Quantum Graph Neural Networks
This work addresses the challenge of executing quantum neural networks on distributed quantum systems, offering a novel framework for quantum machine learning with potential applications in quantum networks and quantum information processing.
The authors tackled the problem of representing quantum processes with graph structures by introducing Quantum Graph Neural Networks (QGNN), including specialized variants like QGRNN and QGCNN, and demonstrated their applicability in four example tasks such as learning Hamiltonian dynamics and graph isomorphism classification.
We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum neural network ansatze which are tailored to represent quantum processes which have a graph structure, and are particularly suitable to be executed on distributed quantum systems over a quantum network. Along with this general class of ansatze, we introduce further specialized architectures, namely, Quantum Graph Recurrent Neural Networks (QGRNN) and Quantum Graph Convolutional Neural Networks (QGCNN). We provide four example applications of QGNNs: learning Hamiltonian dynamics of quantum systems, learning how to create multipartite entanglement in a quantum network, unsupervised learning for spectral clustering, and supervised learning for graph isomorphism classification.