Aspects of scaling and scalability for flow-based sampling of lattice QCD
Aspects of scaling and scalability for flow-based sampling of lattice QCD
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DOI：
10.1140/epja/s10050-023-01154-w
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Ryan Abbott;M. S. Albergo;Aleksandar Botev;D. Boyda;Kyle Cranmer;D. Hackett;A. G. Matthews;S. Raca
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文献类型：
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作者：
Ryan Abbott;M. S. Albergo;Aleksandar Botev;D. Boyda;Kyle Cranmer;D. Hackett;A. G. Matthews;S. Raca
最近机器学习的归一化流在格场理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，这些演示一直在玩具模型的规模，它仍然有待确定，它们是否可以应用于国家的最先进的晶格量子色动力学计算。传统上，使用简单的成本缩放定律来评估格场理论的采样算法的可行性，但正如我们在这项工作中所讨论的那样，它们的实用性对于基于流的方法是有限的。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。
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.