Practical applications of machine-learned flows on gauge fields
Ryan Abbott Michael S. Albergo Denis Boyda Daniel C. Hackett Gurtej Kanwar Fernando Romero-López Phiala E. Shanahan Julian M. Urban
Normalizing flows are machine-learned maps between different lattice theories
which can be used as components in exact sampling and inference schemes.
Ongoing work yields increasingly expressive flows on gauge fields, but it
remains an open question how flows can improve lattice QCD at state-of-the-art
scales. We discuss and demonstrate two applications of flows in replica
exchange (parallel tempering) sampling, aimed at improving topological mixing,
which are viable with iterative improvements upon presently available flows.