<ul><li>Quantum neural networks outperform classical models in terms of convergence speed and accuracy.</li><li>Data augmentation in quantum machine learning is explored using quantum generative adversarial networks (QGANs) with hybrid quantum-classical neural networks (HQCNNs).</li><li>Two strategies are proposed - a general approach to enhance data processing and classification, and a customized strategy for generating samples tailored to specific data categories for HQCNNs.</li><li>Simulation experiments on the MNIST dataset show that QGAN surpasses traditional augmentation methods and classical GANs, achieving comparable performance with fewer parameters.</li></ul>

QGAN-based data augmentation for hybrid quantum-classical neural networks

Discover more