---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Frederik Skovbo
      foaf_name: Moller, Frederik Skovbo
      foaf_surname: Moller
      foaf_workInfoHomepage: http://www.librecat.org/personId=43cbcc83-0564-11f0-a935-e37325525859
  - foaf_Person:
      foaf_givenName: Gabriel
      foaf_name: Fernández-Fernández, Gabriel
      foaf_surname: Fernández-Fernández
  - foaf_Person:
      foaf_givenName: Thomas
      foaf_name: Schweigler, Thomas
      foaf_surname: Schweigler
  - foaf_Person:
      foaf_givenName: Paulin
      foaf_name: De Schoulepnikoff, Paulin
      foaf_surname: De Schoulepnikoff
  - foaf_Person:
      foaf_givenName: Jörg
      foaf_name: Schmiedmayer, Jörg
      foaf_surname: Schmiedmayer
  - foaf_Person:
      foaf_givenName: Gorka
      foaf_name: Muñoz-Gil, Gorka
      foaf_surname: Muñoz-Gil
  bibo_doi: 10.1103/r7pj-gl7r
  bibo_issue: '2'
  bibo_volume: 8
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2643-1564
  dct_language: eng
  dct_publisher: American Physical Society@
  dct_title: Learning minimal representations of many-body physics from snapshots
    of a quantum simulator@
...