This paper is organized as follows: Section 2 includes the graph theory representation of M-HCB topologies and their corresponding adjacency matrices are proposed; Section 3 introduces the whole ...

In this paper, motivated by the natural graph representation of quantum circuits, we propose a Graph Neural Networks (GNNs) based scheme to predict output expectation values of quantum circuits under ...

Abstract: Existing deep learning-based circuit design methods mostly focused on the primary matching of the model itself or circuit data, lacking generalizability and ignoring deep representation of ...

Recently, graph neural networks (GNNs) have been applied to various circuit applications, where circuit topology is leveraged in the learning of the models. However, the aggregation of GNN models has ...

Eulerian Circuit Eulerian circuits must start and end on the same vertex. As it starts and ends on the same vertex, this vertex must be even. Therefore, Eulerian circuits only exist on a graph when it ...