Boost clustering with Gaussian Boson Sampling: a full quantum approach

• URL: http://arxiv.org/abs/2307.13348v1
• Date: Tue, 25 Jul 2023 09:05:24 GMT
• Title: Boost clustering with Gaussian Boson Sampling: a full quantum approach
• Authors: Nicol\`o Bonaldi, Martina Rossi, Daniele Mattioli, Michele Grapulin, Blanca Silva Fern\'andez, Davide Caputo, Marco Magagnini, Arianna Osti, and Fabio Veronese
• Abstract summary: We propose a novel clustering approach based on Gaussian Boson Sampling (GBS) We benchmark our approach with two well-known classical clustering algorithms. Results show that our approach outperforms the two classical algorithms in two out of the three chosen metrics.
• Score: 0.09437521840642138
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Gaussian Boson Sampling (GBS) is a recently developed paradigm of quantum computing consisting of sending a Gaussian state through a linear interferometer and then counting the number of photons in each output mode. When the system encodes a symmetric matrix, GBS can be viewed as a tool to sample subgraphs: the most sampled are those with a large number of perfect matchings, and thus are the densest ones. This property has been the foundation of the novel clustering approach we propose in this work, called GBS-based clustering, which relies solely on GBS, without the need of classical algorithms. The GBS-based clustering has been tested on several datasets and benchmarked with two well-known classical clustering algorithms. Results obtained by using a GBS simulator show that on average our approach outperforms the two classical algorithms in two out of the three chosen metrics, proposing itself as a viable full-quantum clustering option.

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