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Exponential reduction of the sign problem at finite density in the 2+1D XY model via contour deformations

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arxiv 2202.07561 v1 pith:75ABJLKB submitted 2022-02-15 hep-lat cond-mat.stat-mech

classification hep-latcond-mat.stat-mech
keywords problemsigndeformationsexponentialmodelreductionreweightingalleviating
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

We study the 2+1 dimensional XY model at nonzero chemical potential $\mu$ on deformed integration manifolds, with the aim of alleviating its sign problem. We investigate several proposals for the deformations, and considerably improve on the severity of the sign problem with respect to standard reweighting approaches. We present numerical evidence that the reduction of the sign problem is exponential both in $\mu^2$ and in the spatial volume. We also present a new approach to the optimization procedure based on reweighting, that sensibly reduces its computational cost.

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

    hep-lat 2025-02 conditional novelty 5.0 of 10

    Path optimization with machine learning reproduces analytic results in the 1D lattice Thirring model, and dropping the Jacobian from the learning step still works.

  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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