A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing

[2607.16183] A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2607.16183 (cs) [Submitted on 17 Jul 2026 ( v1 ), last revised 14 Aug 2026 (this version, v2)] Title: A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing Authors: Owen Lockwood , Jérémy Béjanin , Joost Bus , Christopher Chamberland , Patrick Huembeli , Frank Schäfer , Guillaume Verdon View a PDF of the paper titled A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing, by Owen Lockwood and 6 other authors View PDF HTML (experimental) Abstract: To help address the escalating energy and latency demands of machine-learning workloads, we introduce a blueprint for an energy-efficient and fast thermodynamic computing stack that leverages stochastic analog processes in physical hardware. In this work, we focus on energy-based thermodynamic computing where the stochastic process is well described by Langevin dynamics with tunable energy potentials. The implementation of such potentials in physical hardware enables us to generate and sample from basic parameterized energy-based models. We demonstrate how to construct and train popular classes of machine learning models based on these hardware-native energy-based models, using the framework of probabilistic graphical models. We analyze the runtime and energy consumption of different models in this thermodynamic paradigm based on theoretical considerations and numerical studies. As a preliminary experimental realization of such hardware, we present our stochastic analog superconducting circuits driven by thermal noise. Together, these results outline a path toward energy-efficient thermodynamic hardware for probabilistic machine learning. Comments: 42 pages, 20 figures Subjects: Machine Learning (cs.LG) ; Emerging Technologies (cs.ET); Applied Physics (physics.app-ph) Cite as: arXiv:2607.16183 [cs.LG] (or arXiv:2607.16183v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.16183 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Owen Lockwood [ view email ] [v1] Fri, 17 Jul 2026 17:57:49 UTC (7,735 KB) [v2] Fri, 14 Aug 2026 23:59:42 UTC (7,736 KB) Full-text links: Access Paper: View a PDF of the paper titled A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing, by Owen Lockwood and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-07 Change to browse by: cs cs.ET physics physics.app-ph References & Citations 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? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) 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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