Quantum-Inspired Graph Partitioning for Data Clustering

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum-inspired graph partitioning offers a novel approach to data clustering, leveraging the principles of quantum mechanics to potentially enhance clustering accuracy and efficiency, particularly in high-dimensional datasets. This paper explores the potential of quantum algorithms to partition data space more effectively, addressing limitations of traditional clustering techniques. We investigate how quantum-inspired graph partitioning could improve the quality of cluster assignments, offering a promising avenue for advancements in data analysis and pattern recognition. The research focuses on the theoretical design and potential implementation of a quantum-based graph partitioning method, with an emphasis on its ability to handle high-dimensional data effectively. The paper will detail the core mechanism of the proposed approach, analyze its potential benefits, and discuss the challenges and future directions of research in this area.

DOI: 10.5281/zenodo.22577399

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