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...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.展开更多
基金support from the RGC of Hong Kong SAR of China(Grant Nos.17303019,17301420,17301721,and Ao E/P-701/20)the National Natural Science Foundation of China(Grant Nos.11974036,11874115,and 11834014)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB33000000)the K.C.Wong Education Foundation(Grant No.GJTD-2020-01)supported by the Seed Funding“Quantum-Inspired explainable-AI”at the HKU-TCL Joint Research Centre for Artifcial Intelligence,Hong Kong。
文摘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.
