User: Guest  Login
Title:

Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks

Document type:
Zeitschriftenaufsatz
Author(s):
Liu, Yu-Jie; Smith, Adam; Knap, Michael; Pollmann, Frank
Abstract:
Quantum convolutional neural networks (QCNNs) have been introduced as classifiers for gapped quantum phases of matter. Here, we propose a model-independent protocol for training QCNNs to discover order parameters that are unchanged under phase-preserving perturbations. We initiate the training sequence with the fixed-point wave functions of the quantum phase and add translation-invariant noise that respects the symmetries of the system to mask the fixed-point structure on short length scales. We...     »
Journal title:
Physical Review Letters 2023-06
Year:
2023
Journal volume:
130
Journal issue:
22
Fulltext / DOI:
doi:10.1103/physrevlett.130.220603
Publisher:
American Physical Society (APS)
E-ISSN:
0031-90071079-7114
Date of publication:
02.06.2023
 BibTeX