Learning a local symmetry with neural networks

Decelle, A. ; Martin-Mayor, V. ; Seoane, B.

Resumen: We explore the capacity of neural networks to detect a symmetry with complex local and non-local patterns: the gauge symmetry Z2. 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.

Idioma: Inglés

DOI: 10.1103/PhysRevE.100.050102

Año: 2019

Publicado en: PHYSICAL REVIEW E 100, 5 (2019), 050102(R) [6 pp.]
ISSN: 2470-0045

Originalmente disponible en: Texto completo de la revista

Factor impacto JCR: 2.296 (2019)

Categ. JCR: PHYSICS, MATHEMATICAL rank: 9 / 55 = 0.164 (2019) - Q1 - T1
Categ. JCR: PHYSICS, FLUIDS & PLASMAS rank: 13 / 34 = 0.382 (2019) - Q2 - T2

Factor impacto SCIMAGO: 0.958 - Condensed Matter Physics (Q1) - Statistical and Nonlinear Physics (Q1) - Statistics and Probability (Q2)

Tipo y forma: Article (Published version)