Quantum semi-supervised generative adversarial network for enhanced data classification

Abstract In this paper, we propose the quantum semi-supervised generative adversarial network (qSGAN). The system is composed of a quantum generator and a classical discriminator/classifier (D/C). The goal is to train both the generator and the D/C, so that the latter may get a high classification a...

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Autores principales:	Kouhei Nakaji, Naoki Yamamoto
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/28d3e769cea14ac5bf6d99df44acb399
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spelling	oai:doaj.org-article:28d3e769cea14ac5bf6d99df44acb3992021-12-02T18:01:49ZQuantum semi-supervised generative adversarial network for enhanced data classification10.1038/s41598-021-98933-62045-2322https://doaj.org/article/28d3e769cea14ac5bf6d99df44acb3992021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98933-6https://doaj.org/toc/2045-2322Abstract In this paper, we propose the quantum semi-supervised generative adversarial network (qSGAN). The system is composed of a quantum generator and a classical discriminator/classifier (D/C). The goal is to train both the generator and the D/C, so that the latter may get a high classification accuracy for a given dataset. Hence the qSGAN needs neither any data loading nor to generate a pure quantum state, implying that qSGAN is much easier to implement than many existing quantum algorithms. Also the generator can serve as a stronger adversary than a classical one thanks to its rich expressibility, and it is expected to be robust against noise. These advantages are demonstrated in a numerical simulation.Kouhei NakajiNaoki YamamotoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kouhei Nakaji Naoki Yamamoto Quantum semi-supervised generative adversarial network for enhanced data classification
description	Abstract In this paper, we propose the quantum semi-supervised generative adversarial network (qSGAN). The system is composed of a quantum generator and a classical discriminator/classifier (D/C). The goal is to train both the generator and the D/C, so that the latter may get a high classification accuracy for a given dataset. Hence the qSGAN needs neither any data loading nor to generate a pure quantum state, implying that qSGAN is much easier to implement than many existing quantum algorithms. Also the generator can serve as a stronger adversary than a classical one thanks to its rich expressibility, and it is expected to be robust against noise. These advantages are demonstrated in a numerical simulation.
format	article
author	Kouhei Nakaji Naoki Yamamoto
author_facet	Kouhei Nakaji Naoki Yamamoto
author_sort	Kouhei Nakaji
title	Quantum semi-supervised generative adversarial network for enhanced data classification
title_short	Quantum semi-supervised generative adversarial network for enhanced data classification
title_full	Quantum semi-supervised generative adversarial network for enhanced data classification
title_fullStr	Quantum semi-supervised generative adversarial network for enhanced data classification
title_full_unstemmed	Quantum semi-supervised generative adversarial network for enhanced data classification
title_sort	quantum semi-supervised generative adversarial network for enhanced data classification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/28d3e769cea14ac5bf6d99df44acb399
work_keys_str_mv	AT kouheinakaji quantumsemisupervisedgenerativeadversarialnetworkforenhanceddataclassification AT naokiyamamoto quantumsemisupervisedgenerativeadversarialnetworkforenhanceddataclassification
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Quantum semi-supervised generative adversarial network for enhanced data classification

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