Lie-Equivariant Quantum Graph Neural Networks
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929602039906304 |
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| author | Neto, Jogi Suda Forestano, Roy T. Gleyzer, Sergei Kong, Kyoungchul Matchev, Konstantin T. Matcheva, Katia |
| author_facet | Neto, Jogi Suda Forestano, Roy T. Gleyzer, Sergei Kong, Kyoungchul Matchev, Konstantin T. Matcheva, Katia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15315 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lie-Equivariant Quantum Graph Neural Networks Neto, Jogi Suda Forestano, Roy T. Gleyzer, Sergei Kong, Kyoungchul Matchev, Konstantin T. Matcheva, Katia Quantum Physics Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology 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. |
| title | Lie-Equivariant Quantum Graph Neural Networks |
| topic | Quantum Physics Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2411.15315 |