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Quantum machine learning is a fast-emerging field that aims to tackle machinelearning using quantum algorithms and quantum computing. Due to the lack ofphysical qubits and an effective means to map real-world data from Euclideanspace to Hilbert space, most of these methods focus on quantum analogies orprocess simulations rather than devising concrete architectures based onqubits. In this paper, we propose a novel hybrid quantum-classical algorithmfor graph-structured data, which we refer to as the Ego-graph based QuantumGraph Neural Network (egoQGNN). egoQGNN implements the GNN theoreticalframework using the tensor product and unity matrix representation, whichgreatly reduces the number of model parameters required. When controlled by aclassical computer, egoQGNN can accommodate arbitrarily sized graphs byprocessing ego-graphs from the input graph using a modestly-sized quantumdevice. The architecture is based on a novel mapping from real-world data toHilbert space. This mapping maintains the distance relations present in thedata and reduces information loss. Experimental results show that the proposedmethod outperforms competitive state-of-the-art models with only 1.68\%parameters compared to those models.

Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach