Graph states are used to represent mathematical graphs as quantum states on quantum computers. They can be formulated through stabilizer codes or directly quantum gates and quantum states. In this paper we show that a quantum graph neural network model can be understood and realized based on graph states. We show that they can be used either as a parameterized quantum circuits to represent neural networks or as an underlying structure to construct graph neural networks on quantum computers.

翻译：图态用于在量子计算机上将数学图表示为量子态，可通过稳定子码或直接通过量子门和量子态进行公式化。本文证明，基于图态可以理解并实现量子图神经网络模型。我们表明，图态既可作为参数化量子电路用于表示神经网络，也可作为基础结构在量子计算机上构建图神经网络。