Dynamic hierarchical quantum secret sharing based on the multiscale entanglement renormalization ansatz

Hong Lai, Josef Pieprzyk, and Lei Pan
Phys. Rev. A 106, 052403 – Published 2 November 2022

Abstract

Tensor networks offer a novel and powerful tool for solving a variety of problems in mathematics, data science, and engineering. One such network is the multiscale entanglement renormalization ansatz (MERA). The MERA exhibits a hierarchical structure of layers, where each layer corresponds to a particular length (or energy) scale. The structure can be easily constructed using isometric and disentangler transformations. The following question arises: Is it possible to use the MERA to build hierarchical quantum secret sharing (HQSS)? The paper answers the question in the affirmative. In particular, it shows how a hierarchy of participant trust and authority relates to a MERA structure. The structure consists of binary and ternary MERA modules, which generate secret shares for participants. Because a binary MERA can be replaced by its ternary sibling and vice versa, our HQSS scheme is dynamic, allowing promotion and demotion of participants from the different layers but also enrollment of new ones and disenrollment of old ones from the same layer. The correctness and security of our dynamic hierarchical quantum secret sharing scheme are discussed.

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  • Received 8 June 2022
  • Accepted 18 October 2022

DOI:https://doi.org/10.1103/PhysRevA.106.052403

©2022 American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & Technology

Authors & Affiliations

Hong Lai1,*, Josef Pieprzyk2,3, and Lei Pan4

  • 1School of Computer and Information Science, Southwest University, Chongqing 400715, China
  • 2Data61, CSIRO, Sydney, New South Wales 2122, Australia
  • 3Institute of Computer Science, Polish Academy of Sciences, Warsaw 01-248, Poland
  • 4School of Information Technology, Deakin University, Geelong, Victoria 3220, Australia

  • *Corresponding author: hlai@swu.edu.cn

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Vol. 106, Iss. 5 — November 2022

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