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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Summary:	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 unsupervised manner. We develop a novel quantum autoencoder that successfully denoises Greenberger-Horne-Zeilinger states subject to spin-flip errors and random unitary noise. Various emergent quantum technologies could benefit from the proposed unsupervised quantum neural networks.
Bibliography:	SourceType-Working Papers-1 ObjectType-Working Paper/Pre-Print-1 content type line 50
ISSN:	2331-8422
DOI:	10.48550/arxiv.1910.09169