基于自适应量子算法的非线性概率模型

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper presents a novel approach to probability modeling leveraging the inherent randomness of quantum computation. We introduce a dynamically adaptive probabilistic model, designed to enhance model precision and improve prediction accuracy through iterative optimization guided by quantum algorithms. The core mechanism centers around utilizing quantum randomness to dynamically adjust probability distributions, enabling the model to adapt to complex environments and achieve superior predictive capabilities. Traditional probabilistic models often struggle with this adaptability. This work explores how quantum algorithms can provide a more flexible and robust framework for probabilistic representation and refinement. The paper details the design of the algorithm, its mathematical foundations, and preliminary experimental results demonstrating its efficacy in specific scenarios.

DOI: 10.5281/zenodo.22578937

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