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.
CITATION STYLE
Bauckhage, C., Brito, E., Cvejoski, K., Ojeda, C., Sifa, R., & Wrobel, S. (2018). Ising models for binary clustering via adiabatic quantum computing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10746 LNCS, pp. 3–17). Springer Verlag. https://doi.org/10.1007/978-3-319-78199-0_1
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