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Adiabatic quantum computing for kernel k = 2 means clustering

: Bauckhage, C.; Ojeda, C.; Sifa, R.; Wrobel, S.

Volltext (PDF; )

Gemulla, R.:
Conference "Lernen, Wissen, Daten, Analysen", LWDA 2018. Proceedings. Online resource : Mannheim, Germany, August 22-24, 2018
Mannheim, 2018 (CEUR Workshop Proceedings 2191)
ISSN: 1613-0073
Conference "Lernen, Wissen, Daten, Analysen" (LWDA) <2018, Mannheim>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
01/S18038C; ML2R
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

Adiabatic quantum computers are tailored towards finding minimum energy states of Ising models. The quest for implementations of machine learning algorithms on such devices thus is the quest for Ising model (re-)formulations of their underlying objective functions. In this paper, we discuss how to accomplish this for the problem of kernel binary clustering. We then discuss how our models can be solved on an adiabatic quantum computing device. Finally, in simulation experiments, we numerically solve the respective Schrödinger equations and observe our approaches to yield convincing results.