Quantum machine learning is a fast-emerging field that aims to tackle machine learning using quantum algorithms and quantum computing. Due to the lack of physical qubits and an effective means to map real-world data from Euclidean space to Hilbert space, most of these methods focus on quantum analogies or process simulations rather than devising concrete architectures based on qubits. In this paper, we propose a novel hybrid quantum-classical algorithm for graph-structured data, which we refer to as the Ego-graph based Quantum Graph Neural Network (egoQGNN). egoQGNN implements the GNN theoretical framework using the tensor product and unity matrix representation, which greatly reduces the number of model parameters required. When controlled by a classical computer, egoQGNN can accommodate arbitrarily sized graphs by processing ego-graphs from the input graph using a modestly-sized quantum device. The architecture is based on a novel mapping from real-world data to Hilbert space. This mapping maintains the distance relations present in the data and reduces information loss. Experimental results show that the proposed method outperforms competitive state-of-the-art models with only 1.68\% parameters compared to those models.

翻译：量子机器学习是一个快速兴起的研究领域，旨在利用量子算法和量子计算解决机器学习问题。由于缺乏物理量子比特以及将现实世界数据从欧几里得空间有效映射到希尔伯特空间的手段，目前大多数方法侧重于量子类比或过程模拟，而非基于量子比特设计具体架构。本文提出了一种面向图结构数据的混合量子-经典算法，称之为基于Ego-Graph的量子图神经网络（egoQGNN）。egoQGNN采用张量积和单位矩阵表示实现图神经网络理论框架，大幅降低了所需的模型参数数量。在经典计算机控制下，该模型可通过在输入图上使用规模适中的量子设备处理Ego-Graph，从而适应任意大小的图。其架构基于一种新型的现实世界数据到希尔伯特空间的映射方法，该映射保持了数据中的距离关系并减少了信息损失。实验结果表明，所提方法仅需对比模型1.68%的参数即可超越当前最先进的模型性能。