Magnetic-field-learning using a single electronic spin in diamond with one-photon-readout at room temperature

TL;DR

This paper demonstrates that machine learning enables magnetic field sensing with a single NV centre in diamond at room temperature using minimal photon collection, achieving sensitivities comparable to cryogenic methods.

Contribution

The study introduces a machine learning approach that processes noisy, low-photon readouts of a single NV centre at room temperature, enhancing practicality for quantum sensing applications.

Findings

Achieved magnetic field sensitivity of 60 nT/Hz^{1/2} at room temperature.

Enabled estimation of dephasing times and dynamic tracking of time-dependent fields.

Reduced photon collection requirement to one photon per algorithm step.

Abstract

Nitrogen-vacancy (NV) centres in diamond are appealing nano-scale quantum sensors for temperature, strain, electric fields and, most notably, for magnetic fields. However, the cryogenic temperatures required for low-noise single-shot readout that have enabled the most sensitive NV-magnetometry reported to date, are impractical for key applications, e.g. biological sensing. Overcoming the noisy readout at room-temperature has until now demanded repeated collection of fluorescent photons, which increases the time-cost of the procedure thus reducing its sensitivity. Here we show how machine learning can process the noisy readout of a single NV centre at room-temperature, requiring on average only one photon per algorithm step, to sense magnetic field strength with a precision comparable to those reported for cryogenic experiments. Analysing large data sets from NV centres in bulk diamond, we report absolute sensitivities of $60$ nT s$^{1/2}$ including initialisation, readout, and computational overheads. We show that dephasing times can be simultaneously estimated, and that time-dependent fields can be dynamically tracked at room temperature. Our results dramatically increase the practicality of early-term single spin sensors.