The 38th International Symposium on Lattice Field Theory

LeapFrogLayers: A Trainable Framework for Effective Topological Sampling

29 Jul 2021, 14:45

15m

Oral presentation Algorithms (including Machine Learning, Quantum Computing, Tensor Networks) Algorithms (including Machine Learning, Quantum Computing, Tensor Networks)

Speaker

Sam Foreman (Argonne National Laboratory)

Description

We introduce LeapfrogLayers, an invertible neural network architecture that can be trained to efficiently sample the topology of a 2D U(1) lattice gauge theory. We show an improvement in the integrated autocorrelation time of the topological charge when compared with traditional HMC, and propose methods for scaling our model to larger lattice volumes.

Presentation materials

579_R3Al_Sam_Foreman

l2hmc_lattice2021.pdf