vqc-trainer

name	vqc-trainer
description	Variational quantum classifier training skill with gradient optimization
allowed-tools	["Bash","Read","Write","Edit","Glob","Grep"]
metadata	{"specialization":"quantum-computing","domain":"science","category":"quantum-ml","phase":6}
graph	{"domains":["domain:quantum-computing"],"specializations":["specialization:quantum-computing"],"skillAreas":["skill-area:mathematical-reasoning","skill-area:machine-learning-frameworks","skill-area:physics-simulation"],"workflows":["workflow:experiment-design"],"roles":["role:research-engineer","role:ml-engineer"]}

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