Machine Learning Topological Phases with a Solid-State Quantum Simulator.
Phys Rev Lett
; 122(21): 210503, 2019 May 31.
Article
in En
| MEDLINE
| ID: mdl-31283312
ABSTRACT
We report an experimental demonstration of a machine learning approach to identify exotic topological phases, with a focus on the three-dimensional chiral topological insulators. We show that the convolutional neural networks-a class of deep feed-forward artificial neural networks with widespread applications in machine learning-can be trained to successfully identify different topological phases protected by chiral symmetry from experimental raw data generated with a solid-state quantum simulator. Our results explicitly showcase the exceptional power of machine learning in the experimental detection of topological phases, which paves a way to study rich topological phenomena with the machine learning toolbox.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Phys Rev Lett
Year:
2019
Document type:
Article