基于自适应的量子算法优化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum algorithms, with their inherent complexity and sensitivity to initial conditions, pose significant challenges for optimization. Traditional optimization methods often struggle to effectively utilize the quantum properties of algorithms, limiting their potential for speed and accuracy. This paper proposes a novel approach to quantum algorithm optimization based on adaptive parameter adjustment. We introduce a system of quantum algorithms that dynamically adjusts parameters based on the algorithm's state, allowing the system to self-optimize and improve performance. The core mechanism of this approach leverages quantum properties to continually refine the algorithm's trajectory, leading to enhanced efficiency and accuracy. We present a preliminary demonstration of this method on a specific benchmark problem, showcasing its ability to outperform traditional optimization techniques. This work addresses a critical gap in the field of quantum algorithm optimization and offers a promising pathway toward more effective utilization of quantum resources.

DOI: 10.5281/zenodo.22195695

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