Neural Networks and Quantum MechanicsConfirmed

by Christian Ferko (Northeastern University and IAIFI)

Friday May 23, 2025, 2:30 p.m. → 4:00 p.m. America/Toronto

PI/3-394 - Skyroom (Perimeter Institute for Theoretical Physics)

In this talk, I will survey recent developments about the connection between neural networks and models of quantum mechanics and quantum field theory. Previous work has shown that the neural network - Gaussian process correspondence can be interpreted as the statement that large-width neural networks share some properties with free, or weakly interacting, quantum field theories (QFTs). Here I will focus on 1d QFTs, or models of quantum mechanics, where one has greater theoretical control. For instance, under mild assumptions, one can prove that any model of a quantum particle admits a representation as a neural network. Cherished features of quantum mechanics, such as uncertainty relations, emerge from specific architectural choices that are made to satisfy the axioms of quantum theory. Based on 2504.05462 with Jim Halverson.