Machine-Learning-Based Sampling in Lattice Field Theory and Quantum Chemistry

Abstract

The application of generative machine-learning models for sampling Boltzmann distributions has first been proposed in the context of quantum chemistry, and has later been successfully applied in lattice quantum field theory. Both research fields have quickly and independently progressed during the last years. With this workshop, we aim to bring together researchers from both communities, in order to foster exchange on common challenges and opportunities.