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Control variates for lattice field theory

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arxiv 2307.14950 v1 pith:UWC62RSA submitted 2023-07-27 hep-lat

classification hep-lat
keywords fieldlatticecontrolmethodsignal-to-noisetheoryvariatesconstructed
verification ladder T0 review T1 audit T2 compute T3 formal
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In most lattice field theories, correlators are plagued by a signal-to-noise problem of exponential difficulty in the time separation. We propose a method for improving the signal-to-noise ratio, in which control variates are systematically constructed from lattice Schwinger-Dyson relations. The method is demonstrated on various two-dimensional lattices in scalar field theory, and a strategy for scaling to larger systems is explored.

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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. Kolmogorov-Arnold Wavefunctions

    nucl-th 2025-06 conditional novelty 6.0 of 10

    KAN-based trial wavefunctions reach about 1 percent ground-state energy accuracy for one-dimensional trapped bosons at roughly 10 times lower cost per training step than MLP-based wavefunctions, aided by a transferabl...

  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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