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Quantum Entanglement Optimization Algorithm

Jincheng Zhang

Published Sep 2, 2026• 0 citations
Publisher

Abstract

Quantum entanglement, a fundamental quantum phenomenon, offers the potential for unprecedented computational capabilities. Harnessing this potential, Quantum Entanglement Optimization Algorithm (QEOA) presents a novel approach to optimize quantum entanglement algorithms, aiming to enhance computational efficiency and security. This paper introduces a dynamic parameter adjustment strategy for entanglement operators, leveraging self-adaptive learning techniques to iteratively refine the entanglement's state. The core mechanism focuses on quantifying and optimizing entanglement parameters to minimize computational overhead and maximize security guarantees. The study explores the practical implications of this algorithm across various entanglement-based tasks, demonstrating significant improvements in both performance and robustness.

DOI
10.5281/zenodo.22243618

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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.
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