We describe a procedure for alleviating the fermion sign problem in which phase fluctuations are explicitly subtracted from the Boltzmann factor. Several ans\"atze for fluctuations are designed and compared. In the absence of a sufficiently high-quality ansatz, a neural network can be trained to parameterize the fluctuations. Demonstrating on the staggered Thirring model in 1+1 dimensions, we examine the performance of this method as deeper neural networks are used, and in conjunction with the well-studied contour deformation methods.
@article{arxiv.2212.14606,
title = {Deep Learning of Fermion Sign Fluctuations},
author = {Scott Lawrence and Yukari Yamauchi},
journal= {arXiv preprint arXiv:2212.14606},
year = {2023}
}