Quantum Sensing of Non-Repeatable Events Enhanced by In-Sensor Quantum Reservoir Computing

Abstract

High-density nitrogen-vacancy (NV) ensembles in diamond enable sensitive magnetometry. Many quantum-sensing protocols rely on reproducible target fields, allowing repeated measurements under different sensing conditions. In dynamical decoupling (DD) magnetometry, sweeping the interval between $π$ pulses across repeated measurements enables estimation of the frequency and amplitude of an unknown alternating field. For non-repeatable events, however, the same field waveform cannot be reproduced for measurements under different settings. Here we propose NV-based in-sensor quantum reservoir computing (NV-QRC) for sensing such events. Field-driven many-body dynamics and simultaneous fluorescence readout from multiple spatial regions provide a classical classifier with multiple features from a single event. For binary phase classification, we benchmark NV-QRC against DD magnetometry using a single pulse sequence fixed in advance. We show that NV-QRC can retain class-dependent information in regimes where the fixed-DD protocol fails to capture it. These results identify non-repeatable-event sensing as a promising application of quantum reservoir computing.