Self-learning Monte Carlo with deep neural networks
Name
PhysRevB.97.205140.pdf
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623.35 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Shen, Huitao
Liu, Junwei
Fu, Liang
Date Issued
May 2018
Journal
Physical Review B
Publisher
American Physical Society
Citation
Shen, Huitao et al. "Self-learning Monte Carlo with deep neural networks." Physical Review B 97, 20 (May 2018): 205140 © 2018 American Physical Society
Version
Final published version
Abstract
The self-learning Monte Carlo (SLMC) method is a general algorithm to speedup MC simulations. Its efficiency has been demonstrated in various systems by introducing an effective model to propose global moves in the configuration space. In this paper, we show that deep neural networks can be naturally incorporated into SLMC, and without any prior knowledge can learn the original model accurately and efficiently. Demonstrated in quantum impurity models, we reduce the complexity for a local update from O(β²) in Hirsch-Fye algorithm to O(βlnβ), which is a significant speedup especially for systems at low temperatures.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1103/PhysRevB.97.205140
