mlsanityData to 7 Oct 2026

Hybrid Quantum-Classical Graph Convolutional Network

arXiv 2101.06189 ↗First posted 15 Jan 2021cs.LG cs.CV hep-excited by 21

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Abstract (arXiv)

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 50% 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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 35 of its 90 references that are in the map. First posted on arXiv 15 Jan 2021.

Builds on

35 of 90 references.

References · 35

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23 more
  1. 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 363
  2. 2019Parameterized quantum circuits as machine learning modelscited by 330
  3. 2015Deep Convolutional Networks on Graph-Structured Datacited by 246
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  5. 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 145
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  13. 2020Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognitioncited by 56
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 3; 2023: 4; 2024: 4; 2025: 2; 2026: 1.

21 papers.

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  1. 2021Federated Quantum Machine Learningcited by 82
  2. 2021Quantum Architecture Search via Deep Reinforcement Learningcited by 58
  3. 2021Variational Quantum Reinforcement Learning via Evolutionary Optimizationcited by 50
  4. 2024Quantum Convolutional Neural Networks are Effectively Classically Simulablecited by 33
  5. 2021Quantum machine learning with differential privacycited by 30
  6. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 29
  7. 2021Quantum Architecture Search via Continual Reinforcement Learningcited by 22
  8. 2021Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstructioncited by 20
  9. 2022Reservoir Computing via Quantum Recurrent Neural Networkscited by 14
  10. 2023Unravelling physics beyond the standard model with classical and quantum anomaly detectioncited by 9
  11. 2022Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approachcited by 8
  12. 20233D-QAE: Fully Quantum Auto-Encoding of 3D Point Cloudscited by 5
9 more
  1. 2022From Quantum Graph Computing to Quantum Graph Learning: A Surveycited by 4
  2. 2023mathbbZ2× mathbbZ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networkscited by 4
  3. 2021Equivariant Quantum Graph Circuitscited by 2
  4. 2024Optimizing Quantum Convolutional Neural Network Architectures for Arbitrary Data Dimensioncited by 2
  5. 2024A unifying primary framework for quantum graph neural networks from quantum graph statescited by 1
  6. 2025Learnable quantum spectral filters for hybrid graph neural networkscited by 1
  7. 2024Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learningcited by 0
  8. 2025A Spatio-Temporal Hybrid Quantum-Classical Graph Convolutional Neural Network Approach for Urban Taxi Destination Predictioncited by 0
  9. 2026Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Eracited by 0