费米子行列式引起的符号问题的路径优化方法

IF 5.3 2区物理与天体物理 Q1 Physics and Astronomy

Physical Review D Pub Date : 2025-05-06 DOI:10.1103/physrevd.111.094503

Kazuki Hisayoshi, Kouji Kashiwa, Yusuke Namekawa, Hayato Takase

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引用次数: 0

摘要

将机器学习路径优化方法应用于具有费米子行列式引起的符号问题的一维大晶格Thirring模型。本研究旨在探讨路径优化方法如何适用于符号问题。结果表明，路径优化方法成功地减小了统计误差，并能再现分析结果。我们还研究了学习过程中雅可比矩阵计算的近似值，并表明它与没有近似值的结果一致。2025年由美国物理学会出版

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Path optimization method for the sign problem caused by the fermion determinant

The path optimization method with machine learning is applied to the one-dimensional massive lattice Thirring model, which has the sign problem caused by the fermion determinant. This study aims to investigate how the path optimization method works for the sign problem. We show that the path optimization method successfully reduces statistical errors and reproduces the analytic results. We also examine an approximation of the Jacobian calculation in the learning process and show that it gives consistent results with those without an approximation.

Published by the American Physical Society

2025

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来源期刊

Physical Review D 物理-天文与天体物理

CiteScore

9.20

自引率

36.00%

发文量

审稿时长

2 months

期刊介绍： Physical Review D (PRD) is a leading journal in elementary particle physics, field theory, gravitation, and cosmology and is one of the top-cited journals in high-energy physics. PRD covers experimental and theoretical results in all aspects of particle physics, field theory, gravitation and cosmology, including: Particle physics experiments, Electroweak interactions, Strong interactions, Lattice field theories, lattice QCD, Beyond the standard model physics, Phenomenological aspects of field theory, general methods, Gravity, cosmology, cosmic rays, Astrophysics and astroparticle physics, General relativity, Formal aspects of field theory, field theory in curved space, String theory, quantum gravity, gauge/gravity duality.