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发展的领域,旨在使用量子算法和量子计算解决机器学习。由于缺乏物理量子比特和有效的映射机制将实际数据从欧几里得空间映射到希尔伯特空间,大多数方法都集中于利用类比或处理模拟,而不是设计基于量子比特的具体体系结构。本文提出了一种新颖的混合量子-经典算法来处理图结构数据,我们称之为基于自我图的量子图神经网络(egoQGNN)。egoQGNN使用张量积和单位矩阵表示实现了GNN理论框架,大大减少了所需的模型参数数量。当由经典计算机控制时,egoQGNN可以通过使用来自输入图的自我图处理器在量子设备上处理任意大小的图。该架构基于一种实现从现实世界数据到希尔伯特空间的新映射。这种映射保留了数据中存在的距离关系并减少了信息丢失。实验结果表明,与具有相似性能的最新模型相比,所提出的方法只需1.68%的参数即可实现更好的性能。