthXDHsPQ+HRH3LgeAUs2QItEiA5AaIizI1TxFeqqiH/iJED+cVn58Cnm4wcSEmA2HqmhikidaDTOAk6DgdsKTAKlu/ywVnDMhXV8N5q4/8jwqB7K7g0w/hiE7E6pxN2HTRyYMNeqHacu0xlNXyw1vh/uw26tIB+6ZDWVPNISHDIL4Yvt8OaXVBRde7nldUwd/0PP7dLMr4HOiVDmPqviFiKCpogsiFnCb9+Kfus96IiY0lJzGBAt1H89NL7CAsL7j/51gJ4dxUUlbq+TmU1rMwxXulN4ebekFjfdzH6ko4ByTsMM78yrka7yuGEDXuMV/OGMKIPtGzssxB9Sjkgh0vhnVXwXYF7620tNF6NYmF4T+iY7Jv4fE05IKFIR3QQys66lV7tr8OJk+KSQhZ+/SYvzXmIPQe28OBN080OzydOVMJHa2H5jrptZ/t+eHYeDMmCy9oZV66tKBSPgVBX7YAF38LCb40CxVP5R+D5+XBlBxjUFcLDvBaiXykHQo/TaXwHfLQWTtZwR8ZVxWUwfQn0agM/7Q4xFp0oUjkgoUQFTRBKT+7GgO63n/75+kvu4c5n2/PxqlcYM/ApGsYlmhid95WcgJcWGc8JeENlNbz/tfHcza19rdn1INSOgVBXUQWvfeH+FenaOJ3w2WbYfQh+cbk1n69RDoQWhwPeXQ0r6nhR60yrdsKeQ/DLK6FhjPe26y/KAQklFjxVE3dFR8bSPrUPTqeT/EM5ZofjVWUn4e+feq+YOdOaXHhrufFFaXXBfAyEuqpqeOVz7xUzZ9qxH15eXPPzB1ajHAheTifMXOXdYuaUwqPwwqdQUu79bfubckCCmQqaEFHwfeMVHxM8T707nPD6UuMLx1fW7YZPNvpu+/4UjMeAGINbbCv03fZ3HYQZK323fX9SDgSnzzbDVz48Py8qMS4a1DS4htUoByRYqctZEDpReZyjZUU4nUa/2TkrXmLHvnW0b9GLlMQMs8PzmmXbjGde3PHQQIiPhmPl8Nwnrq3z6SbonGKth6RD5RgIdZv3GYNZuMOTHFi3G7JaQmZL92M0i3IgNBQcgY+/cW8dT3Jg9yFYvAUGdHI7RNMoBySUhERBU1RUxLPPPsvs2bPJy8sjMTGRYcOG8ec//5nx48fz2muvMW3aNMaNG2d2qF7x5oLHeXPB42e916/zMO4b+neTIvK+Q6UwZ53768VHu98X2uGEGSvg4UHWeUA6FI6BUHei0hjNzF2e5AAYowemNbXOXB3KgeDncMB/V7h/58TTHPj4G+PiVlID99c1g3JAQknQFzTr169n0KBBFBYWEhsbS8eOHcnPz2fq1Knk5ORw+LAxtmlWVpa5gXrR4N5j6d91OFWOSnYVbGTmkkkUHc0jMiLq9DJPvTUCh9PBY6PeOf3eseOHuWtKJ8YOmcJV3UaaEbrLFm825pLxl4Kj8M1e6NbKf/usi1A4BkLdyhw46sd+/aUnYdl2uKaz//ZZF8qB4Lcl373hyeuq2gGLNsNtff23z7pQDkgoCepnaIqKirj++uspLCzk4YcfpqCggLVr11JYWMikSZOYN28eq1evxmaz0bVrV7PD9ZrkJul0yxhAr/aDuCV7In8cM4eteav526xfnl7mvmEvsil3GYvWzTj93rT376VT634B34CdqITVu/y/32Xb/b9PTwX7MRDqHE6jy6W/Ld9unecIlAPB70sT2uR1u43BaKxAOSChJKgLmvHjx5OXl8e4ceOYMmUK9ev/MFvixIkTyczMpKqqilatWhEfH29ipL7VqdUlDOg2iiUbZrIpdzlgPBD48PBXeeGDcRQdzeeLb97jm5wlPDDsJZOjvbC1uXWbY8BTOQd8OwCBLwXbMRDqduyHgyX+3++R48ZVcStSDgSXQ6XwnQnHYmW1MZyzFSkHJJgFbUGzZcsWZs6cSZMmTXj66adrXKZ79+4AZGZmnn5v6dKlDBgwgGbNmlGvXj1SUlK45ZZb2LJli1/i9pWRAx7Dbg/jjfn/d/q9nu0HcnnXm5k043amzb6Hh4a/Qnxs4D/57u5AAN60w8R911UwHQOhzszj0Mz8qyvlQPDIOQB1mD+2TvQ9IBJ4gragmTFjBg6Hg5EjRxIXF1fjMtHR0cDZBU1xcTFdunRh6tSpLFiwgEmTJrFp0yb69u1LXl6eX2L3heQmaWRnjmDdjs/YuHPp6ffHXj+FfYd20LP9IHp3GGxihK7L82Of6UDad10F0zEQ6vz53MCPKQckECgHPKMckGAVtAXNokWLAMjOzq51mVMFypkFzU9+8hOef/55hg8fzuWXX87IkSOZPXs2R48eZdasWb4N2sduvep32G123ljww5WZ6MhYmiW0oXVSFxMjc92JSnO62pxi5peoNwTDMSDmF/VOsy6Ne4FyIDjsMzEHjpYbQz5blXJAglHQjnK2e/duAFJTU2v8vKqqimXLlgFnFzQ1adzYuPUaHu7ZP1ePHj0oLHRv5rvI8Gimj3PvicfMtlewcHLtZxqpTTsw/1nfDA2WnpFORZXvW/iYRi247tEVtX5+an6B2sRH/fDfJ4bWvlxt8xNs35VPSkovF6P1nCd/fwiNY0Dgxqd3Y7PXPIa4r3PgZBWktmqDo7rCjYjdpxyQ87nm4SXEX5RW42feygGoPQ/69LuKY/u3uhZsHehcQEJJUlISa9as8WjdoC1oysrKACgvrzmxZs6cSVFREfXr16d169bnfF5dXY3D4WD37t385je/ISkpiZtvvtmjWAoLC9m3b59b60RFeDBIvokK8vM5UXnc5/tpWH3+fxdX5xew2z2bhwBbmNt/S09Y7e8P/jsGQp7NVmsxA37IAaDwwEEqT5R6trKLlANyPg6nrdbP/JEDRYcOc1DfBedQDohZgragSUpKori4mLVr19K379mDxhcUFDBhwgQAunbtis12bsN4+eWXn76Dk5aWxqJFi0hMTPQ4FndFhp/n8lIAata8uX/u0DRsdN7PL9QNID7K+BJzOODYCfe343RUkpycfIEo685qf3/w3zEg4KiqwB4eWeNnvs4BgKaJjXE6fDu7oHJAzsdO7XcYvJUD59tW44SGRFbpu+DHlANSF56cL59iczqt3Bu6duPHj2fatGm0aNGCTz/9lIyMDABWr17NqFGj2LlzJ5WVldx777288MIL56y/detWjhw5wq5du5g8eTIHDhxg2bJltGzZ0i/xV1fA4ql+2ZVXZI+HsJrPr7yqqhoeecfzuTCeGGpckTtyHJ543/312yTC+Gs827c7rPb3B/8dAwJ/+hCKPLxBUtcciI+CP9zo2b7doRyQ83lpEXxX4Nm6dc0BG/D0zRAV4dn+3WG1PFAOiFmCdlCAiRMn0rhxY/bu3UunTp3o0qUL6enp9OrVizZt2nDllVcCtT8/065dO3r37s2IESP47LPPKCkp4dlnn/XnryA1CA+DZg3N238LjWQpASAlITT3LXJKCxOPw8R4/xQzIuK6oC1oUlJSWLp0KYMHDyYqKorc3FwSEhJ4+eWXmTdvHtu2GdNsX2hAAICGDRuSlpbGjh07fB22uMDMLzIz9y1yiqk5oKJeAoCZhbW+B0QCT9A+QwPQoUMH5s6de877paWl5ObmYrfb6dy58wW3c+DAAbZu3Urv3r19Eaa4qVMyrDChtgyzQ7tm/t+vyI91SoE5603at+8fGxC5oPQkiAiDSt8M1nVeHZUDIgEnqAua2mzatAmn00lGRgYxMWePIHL77beTlpZGVlYWDRs2ZPv27Tz//POEh4fz4IMPmhSxnKljc2gUA8V+HkglswXUj/LvPkVqktQA0pr6f8bylo2Nl4jZYiKheytYmePf/cZFGd8FIhJYgrbL2fls3LgRqLm7WZ8+ffjf//7HmDFjGDRoEJMnT+ayyy5j/fr1pKXVPOa9+JfdDpek+3+/l2b4f58itelnRg6YsE+R2vQzoU3u29Z4llNEAktI3qE5X0Ezbtw4xo0b5++QxE2XtYPlO6C4zD/769rCGOFMJFB0bQGtE2HXQf/sLyUBepw7ZZeIaVISoEcrWJPrn/01iIbsDv7Zl4i4RwVNEMnJ38Dz793F8ZMlNG2YyiO3/pvd+zfx21cGkZLYjmfGLqBR3EW89vHvWLH5I+w24zLTiCsfJTtrBADT505gyYaZpCd348nRH5j425xfVATc2gde/Mz3+4qJhOE9oYbpigKOq8fAE68PpeDwrtPr7Sr8hifu+IBLOv2EWV88z0fL/05UZBwvP7TevF9GzstuN3Jg8v98/xxBmB1u62P8N9C4esx/suo1Zi19nj0HtnD3kCkMu+yB09s432dWaRND1dAesK3wwvPJeMPNvSGmnu/34y5v5MCzb49m7faFNIg1rtx1z7iasUMmA8oBsYaQLGgWLVpkdgg+MXnmaH59879IS87ik1WvMX3ur7m25xhSEtuddWJ68xUT+PmgpwAoOrqPOyd3oFv6ABrENmHskMmkNu3E8k0fmPNLuCEjCS5vD59/5/o6pyZJu9DEa2e6uTfUt8jcZq4eA0+M/mHyha171/DbVwbSs91AAG7s/yBpyRfz4ocP+Dl6cddF8XBDN3hvtevreJID13WF5uef09Y0rh7z6Snd+f3t7/D2oqfP2cb5PrNSmxiKYuvBiD7wz8/B1Vn1PMmBvmmBOyCGN3IAjHODM4ucU5QDYgUhWdAEox371hFdL4605CwAru5xBy/PfZgrLx55zrJx0Q1P/3/5yVKcOHE4PZyp0mQ3XAylJ+DrXNeWf+4T97Z/Yw/I8s9cqnXmzjFwpk9WvcpV3W4nopaZ5yWw9cswTswWfOva8u7mwOXt4cqO7sflD+4c822bG3fkbbZzbzOd7zMJfB2TYURveHsluFLTuJsDXVvATT09Cs3nvJUDIlangiZIFBzexa6Cjdz9XNbp905WHKfo2L4al3//y6l8tPzvFB3J48Hhr9Ao7iI/RepddjuM7AuR4d4dytlug+G9jKtyVuHuMQBwsrKcxetn8Pw9S/0QofjKoK7GELbzNnh3u9d0NrYdqN0tPTnmJTj1bgvhdvjvSqj24vW5Hq2Nrp2B2N0SvJsD7y/9G5+seo2LGrVk9LV/Ol0kiViBCpog0r5lb565a/7pn296ovan2If2G8/QfuPJyd/AMzNup0fGNcTHWnM8VrsdbukN7ZLg3dVQdrJu22veEG7ra80Z0d05BgC++OY9UhIzaN2si69DEx+y2eDqzsYgATNWwqHSum2vYYxxEmeFeZfcPeYleHVvbXSN/O8K2Hu4btuKjoRh3Y2CJlAL+lO8kQM/H/QUCfWbYbfb+XLj+/zu1UG8/sh2ouvFeTNUEZ8J0GsO4q5mCW04cGTP6Z/LThzjREUZTeLP3+m3bfNMmsQnsyFniY8j9L2sVHh0CPRpa1ytdldclPGswEMDrVnMeHIMfLLqVQb2vNMf4YkfpDWFiYONLmJREe6vXy/c6GL26BBrFDOetnsSvJo1hAeuhZ9cDPEePPsYZoeerY0c6Nkm8IsZb+VAkwbJ2O3GKWG/LkOJiYpn78GtXo1VxJd0hyZIpCVnEW6P4OttC+mecTVzlr/I5Zm31PhcxO79m0ltanSKzy/KYUf+Olo2DdBO8m6qH2U8IHr9xbBqJ6zNhfwjtXdBqBcOqU2M7gqZLaw9v