The 38th International Symposium on Lattice Field Theory

A universal neural network for learning phases and criticalities

28 Jul 2021, 22:15

15m

Oral presentation Algorithms (including Machine Learning, Quantum Computing, Tensor Networks) Algorithms (including Machine Learning, Quantum Computing, Tensor Networks)

Speaker

Prof. Fu-Jiun Jiang (National Taiwan Normal University)

Description

A universal supervised neural network (NN) relevant to study phase transitions
is constructed. The validity of the built NN is examined by applying it to
calculate the criticalities of several three-dimensional (3D) and two-dimensional (2D) models including the 3D classical $O(3)$ model, the 3D 5-state ferromagnetic Potts model, a 3D dimerized quantum antiferromagnetic Heisenberg model as well as the 2D $XY$ model. Particularly, while the considered NN is only trained once on a one-dimensional (1D) lattice with 120 sites, it has successfully determined the related critical points of the studied 3D and 2D systems with high accuracy. Moreover, the employed configurations for the prediction are constructed on a 1D lattice of 120 sites as well. As a result, our calculations are ultimately efficient in computation and the applications of the built NN is extremely broaden.

Presentation materials

330_W4Al_Fu-Jiun_Jiang

universal.pdf