自适应量子算法的优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum algorithm optimization centered on dynamically adjusting quantum algorithm parameters. Traditional optimization methods often rely on fixed parameters, which are inadequate for the inherently variable nature of quantum computation, particularly in the context of large-scale quantum systems. We propose a system that leverages a combination of quantum path optimization and adaptive parameter tuning to significantly enhance the efficiency of quantum algorithms. This research focuses on creating a mechanism that can dynamically adjust quantum algorithm parameters based on the current state of the quantum system, leading to improved performance. The core mechanism involves integrating quantum path optimization with a self-adaptive parameter tuning strategy, enabling the algorithm to self-correct and refine its trajectory. The paper details the proposed system's architecture, demonstrates its effectiveness through theoretical analysis, and provides a preliminary evaluation of its capabilities. The objective is to provide a robust and adaptable framework for optimizing quantum algorithms, addressing a key challenge in the field of quantum computing.

DOI: 10.5281/zenodo.22266831

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