Learning minimal representations of many-body physics from snapshots of a quantum simulator
Year: 2026
Type: article
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.
Source: Physical Review Research
DOI: 10.1103/r7pj-gl7r
Language: English
Topic: Quantum many-body systems
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,625
Funders: Centres de Recerca de Catalunya, Institut de Ciències Fotòniques, European Commission, Austrian Science Fund, Generalitat de Catalunya, Ministerio de Ciencia e Innovación, Barcelona Supercomputing Center, QuantERA, Horizon 2020 Framework Programme, HORIZON EUROPE Framework Programme, Agencia Estatal de Investigación, H2020 European Research Council, NextGenerationEU
Awards: G1171162939, G1188902550, G120905174, G1272927172, G1305139904, G1526731822, G1540068383, NExt level Quantum information processing for Science and Technology, G1605908383, G1848969253, G1908469581, G2049473890, Programmable Atomic Large-scale Quantum Simulation 2 - SGA1, G2262748287, G2710326435, G2763573136, G280048558, G2871827553, G2877376689, G2988934680, G3005324705, G3022749744, G3077005769, G3203908756, G3241330029, G3278703066, G3323650233, G3480869486, G3514304032, G356394092, G357246561, G3681454997, G3723121148, G3976037732, G4042783231, G4157035715, G4239348134, G425668148, G4359800586, G4368799831, G4391008399, G4442677895, G4526231891, G4571205363, G4727238542, G4856404060, G4861696250, G5080475149, G526456201, G5302217128, G531036588, G575169067, G5800121100, G5814117976, QuantERA II ERA-NET Cofund in Quantum Technologies, G5922099756, G5987792135, G6166689270, G6482940084, G6552004136, G6656126374, G6743926034, G6835105602, G704744937, G7048927246, G7084143925, G7277596287, G7327758667, G7425137858, G7613962116, G8180681537, G8466379035, G848750135, G8590477180, Optical Topologic Logic, G8665153912, G8684520763, G8959349008
OpenAlex ID: W4416255076