Analysis of weighted quantum secret sharing based on matrix product states Lai, Hong, Pieprzyk, Josef and Pan, Lei 2020, Analysis of weighted quantum secret sharing based on matrix product states, Quantum Information Processing, vol. 19, no. 12, doi: 10.1007/s11128-020-02925-w. Attached Files Name Description MIMEType Size Downloads Title Analysis of weighted quantum secret sharing based on matrix product states Author(s) Lai, Hong Pieprzyk, Josef Pan, Lei orcid.org/0000-0002-4691-8330 Journal name Quantum Information Processing Volume number 19 Issue number 12 Article ID 418 Total pages 16 Publisher Springer Place of publication New York, N.Y. Publication date 2020 ISSN 1570-0755 1573-1332 Keyword(s) tensor networks quantum many-body systems hexagon tensor network weighted quantum secret sharing matrix product state Summary In this paper, motivated by the usefulness of tensor networks to quantum information theory for the progress in study of entanglement in quantum many-body systems, we apply the hexagon tensor network algorithms in terms of Holland’s theory to study the weight allocation and dynamic problems in weighted quantum secret sharing that are not well solved by existing approaches with near-term devices and avoid the instability in the allocation of participants. To be exact, the variety of matrix product state representation of any quantum many-body state can be used to realize dynamic quantum state secret sharing. Language eng DOI 10.1007/s11128-020-02925-w Indigenous content off Field of Research 0105 Mathematical Physics 0206 Quantum Physics 0802 Computation Theory and Mathematics HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2020, Springer Science+Business Media Persistent URL http://hdl.handle.net/10536/DRO/DU:30145567 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Sat, 21 Nov 2020, 05:06:50 EST Sun, 22 Nov 2020, 04:00:45 EST Mon, 23 Nov 2020, 07:05:01 EST Tue, 24 Nov 2020, 05:08:28 EST Thu, 26 Nov 2020, 09:24:56 EST Thu, 26 Nov 2020, 09:26:20 EST Thu, 26 Nov 2020, 09:54:18 EST Sat, 28 Nov 2020, 01:44:19 EST Sat, 09 Jan 2021, 01:38:20 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 24 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Sat, 21 Nov 2020, 05:06:50 EST

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