Quantum autoencoders to denoise quantum data

Entangled states are an important resource for quantum computation, communication, metrology, and the simulation of many-body systems. However, noise limits the experimental preparation of such states. Classical data can be efficiently denoised by autoencoders---neural networks trained in unsupervis...

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Bibliographic Details

Published in:	arXiv.org
Main Authors:	Bondarenko, Dmytro, Feldmann, Polina
Format:	Paper
Language:	English
Published:	Ithaca Cornell University Library, arXiv.org 21.10.2019
Subjects:	Computer simulation Entangled states Neural networks Noise reduction Quantum computing
ISSN:	2331-8422
Online Access:	Get full text
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