Recurrent neural networks for many-body physics

I will discuss our recent work on the use of autoregressive neural networks for many-body physics. In particular, I will discuss two approaches to represent quantum states using these models and their applications to the reconstruction of quantum states, the simulation of real-time dynamics of open quantum systems, and the approximation of ground states of many-body systems displaying long-range order, frustration, and topological order. Finally, I will discuss how annealing in these systems can be used for combinatorial optimization where we observe solutions to problems that are orders of magnitude more accurate than simulated and simulated quantum annealing.