Machine learning phases of an Abelian gauge theory

Jhao Hong Peng, Yuan Heng Tseng, Fu Jiun Jiang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Article number073A03
JournalProgress of Theoretical and Experimental Physics
Issue number7
Publication statusPublished - 2023 Jul 1

ASJC Scopus subject areas

  • General Physics and Astronomy


Dive into the research topics of 'Machine learning phases of an Abelian gauge theory'. Together they form a unique fingerprint.

Cite this