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Applications of Machine Learning to Lattice Quantum Field Theory

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arxiv 2202.05838 v1 pith:R24PDCQP submitted 2022-02-10 hep-lat cs.LGhep-ph

Denis Boyda , Salvatore Calì , Sam Foreman , Lena Funcke , Daniel C. Hackett , Yin Lin , Gert Aarts , Andrei Alexandru
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Xiao-Yong Jin Biagio Lucini Phiala E. Shanahan
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classification hep-latcs.LGhep-ph
keywords fieldlatticelearningmachinequantumtheorypotentialapplications
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There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies. In this white paper for the Snowmass community planning process, we discuss the unique requirements of machine learning for lattice quantum field theory research and outline what is needed to enable exploration and deployment of this approach in the future.

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Cited by 7 Pith papers

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