Dynamic Adaptive Quantum Graph Optimization

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel dynamic adaptive quantum graph optimization algorithm designed to enhance the efficiency and performance of quantum computation. Traditional quantum graph optimization methods are often static, limiting the potential for significant improvements. This work proposes a dynamic approach that automatically adjusts the quantum graph structure during the optimization process, leading to more effective solutions and enhanced computational capabilities. The core mechanism centers around iterative adjustments to the graph, guided by a feedback loop that continuously evaluates the quality of the resulting quantum circuits. We demonstrate the effectiveness of this algorithm through a series of simulations and analyses, showcasing its ability to outperform static optimization techniques in various quantum circuit scenarios.

DOI: 10.5281/zenodo.22447727

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