基于量子纠缠的概率动力系统建模

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the application of quantum entanglement to enhance probabilistic dynamics modeling. Traditional approaches often struggle with complex systems due to computational limitations and potential for suboptimal solutions. We propose a novel framework leveraging quantum entanglement to accelerate the search process and potentially yield improved parameter configurations. The core mechanism focuses on introducing entangled states within the simulation, allowing for a more efficient exploration of the system's state space. This approach offers a fundamentally different perspective on probabilistic dynamics modeling, potentially unlocking new capabilities for complex system analysis. The paper details the theoretical underpinnings of this approach, outlines the implementation process, and presents preliminary results demonstrating the potential benefits of utilizing quantum entanglement in this domain.

DOI: 10.5281/zenodo.22186324

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