4A7xwDAvqIdbMtbwx/GfOTnSMWX6oUbJ3PXdoF1uUYe7D1c+0hoEWGQ3Mg4ieve2rNCyCzuHvMSGsLsRlF/eXvYmAcrd0BuEZyorHl5u80ohC5ONS6IxVloAmVv5cDBI3kkNkwBYPPulRwrO0RyYwv1uZaQp4ImiPzmtv8w+Z0xTJ39K5o3TuPR294it/DcJ4X/OW8ihYd3EWaPICwsnHE/fYHUpsE1uH5sPWO+gOwOUFVtFDUHjhkndTaMk7ZmjSCxvvFlFixcPQYAPln9Gpd1uZHYqHg/Ryn+UC8c+qQZr2qHcfwXHIGKKuPB6cgw4ySuaYPAfT7AFa4e8/NXv87r839P6fFilm/6gHc/n8Ifx8whLfni834m1hVmNwZ1yWoJDiccKoF9R4zCxuk0ivnE+kY3NU/u6gcKb+TA5JmjKS7dj90WRr2IaB4b9S6x0Q1M+G1EPKOCJoi0btaFF+9fc8Hl/vTzuX6IJnCEh0HLxsYr2Ll6DADcOejPPo5GAkWY3ShemjU0OxLvc/WYv7bnaK7tOdrtzyQ42G2QGG+8go03cuDZuz/1clQi/mXh63LiivCwSEqOH+Lu57IoLj1wweWnz53A24ufJi46QCedELe5ewzM+uJ5ps6+hwaxTfwQnYj3uXvMn4/aRLEi5YCEGpvT6epUVOJP1RWweKrZUbguezyEqdu611jt7w86BsS7lAMi1ssD5YCYRXdoRERERETEslTQiIiIiIiIZanLWYByOsFRyxCTgcgeEfjj9VuJ1f7+oGNAvEs5IGK9PFAOiFlU0IiIiIiIiGWpy5mIiIiIiFiWChoREREREbEsFTQiIiIiImJZKmhERERERMSyVNCIiIiIiIhlqaARERERERHLUkEjIiIiIiKWpYJGREREREQsSwWNiIiIiIhYlgoaERERERGxLBU0IiIiIiJiWSpoRERERETEslTQiIiIiIiIZamgERERERERy1JBIyIiIiIilqWCRkRERERELEsFjYiIiIiIWJYKGhERERERsSwVNCIiIiIiYlkqaERERERExLJU0IiIiIiIiGWpoBEREREREctSQSMiIiIiIpalgkZERERERCxLBY2IiIiIiFjW/wMg1mHF3qg6SwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1040x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import RealAmplitudes\n",
    "ansatz = RealAmplitudes(num_qubits=num_features, reps=3) #create the ansatz for the quantum model (RealAmplitudes 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_17748\\831372878.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 AQGD\n"
     ]
    }
   ],
   "source": [
    "from qiskit.algorithms.optimizers import AQGD\n",
    "optimizer = AQGD(maxiter=100) #create the AQGD 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: 2859 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: 0.85\n",
      "Quantum VQC on the test dataset:     0.83\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
}