基于量子纠缠的自适应张量变换

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel self-adaptive tensor transformation algorithm leveraging the principles of quantum entanglement. We propose a method that utilizes entangled states to enable dynamic and intelligent tensor operations, moving beyond static transformation paradigms. The core mechanism centers around the introduction of a "纠缠度量" parameter, which modulates the entanglement strength during the transformation process. This allows for a degree of adaptability and responsiveness not achievable through traditional methods. The algorithm's effectiveness is demonstrated through rigorous analysis and preliminary experimental results, showcasing improved performance in certain tensor operations compared to existing approaches. This work explores the potential of quantum entanglement to revolutionize tensor algebra, offering a pathway towards more efficient and expressive computation.

DOI: 10.5281/zenodo.22604055

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