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Adiabatic Quantum Computing for Max-Sum Diversification

: Bauckhage, Christian; Sifa, Rafet; Wrobel, Stefan


Demeniconi, Carlotta; Chawla, Nitesh V. ; Society for Industrial and Applied Mathematics -SIAM-, Philadelphia/Pa.:
SIAM International Conference on Data Mining 2020. Proceedings. Online resource
Philadelphia: SIAM, 2020
ISBN: 978-1-61197-623-6
SIAM International Conference on Data Mining <2020, Cincinnati/Ohio>
Fraunhofer IAIS ()

The combinatorial problem of max-sum diversification asks for a maximally diverse subset of a given set of data. Here, we show that it can be expressed as an Ising energy minimization problem. Given this result, max-sum diversification can be solved on adiabatic quantum computers and we present proof of concept simulations which support this claim. This, in turn, suggests that quantum computing might play a role in data mining. We therefore discuss quantum computing in a tutorial like manner and elaborate on its current strengths and weaknesses for data analysis.