Probability Matrix Dynamics and Quantum Computing Integration

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the integration of probabilistic matrix modeling with quantum computing to facilitate real-time system state modeling and prediction. Traditional probabilistic matrix approaches often struggle with dynamic environments, necessitating a novel dynamic adjustment mechanism. We introduce a framework that leverages quantum computing's inherent probabilistic properties to dynamically adjust matrix weights, enabling more accurate simulations and precise predictions. This work addresses the limitations of static parameter-based modeling by integrating quantum computation for enhanced system behavior representation. The core mechanism centers on adjusting probabilities based on quantum fluctuations, ultimately improving the responsiveness and accuracy of the modeling process. This represents a significant step towards adaptive system representation, particularly within complex, dynamic systems.

DOI: 10.5281/zenodo.22191367

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