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Optica Sensing Congress

Continuous Real-Time Magnetometry and Feedback Control via Coherent Population Trapping of a NV Center

Ethan Turner, Shuhao Wu, Hailin Wang

Published Jan 1, 2024• 0 citations
Publisher

Abstract

We report the experimental demonstration of continuous real-time magnetometry and feedback control via coherent population trapping of a NV center. The real-time update can take place with the detection of just a single photon.
DOI
10.1364/qsm.2024.qm2c.2

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We report the experimental demonstration of real-time magnetometry by detecting fluorescence from a nitrogen vacancy center in the setting of coherent population trapping and by estimating magnetic fields from the time series of the observed photon counts. We show that by taking into consideration the statistical properties of magnetic field fluctuations, a Bayesian inference-based estimator can effectively update dynamical information of the field in real time with the detection of just a single photon.
We demonstrate real-time magnetometry by detecting fluorescence from a nitrogen-vacancy center in the setting of coherent population trapping and by estimating the magnetic field from the time series of the observed photon counts, which are correlated with the underlying field variation. The proof-of-principle experiment uses an external time-varying magnetic field that follows an Ornstein-Uhlenbeck (OU) process. A conventional estimator based on the use of average photon counts per relevant time interval is susceptible to photon number shot noise and performs poorly at relatively low average photon counts. In comparison, by taking into consideration the statistical properties of the OU process, a Bayesian inference-based estimator can effectively update dynamical information of the field in real time with the detection of just a single photon.
We demonstrate real-time magnetometry by detecting fluorescence from a nitrogen vacancy center in the setting of coherent population trapping and by estimating magnetic field from the time series of the observed photon counts, which are correlated with the underlying field. The proof-of-principle experiment uses an external time-varying magnetic field that follows an Ornstein-Uhlenbeck (OU) process. By taking into consideration the statistical properties of the OU process, a Bayesian inference-based estimator can effectively update dynamical information of the field in real time with the detection of just a single photon.
We demonstrate continuous real-time Bayesian estimations of magnetic-field fluctuations by monitoring single-photon emissions from a diamond nitrogen vacancy center under the setting of coherent population trapping, for which the time sequence of the single-photon emissions is correlated with the underlying magnetic-field fluctuations. The Bayesian estimations are combined with a feedback loop and are followed by a separate verification and optimization process, which uses the spin-dephasing rate in the presence of the feedback as a cost function. The dephasing rate is measured with Ramsey interferometry and is minimized by systematically varying the statistical parameters used in the estimations. The Bayesian estimations aided by the feedback and verification enable continuous real-time sensing at the single-photon level regardless of the prior availability of the statistical parameters of the underlying dynamics.
We propose and theoretically analyze the use of coherent population trapping of a single diamond nitrogen-vacancy (NV) center for continuous real-time sensing. The formation of the dark state in coherent population trapping prevents optical emissions from the NV center. Fluctuating magnetic fields, however, can kick the NV center out of the dark state, leading to a sequence of single-photon emissions. A time series of the photon counts detected can be used for magnetic field estimations, even when the average photon count per update time interval is much smaller than 1. For a theoretical demonstration, the nuclear spin bath in a diamond lattice is used as a model fluctuating magnetic environment. For fluctuations with known statistical properties, such as an Ornstein-Uhlenbeck process, Bayesian inference-based estimators can lead to an estimation variance that approaches the classical Cramer-Rao lower bound and can update dynamical information in real time with the detection of just a single photon. Real-time sensing using coherent population trapping adds a powerful tool to the emerging technology of quantum sensing.