arXiv 2211.075412022-11-14hep-latcond-mat.stat-mechcs.LG

格点量子色动力学基于流采样的标度和可扩展性

Aspects of scaling and scalability for flow-based sampling of lattice QCD

Massachusetts Institute of Technology（麻省理工学院）；New York University（纽约大学）；DeepMind（深度思维）；Argonne National Laboratory（阿贡国家实验室）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）

Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, D… 展开作者

Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

备注：: 22 pages, 8 figures

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摘要：

最近机器学习的归一化流在格点场论中的采样应用表明，这种方法可能能够减轻临界减速和拓扑冻结。然而，这些演示只是玩具模型的规模，它们是否可以应用于最先进的晶格量子色动力学计算还有待确定。评估规模的格场论的采样算法的可行性，传统上已经完成了使用简单的成本缩放法，但我们在这项工作中讨论，他们的效用是有限的基于流的方法。我们的结论是，基于流的采样方法更好地认为是一个广泛的家庭与不同的缩放特性的算法，可扩展性必须进行实验评估。

英文摘要：

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.

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