Neutrino Telescope Event Classification on Quantum Computers

TL;DR

This paper explores the use of current quantum computers for classifying neutrino events, demonstrating comparable performance to classical methods and introducing scalable encoding schemes for large datasets.

Contribution

It presents the first application of quantum machine learning to neutrino event classification, introducing new encoding and preprocessing techniques for scalable analysis.

Findings

Quantum machine learning approaches achieve ~80% accuracy on real hardware.

Both NPQKs and QCNNs perform robustly across a wide energy range.

Results show promising potential for quantum methods in neutrino astronomy.

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.