Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386074054> ?p ?o ?g. }
- W4386074054 abstract "Recurrent neural networks (RNNs), originally developed for natural language processing, hold great promise for accurately describing strongly correlated quantum many-body systems. Here, we employ two-dimensional RNNs to investigate two prototypical quantum many-body Hamiltonians exhibiting topological order. Specifically, we 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. We also find that RNNs favor coherent superpositions of minimally entangled states over minimally entangled states themselves. Overall, our findings demonstrate that RNN wave functions constitute a powerful tool to study phases of matter beyond Landau's symmetry-breaking paradigm." @default.
- W4386074054 created "2023-08-23" @default.
- W4386074054 creator A5022164406 @default.
- W4386074054 creator A5053039276 @default.
- W4386074054 creator A5091069084 @default.
- W4386074054 date "2023-08-22" @default.
- W4386074054 modified "2023-10-10" @default.
- W4386074054 title "Investigating topological order using recurrent neural networks" @default.
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- W4386074054 doi "https://doi.org/10.1103/physrevb.108.075152" @default.
- W4386074054 hasPublicationYear "2023" @default.
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