deleted codes

Author: mhjensen

doc/src/week13/programs/qsvmpytorch.py

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+# Quantum Kernel SVM on Iris (Setosa vs Versicolor) using PennyLane and scikit-learn
+
+import pennylane as qml
+from pennylane import numpy as np
+from sklearn import datasets
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score, classification_report
+
+# Load Iris dataset and select two classes (0: setosa, 1: versicolor)
+iris = datasets.load_iris()
+X = iris.data
+y = iris.target
+mask = y != 2  # drop class '2' (virginica)
+X = X[mask]
+y = y[mask]
+
+# Standardize features and reduce to 2 dimensions via PCA
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+pca = PCA(n_components=2)
+X_reduced = pca.fit_transform(X_scaled)
+
+# Split into train and test sets
+X_train, X_test, y_train, y_test = train_test_split(
+   X_reduced, y, test_size=0.2, random_state=42
+)
+# Define quantum device and feature map (angle encoding on 2 qubits)
+n_qubits = 2
+dev = qml.device('default.qubit', wires=n_qubits)
+
+@qml.qnode(dev)
+def feature_map(x):
+   # Encode 2 features into rotation angles
+   for i in range(n_qubits):
+       qml.RY(x[i] * np.pi, wires=i)
+   # Optional: add entanglement (e.g., ZZ interaction)
+   qml.CNOT(wires=[0, 1])
+   qml.RZ((x[0] + x[1]) * np.pi, wires=1)
+   qml.CNOT(wires=[0, 1])
+   return qml.state()
+
+# Compute quantum kernel (fidelity) between two feature vectors
+def quantum_kernel(x1, x2):
+   # Compute state vectors for each input
+   state1 = feature_map(x1)
+   state2 = feature_map(x2)
+   # Kernel = |<phi(x1)|phi(x2)>|^2
+   overlap = np.vdot(state1, state2)
+   return np.abs(overlap) ** 2
+
+# Build kernel (Gram) matrices for training and test sets
+n_train = len(X_train)
+n_test = len(X_test)
+kernel_train = np.zeros((n_train, n_train))
+for i in range(n_train):
+   for j in range(n_train):
+       kernel_train[i, j] = quantum_kernel(X_train[i], X_train[j])
+
+kernel_test = np.zeros((n_test, n_train))
+for i in range(n_test):
+   for j in range(n_train):
+       kernel_test[i, j] = quantum_kernel(X_test[i], X_train[j])
+
+# Train SVM with precomputed quantum kernel
+svm = SVC(kernel='precomputed')
+svm.fit(kernel_train, y_train)
+
+# Predict on test set and evaluate
+y_pred = svm.predict(kernel_test)
+acc = accuracy_score(y_test, y_pred)
+print("Test Accuracy:", acc)
+print("\nClassification Report:\n", classification_report(y_test, y_pred))