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This study demonstrates the viability of current quantum hardware for classifying neutrino telescope data using QML models like NPQKs and QCNNs, achieving performance comparable to classical methods on real IBM processors. The work validates specific encoding schemes that enable scalable learning on large astrophysics datasets, marking a significant step toward practical quantum applications in high-energy physics.
Neutrino Telescope Event Classification on Quantum Computers
arXiv:2506.16530v3 Announce Type: replace-cross Abstract: Quantum computers represent a new computational paradigm with steadily improving hardware capabilities. In this article, we present the first study exploring how current quantum computers can be used to classify different neutrino event types observed in neutrino telescopes. We investigate two quantum machine learning approaches, Neural Projected Quantum Kernels (NPQKs) and Quantum Convolutional Neural Networks (QCNNs), and find that both achieve classification performance comparable to classical machine learning methods across a wide energy range. By introducing a moment-of-inertia-based encoding scheme and a novel preprocessing approach, we enable efficient and scalable learning with large neutrino astronomy datasets. Tested on both simulators and the IBM Strasbourg quantum processor, the NPQK achieves a testing accuracy near 80%, with robust results above 1 TeV and close agreement between simulation and hardware performance. A simulated QCNN achieves a ~70% accuracy over the same energy range. These results underscore the promise of quantum machine learning for neutrino astronomy, paving the way for future advances as quantum hardware matures.
Abstract:Quantum computers represent a new computational paradigm with steadily improving hardware capabilities. In this article, we present the first study exploring how current quantum computers can be used to classify different neutrino event types observed in neutrino telescopes. We investigate two quantum machine learning approaches, Neural Projected Quantum Kernels (NPQKs) and Quantum Convolutional Neural Networks (QCNNs), and find that both achieve classification performance comparable to classical machine learning methods across a wide energy range. By introducing a moment-of-inertia-based encoding scheme and a novel preprocessing approach, we enable efficient and scalable learning with large neutrino astronomy datasets. Tested on both simulators and the IBM Strasbourg quantum processor, the NPQK achieves a testing accuracy near 80%, with robust results above 1 TeV and close agreement between simulation and hardware performance. A simulated QCNN achieves a ~70% accuracy over the same energy range. These results underscore the promise of quantum machine learning for neutrino astronomy, paving the way for future advances as quantum hardware matures.
High Energy Physics - Experiment (hep-ex); Instrumentation and Methods for Astrophysics (astro-ph.IM); Quantum Physics (quant-ph)
arXiv:2506.16530 [hep-ex]
(or arXiv:2506.16530v3 [hep-ex] for this version)
https://doi.org/10.48550/arXiv.2506.16530 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1088/2058-9565/ae9802 Focus to learn more DOI(s) linking to related resources
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