Reducing autocorrelation time in determinant quantum Monte Carlo using the Wang-Landau algorithm: Application to the Holstein model

Meng Yao, Da Wang, and Qiang-Hua Wang
Phys. Rev. E 104, 025305 – Published 16 August 2021

Abstract

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.

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  • Received 29 December 2020
  • Accepted 29 July 2021

DOI:https://doi.org/10.1103/PhysRevE.104.025305

©2021 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied PhysicsStatistical Physics & Thermodynamics

Authors & Affiliations

Meng Yao1, Da Wang1,2,*, and Qiang-Hua Wang1,2,†

  • 1National Laboratory of Solid State Microstructures & School of Physics, Nanjing University, Nanjing, 210093, China
  • 2Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China

  • *dawang@nju.edu.cn
  • qhwang@nju.edu.cn

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Vol. 104, Iss. 2 — August 2021

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