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.
Highlights
Quantum sensors are likely to be among the first quantum technologies to be translated from laboratory setups to commercial products [1]
We show how machine-learning algorithms [12,13,14,15] can be applied to single-spin magnetometers at room temperature to give a sensitivity that scales with the Heisenberg limit and reduces overheads by requiring only one-photon readout, at each step of the algorithm
Percentile range is shown as shaded areas. (a) Estimated uncertainty σðBestÞ is plotted as a function of the epoch number; data from one sample run is shown as blue circles
Summary
Quantum sensors are likely to be among the first quantum technologies to be translated from laboratory setups to commercial products [1]. In contrast, where spin-selective optical transitions are not resolved in a single shot, readout is typically performed by simultaneously exciting a spin triplet that includes both basis states and observing fluorescence from the subsequent decay, the probabilities for which differ by only approximately 35% Overcoming this classical uncertainty (in addition to quantum projection noise) to allow a precise estimate of the relative spin-state probabilities after a given precession time τ requires repeated Ramsey sequences to produce a large ensemble of fluorescent photons. Such a large readout overhead significantly reduces the sensitivity of NV magnetometry, and, so far, the high sensitivities reported at cryogenic temperatures are out of reach for room-temperature operation by several orders of magnitude. We show that MFL enables the dynamical tracking of timevarying magnetic fields at room temperature
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