Investigating Topological Order with Recurrent Neural Network Wave Functions

          @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}}
          }
          

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.