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Lie-Equivariant Quantum Graph Neural Networks

arXiv 2411.15315 ↗First posted 22 Nov 2024quant-ph cs.LG hep-excited by 0

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Abstract (arXiv)

Discovering new phenomena at the Large Hadron Collider (LHC) involves the identification of rare signals over conventional backgrounds. Thus binary classification tasks are ubiquitous in analyses of the vast amounts of LHC data. We develop a Lie-Equivariant Quantum Graph Neural Network (Lie-EQGNN), a quantum model that is not only data efficient, but also has symmetry-preserving properties. Since Lorentz group equivariance has been shown to be beneficial for jet tagging, we build a Lorentz-equivariant quantum GNN for quark-gluon jet discrimination and show that its performance is on par with its classical state-of-the-art counterpart LorentzNet, making it a viable alternative to the conventional computing paradigm.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 43 references that are in the map. First posted on arXiv 22 Nov 2024.

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References · 27

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15 more
  1. 2022Theory for Equivariant Quantum Neural Networkscited by 62
  2. 2021Particle Cloud Generation with Message Passing Generative Adversarial Networkscited by 46
  3. 2023Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principlescited by 22
  4. 2022What is an equivariant neural network?cited by 21
  5. 2018A rotation-equivariant convolutional neural network model of primary visual cortexcited by 16
  6. 2017A Survey of Quantum Learning Theorycited by 12
  7. 2024Hybrid Quantum Vision Transformers for Event Classification in High Energy Physicscited by 10
  8. 2023Discovering Sparse Representations of Lie Groups with Machine Learningcited by 8
  9. 2024Quantum Vision Transformers for Quark-Gluon Classificationcited by 7
  10. 2023Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6cited by 6
  11. 2023Identifying the Group-Theoretic Structure of Machine-Learned Symmetriescited by 6
  12. 2023A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networkscited by 6
  13. 2023Oracle-Preserving Latent Flowscited by 5
  14. 2023mathbbZ2× mathbbZ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networkscited by 4
  15. 2024Quantum Attention for Vision Transformers in High Energy Physicscited by 1

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