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 unsupervised...

Full description

Saved in:
Bibliographic Details
Published in:Physical review letters Vol. 124; no. 13; p. 130502
Main Authors: Bondarenko, Dmytro, Feldmann, Polina
Format: Journal Article
Language:English
Published: United States American Physical Society 03.04.2020
Subjects:
Computer simulation
Entangled states
Neural networks
Noise reduction
Quantum computing
ISSN:0031-9007, 1079-7114, 1079-7114
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
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, W, Dicke, and cluster states subject to spin-flip errors and random unitary noise. Various emergent quantum technologies could benefit from the proposed unsupervised quantum neural networks.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0031-9007
1079-7114
1079-7114
DOI:10.1103/PhysRevLett.124.130502