Year: 2023
Type: article
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.
Language: English
Topic: Quantum many-body systems
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: Canadian Institute for Advanced Research, Innovation, Science and Economic Development Canada, Compute Canada, Government of Canada, Mitacs, Institut Périmètre de physique théorique, Ministry of Colleges and Universities, Shared Hierarchical Academic Research Computing Network, Natural Sciences and Engineering Research Council of Canada
OpenAlex ID: W4386074054