TH Informal Lattice Meeting

Quantum field-theoretic machine learning and the inverse renormalization group

by Dimitrios Bachtis

Europe/Zurich
online (CERN)

online

CERN

Description

In this talk I will discuss the derivation of machine learning algorithms from quantum field theories and the generation of configurations in absence of the critical slowing down effect via the inverse renormalization group. Based on the Hammersley-Clifford theorem, we will establish an equivalence between lattice field theories and the framework of Markov random fields, hence solidifying a mathematically rigorous connection between the research fields of machine learning, probability theory, statistical mechanics, lattice and constructive field theory. Numerical applications will be additionally discussed. Finally, starting from lattice sizes as small as $V=8^{2}$ in the case of the two-dimensional $\phi^{4}$ theory, we will apply a set of inverse renormalization group transformations to obtain lattice sizes up to $V=512^{2}$, without experiencing the critical slowing down effect. We will then utilize these configurations to calculate two critical exponents. I will conclude by discussing potential future research directions.

Videoconference
Lattice seminars
Zoom Meeting ID
68098777127
Host
Elena Gianolio
Alternative hosts
Ciaran Hughes, Andreas Juttner, Dorota Maria Grabowska, Mattia Dallabrida
Passcode
11900596
Useful links
Join via phone
Zoom URL