---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
PlanS_conform: '1'
_id: '21847'
abstract:
- lang: eng
  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.
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."
article_number: '023094'
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Frederik Skovbo
  full_name: Moller, Frederik Skovbo
  id: 43cbcc83-0564-11f0-a935-e37325525859
  last_name: Moller
- first_name: Gabriel
  full_name: Fernández-Fernández, Gabriel
  last_name: Fernández-Fernández
- first_name: Thomas
  full_name: Schweigler, Thomas
  last_name: Schweigler
- first_name: Paulin
  full_name: De Schoulepnikoff, Paulin
  last_name: De Schoulepnikoff
- first_name: Jörg
  full_name: Schmiedmayer, Jörg
  last_name: Schmiedmayer
- first_name: Gorka
  full_name: Muñoz-Gil, Gorka
  last_name: Muñoz-Gil
citation:
  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>
  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>
  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>.
  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.
  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.
  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>.
  short: F.S. Moller, G. Fernández-Fernández, T. Schweigler, P. De Schoulepnikoff,
    J. Schmiedmayer, G. Muñoz-Gil, Physical Review Research 8 (2026).
date_created: 2026-05-10T22:02:15Z
date_published: 2026-04-29T00:00:00Z
date_updated: 2026-05-11T06:58:56Z
day: '29'
ddc:
- '530'
department:
- _id: EdHa
doi: 10.1103/r7pj-gl7r
external_id:
  arxiv:
  - '2509.13821'
file:
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has_accepted_license: '1'
intvolume: '         8'
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language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '04'
oa: 1
oa_version: Published Version
publication: Physical Review Research
publication_identifier:
  eissn:
  - 2643-1564
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning minimal representations of many-body physics from snapshots of a quantum
  simulator
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  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
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...