Deconvolving the components of the sign problem

S. Tarat, Bo Xiao, R. Mondaini, R. T. Scalettar

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Auxiliary field quantum Monte Carlo simulations of interacting fermions require sampling over a Hubbard-Stratonovich field h introduced to decouple the interactions. The weight for a given configuration involves the products of the determinant of matrices Mσ(h), where σ labels the species, and hence is typically not positive definite. Indeed, the average sign (S) of the determinants goes to zero exponentially with increasing spatial size and decreasing temperature for most Hamiltonians of interest. This statement, however, does not explicitly separate two possible origins for the vanishing of (S). Does (S)→0 because randomly chosen field configurations have det[M(h)]<0, or does the sign problem arise because the specific subset of configurations chosen by the weighting function have a greater preponderance of negative values? In the latter case, the process of weighting the configurations with |det[M(h)]| might steer the simulation to a region of configuration space of h where positive and negative determinants are equally likely, even though randomly chosen h would preferentially have determinants with a single dominant sign. In this paper, we address the relative importance of these two mechanisms for the vanishing of (S) in quantum simulations.

Original languageEnglish
Article numberA77
JournalPhysical Review B
Volume105
Issue number4
DOIs
StatePublished - Jan 15 2022
Externally publishedYes

Funding

R.T.S. was supported by Grant No. DE-SC0014671 funded by the U.S. Department of Energy, Office of Science. R.M. acknowledges support from the National Natural Science Foundation of China (NSFC) Grants No. U1930402, No. 11974039, No. 12050410263 and No. 12111530010. B.X. was supported by the Flatiron Institute. The Flatiron Institute is a division of the Simons Foundation. Computations were performed on the Tianhe-2JK at the Beijing Computational Science Research Center.

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