mlsanityData to 14 Oct 2026

Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction

arXiv 2109.12636 ↗First posted 26 Sep 2021quant-ph cs.LGcited by 20

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Abstract (arXiv)

The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC (HL-LHC). This increase in luminosity will significantly increase the number of particles interacting with the detector. The interaction of particles with a detector is referred to as "hit". The HL-LHC will yield many more detector hits, which will pose a combinatorial challenge by using reconstruction algorithms to determine particle trajectories from those hits. This work explores the possibility of converting a novel Graph Neural Network model, that can optimally take into account the sparse nature of the tracking detector data and their complex geometry, to a Hybrid Quantum-Classical Graph Neural Network that benefits from using Variational Quantum layers. We show that this hybrid model can perform similar to the classical approach. Also, we explore Parametrized Quantum Circuits (PQC) with different expressibility and entangling capacities, and compare their training performance in order to quantify the expected benefits. These results can be used to build a future road map to further develop circuit based Hybrid Quantum-Classical Graph Neural Networks.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 22 of its 71 references that are in the map. First posted on arXiv 26 Sep 2021.

Builds on

22 of 71 references.

References · 22

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10 more
  1. 2020Robust data encodings for quantum classifierscited by 91
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  6. 2021Hybrid Quantum-Classical Graph Convolutional Networkcited by 21
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 5; 2024: 5; 2025: 4; 2026: 3.

20 papers.

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  1. 2023Financial Fraud Detection using Quantum Graph Neural Networkscited by 35
  2. 2023Quantum anomaly detection in the latent space of proton collision events at the LHCcited by 21
  3. 2022Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approachcited by 8
  4. 2022Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC datacited by 7
  5. 2023Quantum Graph Learning: Frontiers and Outlookcited by 6
  6. 2024From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networkscited by 6
  7. 2023Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Taskscited by 5
  8. 2022From Quantum Graph Computing to Quantum Graph Learning: A Surveycited by 4
  9. 2023A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discoverycited by 4
  10. 2024Graph Neural Networks on Quantum Computerscited by 4
  11. 2024Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industriescited by 3
  12. 2024Optimizing Quantum Convolutional Neural Network Architectures for Arbitrary Data Dimensioncited by 2
8 more
  1. 2024A unifying primary framework for quantum graph neural networks from quantum graph statescited by 1
  2. 2025Inductive Graph Representation Learning with Quantum Graph Neural Networkscited by 1
  3. 2025Hybrid Quantum Neural Networks for Efficient Protein-Ligand Binding Affinity Predictioncited by 1
  4. 2026Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learningcited by 1
  5. 2025Guided Graph Compression for Quantum Graph Neural Networkscited by 0
  6. 2025Variational Quantum Algorithms for Particle Track Reconstructioncited by 0
  7. 2026Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Eracited by 0
  8. 2026Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observablescited by 0