Machine learning phases of an Abelian gauge theory

The phase transition of the two-dimensional U(1) quantum link model on the triangular lattice is investigated by employing a supervised neural network (NN) consisting of only one input layer, one hidden layer of two neurons, and one output layer. No information on the studied model is used when the...

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Published inProgress of theoretical and experimental physics Vol. 2023; no. 7
Main Authors Peng, Jhao-Hong, Tseng, Yuan-Heng, Jiang, Fu-Jiun
Format Journal Article
LanguageEnglish
Published Oxford Oxford University Press 01.07.2023
Subjects
Machine learning
Neural networks
Phase transitions
Physics
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ISSN2050-3911
2050-3911
DOI10.1093/ptep/ptad096

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Summary:The phase transition of the two-dimensional U(1) quantum link model on the triangular lattice is investigated by employing a supervised neural network (NN) consisting of only one input layer, one hidden layer of two neurons, and one output layer. No information on the studied model is used when the NN training is conducted. Instead, two artificially made configurations are considered as the training set. Interestingly, the obtained NN not only estimates the critical point accurately but also uncovers the physics correctly. The results presented here imply that a supervised NN, which has a very simple architecture and is trained without any input from the investigated model, can identify the targeted phase structure with high precision.
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ISSN:2050-3911
2050-3911
DOI:10.1093/ptep/ptad096