1.2 量子カーネル法による分子分類

量子カーネル法は、量子コンピューターを使って高次元特徴空間での内積計算を効率化するアプローチです。ADMET（吸収・分布・代謝・排泄・毒性）特性の分類に特に有効です。

from qiskit_machine_learning.kernels import FidelityQuantumKernel
from qiskit_machine_learning.algorithms import QSVC
from qiskit.circuit.library import ZZFeatureMap
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
import pandas as pd

class MolecularClassifier:
    """
    量子カーネルSVM〫よる分子毒性予測
    ADMET特性の分類モデル
    """

    def __init__(self, n_features: int = 4, reps: int = 2):
        self.n_features = n_features
        self.scaler = StandardScaler()

        feature_map = ZZFeatureMap(
            feature_dimension=n_features,
            reps=reps,
            entanglement='circular'
        )
        self.kernel = FidelityQuantumKernel(feature_map=feature_map)
        self.model = QSVC(quantum_kernel=self.kernel)

    def fit(self, X: np.ndarray, y: np.ndarray) -> 'MolecularClassifier':
        X_scaled = self.scaler.fit_transform(X)
        X_normalized = np.arcsin(X_scaled / (np.max(np.abs(X_scaled)) + 1e-8))
        self.model.fit(X_normalized, y)
        return self

    def predict(self, X: np.ndarray) -> np.ndarray:
        X_scaled = self.scaler.transform(X)
        X_normalized = np.arcsin(X_scaled / (np.max(np.abs(X_scaled)) + 1e-8))
        return self.model.predict(X_normalized)

    def evaluate(self, X_test: np.ndarray, y_test: np.ndarray) -> dict:
        y_pred = self.predict(X_test)
        return {
            'classification_report': classification_report(
                y_test, y_pred,
                target_names=['非毒性', '毒性'],
                output_dict=True
            ),
            'confusion_matrix': confusion_matrix(y_test, y_pred).tolist()
        }

def demo_molecular_classification():
    """分子毒性予�測のデモ"""
    np.random.seed(42)
    n_samples = 200
    X = np.random.randn(n_samples, 4)
    y = (X[:, 0] + X[:, 1] > 0).astype(int)

    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42, stratify=y
    )

    classifier = MolecularClassifier(n_features=4)
    classifier.fit(X_train, y_train)
    results = classifier.evaluate(X_test, y_test)