期刊
PHYSICAL REVIEW B
卷 97, 期 20, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevB.97.205140
关键词
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资金
- DOE Office of Basic Energy Sciences, Division of Materials Sciences and Engineering [DE-SC0010526]
- HKUST
- David and Lucile Packard Foundation
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(beta(2)) in Hirsch-Fye algorithm to O(beta ln beta), which is a significant speedup especially for systems at low temperatures.
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