New Pathways in Neutrino Physics via Quantum-Encoded Data Analysis
Ever-increasing amount of data is produced by particle detectors in their
quest to unveil the laws of Nature. The large data rate requires the use of
specialized triggers that promptly reduce the data rate to a manageable level;
however, in doing so, unexpected new phenomena may escape detection.
Additionally, the large data rate is increasingly difficult to analyze
effectively, which has led to a recent revolution on machine learning
techniques. Here, we present a methodology based on recent quantum compression
techniques that has the capacity to store exponentially more amount of
information than classically available methods. To demonstrate this, we encode
the full neutrino telescope event information using parity observables in an
IBM quantum processor using 8 qubits. 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 to muon-neutrinos events in a neutrino
telescope. This new capability would eventually allow us to solve the street
light effect in particle physics, where we only record signatures of particles
with which we are familiar.
Jeffrey Lazar Santiago Giner Olavarrieta Giancarlo Gatti Carlos A. Argüelles Mikel Sanz