Hybrid Quantum-Classical Graph Convolutional Network
arXiv:2101.06189
Abstract
The high energy physics (HEP) community has a long history of dealing with large-scale datasets. To manage such voluminous data, classical machine learning and deep learning techniques have been employed to accelerate physics discovery. Recent advances in quantum machine learning (QML) have indicated the potential of applying these techniques in HEP. However, there are only limited results in QML applications currently available. In particular, the challenge of processing sparse data, common in HEP datasets, has not been extensively studied in QML models. This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data. The proposed framework demonstrates an advantage over classical multilayer perceptron and convolutional neural networks in the aspect of number of parameters. Moreover, in terms of testing accuracy, the QGCNN shows comparable performance to a quantum convolutional neural network on the same HEP dataset while requiring less than of the parameters. Based on numerical simulation results, studying the application of graph convolutional operations and other QML models may prove promising in advancing HEP research and other scientific fields.
References in corpus (11)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Semi-Supervised Classification with Graph Convolutional Networks
- Deep Convolutional Networks on Graph-Structured Data
- Graph Convolutional Matrix Completion
- Interaction Networks for Learning about Objects, Relations and Physics
- Jet Flavor Classification in High-Energy Physics with Deep Neural Networks
- Classification with Quantum Machine Learning: A Survey
- Hybrid quantum-classical classifier based on tensor network and variational quantum circuit
- Recurrent Quantum Neural Networks
- Quantum Machine Learning and its Supremacy in High Energy Physics
- Laplacian Eigenmaps with variational circuits: a quantum embedding of graph data