基于自适应量子纠缠的优化算法

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel optimization algorithm, termed "Quantum-Adaptive Optimization" (QAO), designed to accelerate the optimization of complex systems, particularly within the realms of neural network training and parameter adjustment. The core of QAO lies in leveraging the unique properties of quantum entanglement to facilitate parallel computation during the optimization process. This approach overcomes the limitations of traditional optimization methods, which often struggle with the inherent complexity of these systems. The algorithm incorporates a self-tuning mechanism that adapts to the system's dynamics, enabling a more efficient and robust optimization procedure. This work contributes to the exploration of quantum computing's potential within optimization, offering a promising path toward significant advancements in machine learning and related fields.

DOI: 10.5281/zenodo.22197158

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