https://doi.org/10.1140/epjqt/s40507-025-00418-2
Research
Quantum-classical synergy: enhancing quantum generative adversarial networks for lmage synthesis
1
School of Information Systems Engineering, Information Engineering University, 62 Science Avenue, 450001, Zhengzhou, HeNan, China
2
Department of Electronic Engineering, Tsinghua University, 30 Shuangqing Road, 100084, Beijing, China
3
School of Computer Science and Engineering, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, 610054, Chengdu, SiChuan, China
4
College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, 130012, Changchun, JiLin, China
5
Laboratory for Advanced Computing and Intelligence Engineering, Wuxi, Jiangsu, China
a
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b
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Received:
11
September
2024
Accepted:
13
September
2025
Published online:
29
September
2025
Quantum Generative Adversarial Networks (QGANs), as a rising paradigm in Quantum Machine Learning, have shown promising potential in image generation and processing. However, their output quality remains suboptimal, and existing research is largely limited to small-scale, proof-of-concept studies. In this work, we propose a hybrid quantum-classical GAN architecture, where the generator integrates parameterized quantum circuits (PQCs) and classical neural networks. This integration significantly enhances the visual quality of generated images. Our model leverages the complementary strengths of quantum and classical components and outperforms existing methods (Tsang et al. in IEEE Trans. Quantum Eng. 4:1–19, 2023; Gulrajani et al. in Proceedings of the 31st international conference on neural information processing systems. NIPS’17, Red Hook, pp. 5769–5779, 2017), particularly in terms of image fidelity. Experiments conducted on the MNIST family of datasets show that our hybrid approach achieves a 20.26% average reduction in Fréchet Inception Distance. Furthermore, it improves the Structural Similarity Index Measure, Cosine Similarity, and Peak Signal-to-Noise Ratio by 26.04%, 2.22%, and 7.62%, respectively. These results highlight the effectiveness of combining quantum computing with machine learning, and underscore the potential of hybrid quantum-classical models in advancing generative tasks.
Key words: Quantum machine learning / Quantum generative adversarial networks / Quantum computing
© The Author(s) 2025
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