Neural networks can learn to utilize correlated auxiliary noise

Abstract We demonstrate that neural networks that process noisy data can learn to exploit, when available, access to auxiliary noise that is correlated with the noise on the data. In effect, the network learns to use the correlated auxiliary noise as an approximate key to decipher its noisy input da...

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Autores principales: Aida Ahmadzadegan, Petar Simidzija, Ming Li, Achim Kempf
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/07ae4c39ed6948fc946153bd50bfec10
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spelling oai:doaj.org-article:07ae4c39ed6948fc946153bd50bfec102021-11-08T10:47:32ZNeural networks can learn to utilize correlated auxiliary noise10.1038/s41598-021-00502-42045-2322https://doaj.org/article/07ae4c39ed6948fc946153bd50bfec102021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-00502-4https://doaj.org/toc/2045-2322Abstract We demonstrate that neural networks that process noisy data can learn to exploit, when available, access to auxiliary noise that is correlated with the noise on the data. In effect, the network learns to use the correlated auxiliary noise as an approximate key to decipher its noisy input data. An example of naturally occurring correlated auxiliary noise is the noise due to decoherence. Our results could, therefore, also be of interest, for example, for machine-learned quantum error correction.Aida AhmadzadeganPetar SimidzijaMing LiAchim KempfNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Aida Ahmadzadegan
Petar Simidzija
Ming Li
Achim Kempf
Neural networks can learn to utilize correlated auxiliary noise
description Abstract We demonstrate that neural networks that process noisy data can learn to exploit, when available, access to auxiliary noise that is correlated with the noise on the data. In effect, the network learns to use the correlated auxiliary noise as an approximate key to decipher its noisy input data. An example of naturally occurring correlated auxiliary noise is the noise due to decoherence. Our results could, therefore, also be of interest, for example, for machine-learned quantum error correction.
format article
author Aida Ahmadzadegan
Petar Simidzija
Ming Li
Achim Kempf
author_facet Aida Ahmadzadegan
Petar Simidzija
Ming Li
Achim Kempf
author_sort Aida Ahmadzadegan
title Neural networks can learn to utilize correlated auxiliary noise
title_short Neural networks can learn to utilize correlated auxiliary noise
title_full Neural networks can learn to utilize correlated auxiliary noise
title_fullStr Neural networks can learn to utilize correlated auxiliary noise
title_full_unstemmed Neural networks can learn to utilize correlated auxiliary noise
title_sort neural networks can learn to utilize correlated auxiliary noise
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/07ae4c39ed6948fc946153bd50bfec10
work_keys_str_mv AT aidaahmadzadegan neuralnetworkscanlearntoutilizecorrelatedauxiliarynoise
AT petarsimidzija neuralnetworkscanlearntoutilizecorrelatedauxiliarynoise
AT mingli neuralnetworkscanlearntoutilizecorrelatedauxiliarynoise
AT achimkempf neuralnetworkscanlearntoutilizecorrelatedauxiliarynoise
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