Continuous Real-Time Sensing with a Nitrogen Vacancy Center via Coherent Population Trapping

TL;DR

This paper proposes a method for continuous, real-time magnetic field sensing using a nitrogen vacancy center in diamond, leveraging coherent population trapping to detect magnetic fluctuations through photon emission patterns.

Contribution

It introduces a theoretical framework for using coherent population trapping in NV centers for real-time quantum sensing of magnetic fields, with Bayesian inference for optimal estimation.

Findings

Photon count sequences enable magnetic field estimation even at low photon rates.

Bayesian estimators approach the Cramer-Rao lower bound for variance.

Method provides dynamical magnetic information on short timescales.

Abstract

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 provide dynamical information on a timescale that is comparable to the inverse of the average photon counting rate. Real-time sensing using coherent population trapping adds a new and powerful tool to the emerging technology of quantum sensing.