Learning of error statistics for the detection of quantum phases

authored by

Amit Jamadagni, Javad Kazemi, Hendrik Weimer

Abstract

We present a binary classifier to detect gapped quantum phases based on neural networks. By considering the errors on top of a suitable reference state describing the gapped phase, we show that a neural network trained on the errors can capture the correlation between the errors and can be used to detect the phase boundaries of the gapped quantum phase. We demonstrate the application of the method for matrix product state calculations for different quantum phases exhibiting local symmetry-breaking order, symmetry-protected topological order, and intrinsic topological order.

Organisation(s)

Institute of Theoretical Physics
QuantumFrontiers
CRC 1227 Designed Quantum States of Matter (DQ-mat)

External Organisation(s)

Paul Scherrer Institut (PSI)
Technische Universität Berlin

Type

Article

Journal

Physical Review B

Volume

107

ISSN

2469-9950

Publication date

22.02.2023

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials, Condensed Matter Physics

Electronic version(s)

https://doi.org/10.48550/arXiv.2205.12966 (Access: Open)
https://doi.org/10.1103/PhysRevB.107.075146 (Access: Closed)