PIRSA:25040058

Video URL

https://pirsa.org/25040058

Recurrent neural networks for Rydberg atom arrays

Hibat Allah, M. (2025). Recurrent neural networks for Rydberg atom arrays. Perimeter Institute for Theoretical Physics. https://pirsa.org/25040058

Hibat Allah, Mohamed. Recurrent neural networks for Rydberg atom arrays. Perimeter Institute for Theoretical Physics, May. 02, 2025, https://pirsa.org/25040058 

          @misc{ scivideos_PIRSA:25040058,
            doi = {10.48660/25040058},
            url = {https://pirsa.org/25040058},
            author = {Hibat Allah, Mohamed},
            keywords = {Other Physics},
            language = {en},
            title = {Recurrent neural networks for Rydberg atom arrays},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2025},
            month = {may},
            note = {PIRSA:25040058 see, \url{https://scivideos.org/index.php/pirsa/25040058}}
          }

Mohamed Hibat Allah University of Waterloo

Talk numberPIRSA:25040058
DOI10.48660/25040058
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

Rydberg atom arrays have emerged as powerful quantum simulators, capable of preparing strongly correlated phases of matter that are potentially challenging to access with classical computational methods. A major focus has been on realizing these arrays on frustrated geometries, aiming to stabilize exotic many-body states like spin liquids. In this talk, I will show how two-dimensional recurrent neural network (RNN) wave functions can be used to study the ground states of Rydberg atom arrays on the kagome lattice. For Hamiltonians previously investigated in this geometry, I will demonstrate that the RNN finds no evidence for exotic spin liquid phases or emergent glassiness. In particular, I will argue that signals of glassy behavior, such as a nonzero Edwards-Anderson order parameter seen in quantum Monte Carlo (QMC) studies, may arise from artifacts related to long autocorrelation times. These results highlight the potential of language model-inspired approaches, like RNNs, for advancing the study of frustrated quantum systems and Rydberg atom physics more broadly.

arXiv paper: https://arxiv.org/pdf/2405.20384