quantum-kernel-estimator
Quantum kernel computation skill for quantum machine learning
Profile →npx skills add a5c-ai/babysitter --skill quantum-kernel-estimator --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the quantum-kernel-estimator skill do?
Quantum kernel computation skill for quantum machine learning
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill quantum-kernel-estimator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.