Module quantum_kernel_methods 

Quantum Kernel Methods and Quantum-Inspired Approximations

This module implements quantum kernel approximations and quantum-inspired classical algorithms for kernel methods. These methods simulate quantum feature maps using classical computation while providing theoretical quantum advantage insights.

References

• Havlicek et al. (2019): “Supervised learning with quantum-enhanced feature spaces”
• Schuld & Killoran (2019): “Quantum Machine Learning in Feature Hilbert Spaces”
• Liu et al. (2021): “Rigorous Guarantees for Quantum Computational Advantage”
• Huang et al. (2021): “Power of data in quantum machine learning”

Structs

QuantumKernelApproximation

Quantum Kernel Approximation

QuantumKernelConfig

Configuration for quantum kernel approximation

Enums

EntanglementPattern

Entanglement patterns for quantum circuits

QuantumFeatureMap

Quantum feature map types