QINNs: Quantum-Informed Neural Networks
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918164594425856 |
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| author | Bal, Aritra Klute, Markus Maier, Benedikt Oughton, Melik Pezone, Eric Spannowsky, Michael |
| author_facet | Bal, Aritra Klute, Markus Maier, Benedikt Oughton, Melik Pezone, Eric Spannowsky, Michael |
| contents | Classical deep neural networks can learn rich multi-particle correlations in collider data, but their inductive biases are rarely anchored in physics structure. We propose quantum-informed neural networks (QINNs), a general framework that brings quantum information concepts and quantum observables into purely classical models. While the framework is broad, in this paper, we study one concrete realisation that encodes each particle as a qubit and uses the Quantum Fisher Information Matrix (QFIM) as a compact, basis-independent summary of particle correlations. Using jet tagging as a case study, QFIMs act as lightweight embeddings in graph neural networks, increasing model expressivity and plasticity. The QFIM reveals distinct patterns for QCD and hadronic top jets that align with physical expectations. Thus, QINNs offer a practical, interpretable, and scalable route to quantum-informed analyses, that is, tomography, of particle collisions, particularly by enhancing well-established deep learning approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17984 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QINNs: Quantum-Informed Neural Networks Bal, Aritra Klute, Markus Maier, Benedikt Oughton, Melik Pezone, Eric Spannowsky, Michael High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Quantum Physics Classical deep neural networks can learn rich multi-particle correlations in collider data, but their inductive biases are rarely anchored in physics structure. We propose quantum-informed neural networks (QINNs), a general framework that brings quantum information concepts and quantum observables into purely classical models. While the framework is broad, in this paper, we study one concrete realisation that encodes each particle as a qubit and uses the Quantum Fisher Information Matrix (QFIM) as a compact, basis-independent summary of particle correlations. Using jet tagging as a case study, QFIMs act as lightweight embeddings in graph neural networks, increasing model expressivity and plasticity. The QFIM reveals distinct patterns for QCD and hadronic top jets that align with physical expectations. Thus, QINNs offer a practical, interpretable, and scalable route to quantum-informed analyses, that is, tomography, of particle collisions, particularly by enhancing well-established deep learning approaches. |
| title | QINNs: Quantum-Informed Neural Networks |
| topic | High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Quantum Physics |
| url | https://arxiv.org/abs/2510.17984 |