qLUE: A Quantum Clustering Algorithm for Multi- Dimensional Datasets

TL;DR

qLUE is a novel quantum clustering algorithm designed to efficiently handle high-dimensional, dense datasets by scaling linearly with data points and density, leveraging quantum speedup for improved performance.

Contribution

This paper introduces qLUE, a quantum clustering algorithm inspired by CLUE, that achieves linear scaling in both data points and density, addressing limitations of classical methods.

Findings

qLUE demonstrates effective clustering in high-dimensional datasets.

Numerical tests confirm qLUE's scalability and efficiency.

Quantum speedup enhances clustering performance in complex data scenarios.

Abstract

Clustering algorithms are at the basis of several technological applications, and are fueling the development of rapidly evolving fields such as machine learning. In the recent past, however, it has become apparent that they face challenges stemming from datasets that span more spatial dimensions. In fact, the best-performing clustering algorithms scale linearly in the number of points, but quadratically with respect to the local density of points. In this work, we introduce qLUE, a quantum clustering algorithm that scales linearly in both the number of points and their density. qLUE is inspired by CLUE, an algorithm developed to address the challenging time and memory budgets of Event Reconstruction (ER) in future High-Energy Physics experiments. As such, qLUE marries decades of development with the quadratic speedup provided by quantum computers. We numerically test qLUE in several scenarios, demonstrating its effectiveness and proving it to be a promising route to handle complex data analysis tasks -- especially in high-dimensional datasets with high densities of points.