基于量子势的非线性优化算法与自适应神经网络的融合

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper explores the integration of a novel quantum-enhanced optimization framework, leveraging a quantum potential function to guide the optimization process. We propose a hybrid approach combining classical optimization techniques with a dynamically adjusted neural network, designed to improve the accuracy and efficiency of solutions across a range of optimization problems. The core mechanism centers around the utilization of quantum machine learning algorithms within the quantum potential landscape. This research addresses the limitations of traditional optimization methods by introducing quantum properties that offer a distinct advantage in complex optimization challenges. The paper details the design of this framework, analyzes its performance through experimental validation, and discusses potential future directions for research and development.

DOI: 10.5281/zenodo.22245381

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