Quang Nhat Le-VQC (AQGD-TwoLocal).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_660\\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_660\\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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",
      "text/plain": [
       "<Figure size 1040x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.circuit.library import TwoLocal\n",
    "ansatz = TwoLocal(4, 'ry', 'cx', 'linear', reps=3) #create the ansatz for the quantum model (TwoLocal 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_660\\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: 5164 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: 0.84\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
}