Magnetic-Field Learning Using a Single Electronic Spin in Diamond with One-Photon Readout at Room Temperature

Abstract

Nitrogen-vacancy (NV) centers in diamond are appealing nanoscale quantum sensors for temperature, strain, electric fields, and, most notably, 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 the 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 center 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. Analyzing large datasets from NV centers in bulk diamond, we report absolute sensitivities of60nTs1/2including initialization, 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.

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Plain Language Summary

Nitrogen-vacancy centers are atomic defects that can be found or created in diamond. They allow single electrons to be used for sensing both electric and magnetic fields. Their unique combination of high spatial resolution and sensitivity has led to the investigation of scenarios where the activity of single neurons is monitored and mapped down to the nanoscale. However, such nano-NMR applications are limited by the noise of the optical readout available at room temperature in state-of-the-art setups. Here, we show how machine learning can help overcome these limitations to precisely track a fluctuating magnetic field at room temperature with a sensitivity typically reserved for cryogenic sensors.

Machine learning is particularly apt to distinguish patterns from noisy datasets. In our paper, we show how a Bayesian inference approach can successfully learn the magnetic field and other important physical quantities from intrinsically noisy data. This allows us to relax the complexity of the data readout process at the cost of advanced data processing. Along with the record room-temperature sensitivities we report, these results unlock novel practical applications for nanoscale quantum sensing, ranging from biology to material science.

Nitrogen-vacancy centers have already been successfully employed for striking demonstrations of their sensing capabilities in real-world applications. We expect that the deployment of our techniques can unlock unexplored regimes in a new generation of sensing experiments, where real-time tracking and enhanced sensitivities are crucial ingredients to explore phenomena at the nanoscale.