Pathways in neutrino physics via quantum-encoded data analysis

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.