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Solving the quantum many-body problem with artificial neural networks
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Machine learning and quantum physics: Elucidating the behavior of quantum interacting systems of many particles remains one of the biggest challenges in physics. Traditional numerical methods often work well, but some of the most interesting problems leave them stumped. Carleo and Troyer harnessed the power of machine learning to develop a variational approach to the quantum many-body problem (see the Perspective by Hush). The method performed at least as well as state-of-the-art approaches, setting a benchmark for a prototypical two-dimensional problem. With further development, it may well prove a valuable piece in the quantum toolbox. Science , this issue p. 602 ; see also p. 580
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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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Schuld, M., & Killoran, N. (2022) · PRX Quantum · doi:10.1103/prxquantum.3.030101 · Cited by 267
Metadata
- Authors
- Carleo, G., & Troyer, M.
- Affiliations
- ETH Zurich (CH)
- Microsoft (United States) (US)
- Corresponding author
- Carleo, Giuseppe
- Venue
- Science
- Published
- 2017-02-10
- Volume
- 355
- Issue
- 6325
- Pages
- 602-606
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2419175238
- Cited by
- 2,215
- References
- 53
- Citations by year
- 2026: 178 · 2025: 297 · 2024: 257 · 2023: 250 · 2022: 289 · 2021: 303 · 2020: 255 · 2019: 208 · 2018: 132 · 2017: 46
- FWCI
- 86.50
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- European Commission Directorate-General for Research and Innovation (award287220)
- Microsoft Research (award287221)
- Lincoln Laboratory (award287222)
- Swiss National Science Foundation (award287223)
- 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 problem, artificial neural networks, variational quantum states, reinforcement learning, ground state, unitary evolution
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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