Adaptive quantum state tomography with neural networks

Abstract Current algorithms for quantum state tomography (QST) are costly both on the experimental front, requiring measurement of many copies of the state, and on the classical computational front, needing a long time to analyze the gathered data. Here, we introduce neural adaptive quantum state to...

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Autores principales:	Yihui Quek, Stanislav Fort, Hui Khoon Ng
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/56790f934957464599c19f2edee4f477
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spelling	oai:doaj.org-article:56790f934957464599c19f2edee4f4772021-12-02T17:45:22ZAdaptive quantum state tomography with neural networks10.1038/s41534-021-00436-92056-6387https://doaj.org/article/56790f934957464599c19f2edee4f4772021-06-01T00:00:00Zhttps://doi.org/10.1038/s41534-021-00436-9https://doaj.org/toc/2056-6387Abstract Current algorithms for quantum state tomography (QST) are costly both on the experimental front, requiring measurement of many copies of the state, and on the classical computational front, needing a long time to analyze the gathered data. Here, we introduce neural adaptive quantum state tomography (NAQT), a fast, flexible machine-learning-based algorithm for QST that adapts measurements and provides orders of magnitude faster processing while retaining state-of-the-art reconstruction accuracy. As in other adaptive QST schemes, measurement adaptation makes use of the information gathered from previous measured copies of the state to perform a targeted sensing of the next copy, maximizing the information gathered from that next copy. Our NAQT approach allows for a rapid and seamless integration of measurement adaptation and statistical inference, using a neural-network replacement of the standard Bayes’ update, to obtain the best estimate of the state. Our algorithm, which falls into the machine learning subfield of “meta-learning” (in effect “learning to learn” about quantum states), does not require any ansatz about the form of the state to be estimated. Despite this generality, it can be retrained within hours on a single laptop for a two-qubit situation, which suggests a feasible time-cost when extended to larger systems and potential speed-ups if provided with additional structure, such as a state ansatz.Yihui QuekStanislav FortHui Khoon NgNature 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 Yihui Quek Stanislav Fort Hui Khoon Ng Adaptive quantum state tomography with neural networks
description	Abstract Current algorithms for quantum state tomography (QST) are costly both on the experimental front, requiring measurement of many copies of the state, and on the classical computational front, needing a long time to analyze the gathered data. Here, we introduce neural adaptive quantum state tomography (NAQT), a fast, flexible machine-learning-based algorithm for QST that adapts measurements and provides orders of magnitude faster processing while retaining state-of-the-art reconstruction accuracy. As in other adaptive QST schemes, measurement adaptation makes use of the information gathered from previous measured copies of the state to perform a targeted sensing of the next copy, maximizing the information gathered from that next copy. Our NAQT approach allows for a rapid and seamless integration of measurement adaptation and statistical inference, using a neural-network replacement of the standard Bayes’ update, to obtain the best estimate of the state. Our algorithm, which falls into the machine learning subfield of “meta-learning” (in effect “learning to learn” about quantum states), does not require any ansatz about the form of the state to be estimated. Despite this generality, it can be retrained within hours on a single laptop for a two-qubit situation, which suggests a feasible time-cost when extended to larger systems and potential speed-ups if provided with additional structure, such as a state ansatz.
format	article
author	Yihui Quek Stanislav Fort Hui Khoon Ng
author_facet	Yihui Quek Stanislav Fort Hui Khoon Ng
author_sort	Yihui Quek
title	Adaptive quantum state tomography with neural networks
title_short	Adaptive quantum state tomography with neural networks
title_full	Adaptive quantum state tomography with neural networks
title_fullStr	Adaptive quantum state tomography with neural networks
title_full_unstemmed	Adaptive quantum state tomography with neural networks
title_sort	adaptive quantum state tomography with neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/56790f934957464599c19f2edee4f477
work_keys_str_mv	AT yihuiquek adaptivequantumstatetomographywithneuralnetworks AT stanislavfort adaptivequantumstatetomographywithneuralnetworks AT huikhoonng adaptivequantumstatetomographywithneuralnetworks
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Adaptive quantum state tomography with neural networks

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