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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Aida Ahmadzadegan, Petar Simidzija, Ming Li, Achim Kempf
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/07ae4c39ed6948fc946153bd50bfec10
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:07ae4c39ed6948fc946153bd50bfec10
record_format	dspace
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
_version_	1718442585630965760

Neural networks can learn to utilize correlated auxiliary noise

Ejemplares similares