Quantum Kernel Estimator
SkillDev toolsQuantum kernel computation skill for quantum machine learning
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Quantum Kernel Estimator skill
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/quantum-computing/skills/quantum-kernel-estimator/SKILL.md and read by ahel’s review.
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
- Feature Map Selection: Design quantum feature map for data encoding
- Kernel Computation: Calculate kernel matrix entries via circuit execution
- Alignment Optimization: Tune kernel for target classification task
- SVM Training: Use quantum kernel with classical SVM solvers
- Performance Evaluation: Assess classification accuracy and quantum advantage
Tools/Libraries
- Qiskit Machine Learning
- PennyLane
- scikit-learn
- CVXPY
- NumPy
Signals
- GitHub stars
- 2k
- Forks
- 112
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
quantum-kernel-estimator- Source
- github.com/a5c-ai/babysitter