Sampling the Schwinger Model with Gauge-Equivariant Diffusion

[2606.27481] Sampling the Schwinger Model with Gauge-Equivariant Diffusion Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search High Energy Physics - Lattice arXiv:2606.27481 (hep-lat) [Submitted on 25 Jun 2026 ( v1 ), last revised 14 Aug 2026 (this version, v2)] Title: Sampling the Schwinger Model with Gauge-Equivariant Diffusion Authors: Octavio Vega , Aida X. El-Khadra View a PDF of the paper titled Sampling the Schwinger Model with Gauge-Equivariant Diffusion, by Octavio Vega and 1 other authors View PDF HTML (experimental) Abstract: We present a first study of a diffusion-based approach to accelerated sampling of the $N_f = 2$ lattice Schwinger model. Our work is inspired by recent and growing successes in developing such generative models for ensemble generation in LFT to overcome the well-known critical slowing down problem. We train a U(1)-equivariant score-based generative model to sample gauge link configurations from the marginal Schwinger model. By computing model likelihoods, we obtain unbiased estimates for observables that closely match those produced by MCMC simulations. We also demonstrate improvement over HMC as measured qualitatively by a reduction in topological freezing near critical parameters. Comments: v2: Updated acknowledgements, paper unchanged. Conference paper at PAI 2026. 6 pages, 1 figure Subjects: High Energy Physics - Lattice (hep-lat) ; Strongly Correlated Electrons (cond-mat.str-el); Machine Learning (cs.LG) Cite as: arXiv:2606.27481 [hep-lat] (or arXiv:2606.27481v2 [hep-lat] for this version) https://doi.org/10.48550/arXiv.2606.27481 Focus to learn more arXiv-issued DOI via DataCite Journal reference: 2026 Conference on Physics and AI (PAI26), Stanford University Related DOI : https://doi.org/10.5281/zenodo.21007460 Focus to learn more DOI(s) linking to related resources Submission history From: Octavio Vega [ view email ] [v1] Thu, 25 Jun 2026 19:00:56 UTC (61 KB) [v2] Fri, 14 Aug 2026 19:58:37 UTC (61 KB) Full-text links: Access Paper: View a PDF of the paper titled Sampling the Schwinger Model with Gauge-Equivariant Diffusion, by Octavio Vega and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: hep-lat < prev | next > new | recent | 2026-06 Change to browse by: cond-mat cond-mat.str-el cs cs.LG References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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