Year: 2026
Type: preprint
Abstract: Clustering is a fundamental task in unsupervised learning. The goal is to partition unlabeled data into clusters such that similar elements are assigned to the same cluster, while elements in different clusters are dissimilar. We propose a quantum Monte Carlo approach to density estimation, a key routine in density peak clustering. Building on this approach, we develop quantum algorithms for the full clustering problem and three related tasks: decision clustering, cluster counting, and heavy cluster counting. For each one, we establish a polynomial quantum query speedup under assumptions on the scaling of dataset-dependent parameters. Numerical experiments suggest that these conditions are satisfied for several practically relevant settings.
Source: arXiv (Cornell University)
Authors: Sousa, Gonçalo, Magano, Duarte
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7220963699