基于自适应量子势的优化算法

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm based on adaptive quantum potentials, designed to address the challenges of optimizing complex models effectively. Traditional optimization methods often rely on fixed parameters, limiting their applicability to diverse model structures. Our algorithm dynamically adjusts the quantum potential during the optimization process, adapting to the model's inherent complexity. We demonstrate the algorithm's efficacy through a series of illustrative examples, showcasing its superior performance compared to existing optimization techniques across multiple benchmark datasets. The core mechanism leverages a novel approach to model parameter adjustment based on a dynamically evolving quantum potential, offering enhanced precision and adaptability within complex optimization scenarios.

DOI: 10.5281/zenodo.22296626

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