Interpreting machine learning of topological quantum phase transitions
There has been growing excitement over the possibility of employing artificial neural networks (ANNs) to gain new theoretical insight into the physics of quantum many-body problems. “Interpretability” remains a concern: can we understand the basis for the ANN's decision-making criteria in order...
Main Authors: | Yi Zhang, Paul Ginsparg, Eun-Ah Kim |
---|---|
Formato: | Artigo |
Idioma: | English |
Publicado: |
American Physical Society
2020-06-01
|
Series: | Physical Review Research |
Acceso en liña: | http://doi.org/10.1103/PhysRevResearch.2.023283 |
Títulos similares
-
Quantum Phase Transition and Entanglement in Topological Quantum Wires
por: Jaeyoon Cho, et al.
Publicado: (2017-06-01) -
Topological phase transitions in glassy quantum matter
por: Isac Sahlberg, et al.
Publicado: (2020-01-01) -
Quantum phase transition of fracton topological orders
por: Ting Fung Jeffrey Poon, et al.
Publicado: (2021-11-01) -
Quantum topological phase transitions in skyrmion crystals
por: Kristian Mæland, et al.
Publicado: (2022-08-01) -
Quasiparticles as detector of topological quantum phase transitions
por: Sourav Manna, et al.
Publicado: (2020-12-01)