Radial Basis Function-based Quantum Hybrid Classical Generative Adversarial Networks for Enhanced Image Quality and Training Stability

Abstract Quantum Generative Adversarial Networks (QGANs), as the quantum version to classical Generative Adversarial Networks, exhibit exponential advantages in certain aspects, garnering considerable attention. However, within this nascent field, challenges persist in the synthesis of image quality and the stability of training in QGANs. In this work, we introduce a Hybrid Quantum Classical Generative Adversarial Network (HQCGAN), incorporating a classical discriminator constructed using Radial Basis Function Neural Networks (RBFNN). Harnessing the superior non-linear data processing capabilities and inherent resilience to image noise of RBFNNs, our HQCGAN significantly enhances its proficiency in generating high-fidelity grayscale images characterized by discrete value distributions. Through a series of meticulous experiments that evaluated the training cross-validation scores and the robustness of the loss functions, we have demonstrated the exceptional performance of our HQCGAN model, especially in the presence of noisy input data. These findings contribute meaningfully to the burgeoning field of quantum generative models, underscoring the vital role played by classical machine learning components in augmenting the overall efficacy of quantum approaches. The incorporation of RBFNNs within a quantum framework in our study offers novel perspectives to address prevailing challenges related to image quality and training stability, marking a substantial progression in the evolution of quantum generative adversarial networks.