基于图神经网络的自适应量子计算控制策略

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum computing offers unprecedented computational power, but current control strategies often struggle with the inherent complexity of quantum systems. This paper introduces a novel approach to quantum computing control – a deep learning-based graph neural network (GNN) model – designed to adaptively control quantum systems. We propose a system where the GNN learns a representation of the quantum system's internal graph structure, allowing for dynamic adjustments to control parameters. This approach significantly enhances the robustness and efficiency of quantum computations. The core mechanism involves constructing a dynamic graph representing the quantum state, and training the GNN to optimize the system's evolution within this graph. We demonstrate the efficacy of this method through simulations and preliminary experiments, showcasing improved control stability and enhanced quantum computation efficiency compared to traditional approaches.

DOI: 10.5281/zenodo.22249302

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