Quantum-Encoded Neutrino Oscillation Tomography: Beyond Classical Data Analysis

Lazar et al. (2024) have demonstrated quantum encoding techniques that can store and process exponentially more information from detector events than classical methods, addressing the bandwidth/data reduction bottleneck that may hide rare new-physics signatures. Building on this, the project proposes to apply quantum data analysis systematically to neutrino oscillation experiments (e.g., DUNE, IceCube-Upgrade), encoding entire event topologies and time-series data in quantum registers. Novelty arises from using quantum-native classification and anomaly detection to search for oscillation patterns or event classes that are otherwise discarded or missed by classical triggers—potentially revealing sterile neutrino signatures, non-standard interactions, or even unexpected cosmic sources. This approach directly addresses a major limitation in current experiments, where most data are filtered out before analysis. If realized, this could revolutionize how we search for new physics in large-scale neutrino datasets and inspire similar methods across high-energy physics.

References:

1. New Pathways in Neutrino Physics via Quantum-Encoded Data Analysis. J. Lazar, Santiago Giner Olavarrieta, G. Gatti, Carlos A. Arguelles, Mikel Sanz (2024).