Neural networks for quantum state tomography with constrained measurements

Ma, H; Dong, D; Petersen, IR; Huang, CJ; Xiang, GY

Neural networks for quantum state tomography with constrained measurements

Ma, H Dong, D

Petersen, IR Huang, CJ Xiang, GY

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Quantum Information Processing, 2024, 23, (9), pp. 317
Issue Date:: 2024-09-01

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Field	Value	Language
dc.contributor.author	Ma, H
dc.contributor.author	Dong, D https://orcid.org/0000-0002-7425-3559
dc.contributor.author	Petersen, IR
dc.contributor.author	Huang, CJ
dc.contributor.author	Xiang, GY
dc.date.accessioned	2025-04-07T03:11:25Z
dc.date.available	2025-04-07T03:11:25Z
dc.date.issued	2024-09-01
dc.identifier.citation	Quantum Information Processing, 2024, 23, (9), pp. 317
dc.identifier.issn	1570-0755
dc.identifier.issn	1573-1332
dc.identifier.uri	http://hdl.handle.net/10453/186723
dc.description.abstract	Quantum state tomography (QST) aiming at reconstructing the density matrix of a quantum state plays an important role in various emerging quantum technologies. Recognizing the challenges posed by imperfect measurement data, we develop a unified neural network (NN)-based approach for QST under constrained measurement scenarios, including limited measurement copies, incomplete measurements, and noisy measurements. Through comprehensive comparison with other estimation methods, we demonstrate that our method improves the estimation accuracy in scenarios with limited measurement resources, showcasing notable robustness in noisy measurement settings. These findings highlight the capability of NNs to enhance QST with constrained measurements.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Quantum Information Processing
dc.relation.isbasedon	10.1007/s11128-024-04522-7
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0105 Mathematical Physics, 0206 Quantum Physics, 0802 Computation Theory and Mathematics
dc.subject.classification	Mathematical Physics
dc.subject.classification	5108 Quantum physics
dc.title	Neural networks for quantum state tomography with constrained measurements
dc.type	Journal Article
utslib.citation.volume	23
utslib.for	0105 Mathematical Physics
utslib.for	0206 Quantum Physics
utslib.for	0802 Computation Theory and Mathematics
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-04-07T03:11:24Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	23
utslib.citation.issue	9

Abstract:

Quantum state tomography (QST) aiming at reconstructing the density matrix of a quantum state plays an important role in various emerging quantum technologies. Recognizing the challenges posed by imperfect measurement data, we develop a unified neural network (NN)-based approach for QST under constrained measurement scenarios, including limited measurement copies, incomplete measurements, and noisy measurements. Through comprehensive comparison with other estimation methods, we demonstrate that our method improves the estimation accuracy in scenarios with limited measurement resources, showcasing notable robustness in noisy measurement settings. These findings highlight the capability of NNs to enhance QST with constrained measurements.

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http://hdl.handle.net/10453/186723