Hybrid Quantum-Classical Generative Adversarial Networks with Transfer Learning

[2507.09706] Hybrid Quantum-Classical Generative Adversarial Networks with Transfer Learning Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantum Physics arXiv:2507.09706 (quant-ph) [Submitted on 13 Jul 2025 ( v1 ), last revised 11 Apr 2026 (this version, v2)] Title: Hybrid Quantum-Classical Generative Adversarial Networks with Transfer Learning Authors: Asma Al-Othni , Saif Al-Kuwari , Mohammad Mahdi Nasiri Fatmehsari , Kamila Zaman , Ebrahim Ardeshir-Larijani View a PDF of the paper titled Hybrid Quantum-Classical Generative Adversarial Networks with Transfer Learning, by Asma Al-Othni and 4 other authors View PDF HTML (experimental) Abstract: Generative Adversarial Networks (GANs) have demonstrated immense potential in synthesizing diverse and high-fidelity images. However, critical questions remain unanswered regarding how quantum principles might best enhance their representational and computational capacity. In this paper, we investigate hybrid quantum-classical GAN architectures supplemented by transfer learning to systematically examine whether incorporating Variational Quantum Circuits (VQCs) into the generator, the discriminator, or both improves performance over a fully classical baseline. Our findings indicate that fully hybrid models, which incorporate VQCs in both the generator and the discriminator, produce images with higher quality and achieve more favorable quantitative metrics compared to their fully classical counterparts. In particular, placing the quantum block in the generator appears to accelerate the early emergence of visual structure, whereas placing it in the discriminator slows early visual convergence but improves the final quantitative quality metric. Incorporating quantum blocks into both networks yields the strongest overall performance. Moreover, the model sustains comparable performance even when the dataset size is reduced. Overall, the results underscore that carefully integrating quantum computing with classical adversarial training and pretrained feature extraction can enrich GAN-based image synthesis. These insights open avenues for future work on higher-resolution tasks, alternative quantum circuit designs, and experimentation with emerging quantum hardware. Comments: 14 pages, 11 figures Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2507.09706 [quant-ph] (or arXiv:2507.09706v2 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2507.09706 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Asma Al-Othni [ view email ] [v1] Sun, 13 Jul 2025 16:46:56 UTC (10,962 KB) [v2] Sat, 11 Apr 2026 19:21:20 UTC (11,523 KB) Full-text links: Access Paper: View a PDF of the paper titled Hybrid Quantum-Classical Generative Adversarial Networks with Transfer Learning, by Asma Al-Othni and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2025-07 References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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