The 38th International Symposium on Lattice Field Theory

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.

Presentation materials

65_R3Al_David_Albandea_Jordán

Albandea_Lattice-Conference-2021.pdf