Quantum-Enhanced Generative Adversarial Networks (GANs) for Image Synthesis

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum annealing presents a novel approach to generative adversarial networks (GANs) by incorporating its computational capabilities to enhance image synthesis. This paper investigates the potential of leveraging quantum annealing to improve the diversity and realism of generated images within GANs. We demonstrate a modified GAN architecture that utilizes quantum annealing to optimize the training process, resulting in significantly enhanced output characteristics compared to existing GAN implementations. The core mechanism focuses on leveraging quantum annealing to improve the training of GANs, resulting in more varied and realistic outputs. The research explores the advantages and challenges of applying quantum annealing to this complex task, ultimately aiming to surpass current GAN performance.

DOI: 10.5281/zenodo.22187239

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