基于量子自适应的概率模型优化

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel quantum self-adaptive probability model optimization algorithm. Traditional probabilistic models often rely on manual parameter adjustment, limiting the algorithm's effectiveness. This work leverages the unique properties of quantum entanglement and quantum computing to achieve a more robust and adaptable optimization process. We propose a quantum-enhanced optimization strategy that dynamically adjusts the model parameters based on quantum correlations, resulting in improved model reliability and generalization capabilities. The algorithm's core mechanism centers around establishing and manipulating quantum entanglement between model parameters, facilitating a more efficient and precise parameter search. This approach offers a promising alternative to classical optimization methods, particularly for complex probabilistic models requiring high accuracy and robustness. The results demonstrate the algorithm's effectiveness through a series of simulations and comparisons with established optimization techniques, showcasing its potential for significant advancements in probabilistic modeling.

DOI: 10.5281/zenodo.22329574

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