Network-Initialized Monte Carlo Based on Generative Neural Networks [article]

Hongyu Lu, Chuhao Li, Bin-Bin Chen, Wei Li, Yang Qi, Zi Yang Meng
2022 arXiv   pre-print
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
more » ... rate 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, and expect it can systematically speed up the Monte Carlo simulations especially for the very challenging many-electron problems.
arXiv:2106.00712v6 fatcat:vvruhji4gfewdjmikrtpopg6uq