Adaptive quantum state tomography via linear regression estimation: Theory and two-qubit experiment

Quantum tomography: Adaptivity improves precision Quantum state tomography is an essential task in the development of quantum technology. The key problem is to find a strategy that has a high level of estimation accuracy and is easy to experimentally implement. A group of international scientists fr...

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Autores principales:	Bo Qi, Zhibo Hou, Yuanlong Wang, Daoyi Dong, Han-Sen Zhong, Li Li, Guo-Yong Xiang, Howard M. Wiseman, Chuan-Feng Li, Guang-Can Guo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/cf74ecd82e854942a2f3282b5168091b
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spelling	oai:doaj.org-article:cf74ecd82e854942a2f3282b5168091b2021-12-02T11:41:57ZAdaptive quantum state tomography via linear regression estimation: Theory and two-qubit experiment10.1038/s41534-017-0016-42056-6387https://doaj.org/article/cf74ecd82e854942a2f3282b5168091b2017-04-01T00:00:00Zhttps://doi.org/10.1038/s41534-017-0016-4https://doaj.org/toc/2056-6387Quantum tomography: Adaptivity improves precision Quantum state tomography is an essential task in the development of quantum technology. The key problem is to find a strategy that has a high level of estimation accuracy and is easy to experimentally implement. A group of international scientists from China and Australia presented, and experimentally tested, such a strategy, called recursively adaptive quantum state tomography (RAQST). In RAQST, no prior assumption on the state is made. Numerical results show that RAQST, even with the simplest product measurements, outperforms other proposed protocols wherein nonlocal measurements are involved. With error-compensation techniques, the authors experimentally demonstrated its superiority for two-qubit optical tomography. RAQST is particularly effective when reconstructing states with high purity, which are important resources in quantum information. Their method offers a new basis for designing effective approaches for determining a quantum state and can be widely used in quantum information experiments.Bo QiZhibo HouYuanlong WangDaoyi DongHan-Sen ZhongLi LiGuo-Yong XiangHoward M. WisemanChuan-Feng LiGuang-Can GuoNature PortfolioarticlePhysicsQC1-999Electronic computers. Computer scienceQA75.5-76.95ENnpj Quantum Information, Vol 3, Iss 1, Pp 1-7 (2017)
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 Bo Qi Zhibo Hou Yuanlong Wang Daoyi Dong Han-Sen Zhong Li Li Guo-Yong Xiang Howard M. Wiseman Chuan-Feng Li Guang-Can Guo Adaptive quantum state tomography via linear regression estimation: Theory and two-qubit experiment
description	Quantum tomography: Adaptivity improves precision Quantum state tomography is an essential task in the development of quantum technology. The key problem is to find a strategy that has a high level of estimation accuracy and is easy to experimentally implement. A group of international scientists from China and Australia presented, and experimentally tested, such a strategy, called recursively adaptive quantum state tomography (RAQST). In RAQST, no prior assumption on the state is made. Numerical results show that RAQST, even with the simplest product measurements, outperforms other proposed protocols wherein nonlocal measurements are involved. With error-compensation techniques, the authors experimentally demonstrated its superiority for two-qubit optical tomography. RAQST is particularly effective when reconstructing states with high purity, which are important resources in quantum information. Their method offers a new basis for designing effective approaches for determining a quantum state and can be widely used in quantum information experiments.
format	article
author	Bo Qi Zhibo Hou Yuanlong Wang Daoyi Dong Han-Sen Zhong Li Li Guo-Yong Xiang Howard M. Wiseman Chuan-Feng Li Guang-Can Guo
author_facet	Bo Qi Zhibo Hou Yuanlong Wang Daoyi Dong Han-Sen Zhong Li Li Guo-Yong Xiang Howard M. Wiseman Chuan-Feng Li Guang-Can Guo
author_sort	Bo Qi
title	Adaptive quantum state tomography via linear regression estimation: Theory and two-qubit experiment
title_short	Adaptive quantum state tomography via linear regression estimation: Theory and two-qubit experiment
title_full	Adaptive quantum state tomography via linear regression estimation: Theory and two-qubit experiment
title_fullStr	Adaptive quantum state tomography via linear regression estimation: Theory and two-qubit experiment
title_full_unstemmed	Adaptive quantum state tomography via linear regression estimation: Theory and two-qubit experiment
title_sort	adaptive quantum state tomography via linear regression estimation: theory and two-qubit experiment
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/cf74ecd82e854942a2f3282b5168091b
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Adaptive quantum state tomography via linear regression estimation: Theory and two-qubit experiment

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