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 s1/2 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.
@article{arxiv.1807.09753,
title = {Magnetic-field-learning using a single electronic spin in diamond with one-photon-readout at room temperature},
author = {Raffaele Santagati and Antonio A. Gentile and Sebastian Knauer and Simon Schmitt and Stefano Paesani and Christopher Granade and Nathan Wiebe and Christian Osterkamp and Liam P. McGuinness and Jianwei Wang and Mark G. Thompson and John G. Rarity and Fedor Jelezko and Anthony Laing},
journal= {arXiv preprint arXiv:1807.09753},
year = {2019}
}