Researchers Classify Neutrino Events With A Quantum Computer

Researchers have achieved testing accuracy near 80% with the NPQK and approximately 70% accuracy with the QCNN in classifying events detected by neutrino telescopes using a quantum computer, a result comparable to traditional methods. Pablo Rodriguez-Grasa, University of the Basque Country UPV/EHU and colleagues demonstrated this capability by investigating neural projected quantum kernels and quantum convolutional neural networks.

This work, published August 21, 2026, in Quantum Science and Technology, Number 4, establishes the feasibility of applying quantum machine learning to astronomical data analysis with current hardware. The study explores how quantum computers can distinguish between different types of neutrino events, crucial for understanding rare cosmic phenomena.

NPQK and QCNN Approaches to Neutrino Event Classification

Achieving testing accuracy near 80%, the neural projected quantum kernel (NPQK) approach demonstrated a capacity to classify neutrino events directly on both simulators and the IBM Strasbourg quantum processor. This result suggests a shift toward practical quantum applications in astrophysics. Researchers led by Pablo Rodriguez-Grasa at the University of the Basque Country UPV/EHU detailed this performance in a study published August 21, 2026, in Quantum Science and Technology, Number 4, focusing on distinguishing between muon tracks and hadronic/electromagnetic cascades, key signatures within neutrino telescope data.