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ORIGINAL RESEARCH article

Front. Quantum Sci. Technol.
Sec. Quantum Computing and Simulation
Volume 3 - 2024 | doi: 10.3389/frqst.2024.1462004

qCLUE: A Quantum Clustering Algorithm for Multi-Dimensional Datasets

Provisionally accepted
Dhruv Gopalakrishnan Dhruv Gopalakrishnan 1,2*Luca Dellantonio Luca Dellantonio 3*Antonio Di Pilato Antonio Di Pilato 4*Wahid Redjeb Wahid Redjeb 4,5*Felice Pantaleo Felice Pantaleo 4*Michele Mosca Michele Mosca 1,2*
  • 1 University of Waterloo, Waterloo, Canada
  • 2 Perimeter Institute for Theoretical Physics, Waterloo, Ontario, Canada
  • 3 University of Exeter, Exeter, England, United Kingdom
  • 4 European Organization for Nuclear Research (CERN), Geneva, Geneva, Switzerland
  • 5 RWTH Aachen University, Aachen, North Rhine-Westphalia, Germany

The final, formatted version of the article will be published soon.

    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 bestperforming clustering algorithms scale linearly in the number of points, but quadratically with respect to the local density of points. In this work, we introduce qCLUE, a quantum clustering algorithm that scales linearly in both the number of points and their density. qCLUE 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, qCLUE marries decades of development with the quadratic speedup provided by quantum computers. We numerically test qCLUE 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 densi-

    Keywords: clustering, cern, High energy physics (HEP), Quantum, Machine Learning and Artificial Intelligence, quantum computation (QC)

    Received: 09 Jul 2024; Accepted: 19 Sep 2024.

    Copyright: © 2024 Gopalakrishnan, Dellantonio, Di Pilato, Redjeb, Pantaleo and Mosca. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Dhruv Gopalakrishnan, University of Waterloo, Waterloo, Canada
    Luca Dellantonio, University of Exeter, Exeter, EX4 4PY, England, United Kingdom
    Antonio Di Pilato, European Organization for Nuclear Research (CERN), Geneva, 1211, Geneva, Switzerland
    Wahid Redjeb, RWTH Aachen University, Aachen, 52056, North Rhine-Westphalia, Germany
    Felice Pantaleo, European Organization for Nuclear Research (CERN), Geneva, 1211, Geneva, Switzerland
    Michele Mosca, University of Waterloo, Waterloo, Canada

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