Boost clustering with Gaussian Boson Sampling: a full quantum approach

TL;DR

This paper introduces a novel quantum clustering method leveraging Gaussian Boson Sampling (GBS), which outperforms classical algorithms on certain datasets by directly sampling dense subgraphs without classical preprocessing.

Contribution

The work presents the first full quantum clustering approach based on GBS, demonstrating its effectiveness and potential advantages over classical clustering algorithms.

Findings

GBS-based clustering outperforms classical algorithms on two of three metrics

The approach is viable as a full-quantum clustering method

Results are obtained using a GBS simulator

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.