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Quantum Autoencoders to Denoise Quantum Data

Dmytro Bondarenko and Polina Feldmann
Phys. Rev. Lett. 124, 130502 – Published 31 March 2020
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Abstract

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.

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  • Received 12 November 2019
  • Accepted 24 February 2020

DOI:https://doi.org/10.1103/PhysRevLett.124.130502

© 2020 American Physical Society

Physics Subject Headings (PhySH)

NetworksQuantum Information, Science & Technology

Authors & Affiliations

Dmytro Bondarenko* and Polina Feldmann

  • Institut für Theoretische Physik, Leibniz Universität Hannover, Appelstr. 2, DE-30167 Hannover, Germany

  • *dimbond@live.com
  • polina.feldmann@itp.uni-hannover.de

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Issue

Vol. 124, Iss. 13 — 3 April 2020

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