基于量子自旋的拓扑结构优化算法

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel quantum spin-based optimization algorithm designed to enhance the performance of quantum computing algorithms. Traditional optimization methods rely on fixed parameters, often limiting their effectiveness in complex problems. Our approach leverages the inherent adaptability of quantum spin systems to dynamically adjust optimization parameters, promising significant improvements in computational efficiency. The core mechanism centers around utilizing the self-steering properties of quantum spin systems to guide the optimization process, resulting in more robust and efficient solutions compared to conventional algorithms. This work demonstrates the potential of this algorithm to address challenges in the design and implementation of quantum algorithms.

DOI: 10.5281/zenodo.22193019

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