%0 Journal Article
%A Decelle, A.
%A Martín Mayor, Víctor
%A Seoane, B.
%T Learning a local symmetry with neural networks
%D 2019
%@ 2470-0045
%U https://hdl.handle.net/20.500.14352/5980
%X We explore the capacity of neural networks to detect a symmetry with complex local and non-local patterns: the gauge symmetry Z(2). This symmetry is present in physical problems from topological transitions to quantum chromodynamics, and controls the computational hardness of instances of spin-glasses. Here, we show how to design a neural network, and a dataset, able to learn this symmetry and to find compressed latent representations of the gauge orbits. Our method pays special attention to system-wrapping loops, the so-called Polyakov loops, known to be particularly relevant for computational complexity.
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