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Cited by 221Quantum many-body systemsQuantum Computing Algorithms and ArchitectureCold Atom Physics and Bose-Einstein Condensates

Quantum Many-Body Dynamics in Two Dimensions with Artificial Neural Networks

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Schmitt, Markus, Heyl, Markus·2020·Physical Review Letters
DOI 10.1103/physrevlett.125.100503
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Abstract

The efficient numerical simulation of nonequilibrium real-time evolution in isolated quantum matter constitutes a key challenge for current computational methods. This holds in particular in the regime of two spatial dimensions, whose experimental exploration is currently pursued with strong efforts in quantum simulators. In this work we present a versatile and efficient machine learning inspired approach based on a recently introduced artificial neural network encoding of quantum many-body wave functions. We identify and resolve key challenges for the simulation of time evolution, which previously imposed significant limitations on the accurate description of large systems and long-time dynamics. As a concrete example, we study the dynamics of the paradigmatic two-dimensional transverse-field Ising model, as recently also realized experimentally in systems of Rydberg atoms. Calculating the nonequilibrium real-time evolution across a broad range of parameters, we, for instance, observe collapse and revival oscillations of ferromagnetic order and demonstrate that the reached timescales are comparable to or exceed the capabilities of state-of-the-art tensor network methods.

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    “In practice, the matrix S is typically ill-conditioned and needs to be regularized, for which there exist several approaches 47,56,121 .”

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Cited by

Sorted by citations

Authors
Schmitt, M., & Heyl, M.
Affiliations
  • University of California, Berkeley (US)
  • Max Planck Institute for the Physics of Complex Systems (DE)
ORCID
Venue
Physical Review Letters
Publisher
American Physical Society (APS)
Published
2020-09-02
Volume
125
Issue
10
Pages
100503-100503
ISSN
  • 0031-9007 (ISSN-L)
  • 1079-7114
DOI
10.1103/physrevlett.125.100503
OpenAlex
W2995786632
Cited by
221
References
9
Citations by year
2026: 21 · 2025: 47 · 2024: 40 · 2023: 32 · 2022: 27 · 2021: 38 · 2020: 15 · 2019: 1
FWCI
11.39
Type
journal article
Language
en
Open access
Licence
link.aps.org/licenses/aps-default-license (vor)
Locations
  • Physical Review Letters (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
  • OPUS (Augsburg University) (submittedVersion)
  • arXiv (Cornell University)
  • Bulletin of the American Physical Society
Funders
  • German National Academy of Sciences Leopoldina (LPDS 2018-07)
  • Simons Foundation
  • Deutsche Forschungsgemeinschaft
  • Max-Planck-Gesellschaft
  • H2020 European Research Council (853443)
  • Horizon 2020 Framework Programme
  • John von Neumann Institute for Computing
Topics
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Cold Atom Physics and Bose-Einstein Condensates
Keywords
quantum many-body dynamics, two-dimensional quantum systems, artificial neural networks, neural quantum states, transverse-field Ising model, Rydberg atoms, ferromagnetic ordering, collapse and revival, long-time behavior

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