quantum-kernel-estimator

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Quantum kernel computation skill for quantum machine learning

General

What this skill does


# 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

Files: 1

Size: 1.2 KB

Complexity: 8/100

Category: General

Source: https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/science/quantum-computing/skills/quantum-kernel-estimator

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