Trainability of Dissipative Perceptron-Based Quantum Neural Networks

Several architectures have been proposed for quantum neural networks (QNNs), with the goal of efficiently performing machine learning tasks on quantum data. Rigorous scaling results are urgently needed for specific QNN constructions to understand which, if any, will be trainable at a large scale. He...

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Veröffentlicht in:	Physical review letters Jg. 128; H. 18; S. 180505
Hauptverfasser:	Sharma, Kunal, Cerezo, M., Cincio, Lukasz, Coles, Patrick J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States American Physical Society (APS) 06.05.2022
Schlagworte:	artificial neural networks CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS machine learning quantum algorithms quantum computation quantum information processing quantum networks
ISSN:	0031-9007, 1079-7114, 1079-7114
Online-Zugang:	Volltext
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