mlsanityData to 7 Oct 2026

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

arXiv 2201.05158 ↗First posted 13 Jan 2022quant-ph cs.LGcited by 8

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Abstract (arXiv)

Quantum machine learning is a fast-emerging field that aims to tackle machine learning using quantum algorithms and quantum computing. Due to the lack of physical qubits and an effective means to map real-world data from Euclidean space to Hilbert space, most of these methods focus on quantum analogies or process simulations rather than devising concrete architectures based on qubits. In this paper, we propose a novel hybrid quantum-classical algorithm for graph-structured data, which we refer to as the Ego-graph based Quantum Graph Neural Network (egoQGNN). egoQGNN implements the GNN theoretical framework using the tensor product and unity matrix representation, which greatly reduces the number of model parameters required. When controlled by a classical computer, egoQGNN can accommodate arbitrarily sized graphs by processing ego-graphs from the input graph using a modestly-sized quantum device. The architecture is based on a novel mapping from real-world data to Hilbert space. This mapping maintains the distance relations present in the data and reduces information loss. Experimental results show that the proposed method outperforms competitive state-of-the-art models with only 1.68% parameters compared to those models.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 33 of its 68 references that are in the map. First posted on arXiv 13 Jan 2022.

Builds on

33 of 68 references.

References · 33

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21 more
  1. 2019Hyperbolic Graph Neural Networkscited by 244
  2. 2018Graph networks as learnable physics engines for inference and controlcited by 242
  3. 2019Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernelscited by 160
  4. 2019Multi-relational Poincar'e Graph Embeddingscited by 126
  5. 2021DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networkscited by 117
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  7. 2017Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approachcited by 102
  8. 2020Transfer Learning of Graph Neural Networks with Ego-graph Information Maximizationcited by 93
  9. 2019Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matchingcited by 83
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  17. 2021Hybrid Quantum-Classical Graph Convolutional Networkcited by 21
  18. 2021Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstructioncited by 20
  19. 2021Quantum Graph Convolutional Neural Networkscited by 18
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 1; 2026: 2.

8 papers.

  1. 2023Quantum Graph Learning: Frontiers and Outlookcited by 6
  2. 2024From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networkscited by 6
  3. 2023Time-Warping Invariant Quantum Recurrent Neural Networks via Quantum-Classical Adaptive Gatingcited by 5
  4. 2023Shot Optimization in Quantum Machine Learning Architectures to Accelerate Trainingcited by 5
  5. 2024A unifying primary framework for quantum graph neural networks from quantum graph statescited by 1
  6. 2025Inductive Graph Representation Learning with Quantum Graph Neural Networkscited by 1
  7. 2026Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Eracited by 0
  8. 2026Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchycited by 0