Quantum Entanglement-Based Dynamic Probability Model Optimization (QEDMO)

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel algorithm, Quantum Entanglement-Based Dynamic Probability Model Optimization (QEDMO), designed to enhance the accuracy of probabilistic models through the utilization of quantum entanglement. Traditional methods often struggle to fully capture the intricate relationships within probabilistic systems, limiting their predictive power. QEDMO dynamically adjusts probability model parameters based on entanglement, creating a system that adapts to the data and improves overall model performance. We explore the core mechanism and preliminary results demonstrating the effectiveness of this approach in optimizing probabilistic models.

DOI: 10.5281/zenodo.22188118

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