Quantum-Assisted Cluster Analysis on a Quantum Annealing Device

We present an algorithm for quantum-assisted cluster analysis that makes use of the topological properties of a D-Wave 2000Q quantum processing unit. Clustering is a form of unsupervised machine learning, where instances are organized into groups whose members share similarities. The assignments are...

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Bibliographic Details
Published in:Frontiers in physics Vol. 6
Main Authors: Neukart, Florian, Dollen, David Von, Seidel, Christian
Format: Journal Article
Language:English
Published: Frontiers Media S.A 14.06.2018
Subjects:
clustering
machine learning
quantum algorithms
quantum computing
quantum physics
quantum-assisted
ISSN:2296-424X, 2296-424X
Online Access:Get full text
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Summary:We present an algorithm for quantum-assisted cluster analysis that makes use of the topological properties of a D-Wave 2000Q quantum processing unit. Clustering is a form of unsupervised machine learning, where instances are organized into groups whose members share similarities. The assignments are, in contrast to classification, not known a priori, but generated by the algorithm. We explain how the problem can be expressed as a quadratic unconstrained binary optimization problem and show that the introduced quantum-assisted clustering algorithm is, regarding accuracy, equivalent to commonly used classical clustering algorithms. Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum annealing machines produced by D-Wave Systems [1], have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization, sampling, and clustering [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. Our first and foremost aim is to explain how to represent and solve parts of these problems with the help of the QPU, and not to prove supremacy over every existing classical clustering algorithm.
ISSN:2296-424X
2296-424X
DOI:10.3389/fphy.2018.00055