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  4. Adiabatic quantum computing for kernel k = 2 means clustering
 
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2018
Conference Paper
Title

Adiabatic quantum computing for kernel k = 2 means clustering

Abstract
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.
Author(s)
Bauckhage, Christian  
Ojeda, César  
Sifa, Rafet  
Wrobel, Stefan  
Mainwork
Conference "Lernen, Wissen, Daten, Analysen", LWDA 2018. Proceedings. Online resource  
Project(s)
ML2R
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)  
Conference
Conference "Lernen, Wissen, Daten, Analysen" (LWDA) 2018  
Link
Link
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
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