The 38th International Symposium on Lattice Field Theory

Machine learning phase transitions in a scalable manner

29 Jul 2021, 06:15

15m

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

Speaker

Marina Krstic Marinkovic (ETH Zurich)

Description

Machine learning is becoming an established area of research in lattice field theories, with prominent applications to phase classification, configuration generation, and noise reduction. When moving beyond the toy models, scalable methods to learn phases of matter are needed. In this talk, we compare two possible avenues to speed up the methods for classifying phase transitions in the 2D Ising model: parallelization of kernel methods, and quantum machine learning. Finally, we discuss the extension of the two approaches to the low-dimensional quantum field theories.

Presentation materials

616_R1Al_Marina_Krstic_Marinkovic