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Computer Vision and Image Understanding

High resolution image generation based on quantum generative adversarial networks

Yunwei Deng, Mingxuan Chen, Chen Gong, Nanrun Zhou

Published Jul 29, 2026• 1 citation
Publisher
DOI
10.1016/j.cviu.2026.104884

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Quantum generative adversarial networks (QGANs) have attracted increasing attention for their potential to surpass classical generative models by leveraging the expressive power of parameterized quantum circuits (PQCs). However, in the current noisy intermediate-scale quantum era, the implementation of fully quantum algorithms is hindered by hardware limitations, restricting most existing approaches to low-resolution generated image. To address this challenge, we propose a hybrid quantum-classical generative adversarial network for patch-based image generation. Our method employs dual PQC-based generators that decompose images into structured patches, while local and global classical discriminators jointly capture local details and preserve global structural coherence. This design addresses scalability limitations of existing QGANs and mitigates patch-boundary artifacts, thereby enabling high-resolution image generation on hardware with restricted qubit counts. Experimental results validate the effectiveness of the proposed method, demonstrating improvements in image fidelity over existing baselines, and providing insights into the influence of qubit count on generative performance.
Generative adversarial network (GAN) is one of the most promising methods for unsupervised learning in recent years. GAN works via adversarial training concept and has shown excellent performance in the fields image synthesis, image super-resolution, video generation, image translation, etc. Compared with classical algorithms, quantum algorithms have their unique advantages in dealing with complex tasks, quantum machine learning (QML) is one of the most promising quantum algorithms with the rapid development of quantum technology. Specifically, Quantum generative adversarial network (QGAN) has shown the potential exponential quantum speedups in terms of performance. Meanwhile, QGAN also exhibits some problems, such as barren plateaus, unstable gradient, model collapse, absent complete scientific evaluation system, etc. How to improve the theory of QGAN and apply it that have attracted some researcher. In this paper, we comprehensively and deeply review recently proposed GAN and QAGN models and their applications, and we discuss the existing problems and future research trends of QGAN.