Yao Meng, Wang Da, Wang Qiang-Hua
National Laboratory of Solid State Microstructures & School of Physics, Nanjing University, Nanjing, 210093, China.
Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.
Phys Rev E. 2021 Aug;104(2-2):025305. doi: 10.1103/PhysRevE.104.025305.
When performing a Monte Carlo calculation, the running time should, in principle, be much longer than the autocorrelation time in order to get reliable results. Among different lattice fermion models, the Holstein model is notorious for its particularly long autocorrelation time. In this paper, we employ the Wang-Landau algorithm in the determinant quantum Monte Carlo to achieve the flat-histogram sampling in the "configuration weight space," which can greatly reduce the autocorrelation time by sacrificing some sampling efficiency. The proposal is checked in the Holstein model on both square and honeycomb lattices. Based on such a Wang-Landau assisted determinant quantum Monte Carlo method, some models with long autocorrelation times can now be simulated possibly.
在进行蒙特卡罗计算时，原则上运行时间应远长于自相关时间，以便获得可靠的结果。在不同的晶格费米子模型中，霍尔斯坦模型因其特别长的自相关时间而声名狼藉。在本文中，我们在行列式量子蒙特卡罗中采用王 - 兰道算法，以在“构型权重空间”中实现平直方图采样，这可以通过牺牲一些采样效率来大大减少自相关时间。该方案在方形和蜂窝晶格上的霍尔斯坦模型中进行了检验。基于这种王 - 兰道辅助的行列式量子蒙特卡罗方法，现在有可能模拟一些具有长自相关时间的模型。
