Hibat Allah, M. (2023). Investigating Topological Order with Recurrent Neural Network Wave Functions. Perimeter Institute for Theoretical Physics. https://pirsa.org/23060039
MLA
Hibat Allah, Mohamed. Investigating Topological Order with Recurrent Neural Network Wave Functions. Perimeter Institute for Theoretical Physics, Jun. 14, 2023, https://pirsa.org/23060039
BibTex
@misc{ scivideos_PIRSA:23060039,
doi = {10.48660/23060039},
url = {https://pirsa.org/23060039},
author = {Hibat Allah, Mohamed},
keywords = {Quantum Matter},
language = {en},
title = {Investigating Topological Order with Recurrent Neural Network Wave Functions},
publisher = {Perimeter Institute for Theoretical Physics},
year = {2023},
month = {jun},
note = {PIRSA:23060039 see, \url{https://scivideos.org/index.php/pirsa/23060039}}
}
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.