Quantum-Enhanced Data Classification with a Variational Entangled Sensor Network

Variational quantum circuits (VQCs) built upon noisy intermediate-scale quantum (NISQ) hardware, in conjunction with classical processing, constitute a promising architecture for quantum simulations, classical optimization, and machine learning. However, the required VQC depth to demonstrate a quant...

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Autores principales:	Yi Xia, Wei Li, Quntao Zhuang, Zheshen Zhang
Formato:	article
Lenguaje:	EN
Publicado:	American Physical Society 2021
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/b612f9eb6956406a8b68916d14954d7b
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spelling	oai:doaj.org-article:b612f9eb6956406a8b68916d14954d7b2021-12-02T14:38:52ZQuantum-Enhanced Data Classification with a Variational Entangled Sensor Network10.1103/PhysRevX.11.0210472160-3308https://doaj.org/article/b612f9eb6956406a8b68916d14954d7b2021-06-01T00:00:00Zhttp://doi.org/10.1103/PhysRevX.11.021047http://doi.org/10.1103/PhysRevX.11.021047https://doaj.org/toc/2160-3308Variational quantum circuits (VQCs) built upon noisy intermediate-scale quantum (NISQ) hardware, in conjunction with classical processing, constitute a promising architecture for quantum simulations, classical optimization, and machine learning. However, the required VQC depth to demonstrate a quantum advantage over classical schemes is beyond the reach of available NISQ devices. Supervised learning assisted by an entangled sensor network (SLAEN) is a distinct paradigm that harnesses VQCs trained by classical machine-learning algorithms to tailor multipartite entanglement shared by sensors for solving practically useful data-processing problems. Here, we report the first experimental demonstration of SLAEN and show an entanglement-enabled reduction in the error probability for classification of multidimensional radio-frequency signals. Our work paves a new route for quantum-enhanced data processing and its applications in the NISQ era.Yi XiaWei LiQuntao ZhuangZheshen ZhangAmerican Physical SocietyarticlePhysicsQC1-999ENPhysical Review X, Vol 11, Iss 2, p 021047 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 Yi Xia Wei Li Quntao Zhuang Zheshen Zhang Quantum-Enhanced Data Classification with a Variational Entangled Sensor Network
description	Variational quantum circuits (VQCs) built upon noisy intermediate-scale quantum (NISQ) hardware, in conjunction with classical processing, constitute a promising architecture for quantum simulations, classical optimization, and machine learning. However, the required VQC depth to demonstrate a quantum advantage over classical schemes is beyond the reach of available NISQ devices. Supervised learning assisted by an entangled sensor network (SLAEN) is a distinct paradigm that harnesses VQCs trained by classical machine-learning algorithms to tailor multipartite entanglement shared by sensors for solving practically useful data-processing problems. Here, we report the first experimental demonstration of SLAEN and show an entanglement-enabled reduction in the error probability for classification of multidimensional radio-frequency signals. Our work paves a new route for quantum-enhanced data processing and its applications in the NISQ era.
format	article
author	Yi Xia Wei Li Quntao Zhuang Zheshen Zhang
author_facet	Yi Xia Wei Li Quntao Zhuang Zheshen Zhang
author_sort	Yi Xia
title	Quantum-Enhanced Data Classification with a Variational Entangled Sensor Network
title_short	Quantum-Enhanced Data Classification with a Variational Entangled Sensor Network
title_full	Quantum-Enhanced Data Classification with a Variational Entangled Sensor Network
title_fullStr	Quantum-Enhanced Data Classification with a Variational Entangled Sensor Network
title_full_unstemmed	Quantum-Enhanced Data Classification with a Variational Entangled Sensor Network
title_sort	quantum-enhanced data classification with a variational entangled sensor network
publisher	American Physical Society
publishDate	2021
url	https://doaj.org/article/b612f9eb6956406a8b68916d14954d7b
work_keys_str_mv	AT yixia quantumenhanceddataclassificationwithavariationalentangledsensornetwork AT weili quantumenhanceddataclassificationwithavariationalentangledsensornetwork AT quntaozhuang quantumenhanceddataclassificationwithavariationalentangledsensornetwork AT zheshenzhang quantumenhanceddataclassificationwithavariationalentangledsensornetwork
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Quantum-Enhanced Data Classification with a Variational Entangled Sensor Network

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