Training deep quantum neural networks

It is hard to design quantum neural networks able to work with quantum data. Here, the authors propose a noise-robust architecture for a feedforward quantum neural network, with qudits as neurons and arbitrary unitary operations as perceptrons, whose training procedure is efficient in the number of...

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Main Authors:	Kerstin Beer, Dmytro Bondarenko, Terry Farrelly, Tobias J. Osborne, Robert Salzmann, Daniel Scheiermann, Ramona Wolf
Format:	article
Language:	EN
Published:	Nature Portfolio 2020
Subjects:	Science Q
Online Access:	https://doaj.org/article/d9ba2b872323438c9255d08c30daee30
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id	oai:doaj.org-article:d9ba2b872323438c9255d08c30daee30
record_format	dspace
spelling	oai:doaj.org-article:d9ba2b872323438c9255d08c30daee302021-12-02T15:37:08ZTraining deep quantum neural networks10.1038/s41467-020-14454-22041-1723https://doaj.org/article/d9ba2b872323438c9255d08c30daee302020-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-14454-2https://doaj.org/toc/2041-1723It is hard to design quantum neural networks able to work with quantum data. Here, the authors propose a noise-robust architecture for a feedforward quantum neural network, with qudits as neurons and arbitrary unitary operations as perceptrons, whose training procedure is efficient in the number of layers.Kerstin BeerDmytro BondarenkoTerry FarrellyTobias J. OsborneRobert SalzmannDaniel ScheiermannRamona WolfNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-6 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Kerstin Beer Dmytro Bondarenko Terry Farrelly Tobias J. Osborne Robert Salzmann Daniel Scheiermann Ramona Wolf Training deep quantum neural networks
description	It is hard to design quantum neural networks able to work with quantum data. Here, the authors propose a noise-robust architecture for a feedforward quantum neural network, with qudits as neurons and arbitrary unitary operations as perceptrons, whose training procedure is efficient in the number of layers.
format	article
author	Kerstin Beer Dmytro Bondarenko Terry Farrelly Tobias J. Osborne Robert Salzmann Daniel Scheiermann Ramona Wolf
author_facet	Kerstin Beer Dmytro Bondarenko Terry Farrelly Tobias J. Osborne Robert Salzmann Daniel Scheiermann Ramona Wolf
author_sort	Kerstin Beer
title	Training deep quantum neural networks
title_short	Training deep quantum neural networks
title_full	Training deep quantum neural networks
title_fullStr	Training deep quantum neural networks
title_full_unstemmed	Training deep quantum neural networks
title_sort	training deep quantum neural networks
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/d9ba2b872323438c9255d08c30daee30
work_keys_str_mv	AT kerstinbeer trainingdeepquantumneuralnetworks AT dmytrobondarenko trainingdeepquantumneuralnetworks AT terryfarrelly trainingdeepquantumneuralnetworks AT tobiasjosborne trainingdeepquantumneuralnetworks AT robertsalzmann trainingdeepquantumneuralnetworks AT danielscheiermann trainingdeepquantumneuralnetworks AT ramonawolf trainingdeepquantumneuralnetworks
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Training deep quantum neural networks

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