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Machine Learning Many-Body Localization: Search for the Elusive Nonergodic Metal
Yi-Ting Hsu1, Xiao Li1, Dong-Ling Deng1,2
1Condensed Matter Theory Center and Joint Quantum Institute, University of Maryland, College Park, Maryland 20742, USA.
Physical Review Letters
|January 5, 2019
Summary
Researchers used a neural network to find evidence of a nonergodic metallic phase in quantum systems. This method helps identify novel phases and locate phase boundaries in complex systems.
Area of Science:
- Condensed matter physics
- Quantum mechanics
- Statistical mechanics
Background:
- Ergodicity breaking in isolated quantum systems is not fully understood.
- The existence of a nonergodic metallic phase in 1D quasiperiodic systems is debated.
Purpose of the Study:
- Investigate the existence of a nonergodic metallic phase in a prototype model.
- Utilize many-body entanglement spectra as a diagnostic tool.
- Apply a neural-network-based approach to analyze quantum systems.
Main Methods:
- Developed a novel neural-network-based approach.
- Employed many-body entanglement spectra as the sole diagnostic.
- Analyzed a prototype model with a one-dimensional quasiperiodic potential.
Main Results:
- Identified a nonergodic metallic phase with high confidence.
- Located this phase in the midspectrum.
- Found the phase at an intermediate quasiperiodic potential strength.
Conclusions:
- The neural-network approach successfully identified a novel nonergodic metallic phase.
- Supervised machine learning can locate phase boundaries and examine novel phases.
- This method provides a definitive way to study complex quantum phenomena.
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