Invariant and Equivariant Classical and Quantum Graph Neural Networks

Deep geometric methods, such as graph neural networks (GNNs), can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. We test classical GNNs and equivariant GNNs (EGNNs) and their quantum counterparts, namely quantum GNNs (QGNNs) and equivariant quantum GNNs (EQGNN). Based on their AUC scores, the quantum networks were shown to outperform the classical networks. (2023 Google Summer of Code Project)