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Fermionic Sign Problem Minimization by Constant Path Integral Contour Shifts

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arxiv 2307.06785 v1 pith:OIQD5LXO submitted 2023-07-13 cond-mat.str-el hep-latphysics.comp-ph

classification cond-mat.str-elhep-latphysics.comp-ph
keywords problemsigncontourintegralpathshiftscomputationalconstant
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The path integral formulation of quantum mechanical problems including fermions is often affected by a severe numerical sign problem. We show how such a sign problem can be alleviated by a judiciously chosen constant imaginary offset to the path integral. Such integration contour deformations introduce no additional computational cost to the Hybrid Monte Carlo algorithm, while its effective sample size is greatly increased. This makes otherwise unviable simulations efficient for a wide range of parameters. Applying our method to the Hubbard model, we find that the sign problem is significantly reduced. Furthermore, we prove that it vanishes completely for large chemical potentials, a regime where the sign problem is expected to be particularly severe without imaginary offsets. In addition to a numerical analysis of such optimized contour shifts, we analytically compute the shifts corresponding to the leading and next-to-leading order corrections to the action. We find that such simple approximations, free of significant computational cost, suffice in many cases.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Exploring Group Convolutional Networks for Sign Problem Mitigation via Contour Deformation

    cond-mat.dis-nn 2025-02 conditional novelty 5.0 of 10

    Group convolutional networks with built-in lattice symmetries outperform fully connected networks for learned contour deformations in small Hubbard-model sign-problem simulations, but transfer learning across paramete...

  2. Machine-learning approaches to accelerating lattice simulations

    hep-lat 2025-02 unverdicted

    A review of unbiased machine-learning acceleration methods for lattice field theory, covering flow-based sampling, contour deformations, control variates, and surrogate observables.

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