基于自适应高阶矩阵的量子算法优化

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper presents a novel quantum algorithm optimization framework based on adaptive high-order matrices. Traditional quantum algorithm optimization often relies on static parameter adjustments, limiting its applicability to complex algorithms. Our proposed method dynamically adjusts high-order matrix parameters to optimize quantum algorithm performance, thereby significantly enhancing computational efficiency. We detail the framework's architecture, explain the key optimization strategy, and provide illustrative examples demonstrating its effectiveness across a range of quantum algorithms. The core mechanism lies in the iterative refinement of high-order matrix configurations to achieve optimal performance.

DOI: 10.5281/zenodo.22629967

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