vqc-trainer
Variational quantum classifier training skill with gradient optimization
npx skills add majiayu000/claude-skill-registry --skill vqc-trainer --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.
# VQC Trainer ## Purpose Provides expert guidance on training variational quantum classifiers, including data encoding, circuit design, and gradient-based optimization. ## Capabilities - Data encoding circuit design - Variational layer construction - Gradient-based optimization (SPSA, Adam) - Cross-validation for QML - Hyperparameter tuning - Overfitting detection - Learning curve analysis - Ensemble methods ## Usage Guidelines 1. **Data Preparation**: Preprocess classical data for quantum encoding 2. **Encoding Design**: Select appropriate data encoding strategy 3. **Ansatz Design**: Build variational circuit with trainable parameters 4. **Training Setup**: Configure optimizer, learning rate, and batch size 5. **Evaluation**: Assess model on test set with proper metrics ## Tools/Libraries - Qiskit Machine Learning - PennyLane - TensorFlow Quantum - PyTorch - scikit-learn
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the vqc-trainer skill do?
Variational quantum classifier training skill with gradient optimization
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill vqc-trainer --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 majiayu000/claude-skill-registry, a repository with 534 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.