基于量子纠缠的自适应图的优化算法

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm based on quantum entanglement, designed to improve the stability and robustness of optimization processes. Traditional optimization methods often overlook the inherent properties of entanglement, offering a potential pathway for enhanced performance. This work proposes a framework wherein parameters are optimized through the manipulation of entangled states, leading to a more resilient and reliable solution. We detail the core mechanism of utilizing entanglement to guide the optimization process, emphasizing the stability gained through this approach. The research explores the potential benefits of this quantum-enhanced optimization strategy for a range of problems, aiming to provide a significant advancement over existing algorithms.

DOI: 10.5281/zenodo.22221904

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