基于量子计算的自适应概率模型

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper investigates the application of quantum computing to enhance probabilistic modeling, specifically focusing on the development of a self-adaptive probability model. The core challenge lies in efficiently simulating complex, dynamic probability processes, which is often computationally demanding. This research proposes a novel quantum algorithm that leverages quantum superposition and entanglement to represent and manipulate probability distributions, enabling dynamic adjustment and improved predictive accuracy. The proposed mechanism addresses limitations of traditional probabilistic models by harnessing quantum phenomena for enhanced computational efficiency and robust prediction. We present preliminary results demonstrating the model's capacity to handle complex scenarios with enhanced precision compared to existing approaches. This work lays the foundation for a new paradigm in probabilistic modeling, potentially revolutionizing simulations in fields requiring high accuracy and adaptability.

DOI: 10.5281/zenodo.22514174

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