Machine learning for simulations of gauge theories

As a statistical tool, external pagelattice QCDcall_made has been successfully used to determine many parameters of the Standard Model, including quark masses and the strong interaction coupling constant, see external page[2]call_made, external page[3]call_made and external page[4]call_made. Despite the success of lattice QCD, limitations of the current statistical algorithms still exist, leading to problems such as critical slowing down of the simulations external page[5]call_made. New approaches are required to circumvent these limitations. Machine learning algorithms provide a viable approach to address some of these difficulties. We explore deep generative models, such as normalizing flows external page[6]call_made and external page[7]call_made, to develop alternatives to standard algorithms for generating lattice field configurations external page[8]call_made.