Recurrent neural networks for Rydberg atom arrays
APA
Hibat Allah, M. (2025). Recurrent neural networks for Rydberg atom arrays. Perimeter Institute. https://pirsa.org/25040058
MLA
Hibat Allah, Mohamed. Recurrent neural networks for Rydberg atom arrays. Perimeter Institute, May. 02, 2025, https://pirsa.org/25040058
BibTex
@misc{ pirsa_PIRSA:25040058, doi = {10.48660/25040058}, url = {https://pirsa.org/25040058}, author = {Hibat Allah, Mohamed}, keywords = {Other}, language = {en}, title = {Recurrent neural networks for Rydberg atom arrays}, publisher = {Perimeter Institute}, year = {2025}, month = {may}, note = {PIRSA:25040058 see, \url{https://pirsa.org}} }
Mohamed Hibat Allah University of Waterloo
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