quantum-kernel-estimator
Quantum kernel computation skill for quantum machine learning
Best use case
quantum-kernel-estimator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Quantum kernel computation skill for quantum machine learning
Teams using quantum-kernel-estimator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/quantum-kernel-estimator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How quantum-kernel-estimator Compares
| Feature / Agent | quantum-kernel-estimator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Quantum kernel computation skill for quantum machine learning
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
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