Network-Initialized Monte Carlo Based on Generative Neural Networks
Hongyu Lu1 , Chuhao Li2,3 , Bin-Bin Chen1 , Wei Li4,5* , Yang Qi6,7* , and Zi Yang Meng1*
1 Department of Physics and HKU-UCAS Joint Institute of Theoretical and Computational Physics, The University of Hong Kong, Hong Kong, China2 Beijing National Laboratory for Condensed Matter Physics, and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China3 School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China4 Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China5 School of Physics, Beihang University, Beijing 100191, China6 State Key Laboratory of Surface Physics, Fudan University, Shanghai 200438, China7 Center for Field Theory and Particle Physics, Department of Physics, Fudan University, Shanghai 200433, China
Abstract ：We design generative neural networks that generate Monte Carlo configurations with complete absence of autocorrelation from which only short Markov chains are needed before making measurements for physical observables, irrespective of the system locating at the classical critical point, fermionic Mott insulator, Dirac semimetal, or quantum critical point. We further propose a network-initialized Monte Carlo scheme based on such neural networks, which provides independent samplings and can accelerate the Monte Carlo simulations by significantly reducing the thermalization process. We demonstrate the performance of our approach on the two-dimensional Ising and fermion Hubbard models, expect that it can systematically speed up the Monte Carlo simulations especially for the very challenging many-electron problems.
收稿日期: 2022-02-16
出版日期: 2022-04-29
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2022, Vol. 39 
