Quantum Graph Embeddings for Complex Data Analysis

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Graph Embeddings represent a novel approach to data analysis, leveraging the power of quantum algorithms to create and manipulate graph structures. This paper introduces a method for generating quantum graphs with enhanced representation capabilities, specifically designed for complex data analysis. We explore the potential of quantum algorithms to efficiently explore graph structure, identify key nodes and connections, and ultimately uncover hidden patterns and relationships within datasets. The core mechanism focuses on creating a scalable and adaptable framework for data representation, addressing limitations inherent in traditional graph embedding techniques. This work proposes a method that is inherently scalable, potentially offering significant advantages in the analysis of intricate and high-dimensional datasets.

DOI: 10.5281/zenodo.22291231

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