%A Hongyu Lu, Chuhao Li, Bin-Bin Chen, Wei Li, Yang Qi, and Zi Yang Meng %T Network-Initialized Monte Carlo Based on Generative Neural Networks %0 Journal Article %D 2022 %J Chin. Phys. Lett. %R 10.1088/0256-307X/39/5/050701 %P 050701%V 39 %N 5 %U {https://cpl.iphy.ac.cn/CN/abstract/article_116124.shtml} %8 2022-04-01 %X 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.