基于自适应的非线性矩阵运算的量子算法 (QMAPQ)

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum Algorithm Mapping and Parallelization with Adaptive Matrix Operations (QMAPQ) presents a novel quantum algorithm designed to address the limitations of traditional matrix operations in quantum computing. This research explores the implementation of a dynamically adjustable matrix operation scheme, leveraging quantum algorithms and adaptive complexity control to enhance the efficiency of large-scale matrix computations. The core of QMAPQ is based on the concept of iteratively adjusting the computational complexity of matrix operations through quantum entanglement and superposition. This allows the algorithm to potentially surpass the performance limitations of fixed-parameter algorithms, offering a pathway toward more scalable and efficient quantum computations. The paper details the design of the adaptive matrix operation module, the quantum circuit implementation, and preliminary results demonstrating the algorithm's potential for increased computational throughput.

DOI: 10.5281/zenodo.22608862

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