Quantum Clustering Algorithms
Abstract
By the term "quantization", we refer to the process of using quantum mechanics in order to improve a classical algorithm, usually by making it go faster. In this paper, we initiate the idea of quantizing clustering algorithms by using variations on a celebrated quantum algorithm due to Grover. After having introduced this novel approach to unsupervised learning, we illustrate it with a quantized version of three standard algorithms: divisive clustering, k -medians and an algorithm for the construction of a neighbourhood graph. We obtain a significant speedup compared to the classical approach.
Cite
Text
Aïmeur et al. "Quantum Clustering Algorithms." International Conference on Machine Learning, 2007. doi:10.1145/1273496.1273497Markdown
[Aïmeur et al. "Quantum Clustering Algorithms." International Conference on Machine Learning, 2007.](https://mlanthology.org/icml/2007/aimeur2007icml-quantum/) doi:10.1145/1273496.1273497BibTeX
@inproceedings{aimeur2007icml-quantum,
title = {{Quantum Clustering Algorithms}},
author = {Aïmeur, Esma and Brassard, Gilles and Gambs, Sébastien},
booktitle = {International Conference on Machine Learning},
year = {2007},
pages = {1-8},
doi = {10.1145/1273496.1273497},
url = {https://mlanthology.org/icml/2007/aimeur2007icml-quantum/}
}