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arXiv · 1910.09169

Quantum autoencoders to denoise quantum data

Dmytro Bondarenko·Polina Feldmann
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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 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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Dmytro Bondarenko, Polina Feldmann. 2019-10-21. Quantum autoencoders to denoise quantum data. https://doi.org/10.1103/physrevlett.124.130502

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