Network-Initialized Monte Carlo Based on Generative Neural Networks

• 作者: Hongyu Lu Chuhao Li Bin-Bin Chen Wei Li Yang Qi Zi Yang Meng
• 来源期刊: 中国物理快报（英文版）
• 年，卷（期）: 2022,（5）
• 所属期刊栏目: GENERAL
• 页码范围: 39-47
• 页数: 9页
• 语种: 英文
• DOI: 10.1088/0256-307X/39/5/050701

摘要

We design generative neural networks that generate Monte Carlo configurations with complete absence of autocor-relation 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 net-works,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.