https://doi.org/10.1140/epja/s10050-023-01154-w
Special Article - New Tools and Techniques
Aspects of scaling and scalability for flow-based sampling of lattice QCD
1
Center for Theoretical Physics, Massachusetts Institute of Technology, 02139, Cambridge, MA, USA
2
The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Cambridge, USA
3
Center for Cosmology and Particle Physics, New York University, 10003, New York, NY, USA
4
Argonne Leadership Computing Facility, Argonne National Laboratory, 60439, Lemont, IL, USA
5
Physics Department, University of Wisconsin-Madison, 53706, Madison, WI, USA
6
DeepMind, London, UK
Received:
28
November
2022
Accepted:
4
October
2023
Published online:
4
November
2023
Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing. However, these demonstrations have been at the scale of toy models, and it remains to be determined whether they can be applied to state-of-the-art lattice quantum chromodynamics calculations. Assessing the viability of sampling algorithms for lattice field theory at scale has traditionally been accomplished using simple cost scaling laws, but as we discuss in this work, their utility is limited for flow-based approaches. We conclude that flow-based approaches to sampling are better thought of as a broad family of algorithms with different scaling properties, and that scalability must be assessed experimentally.
© The Author(s) 2023
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