[{"publisher":"American Physical Society","quality_controlled":"1","citation":{"apa":"Moller, F. S., Fernández-Fernández, G., Schweigler, T., De Schoulepnikoff, P., Schmiedmayer, J., &#38; Muñoz-Gil, G. (2026). Learning minimal representations of many-body physics from snapshots of a quantum simulator. <i>Physical Review Research</i>. American Physical Society. <a href=\"https://doi.org/10.1103/r7pj-gl7r\">https://doi.org/10.1103/r7pj-gl7r</a>","ama":"Moller FS, Fernández-Fernández G, Schweigler T, De Schoulepnikoff P, Schmiedmayer J, Muñoz-Gil G. Learning minimal representations of many-body physics from snapshots of a quantum simulator. <i>Physical Review Research</i>. 2026;8(2). doi:<a href=\"https://doi.org/10.1103/r7pj-gl7r\">10.1103/r7pj-gl7r</a>","chicago":"Moller, Frederik Skovbo, Gabriel Fernández-Fernández, Thomas Schweigler, Paulin De Schoulepnikoff, Jörg Schmiedmayer, and Gorka Muñoz-Gil. “Learning Minimal Representations of Many-Body Physics from Snapshots of a Quantum Simulator.” <i>Physical Review Research</i>. American Physical Society, 2026. <a href=\"https://doi.org/10.1103/r7pj-gl7r\">https://doi.org/10.1103/r7pj-gl7r</a>.","ista":"Moller FS, Fernández-Fernández G, Schweigler T, De Schoulepnikoff P, Schmiedmayer J, Muñoz-Gil G. 2026. Learning minimal representations of many-body physics from snapshots of a quantum simulator. Physical Review Research. 8(2), 023094.","ieee":"F. S. Moller, G. Fernández-Fernández, T. Schweigler, P. De Schoulepnikoff, J. Schmiedmayer, and G. Muñoz-Gil, “Learning minimal representations of many-body physics from snapshots of a quantum simulator,” <i>Physical Review Research</i>, vol. 8, no. 2. American Physical Society, 2026.","short":"F.S. Moller, G. Fernández-Fernández, T. Schweigler, P. De Schoulepnikoff, J. Schmiedmayer, G. Muñoz-Gil, Physical Review Research 8 (2026).","mla":"Moller, Frederik Skovbo, et al. “Learning Minimal Representations of Many-Body Physics from Snapshots of a Quantum Simulator.” <i>Physical Review Research</i>, vol. 8, no. 2, 023094, American Physical Society, 2026, doi:<a href=\"https://doi.org/10.1103/r7pj-gl7r\">10.1103/r7pj-gl7r</a>."},"ddc":["530"],"day":"29","oa_version":"Published Version","article_processing_charge":"Yes","_id":"21847","scopus_import":"1","article_type":"original","department":[{"_id":"EdHa"}],"intvolume":"         8","date_updated":"2026-05-11T06:58:56Z","abstract":[{"text":"Analog quantum simulators provide access to many-body dynamics beyond the reach of classical computation. However, extracting physical insights from experimental data is often hindered by measurement noise, limited observables, and incomplete knowledge of the underlying microscopic model. Here, we develop a machine learning approach based on a variational autoencoder (VAE) to analyze interference measurements of tunnel-coupled one-dimensional Bose gases, which realize the sine-Gordon quantum field theory. Trained in an unsupervised manner, the VAE learns a minimal latent representation that strongly correlates with the equilibrium control parameter of the system. Applied to nonequilibrium protocols, the latent space uncovers signatures of frozen-in solitons following rapid cooling, and reveals anomalous postquench dynamics not captured by conventional correlation-based methods. These results demonstrate that generative models can extract physically interpretable variables directly from noisy and sparse experimental data, providing complementary probes of equilibrium and nonequilibrium physics in quantum simulators. More broadly, our work highlights how machine learning can supplement established field-theoretical techniques, paving the way for scalable, data-driven discovery in quantum many-body systems.","lang":"eng"}],"author":[{"last_name":"Moller","first_name":"Frederik