Quantum State-Based Dynamic Probability Model Optimization (QSPMO)

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel dynamic probability model optimization algorithm, QSPMO, which leverages quantum state representations to dynamically adjust probability model parameters, leading to improved model prediction accuracy. Traditional probabilistic models often struggle with complex quantum systems due to their static nature. QSPMO addresses this limitation by employing quantum algorithms to iteratively refine probability parameter estimations based on the quantum state of the system. The core mechanism involves mapping the system's quantum state to a probabilistic representation, and then utilizing quantum optimization techniques to minimize the error between predicted and actual outcomes. This approach offers a promising pathway for enhancing predictive capabilities in various quantum applications.

DOI: 10.5281/zenodo.22187983

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