Quantum Graph Convolutional Neural Networks

Abstract

At present, there are a large number of quantum neural network models to deal with Euclidean spatial data, while little research have been conducted on non-Euclidean spatial data. In this paper, we propose a novel quantum graph convolutional neural network (QGCN) model based on quantum parametric circuits and utilize the computing power of quantum systems to accomplish graph classification tasks in traditional machine learning. The proposed QGCN model has a similar architecture as the classical graph convolutional neural networks, which can illustrate the topology of the graph type data and efficiently learn the hidden layer representation of node features as well. Numerical simulation results on a graph dataset demonstrate that the proposed model can be effectively trained and has good performance in graph level classification tasks.

References in corpus (12)

• Semi-Supervised Classification with Graph Convolutional Networks
• Quantum Machine Learning
• Quantum-Chemical Insights from Deep Tensor Neural Networks
• Quantum Convolutional Neural Networks
• Quantum advantage with shallow circuits
• Minimal Universal Two-qubit Quantum Circuits
• A universal quantum circuit for two-qubit transformations with three CNOT gates
• An Artificial Neuron Implemented on an Actual Quantum Processor
• PennyLane: Automatic differentiation of hybrid quantum-classical computations
• Circuit-Based Quantum Random Access Memory for Classical Data
• Toward Trainability of Quantum Neural Networks
• End-to-End Quantum Machine Learning Implemented with Controlled Quantum Dynamics

Cited by in corpus (1)

• A review of Quantum Neural Networks: Methods, Models, Dilemma