272 citations · 3.9k across the 110 of their papers we have counts for
17 papers · 1 filter
Aspects of scaling and scalability for flow-based sampling of lattice QCD
Ryan Abbott, Michael S. Albergo, Aleksandar Botev +10
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…
The Future of High Energy Physics Software and Computing
V. Daniel Elvira, Steven Gottlieb, Oliver Gutsche +25
Software and Computing (S&C) are essential to all High Energy Physics (HEP) experiments and many theoretical studies. The size and complexity of S&C are now commensurate with that…
Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning
Phiala Shanahan, Kazuhiro Terao, Daniel Whiteson
The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of…
Strategies for quantum-optimized construction of interpolating operators in classical simulations of lattice quantum field theories
A. Avkhadiev, P. E. Shanahan, R. D. Young
It has recently been argued that noisy intermediate-scale quantum computers may be used to optimize interpolating operator constructions for lattice quantum field theory (LQFT) cal…
Sampling QCD field configurations with gauge-equivariant flow models
Ryan Abbott, Michael S. Albergo, Aleksandar Botev +11
Machine learning methods based on normalizing flows have been shown to address important challenges, such as critical slowing-down and topological freezing, in the sampling of gaug…
Neural-network preconditioners for solving the Dirac equation in lattice gauge theory
Salvatore Calì, Daniel C. Hackett, Yin Lin +2
This work develops neural-network--based preconditioners to accelerate solution of the Wilson-Dirac normal equation in lattice quantum field theories. The approach is implemented f…