Abstract
One of the major benefits of quantum computing is the potential to resolve complex computational problems faster than can be done by classical methods. There are many prototype-based clustering methods in use today, and the selection of the starting nodes for the center points is often done randomly. Clustering often suffers from accepting a local minima as a valid solution when there are possibly better solutions. We will present the results of a study to leverage the benefits of quantum computing for finding better starting centroids for prototype-based clustering. We will present findings from our quantum algorithms that, despite the limitations of current quantum hardware, are comparable to those obtained using more traditional techniques since the quantum algorithms do not suffer from the local minima problem.
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Allgood, N.R., Borle, A., Nicholas, C.K. (2023). Quantum Optimized Centroid Initialization. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2023, Volume 2. FTC 2023. Lecture Notes in Networks and Systems, vol 814. Springer, Cham. https://doi.org/10.1007/978-3-031-47451-4_5
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DOI: https://doi.org/10.1007/978-3-031-47451-4_5
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