Quang Nhat Le-VQC (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_14764\\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_14764\\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_14764\\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: 609 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: 0.81\n",
      "Quantum VQC on the test dataset:     0.73\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": []
  }
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