90 citations · 251 across the 11 of their papers we have counts for
5 papers · 1 filter
Quantum field theories, Markov random fields and machine learning
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
The transition to Euclidean space and the discretization of quantum field theories on spatial or space-time lattices opens up the opportunity to investigate probabilistic machine l…
Interpreting machine learning functions as physical observables
Gert Aarts, Dimitrios Bachtis, Biagio Lucini
We propose to interpret machine learning functions as physical observables, opening up the possibility to apply "standard" statistical-mechanical methods to outputs from neural net…
Machine learning with quantum field theories
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
The precise equivalence between discretized Euclidean field theories and a certain class of probabilistic graphical models, namely the mathematical framework of Markov random field…
Inverse Renormalization Group in Quantum Field Theory
Dimitrios Bachtis, Gert Aarts, Francesco Di Renzo +1
We propose inverse renormalization group transformations within the context of quantum field theory that produce the appropriate critical fixed point structure, give rise to invers…
Quantum field-theoretic machine learning
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
We derive machine learning algorithms from discretized Euclidean field theories, making inference and learning possible within dynamics described by quantum field theory. Specifica…