基于量子势场的自适应拓扑优化算法

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper introduces a novel algorithm for parameter adjustment based on quantum fields, leveraging quantum entanglement to achieve more efficient optimization. Traditional optimization algorithms often struggle with complex problems due to their fixed parameter settings. This proposed approach utilizes a dynamic quantum field representation and quantum entanglement to bypass local optima and potentially unlock significant improvements in parameter tuning across a wide range of problems. The algorithm's core mechanism centers on encoding the problem's parameters within the quantum field, enabling a more sophisticated exploration of the parameter space. We demonstrate its effectiveness through a series of illustrative examples, showcasing enhanced performance and a novel approach to parameter optimization.

DOI: 10.5281/zenodo.22154066

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