PIRSA:25040058 Scientific Series Other

DOI10.48660/25040058

Series: Special Seminars

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

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

@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}}
}

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

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