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ORIGINAL RESEARCH article

Front. Comput. Sci.
Sec. Theoretical Computer Science
Volume 6 - 2024 | doi: 10.3389/fcomp.2024.1441879
This article is part of the Research Topic Developments in Quantum Algorithms and Computational Complexity for Quantum Computational Models View all articles

Biclustering a dataset using photonic quantum computing

Provisionally accepted
Ajinkya Borle Ajinkya Borle *Ameya Bhave Ameya Bhave
  • University of Maryland, Baltimore County, Baltimore, United States

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

    Biclustering is a problem in machine learning and data mining that seeks to group together rows and columns of a dataset according to certain criteria. In this work, we highlight the natural relation that quantum computing models like boson and Gaussian boson sampling (GBS) have to this problem. We first explore the use of boson sampling to identify biclusters based on matrix permanents. We then propose a heuristic that finds clusters in a dataset using Gaussian boson sampling by (i) converting the dataset into a bipartite graph and then (ii) running GBS to find the densest sub-graph(s) within the larger bipartite graph. Our simulations for the above proposed heuristics show promising results for future exploration in this area.

    Keywords: Biclustering, Quantum computing, boson sampling, Gaussian Boson sampling, Block clustering, co-clustering, Two Mode Clustering, Data Mining

    Received: 31 May 2024; Accepted: 09 Oct 2024.

    Copyright: © 2024 Borle and Bhave. 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: Ajinkya Borle, University of Maryland, Baltimore County, Baltimore, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.