基于量子自适应的概率模型优化算法

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel probabilistic model optimization algorithm based on quantum self-adaptation. Traditional optimization methods often rely on manual parameter adjustments, lacking adaptability. We propose a new algorithm that leverages the unique properties of quantum probabilistic models to dynamically adjust model parameters, resulting in significantly more accurate probability predictions. The core mechanism centers around a quantum probabilistic model framework, enabling automated parameter adjustment and improved predictive accuracy. This algorithm offers a promising advancement in probabilistic modeling and optimization, particularly for complex systems with high-dimensional data.

DOI: 10.5281/zenodo.22325963

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