基于量子纠缠的动态量子算法优化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement offers a novel approach to algorithm optimization, enabling dynamic parameter adjustments based on quantum correlations. This paper investigates the design of a quantum algorithm optimization framework leveraging entanglement to adaptively adjust algorithm parameters. We outline the core mechanism, highlighting the use of quantum states to dynamically influence the optimization process. The proposed method promises to surpass traditional optimization techniques by exploiting inherent quantum properties to achieve superior results with significantly reduced computational complexity. Furthermore, we analyze the potential benefits and challenges associated with this approach, including the required infrastructure and theoretical limitations.

DOI: 10.5281/zenodo.22205506

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