Skovbo","id":"43cbcc83-0564-11f0-a935-e37325525859","full_name":"Moller, Frederik Skovbo"},{"last_name":"Fernández-Fernández","first_name":"Gabriel","full_name":"Fernández-Fernández, Gabriel"},{"last_name":"Schweigler","first_name":"Thomas","full_name":"Schweigler, Thomas"},{"first_name":"Paulin","last_name":"De Schoulepnikoff","full_name":"De Schoulepnikoff, Paulin"},{"last_name":"Schmiedmayer","first_name":"Jörg","full_name":"Schmiedmayer, Jörg"},{"full_name":"Muñoz-Gil, Gorka","last_name":"Muñoz-Gil","first_name":"Gorka"}],"title":"Learning minimal representations of many-body physics from snapshots of a quantum simulator","external_id":{"arxiv":["2509.13821"]},"date_created":"2026-05-10T22:02:15Z","date_published":"2026-04-29T00:00:00Z","OA_type":"gold","article_number":"023094","publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2026-05-11T06:56:58Z","publication":"Physical Review Research","publication_identifier":{"eissn":["2643-1564"]},"status":"public","OA_place":"publisher","volume":8,"type":"journal_article","acknowledgement":"We thank Sebastian Erne and Igor Mazets for helpful discussions and sharing codes for the transfer matrix sampling. This research was funded in part by the European Research Council: ERC Advanced Grant “Emergence in Quantum Physics” (EmQ) under Grant Agreement No. 101097858 and ERC Advanced Grant “Artificial agency and learning in quantum environments” (QuantAI) under Grant Agreement No. 101055129. This work was also supported by the Austrian Science Fund (FWF) (SFB BeyondC F7102, 10.55776/F71). G.F.-F. acknowledges the European Research Council AdG NOQIA; MCIN/AEI [PGC2018-0910.13039/501100011033, CEX2019-000910-S/10.13039/501100011033, Plan National FIDEUA PID2019-106901GB-I00, Plan National STAMEENA PID2022-139099NB, I00, project funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR” (PRTR-C17.I1), FPI]; QUANTERA DYNAMITE PCI2022-132919 under Grant Agreement No. 101017733; Ministry for Digital Transformation and of Civil Service of the Spanish Government through the QUANTUM ENIA project call—Quantum Spain project, and by the European Union through the Recovery, Transformation and Resilience Plan—NextGenerationEU within the framework of the Digital Spain 2026 Agenda; Fundació Cellex; Fundació Mir-Puig; Generalitat de Catalunya (European Social Fund FEDER and CERCA program); Barcelona Supercomputing Center MareNostrum (FI-2023-3-0024); (HORIZON-CL4-2022-QUANTUM-02-SGA PASQuanS2.1, 101113690, EU Horizon 2020 FET-OPEN OPTOlogic, Grant No. 899794, QU-ATTO, 101168628), EU Horizon Europe Program (This project has received funding from the European Union's Horizon Europe research and innovation program under Grant Agreement No. 101080086 NeQST); ICFO Internal “QuantumGaudi” project. This research was funded in whole or in part by the Austrian Science Fund (FWF) [10.55776/COE1] through the Cluster of Excellence quantA (Quantum Science Austria).\r\n\r\nThe views and opinions expressed in this article are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council—neither the European Union nor the granting authority can be held responsible for them.","arxiv":1,"DOAJ_listed":"1","month":"04","license":"https://creativecommons.org/licenses/by/4.0/","has_accepted_license":"1","PlanS_conform":"1","issue":"2","doi":"10.1103/r7pj-gl7r","oa":1,"year":"2026","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"language":[{"iso":"eng"}],"file":[{"date_updated":"2026-05-11T06:56:58Z","file_name":"2026_PhysicalReviewResearch_Moller.pdf","file_size":1829628,"content_type":"application/pdf","checksum":"dbfc58e1e176f7b63e0d274eb0d1bffa","relation":"main_file","date_created":"2026-05-11T06:56:58Z","creator":"dernst","success":1,"access_level":"open_access","file_id":"21852"}]}]