Sampling using $SU(N)$ gauge equivariant flows

Boyda, Denis, Kanwar, Gurtej, Racanière, Sébastien, Rezende, Danilo Jimenez, Albergo, Michael S., Cranmer, Kyle, Hackett, Daniel C., Shanahan, Phiala E.

arXiv.org Machine Learning 

In Ref. [11], this approach was demonstrated in the Gauge theories based on SU(N) or U(N) groups describe context of U(1) gauge theory. Here, we develop a class of many aspects of nature. For example, the Standard kernels for SU(N) group elements (and describe a similar Model of nuclear and particle physics is a nonabelian construction for U(N) group elements). We show that if gauge theory with the symmetry group U(1) an invertible transformation acts only on the eigenvalues SU(2) SU(3), candidate theories for physics beyond the of a matrix and is equivariant under permutation of those Standard Model can be defined based on strongly interacting eigenvalues, then it is equivariant under matrix conjugation SU(N) gauge theories [1, 2], SU(N) gauge symmetries and may be used as a kernel. Moreover, by making emerge in various condensed matter systems [3-7], a connection to the maximal torus within the group and and SU(N) and U(N) gauge symmetries feature in the to the Weyl group of the root system, we show that this low energy limit of certain string-theory vacua [8]. In is in fact a universal way to define a kernel for unitary the context of the rapidly-developing area of machinelearning groups.

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