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  Distributed Partial Quantum Consensus of Qubit Networks With Connected Topologies

Jin, X., Cao, Z., Tang, Y., Kurths, J. (2024): Distributed Partial Quantum Consensus of Qubit Networks With Connected Topologies. - IEEE Transactions on Cybernetics, 54, 9, 4986-4997.
https://doi.org/10.1109/TCYB.2024.3358905

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 Creators:
Jin, Xin1, Author
Cao, Zhu1, Author
Tang, Yang1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: In this article, we consider the partial quantum consensus problem of a qubit network in a distributed view. The local quantum operation is designed based on the Hamiltonian by using the local information of each quantum system in a network of qubits. We construct the unitary transformation for each quantum system to achieve the partial quantum consensus, that is, the directions of the quantum states in the Bloch ball will reach an agreement. A simple case of two-qubit quantum systems is considered first, and a minimum completing time of reaching partial consensus is obtained based on the geometric configuration of each qubit. Furthermore, we extend the approaches to deal with the more general N -qubit networks. Two partial quantum consensus protocols, based on the Lyapunov method for chain graphs and the geometry method for connected graphs, are proposed. The geometry method can be utilized to deal with more general connected graphs, while for the Lyapunov method, the global consensus can be obtained. The numerical simulation over a qubit network is demonstrated to verify the validity and the effectiveness of the theoretical results.

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Language(s): eng - English
 Dates: 2024-02-192024-09-01
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/TCYB.2024.3358905
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
 Degree: -

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Title: IEEE Transactions on Cybernetics
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 54 (9) Sequence Number: - Start / End Page: 4986 - 4997 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/transactions-on-cybernetics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)