quantum-kernel-estimator

# Quantum Kernel Estimator ## Purpose Provides expert guidance on quantum kernel methods for machine learning, enabling kernel-based classifiers and regressors with quantum feature maps. ## Capabilities - Fidelity quantum kernel - Projected quantum kernel - Kernel alignment optimization - Feature map design - SVM integration with quantum kernels - Kernel matrix visualization - Bandwidth tuning - Trainable kernel circuits ## Usage Guidelines 1. **Feature Map Selection**: Design quantum feature map for data encoding 2. **Kernel Computation**: Calculate kernel matrix entries via circuit execution 3. **Alignment Optimization**: Tune kernel for target classification task 4. **SVM Training**: Use quantum kernel with classical SVM solvers 5. **Performance Evaluation**: Assess classification accuracy and quantum advantage ## Tools/Libraries - Qiskit Machine Learning - PennyLane - scikit-learn - CVXPY - NumPy