基于量子纠缠的自适应图谱构建

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the potential of leveraging quantum entanglement for adaptive graph construction. Traditional graph representation methods often require manual parameter adjustments, limiting their applicability in dynamic environments. We propose a novel algorithm that utilizes quantum entanglement to dynamically update and adjust graph representations, offering a fundamentally different approach to graph modeling. This research investigates the feasibility of establishing a quantum-enhanced graph structure capable of adapting to evolving data. We demonstrate a preliminary implementation of this algorithm, showcasing its ability to dynamically update graph connectivity based on measured data. This work introduces a new paradigm for graph construction, potentially opening doors to more sophisticated data representation and analysis. The core mechanism centers around exploiting entanglement to facilitate rapid and localized adjustments to the graph's connectivity, providing a method for representing evolving data. The paper concludes with a discussion of future research directions and potential applications of this quantum-inspired graph construction technique.

DOI: 10.5281/zenodo.22193160

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