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Quantum Machine Learning Quantum Simulation

Biclustering a dataset using photonic quantum computing

arXiv
Authors: Ajinkya Borle, Ameya Bhave

Year

2024

Paper ID

67177

Status

Preprint

Abstract Read

~2 min

Abstract Words

119

Citations

N/A

Abstract

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.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2024 reference point for readers tracking recent quantum research.
  • 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.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2405.18622 [2] arXiv https://arxiv.org/abs/2405.18622 [3] Publisher https://arxiv.org/abs/2405.18622

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