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Felipe Fernandes Fanchini

Machine learning applied to physical problems : a brief review focusing on quantum phase transitions

mardi 10 juillet 2018, 10h30

Salle de conférences de l’observatoire

Felipe Fernandes FANCHINI

Department of Physics, São Paulo State University

Résumé :
Machine Learning (ML) strategies have been shown to be extremely efficient to find patterns in huge amount of data. That makes it important in a broad area of science, including economy, biology, physics and many other fields of knowledge. Recently, the use of ML techniques to solve physical problems has grown significantly [1-5]. Here, we will present a brief review on the advancement in the use of ML strategies when applied to physical problems. We will focus mainly on quantum phase transitions [6, 7] and we will present our new findings concerning the one-dimensional spin-1/2 Ising model with nearest and next-nearest-neighbour interactions in the presence of a transverse magnetic field.


[1] G. Torlai et al., Nature Physics 14, 447 (2018).
[2] A. A. Melnikov et al., arXiv:1706.00868.
[3] X.-Y. Dong, F. Pollmann, and X.-F., Zhang, arxiv:1806.00829.
[4] K. Ch’ng, J. Carrasquilla, R. G. Melko, and E. Khatami, Phys. Rev. X 7, 031038 (2017).
[5] J. Carrasquilla and R. G. Melko, Nature Physics 13, 431 (2017).
[6] A. L. Malvezzi et al. Phys. Rev. B 93, 184428 (2016).
[7] G. Karpat, B. Çakmak, and F. F. Fanchini, Phys. Rev. B 90, 104431 (2014).