Finding steady-state solutions to the Schrödinger equation involves solving an exponentially large eigenvalue problem. In this talk, I show how neural networks can be used to parameterize functions representing the eigenvector associated with the lowest eigenvalue. For interacting quantum systems, this solution is the ground state wavefunction and describes the behavior at zero temperature. I will then discuss neural network simulations have been used to address open questions in quantum many-body physics. I will focus on Hubbard-like models and show that neural networks can be used to understand the interplay between parent insulating phases and the superconductors that emerge upon doping.

Refreshments will be served preceding the talk, starting at 2:45.