Quantum density peak clustering

TL;DR

This paper introduces a quantum version of the density peak clustering algorithm that leverages quantum routines for minimum finding, offering potential speedups especially for high-dimensional datasets, and demonstrates its feasibility on a real quantum device.

Contribution

The paper presents a novel quantum algorithm for density peak clustering, providing theoretical speedup analysis and practical benchmarking on a quantum device.

Findings

Quantum speedup depends on dataset structure, particularly the heights of trees in the nearest-higher graph.

The quantum clustering algorithm is especially effective for high-dimensional data.

Benchmarking shows the algorithm's feasibility on current quantum hardware.

Abstract

Clustering algorithms are of fundamental importance when dealing with large unstructured datasets and discovering new patterns and correlations therein, with applications ranging from scientific research to medical imaging and marketing analysis. In this work, we introduce a quantum version of the density peak clustering algorithm, built upon a quantum routine for minimum finding. We prove a quantum speedup for a decision version of density peak clustering depending on the structure of the dataset. Specifically, the speedup is dependent on the heights of the trees of the induced graph of nearest-highers, i.e., the graph of connections to the nearest elements with higher density. We discuss this condition, showing that our algorithm is particularly suitable for high-dimensional datasets. Finally, we benchmark our proposal with a toy problem on a real quantum device.