vqc-trainer
// Variational quantum classifier training skill with gradient optimization
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stars:384
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updated:March 4, 2026
SKILL.mdreadonly
SKILL.md Frontmatter
namevqc-trainer
descriptionVariational quantum classifier training skill with gradient optimization
allowed-toolsBash,Read,Write,Edit,Glob,Grep
metadata[object Object]
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
- Data Preparation: Preprocess classical data for quantum encoding
- Encoding Design: Select appropriate data encoding strategy
- Ansatz Design: Build variational circuit with trainable parameters
- Training Setup: Configure optimizer, learning rate, and batch size
- Evaluation: Assess model on test set with proper metrics
Tools/Libraries
- Qiskit Machine Learning
- PennyLane
- TensorFlow Quantum
- PyTorch
- scikit-learn