Path optimization with machine learning reproduces analytic results in the 1D lattice Thirring model, and dropping the Jacobian from the learning step still works.
Exponential reduction of the sign problem at finite density in the 2+1D XY model via contour deformations
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
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.
citation-role summary
background 1
citation-polarity summary
fields
hep-lat 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
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.