A Novel Clustering Algorithm Based on Quantum Random Walk

TL;DR

This paper introduces two new data clustering algorithms that leverage quantum random walks, demonstrating improved efficiency and effectiveness through experimental validation and comparison with existing methods.

Contribution

The paper develops novel clustering algorithms based on quantum random walks, combining quantum computing principles with data clustering to enhance performance.

Findings

Algorithms cluster data reasonably and efficiently.

Clustering algorithms exhibit fast convergence.

Experimental results outperform some existing methods.

Abstract

The enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum random walk (QRW) with the problem of data clustering, and develop two clustering algorithms based on the one dimensional QRW. Then, the probability distributions on the positions induced by QRW in these algorithms are investigated, which also indicates the possibility of obtaining better results. Consequently, the experimental results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the clustering algorithms are of fast rates of convergence. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.