Self-learning quantum Monte Carlo method in interacting fermion systems

Xu, Xiao Yan; Qi, Yang; Liu, Junwei; Fu, Liang; Meng, Zi Yang

doi:10.1103/PhysRevB.96.041119

Title: Self-learning quantum Monte Carlo method in interacting fermion systems

Journal Article · 18 July 2017 · Physical Review B

DOI: https://doi.org/10.1103/PhysRevB.96.041119 · OSTI ID:1424921

Xu, Xiao Yan ^[1]; Qi, Yang ^[2]; Liu, Junwei ^[2]; Fu, Liang ^[2]; Meng, Zi Yang ^[1]

Chinese Academy of Sciences, Beijing (China). Beijing National Laboratory for Condensed Matter Physics and Institute of Physics; University of Chinese Academy of Sciences, Beijing (China). School of Physical Sciences
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Department of Physics

We present the self-learning Monte Carlo method is a powerful general-purpose numerical method recently introduced to simulate many-body systems. In this work, we extend it to an interacting fermion quantum system in the framework of the widely used determinant quantum Monte Carlo. This method can generally reduce the computational complexity and moreover can greatly suppress the autocorrelation time near a critical point. This enables us to simulate an interacting fermion system on a $100 × 100$ lattice even at the critical point and obtain critical exponents with high precision.

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Research Organization:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division; USDOE

Grant/Contract Number:: SC0010526

OSTI ID:: 1424921

Alternate ID(s):: OSTI ID: 1371802

Journal Information:: Physical Review B, Vol. 96, Issue 4; ISSN 2469-9950

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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