Continuous-Variable Quantum K-Means Clustering with Photonic Gaussian States

This paper presents a novel approach to the K-Means clustering algorithm using continuous variable (CV) quantum computing, specifically leveraging Gaussian states on quantum photonic hardware. We implement the algorithm uti-lizing Strawberry Fields, a platform for simulating and executing photonic quantum computations. The theoretical framework integrates Gaussian state preparation, quantum interference, and homodyne detection to perform the clustering tasks. We evaluate the performance of the CV quantum K-Means algorithm on var-ious datasets, focusing on clustering accuracy and compactness compared to classical K-Means. Our findings provide valuable insights into the potential of using CV quantum methods for clustering and highlight the feasibility of CV quantum algorithms in machine learning. These results suggest promising directions for future research to further enhance their performance.