Quantum data encoding strategies for quantum machine learning

Common infrastructure for quantum generative adversarial networks (QGANs)

Common infrastructure for quantum kernel methods

Training algorithms and cost functions for quantum machine learning

Variational Quantum Classifier (VQC) implementation.

VQC is a quantum machine learning algorithm that uses parameterized quantum circuits 
to perform classification tasks. It combines feature maps for encoding classical data
with variational ansatzes optimized to maximize classification accuracy.

Key Features:
- Multiple feature map types (angle, amplitude, basis, IQP)
- Integration with existing ansatz types from QClojure
- Classification-specific objective functions and metrics
- Support for binary and multi-class classification
- Comprehensive analysis including confusion matrix and decision boundaries

Algorithm Flow:
1. Encode classical features using quantum feature maps
2. Apply parameterized ansatz circuit
3. Measure classification outputs
4. Optimize parameters to maximize classification accuracy
5. Analyze results and performance metrics

This implementation is designed for production use with real quantum hardware.