PIRSA:23060039 Conference Condensed Matter

DOI10.48660/23060039

Conference/School: Machine Learning for Quantum Many-Body Systems

Hibat Allah, M. (2023). Investigating Topological Order with Recurrent Neural Network Wave Functions. Perimeter Institute. https://pirsa.org/23060039

Hibat Allah, Mohamed. Investigating Topological Order with Recurrent Neural Network Wave Functions. Perimeter Institute, Jun. 14, 2023, https://pirsa.org/23060039 

@misc{ pirsa_PIRSA:23060039,
  doi = {10.48660/23060039},
  url = {https://pirsa.org/23060039},
  author = {Hibat Allah, Mohamed},
  keywords = {Condensed Matter},
  language = {en},
  title = {Investigating Topological Order with Recurrent Neural Network Wave Functions},
  publisher = {Perimeter Institute},
  year = {2023},
  month = {jun},
  note = {PIRSA:23060039 see, \url{https://pirsa.org}}
}

Abstract

Recurrent neural networks (RNNs), originally developed for natural language processing, hold great promise for accurately describing strongly correlated quantum many-body systems. In this talk, we will illustrate how to use 2D RNNs to investigate two prototypical quantum many-body Hamiltonians exhibiting topological order. Specifically, we will demonstrate that RNN wave functions can effectively capture the topological order of the toric code and a Bose-Hubbard spin liquid on the kagome lattice by estimating their topological entanglement entropies. Overall, we will show that RNN wave functions constitute a powerful tool for studying phases of matter beyond Landau's symmetry-breaking paradigm.

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