from qiskit import Aer
from qiskit.utils import QuantumInstance
from qiskit.circuit.library import ZZFeatureMap
from qiskit.algorithms.optimizers import COBYLA
from qiskit_machine_learning.algorithms import VQC
from qiskit_machine_learning.datasets import ad_hoc_data
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler

def run_qml_classifier():
    """Runs a QML classifier on a sample dataset."""
    # 1. Load and preprocess the dataset
    # For simplicity, we use a sample dataset from Qiskit Machine Learning
    feature_dim = 2
    training_dataset_size = 20
    testing_dataset_size = 10

    sample_total, training_input, test_input, class_labels = ad_hoc_data(
        training_size=training_dataset_size,
        test_size=testing_dataset_size,
        n=feature_dim,
        gap=0.3,
        plot_data=False
    )

    # 2. Set up the VQC
    # Choose a feature map to encode the data
    feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2, entanglement='linear')

    # Choose an optimizer
    optimizer = COBYLA(maxiter=100)

    # Choose a quantum backend (simulator)
    backend = Aer.get_backend('statevector_simulator')
    quantum_instance = QuantumInstance(backend)

    # Create the VQC instance
    vqc = VQC(optimizer=optimizer, feature_map=feature_map, quantum_instance=quantum_instance)

    # 3. Train the VQC
    vqc.fit(training_input['A'], class_labels[0])

    # 4. Test the VQC
    score = vqc.score(test_input['A'], class_labels[0])
    print(f"Accuracy: {score}")

if __name__ == "__main__":
    run_qml_classifier()