On how neural networks enhance quantum state tomography with limited resources

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dc.contributor.authorPetersen, Ian
dc.contributor.authorMa, Hailan
dc.contributor.authorDong, Daoyi
dc.contributor.editorEgerstedt, Magnus
dc.coverage.spatialvirtual
dc.date.accessioned2024-01-31T04:44:21Z
dc.date.createdDecember 13-17
dc.date.issued2021
dc.date.updated2022-10-02T07:18:53Z
dc.description.abstractQuantum state tomography is defined as a process of reconstructing the density matrix of a quantum state and is an important task for various emerging quantum technologies. In this work, we propose a general quantum state tomography framework that employs deep neural networks to reconstruct quantum states from a set of measurements with high efficiency. In particular, we apply it to two cases, including few measurement copies and incomplete measurement. Numerical results demonstrate that the proposed method exhibits a significant potential to achieve high fidelity for quantum state tomography when measurement resources are limited.en_AU
dc.description.sponsorshipThis work was supported by the Australian Research Council under Grants DP180101805 and DP190103615.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.isbn9781665436601en_AU
dc.identifier.urihttp://hdl.handle.net/1885/312474
dc.language.isoen_AUen_AU
dc.provenancehttps://www.ieee.org/publications/rights/author-posting-policy.html..."The policy reaffirms the principle that authors are free to post their own version of their IEEE periodical or conference articles on their personal Web sites, those of their employers, or their funding agencies for the purpose of meeting public availability requirements prescribed by their funding agencies. " from the publisher site (as at 19 Feb 2024) © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
dc.publisherIEEEen_AU
dc.relationhttp://purl.org/au-research/grants/arc/DP180101805en_AU
dc.relationhttp://purl.org/au-research/grants/arc/DP190103615en_AU
dc.relation.ispartofseries60th IEEE Conference on Decision and Control (CDC)en_AU
dc.rights© 2021 IEEEen_AU
dc.sourceProceedings of the 60th IEEE Conference on Decision and Control (CDC)en_AU
dc.titleOn how neural networks enhance quantum state tomography with limited resourcesen_AU
dc.typeConference paperen_AU
dcterms.accessRightsOpen Access
local.bibliographicCitation.lastpage4151en_AU
local.bibliographicCitation.startpage4146en_AU
local.contributor.affiliationPetersen, Ian, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationMa, Hailan, University of New South Walesen_AU
local.contributor.affiliationDong, Daoyi, University of New South Walesen_AU
local.contributor.authoruidPetersen, Ian, u4036493en_AU
local.description.notesImported from ARIESen_AU
local.description.refereedYes
local.identifier.absfor461104 - Neural networksen_AU
local.identifier.absfor461307 - Quantum computationen_AU
local.identifier.ariespublicationa383154xPUB29566en_AU
local.identifier.doi10.1109/CDC45484.2021.9683315en_AU
local.identifier.scopusID2-s2.0-85126009373
local.publisher.urlhttps://www.ieee.org/en_AU
local.type.statusAccepted Versionen_AU

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