mlsanityData to 14 Oct 2026

Quantum Graph Learning: Frontiers and Outlook

arXiv 2302.00892 ↗First posted 2 Feb 2023cs.LGcited by 6

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Abstract (arXiv)

Quantum theory has shown its superiority in enhancing machine learning. However, facilitating quantum theory to enhance graph learning is in its infancy. This survey investigates the current advances in quantum graph learning (QGL) from three perspectives, i.e., underlying theories, methods, and prospects. We first look at QGL and discuss the mutualism of quantum theory and graph learning, the specificity of graph-structured data, and the bottleneck of graph learning, respectively. A new taxonomy of QGL is presented, i.e., quantum computing on graphs, quantum graph representation, and quantum circuits for graph neural networks. Pitfall traps are then highlighted and explained. This survey aims to provide a brief but insightful introduction to this emerging field, along with a detailed discussion of frontiers and outlook yet to be investigated.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 18 of its 71 references that are in the map. First posted on arXiv 2 Feb 2023.

Builds on

18 of 71 references.

References · 18

  1. 2018Supervised learning with quantum enhanced feature spacescited by 665
  2. 2020Power of data in quantum machine learningcited by 338
  3. 2020A rigorous and robust quantum speed-up in supervised machine learningcited by 271
  4. 2013Ising formulations of many NP problemscited by 207
  5. 2021Quantum advantage in learning from experimentscited by 188
  6. 2022Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanismcited by 171
  7. 2021Graph Learning: A Surveycited by 141
  8. 2019Quantum Graph Neural Networkscited by 66
  9. 2020A divide-and-conquer algorithm for quantum state preparationcited by 26
  10. 2021Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstructioncited by 20
  11. 2018Graph Convolutional Neural Networks based on Quantum Vertex Saliencycited by 12
  12. 2020Multivariate Relations Aggregation Learning in Social Networkscited by 10
6 more
  1. 2022Graph Augmentation Learningcited by 9
  2. 2022Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approachcited by 8
  3. 2020Big Networks: A Surveycited by 6
  4. 2022From Quantum Graph Computing to Quantum Graph Learning: A Surveycited by 4
  5. 2022Detecting Outlier Patterns with Query-based Artificially Generated Searching Conditionscited by 4
  6. 2022CHIEF: Clustering with Higher-order Motifs in Big Networkscited by 3

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Cited by year

Citing papers per year: 2024: 3; 2025: 3.

6 papers.

  1. 2025Biologically Plausible Brain Graph Transformercited by 10
  2. 2024GQWformer: A Quantum-based Transformer for Graph Representation Learningcited by 1
  3. 2025Learnable quantum spectral filters for hybrid graph neural networkscited by 1
  4. 2024A Laplacian-based Quantum Graph Neural Network for Semi-Supervised Learningcited by 0
  5. 2024Can Geometric Quantum Machine Learning Lead to Advantage in Barcode Classification?cited by 0
  6. 2025Enhancement of Quantum Semi-Supervised Learning via Improved Laplacian and Poisson Methodscited by 0