基于量子隐马计算的自适应模型参数调整

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper introduces a novel algorithm for model parameter adjustment based on quantum entanglement, aiming to enhance model performance through dynamic adjustment. Traditional parameter tuning methods often rely on iterative optimization, which can be computationally expensive. We propose a quantum entanglement-driven approach that utilizes the inherent properties of quantum entanglement to dynamically adjust model parameters, fostering improved stability and performance. The core mechanism leverages quantum entanglement to create a correlated state that allows for efficient and nuanced parameter optimization. This approach offers a potential paradigm shift in model training, particularly for complex and high-dimensional models.

DOI: 10.5281/zenodo.22225337

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