Practical applications of machine-learned flows on gauge fields
This work addresses a specific bottleneck in high-energy physics simulations, offering incremental improvements for researchers in lattice QCD.
The paper tackles the challenge of improving topological mixing in lattice QCD simulations by applying machine-learned normalizing flows in replica exchange sampling, demonstrating viable applications with iterative enhancements to existing flows.
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.