The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Restricted Boltzmann machine learning for solving strongly correlated quantum systems
Open accessAbstract
We develop a machine learning method to construct accurate ground-state wave functions of strongly interacting and entangled quantum spin as well as fermionic models on lattices. A restricted Boltzmann machine algorithm in the form of an artificial neural network is combined with a conventional variational Monte Carlo method with pair product (geminal) wave functions and quantum number projections. The combination allows an application of the machine learning scheme to interacting fermionic systems. The combined method substantially improves the accuracy beyond that ever achieved by each method separately, in the Heisenberg as well as Hubbard models on square lattices, thus proving its power as a highly accurate quantum many-body solver.
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Cites
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Machine learning phases of matter
Carrasquilla, J., & Melko, R. G. (2017) · Nature Physics · doi:10.1038/nphys4035 · Cited by 1,448
Cited by
The Variational Quantum Eigensolver: A review of methods and best practices
Tilly, J., Chen, H., Cao, S., Picozzi, D., Setia, K., Li, Y., Grant, E., Wossnig, L., Rungger, I., Booth, G. H., et al. (2022) · Physics Reports · doi:10.1016/j.physrep.2022.08.003 · Cited by 1,031
Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines
Cheng, S., Chen, J., & Wang, L. (2018) · Entropy · doi:10.3390/e20080583 · Cited by 104
Variational Quantum Boltzmann Machines
Zoufal, C., Lucchi, A., & Woerner, S. (2020) · arXiv · doi:10.48550/arxiv.2006.06004 · Cited by 99
Quenches near Ising quantum criticality as a challenge for artificial neural networks
Czischek, S., Gärttner, M., & Gasenzer, T. (2018) · Physical Review B · doi:10.1103/physrevb.98.024311 · Cited by 85
Variational benchmarks for quantum many-body problems
Wu, D., Rossi, R., Vicentini, F., Astrakhantsev, N., Becca, F., Cao, X., Carrasquilla, J., Ferrari, F., Georges, A., Hibat-Allah, M., et al. (2024) · Science · doi:10.1126/science.adg9774 · Cited by 79
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Metadata
- Authors
- Nomura, Y., Darmawan, A. S., Yamaji, Y., & Imada, M.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2017-11-29
- Volume
- 96
- Issue
- 20
- ISSN
- 2469-9950 (ISSN-L)
- 2469-9969
- OpenAlex
- W2755695124
- Cited by
- 318
- References
- 6
- FWCI
- 14.70
- Type
- journal article
- Language
- en
- Open access
- green
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