vqc-trainer

# 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