PIRSA:22110076

Recurrent neural networks for many-body physics

Carrasquilla Álvarez, J.F. (2022). Recurrent neural networks for many-body physics. Perimeter Institute. https://pirsa.org/22110076

Carrasquilla Álvarez, Juan Felipe. Recurrent neural networks for many-body physics. Perimeter Institute, Nov. 14, 2022, https://pirsa.org/22110076 

          @misc{ pirsa_PIRSA:22110076,
            doi = {10.48660/22110076},
            url = {https://pirsa.org/22110076},
            author = {Carrasquilla {\'A}lvarez, Juan Felipe},
            keywords = {Condensed Matter},
            language = {en},
            title = {Recurrent neural networks for many-body physics},
            publisher = {Perimeter Institute},
            year = {2022},
            month = {nov},
            note = {PIRSA:22110076 see, \url{https://pirsa.org}}
          }

Juan Carrasquilla Vector Institute for Artificial Intelligence

DOI 10.48660/22110076
Collection
Talk Type Conference
Subject

Abstract

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.