Dynamic Multi-Particle Quantum Entanglement

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper investigates the development of a novel dynamic parameter adjustment algorithm for quantum systems, leveraging the unique properties of quantum entanglement. Traditional parameter adjustment methods often rely on manual intervention, limiting precision and efficiency. Our approach utilizes machine learning to dynamically adjust quantum system parameters, offering a significant advancement in modeling and control, particularly within the realms of quantum computing and quantum communication. The core mechanism centers around learning and automated parameter optimization based on a carefully designed feedback loop. We demonstrate the effectiveness of this algorithm through simulations and theoretical analysis, showcasing its potential to achieve accurate and efficient modeling of complex quantum systems.

DOI: 10.5281/zenodo.22296239

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