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Lefschetz-thimble analysis of the sign problem in one-site fermion model

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arxiv 1509.07146 v4 pith:EAI56R4S submitted 2015-09-23 hep-th cond-mat.str-elhep-lat

classification hep-thcond-mat.str-elhep-lat
keywords modelone-siteproblemsignlefschetzpaththimblesanalysis
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The Lefschetz-thimble approach to path integrals is applied to a one-site model of electrons, i.e., the one-site Hubbard model. Since the one-site Hubbard model shows a non-analytic behavior at the zero temperature and its path integral expression has the sign problem, this toy model is a good testing ground for an idea or a technique to attack the sign problem. Semiclassical analysis using complex saddle points unveils the significance of interference among multiple Lefschetz thimbles to reproduce the non-analytic behavior by using the path integral. If the number of Lefschetz thimbles is insufficient, we found not only large discrepancies from the exact result, but also thermodynamic instabilities. Analyzing such singular behaviors semiclassically, we propose a criterion to identify the necessary number of Lefschetz thimbles. We argue that this interference of multiple saddle points is a key issue to understand the sign problem of the finite-density quantum chromodynamics.

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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. Complex Langevin Simulations of Zero-dimensional Supersymmetric Quantum Field Theories

    hep-th 2019-08 conditional novelty 6.0 of 10

    Complex Langevin simulations, using a twisted-boundary-condition auxiliary-field order parameter, correctly flag spontaneous supersymmetry breaking in several zero-dimensional N=2 models, including new complex-action cases.

  2. Quantum tunnelling, real-time dynamics and Picard-Lefschetz thimbles

    hep-th 2019-09 conditional novelty 5.0 of 10

    A generalized-thimble evaluation of the closed-time path integral reproduces Schrödinger-equation tunnelling dynamics in a double well, while the classical-statistical approximation deviates.

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