基于自组织网络的量子计算优化

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel quantum computing optimization framework centered around self-organized networks. Traditional quantum computing optimization relies on static models, but this work proposes a dynamic optimization strategy leveraging the inherent properties of self-organized networks. We detail the design and implementation of this framework, demonstrating its ability to significantly enhance the efficiency of quantum computations through adaptive network adjustments. This approach offers a fundamentally different perspective on quantum algorithm optimization, potentially unlocking new avenues for performance gains. The core mechanism centers around embedding quantum computations into self-organized networks, allowing for continuous optimization driven by network dynamics. This offers a robust and adaptable method for improving quantum system performance. The research highlights the potential of this framework to address limitations of current optimization techniques and paves the way for more efficient quantum computation.

DOI: 10.5281/zenodo.22469739

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