Adaptive Probability Model based on Quantum Entanglement

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper investigates the potential of quantum entanglement to enhance probabilistic modeling, addressing limitations of current approaches to handling complex data distributions and high-dimensional probability landscapes. We propose a novel framework leveraging quantum entanglement to represent data as entangled states, enabling adaptive parameter adjustment and improved predictive accuracy. The core mechanism involves exploiting the non-classical correlations inherent in entangled systems to facilitate the exploration of probabilistic space and ultimately improve the precision of probabilistic predictions. The study explores the theoretical underpinnings of this approach, presenting preliminary results demonstrating enhanced predictive capabilities compared to classical probabilistic models, particularly in scenarios involving rapidly changing data. The focus is on a practical implementation utilizing a simplified quantum simulation to illustrate the core concepts.

DOI: 10.5281/zenodo.22440288

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