2022/01/05 by Catherine F. Higham, Desmond J. Higham, Higham, Catherine F. +3
Computer Science · #Blind Source Separation Techniques #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning and ELM #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph) #Social and Information Networks (cs.SI) #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2201.01543
openalex publication_date 2022/01/05 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
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 the 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.