基于量子自适应的计算图优化

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel computational graph optimization technique leveraging quantum self-adaptivity. Traditional graph optimization methods often rely on static parameter settings, which struggle to effectively address the dynamic nature of complex systems. We propose a quantum self-adaptative algorithm that dynamically adjusts quantum state parameters to enhance both computational efficiency and accuracy. The core mechanism involves constructing a dynamic graph structure and employing an iterative adjustment process based on quantum state transformations. This approach offers a significant advancement over existing methods by providing a more flexible and adaptive optimization strategy, particularly suitable for systems exhibiting complex dynamic behavior. The proposed method demonstrates substantial improvements in various graph optimization tasks, highlighting its potential for revolutionizing the field of computational graph design.

DOI: 10.5281/zenodo.22592749

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