Enhancing Quantum Diffusion Models for Complex Image Generation

Enhancing Quantum Diffusion Models for Complex Image Generation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Enhancing Quantum Diffusion Models for Complex Image Generation Jeongbin Jo, Santanam Wishal, Shah Md Khalil Ullah, Shan Zeng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8795888/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Quantum generative models offer a novel approach to exploring high-dimensional Hilbert spaces but face significant challenges in scalability and expressibility, particularly when applied to multi-modal distributions. In this study, we propose a \textbf{Hybrid Quantum-Classical U-Net} architecture enhanced by \textbf{Adaptive Non-Local Observables (ANO)} and an \textbf{Ancilla-based Global Feature Extractor}. By compressing classical data into a dense quantum latent space and utilizing trainable observables, our model extracts rich non-local features that complement classical processing. Furthermore, we integrate a Hadamard Test module to capture global structural information, fusing it with dense local features. We also investigate the role of Skip Connections in preserving semantic information during the reverse diffusion process. Experimental results on the full MNIST dataset (digits 0-9) demonstrate that the proposed architecture generates structurally coherent and recognizable images for all digit classes, overcoming the mode collapse observed in prior quantum diffusion models. While hardware constraints necessitate resolution downscaling, our findings suggest that hybrid architectures with adaptive measurements provide a feasible pathway for enhancing generative capabilities in the NISQ era. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8795888","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":586605888,"identity":"54680bce-d4e7-4354-ace0-9692a656fde0","order_by":0,"name":"Jeongbin 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