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Path integral contour deformations for observables in $SU(N)$ gauge theory
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abstract
Path integral contour deformations have been shown to mitigate sign and signal-to-noise problems associated with phase fluctuations in lattice field theories. We define a family of contour deformations applicable to $SU(N)$ lattice gauge theory that can reduce sign and signal-to-noise problems associated with complex actions and complex observables. For observables, these contours can be used to define deformed observables with identical expectation value but different variance. As a proof-of-principle, we apply machine learning techniques to optimize the deformed observables associated with Wilson loops in two dimensional $SU(2)$ and $SU(3)$ gauge theory. We study loops consisting of up to 64 plaquettes and achieve variance reduction of up to 4 orders of magnitude.
Forward citations
Cited by 2 Pith papers
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Path optimization method for the sign problem caused by fermion determinant
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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