Optimizing Entangling Power and Preserving Circuit Symmetry in Hybrid Quantum Generative Adversarial Networks for Expressive Variational Generation

Optimizing Entangling Power and Preserving Circuit Symmetry in Hybrid Quantum Generative Adversarial Networks for Expressive Variational 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 Research Article Optimizing Entangling Power and Preserving Circuit Symmetry in Hybrid Quantum Generative Adversarial Networks for Expressive Variational Generation Wei Ding, Yan Liang, Zijun Guo, Jiazhao Shen, Hongyang Ma, Litong Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6902359/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract This work presents a Hybrid Quantum Generative Adversarial Network (HQGAN) that combines symmetry-preserving variational quantum circuits with entangling power optimization, targeting improved expressivity and training stability on NISQ devices. The quantum generator employs layered parameterized circuits composed of Ising-type entangling gates and symmetric single-qubit rotations, inspired by convolutional neural networks. To guide circuit design, we quantify the entanglement capability of each layer using the entangling power (EP) metric via Monte Carlo sampling. Compared to traditional fully connected architectures, HQGAN introduces three major improvements: (1) symmetry-preserving gates preserve translational invariance and enhance feature extraction; (2) modular subcircuit designs with patch-wise outputs reduce parameter overhead by 50% while improving convergence; (3) optimized entanglement via EP leads to better initialization and performance. Experimental results on Fashion-MNIST and OptDigits demonstrate that HQGAN achieves the same level of fidelity as the original model within 400 epochs,The KL divergence of the entanglement-enhanced Ising-based model is reduced to 0.023, compared to the baseline model. Moreover,parameter complexity is reduced from ( O(N^2) ) to ( O(\mathrm{poly}(N)) ), alleviating overfitting and improving generalization. These results confirm the importance of structure-aware circuit design and entanglement-driven optimization for quantum generative modeling under near-term constraints. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 Jul, 2025 Reviewers invited by journal 08 Jul, 2025 Editor assigned by journal 18 Jun, 2025 Submission checks completed at journal 18 Jun, 2025 First submitted to journal 16 Jun, 2025 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. 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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-6902359","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483357570,"identity":"c2c366e7-95ce-458e-9572-ac014ceda844","order_by":0,"name":"Wei Ding","email":"","orcid":"","institution":"School of Science, Qingdao University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Ding","suffix":""},{"id":483357571,"identity":"a38c25d9-d499-4820-adda-ce9cc2966934","order_by":1,"name":"Yan Liang","email":"","orcid":"","institution":"School of Science, Qingdao University of 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Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6902359/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6902359/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This work presents a Hybrid Quantum Generative Adversarial Network (HQGAN) that combines symmetry-preserving variational quantum circuits with entangling power optimization, targeting improved expressivity and training stability on NISQ devices. The quantum generator employs layered parameterized circuits composed of Ising-type entangling gates and symmetric single-qubit rotations, inspired by convolutional neural networks. To guide circuit design, we quantify the entanglement capability of each layer using the entangling power (EP) metric via Monte Carlo sampling. Compared to traditional fully connected architectures, HQGAN introduces three major improvements: (1) symmetry-preserving gates preserve translational invariance and enhance feature extraction; (2) modular subcircuit designs with patch-wise outputs reduce parameter overhead by 50\\% while improving convergence; (3) optimized entanglement via EP leads to better initialization and performance. Experimental results on Fashion-MNIST and OptDigits demonstrate that HQGAN achieves the same level of fidelity as the original model within 400 epochs,The KL divergence of the entanglement-enhanced Ising-based model is reduced to 0.023, compared to the baseline model. Moreover,parameter complexity is reduced from \\( O(N^2) \\) to \\( O(\\mathrm{poly}(N)) \\), alleviating overfitting and improving generalization. These results confirm the importance of structure-aware circuit design and entanglement-driven optimization for quantum generative modeling under near-term constraints.","manuscriptTitle":"Optimizing Entangling Power and Preserving Circuit Symmetry in Hybrid Quantum Generative Adversarial Networks for Expressive Variational Generation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-11 05:10:48","doi":"10.21203/rs.3.rs-6902359/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"169477602774982697841878049022318243175","date":"2025-07-09T03:09:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T02:28:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-18T09:32:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-18T09:31:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"EPJ Quantum Technology","date":"2025-06-16T06:48:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"epj-quantum-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epjq","sideBox":"Learn more about [EPJ Quantum Technology](http://epjquantumtechnology.springeropen.com)","snPcode":"40507","submissionUrl":"https://submission.nature.com/new-submission/40507/3","title":"EPJ Quantum Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e0d967d1-773c-4671-9775-3bad75aba945","owner":[],"postedDate":"July 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-11T05:10:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-11 05:10:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6902359","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6902359","identity":"rs-6902359","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}