Practical distributed quantum information processing with LOCCNet

Abstract Distributed quantum information processing is essential for building quantum networks and enabling more extensive quantum computations. In this regime, several spatially separated parties share a multipartite quantum system, and the most natural set of operations is Local Operations and Cla...

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Autores principales:	Xuanqiang Zhao, Benchi Zhao, Zihe Wang, Zhixin Song, Xin Wang
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/0e2dc0900f9a429fa9c25c7adb2eedd2
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spelling	oai:doaj.org-article:0e2dc0900f9a429fa9c25c7adb2eedd22021-11-08T10:44:54ZPractical distributed quantum information processing with LOCCNet10.1038/s41534-021-00496-x2056-6387https://doaj.org/article/0e2dc0900f9a429fa9c25c7adb2eedd22021-11-01T00:00:00Zhttps://doi.org/10.1038/s41534-021-00496-xhttps://doaj.org/toc/2056-6387Abstract Distributed quantum information processing is essential for building quantum networks and enabling more extensive quantum computations. In this regime, several spatially separated parties share a multipartite quantum system, and the most natural set of operations is Local Operations and Classical Communication (LOCC). As a pivotal part in quantum information theory and practice, LOCC has led to many vital protocols such as quantum teleportation. However, designing practical LOCC protocols is challenging due to LOCC’s intractable structure and limitations set by near-term quantum devices. Here we introduce LOCCNet, a machine learning framework facilitating protocol design and optimization for distributed quantum information processing tasks. As applications, we explore various quantum information tasks such as entanglement distillation, quantum state discrimination, and quantum channel simulation. We discover protocols with evident improvements, in particular, for entanglement distillation with quantum states of interest in quantum information. Our approach opens up new opportunities for exploring entanglement and its applications with machine learning, which will potentially sharpen our understanding of the power and limitations of LOCC. An implementation of LOCCNet is available in Paddle Quantum, a quantum machine learning Python package based on PaddlePaddle deep learning platform.Xuanqiang ZhaoBenchi ZhaoZihe WangZhixin SongXin WangNature PortfolioarticlePhysicsQC1-999Electronic computers. Computer scienceQA75.5-76.95ENnpj Quantum Information, Vol 7, Iss 1, Pp 1-7 (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 Xuanqiang Zhao Benchi Zhao Zihe Wang Zhixin Song Xin Wang Practical distributed quantum information processing with LOCCNet
description	Abstract Distributed quantum information processing is essential for building quantum networks and enabling more extensive quantum computations. In this regime, several spatially separated parties share a multipartite quantum system, and the most natural set of operations is Local Operations and Classical Communication (LOCC). As a pivotal part in quantum information theory and practice, LOCC has led to many vital protocols such as quantum teleportation. However, designing practical LOCC protocols is challenging due to LOCC’s intractable structure and limitations set by near-term quantum devices. Here we introduce LOCCNet, a machine learning framework facilitating protocol design and optimization for distributed quantum information processing tasks. As applications, we explore various quantum information tasks such as entanglement distillation, quantum state discrimination, and quantum channel simulation. We discover protocols with evident improvements, in particular, for entanglement distillation with quantum states of interest in quantum information. Our approach opens up new opportunities for exploring entanglement and its applications with machine learning, which will potentially sharpen our understanding of the power and limitations of LOCC. An implementation of LOCCNet is available in Paddle Quantum, a quantum machine learning Python package based on PaddlePaddle deep learning platform.
format	article
author	Xuanqiang Zhao Benchi Zhao Zihe Wang Zhixin Song Xin Wang
author_facet	Xuanqiang Zhao Benchi Zhao Zihe Wang Zhixin Song Xin Wang
author_sort	Xuanqiang Zhao
title	Practical distributed quantum information processing with LOCCNet
title_short	Practical distributed quantum information processing with LOCCNet
title_full	Practical distributed quantum information processing with LOCCNet
title_fullStr	Practical distributed quantum information processing with LOCCNet
title_full_unstemmed	Practical distributed quantum information processing with LOCCNet
title_sort	practical distributed quantum information processing with loccnet
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0e2dc0900f9a429fa9c25c7adb2eedd2
work_keys_str_mv	AT xuanqiangzhao practicaldistributedquantuminformationprocessingwithloccnet AT benchizhao practicaldistributedquantuminformationprocessingwithloccnet AT zihewang practicaldistributedquantuminformationprocessingwithloccnet AT zhixinsong practicaldistributedquantuminformationprocessingwithloccnet AT xinwang practicaldistributedquantuminformationprocessingwithloccnet
_version_	1718442587194392576

Practical distributed quantum information processing with LOCCNet

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