The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Machine Learning Phases of Strongly Correlated Fermions
Open accessAbstract
Machine learning has strong potential as a tool for understanding how to classify phases in condensed matter physics. A new investigation shows that an artificial neural network can be trained to identify changes in the collective magnetic properties of electrons on a lattice and predict trends in the transition when some of the electrons are removed.
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Citations
Cites
Solving the quantum many-body problem with artificial neural networks
Carleo, G., & Troyer, M. (2017) · Science · doi:10.1126/science.aag2302 · Cited by 2,215
Machine learning phases of matter
Carrasquilla, J., & Melko, R. G. (2017) · Nature Physics · doi:10.1038/nphys4035 · Cited by 1,448
Self-citationSign problem in the numerical simulation of many-electron systems
Loh, E. Y., Gubernatis, J. E., Scalettar, R. T., White, S. R., Scalapino, D. J., & Sugar, R. L. (1990) · Physical Review B · doi:10.1103/physrevb.41.9301 · Cited by 801
Cited by
From DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852
Transfer learning in hybrid classical-quantum neural networks
Mari, A., Bromley, T. R., Izaac, J., Schuld, M., & Killoran, N. (2020) · Quantum · doi:10.22331/q-2020-10-09-340 · Cited by 435
Expressive power of parametrized quantum circuits
Du, Y., Hsieh, M.-H., Liu, T., & Tao, D. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.033125 · Cited by 318
Machine learning action parameters in lattice quantum chromodynamics
Shanahan, P. E., Trewartha, D., & Detmold, W. (2018) · Physical Review D · doi:10.1103/physrevd.97.094506 · Cited by 65
Machine learning meets quantum foundations: A brief survey
Bharti, K., Haug, T., Vedral, V., & Kwek, L.-C. (2020) · AVS Quantum Science · doi:10.1116/5.0007529 · Cited by 59
Metadata
- Authors
- Ch’ng, K., Carrasquilla, J., Melko, R. G., & Khatami, E.
- Venue
- Physical Review X
- Publisher
- American Physical Society (APS)
- Published
- 2017-08-30
- Volume
- 7
- Issue
- 3
- ISSN
- 2160-3308 (ISSN-L)
- OpenAlex
- W2520014252
- Cited by
- 251
- References
- 3
- FWCI
- 21.75
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ch’ng, K., Carrasquilla, J., Melko, R. G., & Khatami, E. (2017). Machine Learning Phases of Strongly Correlated Fermions. Physical Review X. https://doi.org/10.1103/physrevx.7.031038
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