Quang Nhat Le-QSVC.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_9676\\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_9676\\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": "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": 8,
   "id": "bc75fbbd-5931-4fb6-ac23-8267799bd455",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qiskit_algorithms.state_fidelities import ComputeUncompute\n",
    "from qiskit_machine_learning.kernels import FidelityQuantumKernel\n",
    "from qiskit_machine_learning.algorithms import QSVC\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "fidelity = ComputeUncompute(sampler=sampler) #define the quantum kernel \n",
    "kernel = FidelityQuantumKernel(fidelity=fidelity, feature_map=feature_map)\n",
    "matrix_train = kernel.evaluate(x_vec=train_features)\n",
    "matrix_test = kernel.evaluate(x_vec=test_features, y_vec=train_features)\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "axs[0].imshow(\n",
    "    np.asmatrix(matrix_train), interpolation=\"nearest\", origin=\"upper\", cmap=\"Blues\"\n",
    ")\n",
    "axs[0].set_title(\"Training kernel matrix\")\n",
    "axs[1].imshow(np.asmatrix(matrix_test), interpolation=\"nearest\", origin=\"upper\", cmap=\"Reds\")\n",
    "axs[1].set_title(\"Testing kernel matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0af53699-c32f-45bc-9180-fb3021344982",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>QSVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "     decision_function_shape=&#x27;ovr&#x27;, degree=3, gamma=&#x27;scale&#x27;, max_iter=-1,\n",
       "     probability=False,\n",
       "     quantum_kernel=&lt;qiskit_machine_learning.kernels.fidelity_quantum_kernel.FidelityQuantumKernel object at 0x000002129270F520&gt;,\n",
       "     random_state=123, shrinking=True, tol=0.001, verbose=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;QSVC<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>QSVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "     decision_function_shape=&#x27;ovr&#x27;, degree=3, gamma=&#x27;scale&#x27;, max_iter=-1,\n",
       "     probability=False,\n",
       "     quantum_kernel=&lt;qiskit_machine_learning.kernels.fidelity_quantum_kernel.FidelityQuantumKernel object at 0x000002129270F520&gt;,\n",
       "     random_state=123, shrinking=True, tol=0.001, verbose=False)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "QSVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "     decision_function_shape='ovr', degree=3, gamma='scale', max_iter=-1,\n",
       "     probability=False,\n",
       "     quantum_kernel=<qiskit_machine_learning.kernels.fidelity_quantum_kernel.FidelityQuantumKernel object at 0x000002129270F520>,\n",
       "     random_state=123, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qsvc = QSVC( #use QSVC to train the quantum model\n",
    "    quantum_kernel=kernel,\n",
    ")\n",
    "qsvc.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1da06b0b-a9e2-40d3-9090-48e3e1bd28fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QSVC classification train score: 0.9916666666666667\n",
      "QSVC classification test score: 0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "qsvc_train_score = qsvc.score(train_features, train_labels) #Print the scores of the quantum model\n",
    "qsvc_test_score = qsvc.score(test_features, test_labels)\n",
    "print(f\"QSVC classification train score: {qsvc_train_score}\")\n",
    "print(f\"QSVC classification test score: {qsvc_test_score}\")"
   ]
  }
 ],
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