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arXiv · 1903.08256

A Quantum Annealing-Based Approach to Extreme Clustering

Tim Jaschek·Marko Bucyk·Jaspreet S. Oberoi
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Abstract

Clustering, or grouping, dataset elements based on similarity can be used not only to classify a dataset into a few categories, but also to approximate it by a relatively large number of representative elements. In the latter scenario, referred to as extreme clustering, datasets are enormous and the number of representative clusters is large. We have devised a distributed method that can efficiently solve extreme clustering problems using quantum annealing. We prove that this method yields optimal clustering assignments under a separability assumption, and show that the generated clustering assignments are of comparable quality to those of assignments generated by common clustering algorithms, yet can be obtained a full order of magnitude faster.

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Tim Jaschek, Marko Bucyk, Jaspreet S. Oberoi. 2019-03-19. A Quantum Annealing-Based Approach to Extreme Clustering. https://arxiv.org/abs/1903.08256

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