aiqbench analysis
This research demonstrates how in-sensor quantum reservoir computing can detect non-repeatable magnetic events using nitrogen-vacancy centers, overcoming limitations of traditional dynamical decoupling protocols that require reproducible fields. By leveraging field-driven many-body dynamics and simultaneous fluorescence readout, the method extracts class-dependent information from single-shot measurements where standard techniques fail. This advancement highlights a practical application for quantum sensing in dynamic environments where event replication is impossible.
Quantum Sensing of Non-Repeatable Events Enhanced by In-Sensor Quantum Reservoir Computing
arXiv:2609.06292v1 Announce Type: new 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 $\pi$ 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.
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 $\pi$ 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.
arXiv:2609.06292 [quant-ph]
(or arXiv:2609.06292v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2609.06292 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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