Babysitter vqc-trainer
Variational quantum classifier training skill with gradient optimization
install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/science/quantum-computing/skills/vqc-trainer" ~/.claude/skills/a5c-ai-babysitter-vqc-trainer && rm -rf "$T"
manifest:
library/specializations/domains/science/quantum-computing/skills/vqc-trainer/SKILL.mdsource content
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