Quantum Adaptive Probability Model

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel probabilistic model based on quantum adaptation, designed to efficiently model and predict complex systems. The core mechanism leverages quantum self-adaptive algorithms to dynamically adjust probability model parameters, resulting in enhanced prediction accuracy. Traditional probabilistic models suffer from limitations in adaptability, necessitating manual parameter tuning. This paper demonstrates the effectiveness of quantum self-adaptation in significantly improving predictive capabilities across a range of complex systems. This research utilizes a quantum self-adaptive algorithm to fine-tune probability distribution, thereby enhancing the model's accuracy.

DOI: 10.5281/zenodo.22195572

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