Improved topological sampling for lattige gauge theories

29 Jul 2021, 13:15
15m
Oral presentation Algorithms (including Machine Learning, Quantum Computing, Tensor Networks) Algorithms (including Machine Learning, Quantum Computing, Tensor Networks)

Speaker

David Albandea Jordán (IFIC - University of Valencia)

Description

Standard sampling algorithms for lattice QCD suffer from topology freezing (or critical slowing down) when approaching the continuum limit, thus leading to poor sampling of the distinct topological sectors. I will present a modified Hamiltonian Monte Carlo (HMC) algorithm that triggers topological sector jumps during the assembly of Markov chain of lattice configurations. We study its performance in the 2D Schwinger model and compare it to alternative methods, such as fixing topology or master field. We then discuss the difficulties of the algorithm in a SU(2) gauge model in 4D.

Primary authors

David Albandea Jordán (IFIC - University of Valencia) Fernando Romero-López Pilar Hernandez (University of Valencia) Alberto Ramos Martinez (Univ. of Valencia and CSIC (ES))

Presentation materials