a5c-ai/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