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Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Zeitschriftentitel:
Physical Review Letters 2023-06
Jahr:
2023
Band / Volume:
130
Heft / Issue:
22
Volltext / DOI:
doi:10.1103/physrevlett.130.220603
Verlag / Institution:
American Physical Society (APS)
E-ISSN:
0031-90071079-7114
Publikationsdatum:
02.06.2023
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