自适应线性矩阵分解的量子算法

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel approach to quantum algorithm design, focusing on the implementation of matrix multiplication through an adaptive linear matrix decomposition (ALM) framework. The core principle of this algorithm leverages quantum entanglement and superposition to achieve enhanced computational efficiency, specifically designed to optimize matrix multiplication operations. We demonstrate the effectiveness of this design through theoretical analysis and preliminary experiments, showcasing significant performance improvements over existing quantum algorithms for matrix multiplication. The study highlights the potential of ALM to unlock new capabilities in quantum computation, offering a pathway towards more robust and scalable quantum algorithms.

DOI: 10.5281/zenodo.22186484

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