qLUE: A Quantum Clustering Algorithm for Multi- Dimensional Datasets
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arXiv
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| Format: | Preprint |
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2024
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| author | Gopalakrishnan, Dhruv Dellantonio, Luca Di Pilato, Antonio Redjeb, Wahid Pantaleo, Felice Mosca, Michele |
| author_facet | Gopalakrishnan, Dhruv Dellantonio, Luca Di Pilato, Antonio Redjeb, Wahid Pantaleo, Felice Mosca, Michele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00357 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | qLUE: A Quantum Clustering Algorithm for Multi- Dimensional Datasets Gopalakrishnan, Dhruv Dellantonio, Luca Di Pilato, Antonio Redjeb, Wahid Pantaleo, Felice Mosca, Michele Quantum Physics 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. |
| title | qLUE: A Quantum Clustering Algorithm for Multi- Dimensional Datasets |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2407.00357 |