Core-periphery Partitioning and Quantum Annealing

Higham, C. F. , Higham, D. J. and Tudisco, F. (2022) Core-periphery Partitioning and Quantum Annealing. In: KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, D.C., USA, 14-18 Aug 2022, pp. 565-573. ISBN 9781450393850 (doi: 10.1145/3534678.3539261)

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We propose a new kernel that quantifies success for the task of computing a core-periphery partition for an undirected network. Finding the associated optimal partitioning may be expressed in the form of a quadratic unconstrained binary optimization (QUBO) problem, to which a state-of-the-art quantum annealer may be applied. We therefore make use of the new objective function to (a) judge the performance of a quantum annealer, and (b) compare this approach with existing heuristic core-periphery partitioning methods. The quantum annealing is performed on a commercially available D-Wave machine. The QUBO problem involves a full matrix even when the underlying network is sparse. Hence, we develop and test a sparsified version of the original QUBO which increases the available problem dimension for the quantum annealer. Results are provided on both synthetic and real data sets, and we conclude that the QUBO/quantum annealing approach offers benefits in terms of optimizing this new quantity of interest.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Higham, Dr Catherine
Authors: Higham, C. F., Higham, D. J., and Tudisco, F.
College/School:College of Science and Engineering > School of Computing Science
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
190841UK Quantum Technology Hub in Enhanced Quantum ImagingMiles PadgettEngineering and Physical Sciences Research Council (EPSRC)EP/M01326X/1P&S - Physics & Astronomy