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Parallel tempering algorithm for integration over Lefschetz thimbles

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arxiv 1703.00861 v3 pith:OGMGXKOZ submitted 2017-03-02 hep-lat cond-mat.stat-mechhep-thnucl-th

classification hep-latcond-mat.stat-mechhep-thnucl-th
keywords algorithmflowtemperingthimblestimeintegrationlargelefschetz
verification ladder T0 review T1 audit T2 compute T3 formal
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The algorithm based on integration over Lefschetz thimbles is a promising method to resolve the sign problem for complex actions. However, this algorithm often meets a difficulty in actual Monte Carlo calculations because the configuration space is not easily explored due to the infinitely high potential barriers between different thimbles. In this paper, we propose to use the flow time of the antiholomorphic gradient flow as an auxiliary variable for the highly multimodal distribution. To illustrate this, we implement the parallel tempering method by taking the flow time as a tempering parameter. In this algorithm, we can take the maximum flow time to be sufficiently large such that the sign problem disappears there, and two separate modes are connected through configurations at small flow times. To exemplify that this algorithm does work, we investigate the (0+1)-dimensional massive Thirring model at finite density and show that our algorithm correctly reproduces the analytic results for large flow times such as T=2.

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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: Insights from random matrix models

    hep-lat 2026-07 conditional novelty 5.0 of 10

    Path optimization improves the average phase factor in the Stephanov model at high chemical potential but not at low chemical potential or in the chiral random matrix model, pointing to the global sign problem as the ...

  2. 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.

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