基于自适应的图神经网络的量子计算

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum computing, leveraging the principles of quantum mechanics, holds immense promise for solving complex problems intractable for classical computers. However, the development of practical quantum algorithms is hampered by the inherent challenges of quantum data representation and manipulation. This paper presents a novel approach to quantum computation, employing a dynamically adaptive graph neural network (DANN) framework. The core innovation lies in the automatic learning and optimization of the DANN, enabling efficient data distribution modeling and significantly enhancing computational efficiency. We introduce a DANN architecture designed to adapt to the specific characteristics of quantum data, mitigating the complexities associated with traditional graph neural networks. The resulting framework demonstrates improved performance in various quantum computation tasks, showcasing the potential of this adaptive approach to accelerate quantum algorithm development and implementation. This work contributes to the advancement of quantum computing by offering a robust and adaptable methodology for data representation and learning within the quantum realm.

DOI: 10.5281/zenodo.22480796

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