基于自适应矩阵的量子算法优化

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper presents a novel quantum algorithm optimization framework based on adaptive matrix parameters. Traditional matrix optimization methods are often static, failing to fully leverage the nuances of specific quantum algorithms and data characteristics. This work introduces a dynamic adjustment mechanism that iteratively refines matrix parameters to enhance algorithm performance, effectively mitigating potential errors inherent in quantum computations. The proposed approach offers a significant improvement over existing methods by providing a flexible and adaptive optimization strategy. We detail the framework's architecture, the optimization process, and preliminary experimental results demonstrating its effectiveness across a range of quantum algorithms.

DOI: 10.5281/zenodo.22152709

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