拓扑-量子融合算法的优化

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm, termed "拓扑-量子融合算法", designed to address complex system parameter configuration optimization. Traditional optimization methods often rely on classical computation, while this approach integrates topological and quantum entanglement to enhance computational efficiency. The algorithm leverages quantum entanglement to accelerate the optimization process, leading to improved parameter configuration. We present a detailed theoretical analysis and preliminary implementation demonstrating the algorithm's effectiveness in solving specific optimization problems. The core mechanism centers around constructing a topological structure that guides the exploration of parameter space and utilizing entanglement to accelerate the search for optimal configurations. This paper explores the potential of this integrated approach to enhance optimization performance, particularly for systems with intricate parameter landscapes.

DOI: 10.5281/zenodo.22322122

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