Prof. Dr.

Wrobel, Stefan

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Publication

Adiabatic quantum computing for kernel k = 2 means clustering

2018 , Bauckhage, Christian , Ojeda, César , Sifa, Rafet , Wrobel, Stefan

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.

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Publication

Ising models for binary clustering via adiabatic quantum computing

2018 , Bauckhage, Christian , Brito, Eduardo , Cvejoski, Kostadin , Ojeda, César , Sifa, Rafet , Wrobel, Stefan

Existing adiabatic quantum computers are tailored towards minimizing the energies of Ising models. The quest for implementations of pattern recognition or machine learning algorithms on such devices can thus be seen as the quest for Ising model (re-)formulations of their objective functions. In this paper, we present Ising models for the tasks of binary clustering of numerical and relational data and discuss how to set up corresponding quantum registers and Hamiltonian operators. In simulation experiments, we numerically solve the respective Schrödinger equations and observe our approaches to yield convincing results.

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Publication

Informed machine learning through functional composition

2018 , Bauckhage, Christian , Ojeda, César , Schücker, Jannis , Sifa, Rafet , Wrobel, Stefan

Addressing general problems with applied machine learning, we sketch an approach towards informed learning. The general idea is to treat data driven learning not as a parameter estimation problem but as a problem of sequencing predefined operations. We show by means of an example that this allows for incorporating expert knowledge and leads to traceable or explainable decision making systems.

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