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Pathways in neutrino physics via quantum-encoded data analysis

Jeffrey Lazar1,*, Santiago Giner Olavarrieta2,†, Giancarlo Gatti3,4,5,‡, Carlos A. Argüelles2,§, and Mikel Sanz3,4,6,7,∥

  • *Contact author: jeffreylazar@fas.harvard.edu
  • †Contact author: santiagoginer@college.harvard.edu
  • ‡Contact author: gatti.gianc@gmail.com
  • §Contact author: carguelles@fas.harvard.edu
  • ∥Contact author: mikel.sanz@ehu.es
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Phys. Rev. Research 7, 043216 – Published 24 November, 2025

DOI: https://doi.org/10.1103/j7gt-zm6f

Abstract

In the quest to unveil the laws of nature, particle detectors produce an ever-increasing amount of data. Large data rates require the use of specialized triggers that promptly reduce the data rate to a manageable level; however, in doing so, unexpected phenomena may escape detection. In this work, we present a methodology based on recent quantum compression techniques that can store exponentially more information than classically available methods. We encode the full neutrino telescope event information using parity observables in an IBM quantum processor using eight qubits to demonstrate this. Then, we show that we can recover the information stored on the quantum computer with a fidelity of 84%. Finally, we illustrate the use of our protocol by performing a classification task that separates electron-neutrino events from muon-neutrino events in a neutrino telescope. We find that this classification method is limited by our ability to find adequately faithful representations of the classical data within the quantum systems, necessitating further investigations into finding these representations.

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Phys. Rev. Research 7, 043216– Published 24 November, 2025
  • Received 12 December 2024
  • Accepted 16 July 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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