2018/05/31 by Yi-Ting Hsu, Yi‐Ting Hsu, Xiao Li +2 · 1 citation
Physics and Astronomy · #Artificial intelligence #Artificial neural network #Computer science #Condensed matter physics #Ergodicity #Machine learning #Phase (matter) #Physics #Quantum #Quantum and electron transport phenomena #Quantum entanglement #Quantum many-body systems #Quantum mechanics #Quasiperiodic function #Statistical physics #Theoretical physics #Topological Materials and Phenomena #cond-mat.dis-nn
paper · pdf · doi:10.1103/physrevlett.121.245701
published as Phys. Rev. Lett. 121, 245701 (2018) · 5 pages, 3 figures
openalex publication_date 2018/12/10 · arxiv created 2018/12/11 · arxiv updated 2018/12/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The breaking of ergodicity in isolated quantum systems with a single-particle mobility edge is an intriguing subject that has not yet been fully understood. In particular, whether a nonergodic but metallic phase exists or not in the presence of a one-dimensional quasiperiodic potential is currently under active debate. In this Letter, we develop a neural-network-based approach to investigate the existence of this nonergodic metallic phase in a prototype model using many-body entanglement spectra as the sole diagnostic. We find that such a method identifies with high confidence the existence of a nonergodic metallic phase in the midspectrum at an intermediate quasiperiodic potential strength. Our neural-network-based approach shows how supervised machine learning can be applied not only in locating phase boundaries but also in providing a way to definitively examine the existence or not of a novel phase.