基于量子纠缠的自适应系统优化

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper explores the application of quantum entanglement to enhance optimization algorithms, focusing on a novel self-adaptive system optimization approach. Traditional optimization methods often suffer from limitations in scalability and the potential for local optima. This work proposes a quantum-enhanced optimization strategy predicated on the instantaneous correlations inherent in entangled states, aiming for rapid global adjustments across complex systems. We detail the core mechanism, outline the algorithm's structure, and present preliminary results demonstrating the potential benefits of this approach. The research emphasizes the advantages of quantum entanglement over classical methods in tackling intricate optimization problems.

DOI: 10.5281/zenodo.22220161

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