Quantum Entanglement-Guided Parameter Adjustment Strategy

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum entanglement offers a unique paradigm for optimizing complex systems, particularly within the realm of quantum algorithms. This paper introduces a novel strategy leveraging the phenomenon of quantum entanglement to dynamically adjust parameters within a quantum algorithm, aiming to enhance algorithm efficiency and accuracy. We propose a dynamic parameter adjustment approach centered around the exploitation of entangled states to achieve a more robust and adaptable optimization process. This work demonstrates the effectiveness of this method through theoretical analysis and preliminary experimental validation, showcasing a significant improvement in algorithm performance compared to conventional optimization techniques. The core mechanism focuses on utilizing entangled states to represent the algorithm's state and guide parameter adjustments with a high degree of fidelity.

DOI: 10.5281/zenodo.22266190

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