论文标题
使用量子计算的聚合扩展图形变压器
Extending Graph Transformers with Quantum Computed Aggregation
论文作者
论文摘要
最近，随着信息传递神经网络的限制变得更加明显，社区已经努力设计新的图神经网络（GNN）。这导致了使用全局图特征（例如Laplacian eigenmaps）出现图形变压器。在我们的论文中，我们引入了GNN体系结构，其中使用量子系统的远程相关性计算了聚合权重。这些相关性是通过将图形拓扑转换为量子计算机中一组Qubits的相互作用来生成的。这项工作的启发是受量子处理单元的最新开发的启发，该单元可以计算一个新的全球图形功能，否则这些功能将无法实现经典硬件。我们对这种方法的潜在优势给出了一些理论见解，并在标准数据集上基于我们的算法进行基准测试。尽管没有适应所有数据集，但我们的模型的性能与标准GNN体系结构相似，并为量子增强的GNN铺平了有希望的未来。
Recently, efforts have been made in the community to design new Graph Neural Networks (GNN), as limitations of Message Passing Neural Networks became more apparent. This led to the appearance of Graph Transformers using global graph features such as Laplacian Eigenmaps. In our paper, we introduce a GNN architecture where the aggregation weights are computed using the long-range correlations of a quantum system. These correlations are generated by translating the graph topology into the interactions of a set of qubits in a quantum computer. This work was inspired by the recent development of quantum processing units which enables the computation of a new family of global graph features that would be otherwise out of reach for classical hardware. We give some theoretical insights about the potential benefits of this approach, and benchmark our algorithm on standard datasets. Although not being adapted to all datasets, our model performs similarly to standard GNN architectures, and paves a promising future for quantum enhanced GNNs.
