基于量子计算的自适应优化算法

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel quantum computing-based optimization algorithm designed for enhanced performance and generalization capabilities. Traditional optimization algorithms often struggle with complex, high-dimensional problems, requiring extensive parameter tuning. We propose a quantum algorithm that leverages quantum entanglement to dynamically adjust optimization parameters during the optimization process. This dynamic adjustment fosters a more efficient and robust optimization pathway, potentially surpassing the capabilities of classical algorithms in certain scenarios. The core mechanism relies on exploiting quantum properties to explore the solution space more effectively. We analyze the algorithm's potential benefits, including improved convergence speed and robustness against local optima, and provide a preliminary assessment of its applicability to real-world problems. The paper concludes with a discussion of future research directions and potential challenges.

DOI: 10.5281/zenodo.22485931

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