Quantum generative adversarial networks with multiple superconducting qubits

Abstract Generative adversarial networks are an emerging technique with wide applications in machine learning, which have achieved dramatic success in a number of challenging tasks including image and video generation. When equipped with quantum processors, their quantum counterparts—called quantum...

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Autores principales:	Kaixuan Huang, Zheng-An Wang, Chao Song, Kai Xu, Hekang Li, Zhen Wang, Qiujiang Guo, Zixuan Song, Zhi-Bo Liu, Dongning Zheng, Dong-Ling Deng, H. Wang, Jian-Guo Tian, Heng Fan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/30e6fcf2e9ea43b394baa841def6964c
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spelling	oai:doaj.org-article:30e6fcf2e9ea43b394baa841def6964c2021-12-05T12:10:21ZQuantum generative adversarial networks with multiple superconducting qubits10.1038/s41534-021-00503-12056-6387https://doaj.org/article/30e6fcf2e9ea43b394baa841def6964c2021-12-01T00:00:00Zhttps://doi.org/10.1038/s41534-021-00503-1https://doaj.org/toc/2056-6387Abstract Generative adversarial networks are an emerging technique with wide applications in machine learning, which have achieved dramatic success in a number of challenging tasks including image and video generation. When equipped with quantum processors, their quantum counterparts—called quantum generative adversarial networks (QGANs)—may even exhibit exponential advantages in certain machine learning applications. Here, we report an experimental implementation of a QGAN using a programmable superconducting processor, in which both the generator and the discriminator are parameterized via layers of single- and two-qubit quantum gates. The programmed QGAN runs automatically several rounds of adversarial learning with quantum gradients to achieve a Nash equilibrium point, where the generator can replicate data samples that mimic the ones from the training set. Our implementation is promising to scale up to noisy intermediate-scale quantum devices, thus paving the way for experimental explorations of quantum advantages in practical applications with near-term quantum technologies.Kaixuan HuangZheng-An WangChao SongKai XuHekang LiZhen WangQiujiang GuoZixuan SongZhi-Bo LiuDongning ZhengDong-Ling DengH. WangJian-Guo TianHeng FanNature PortfolioarticlePhysicsQC1-999Electronic computers. Computer scienceQA75.5-76.95ENnpj Quantum Information, Vol 7, Iss 1, Pp 1-5 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95 Kaixuan Huang Zheng-An Wang Chao Song Kai Xu Hekang Li Zhen Wang Qiujiang Guo Zixuan Song Zhi-Bo Liu Dongning Zheng Dong-Ling Deng H. Wang Jian-Guo Tian Heng Fan Quantum generative adversarial networks with multiple superconducting qubits
description	Abstract Generative adversarial networks are an emerging technique with wide applications in machine learning, which have achieved dramatic success in a number of challenging tasks including image and video generation. When equipped with quantum processors, their quantum counterparts—called quantum generative adversarial networks (QGANs)—may even exhibit exponential advantages in certain machine learning applications. Here, we report an experimental implementation of a QGAN using a programmable superconducting processor, in which both the generator and the discriminator are parameterized via layers of single- and two-qubit quantum gates. The programmed QGAN runs automatically several rounds of adversarial learning with quantum gradients to achieve a Nash equilibrium point, where the generator can replicate data samples that mimic the ones from the training set. Our implementation is promising to scale up to noisy intermediate-scale quantum devices, thus paving the way for experimental explorations of quantum advantages in practical applications with near-term quantum technologies.
format	article
author	Kaixuan Huang Zheng-An Wang Chao Song Kai Xu Hekang Li Zhen Wang Qiujiang Guo Zixuan Song Zhi-Bo Liu Dongning Zheng Dong-Ling Deng H. Wang Jian-Guo Tian Heng Fan
author_facet	Kaixuan Huang Zheng-An Wang Chao Song Kai Xu Hekang Li Zhen Wang Qiujiang Guo Zixuan Song Zhi-Bo Liu Dongning Zheng Dong-Ling Deng H. Wang Jian-Guo Tian Heng Fan
author_sort	Kaixuan Huang
title	Quantum generative adversarial networks with multiple superconducting qubits
title_short	Quantum generative adversarial networks with multiple superconducting qubits
title_full	Quantum generative adversarial networks with multiple superconducting qubits
title_fullStr	Quantum generative adversarial networks with multiple superconducting qubits
title_full_unstemmed	Quantum generative adversarial networks with multiple superconducting qubits
title_sort	quantum generative adversarial networks with multiple superconducting qubits
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/30e6fcf2e9ea43b394baa841def6964c
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Quantum generative adversarial networks with multiple superconducting qubits

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