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Cited by 18Quantum many-body systemsPhysics of Superconductivity and MagnetismQuantum Computing Algorithms and Architecture

Neural network approach to quasiparticle dispersions in doped antiferromagnets

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Lange, Hannah, Döschl, Fabian, Carrasquilla, Juan, Bohrdt, Annabelle·2024·Communications Physics
DOI 10.1038/s42005-024-01678-7
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Abstract

Numerically simulating large, spinful, fermionic systems is of great interest in condensed matter physics. However, the exponential growth of the Hilbert space dimension with system size renders exact quantum state parameterizations impractical. Owing to their representative power, neural networks often allow to overcome this exponential scaling. Here, we investigate the ability of neural quantum states (NQS) to represent the bosonic and fermionic t − J model – the high interaction limit of the Hubbard model – on various 1D and 2D lattices. Using autoregressive, tensorized recurrent neural networks (RNNs), we study ground state representations upon hole doping the half-filled system. Additionally, we propose a method to calculate quasiparticle dispersions, applicable to any network architecture or lattice geometry, and allowing to infer the low-energy physics from NQS. By analyzing the strengths and weaknesses of the RNN ansatz we shed light on the challenges and promises of NQS for simulating bosonic and fermionic systems.

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  • Recent results indicate that the autoregressive property of the models may restrict the expressiveness of the model.

    “since recent results indicate that the autoregressive property of the models may restrict the expressiveness of the model 126,132 .”

    From Beyond-classical computation in quantum simulation (2025) · cites this paper as [126]

    The Comparison of optimizers (p. 10) identifies a problem associated with autoregressive architectures, but describes it as a flat optimization landscape, not restricted expressivity. The Summary and outlook (p. 12) explicitly concludes that, for the fermionic spinful systems studied, the bottleneck is training rather than the expressivity of the ansatz. (LLM, conf=0.84)

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Cited by

Sorted by citations

Authors
Lange, H., Döschl, F., Carrasquilla, J., & Bohrdt, A.
Affiliations
  • Max Planck Institute of Quantum Optics (DE)
  • Munich Center for Quantum Science and Technology (DE)
  • University of Waterloo (CA)
Corresponding author
Lange, Hannah
ORCID
Venue
Communications Physics
Publisher
Springer Science and Business Media LLC
Published
2024-06-12
Volume
7
Issue
1
ISSN
2399-3650 (ISSN-L)
DOI
10.1038/s42005-024-01678-7
OpenAlex
W4399567381
Cited by
18
References
5
Citations by year
2026: 2 · 2025: 13 · 2024: 3
FWCI
2.87
Type
journal article
Language
en
Open access
Licence
Locations
  • Communications Physics (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
  • arXiv (Cornell University)
  • ETH Zürich Research Collection
Funders
  • Canadian Institute for Advanced Research
  • International Max Planck Research School for Environmental, Cellular and Molecular Microbiology
  • Government of Canada
  • European Commission (HORIZON2020)
  • Deutsche Forschungsgemeinschaft (EXC 2111, 390814868)
  • Natural Sciences and Engineering Research Council of Canada
Topics
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
  • Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Physics of Superconductivity and Magnetism
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
Keywords
neural quantum states, quasiparticle dispersion, doped antiferromagnets, t-J model, Hubbard model, p-type doping, recurrent neural networks, ground state representation, fermionic systems, bosonic systems, quantum many-body simulation, low-energy physics

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