LeapfrogLayers: A Trainable Framework for Effective Topological Sampling
Sam Foreman, Xiao-Yong Jin, James C. Osborn Β· Proceedings of The 38th International Symposium on Lattice Field Theory β PoS(LATTICE2021) Β· 2022
We introduce LeapfrogLayers, an invertible neural network architecture that can be trained to efficiently sample the topology of a 2D π(1) lattice gauge theory. We show an improvement in the integrated autocorrelation time of the topological charge when compared with traditional HMC, and look at how different quantities transform under our model. Our implementation is open source, and is publicly available on GitHub at https://www.github.com/saforem2/l2hmc-qcd.