基于量子拓扑的自适应参数优化

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper presents a novel algorithm for neural network parameter optimization based on quantum topological principles. Traditional optimization methods often struggle with complex network architectures and require substantial manual tuning. We introduce a quantum-enhanced optimization framework that leverages quantum entanglement and topological structures to achieve adaptive parameter adjustments, potentially leading to improved model generalization. The core mechanism involves utilizing quantum entanglement to create a dynamic, self-regulating optimization process, enabling the algorithm to explore a wider parameter space and discover optimal configurations. We provide a detailed mathematical framework, including a quantum circuit representation of the optimization process, and discuss the potential benefits of this approach in enhancing neural network performance.

DOI: 10.5281/zenodo.22446540

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