Quantum Entanglement-Based Probability Model Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel probabilistic model optimization algorithm based on quantum entanglement. Traditional optimization methods often struggle with the complexities of quantum states, necessitating a shift towards quantum-inspired approaches. We propose a framework that leverages quantum entanglement to enhance the optimization process, resulting in improved model accuracy and stability. The core mechanism centers around introducing entangled quantum states into the model parameters, facilitating a more nuanced and effective parameter space exploration. This approach addresses limitations of classical optimization techniques and offers a promising pathway for advanced probabilistic modeling.

DOI: 10.5281/zenodo.22265088

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