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.

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Title Analysis of weighted quantum secret sharing based on matrix product states
Author(s) Lai, Hong
Pieprzyk, Josef
Pan, LeiORCID iD for Pan, Lei
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
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
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