Efficient real-time spin readout of nitrogen-vacancy centers based on Bayesian estimation

TL;DR

This paper introduces a real-time Bayesian estimation algorithm to enhance the spin readout efficiency of nitrogen-vacancy centers, surpassing traditional methods and improving signal-to-noise ratio in experiments.

Contribution

The work develops and experimentally validates a Bayesian estimation approach for NV center spin readout, achieving higher efficiency and better noise performance than conventional photon summation.

Findings

Surpassed Cramer-Rao lower bound in simulations

Improved signal-to-noise ratio by 28.6% in experiments

Demonstrated potential for advanced quantum sensing applications

Abstract

In this work, to improve the spin readout efficiency of the nitrogen vacancy (NV) center, a real-time Bayesian estimation algorithm is proposed, which combines both the prior probability distribution and the fluorescence likelihood function established by the implementation of the NV center dynamics model. The theoretical surpass of the Cramer-Rao lower bound of the readout variance and the improvement of the readout efficiency in the simulation indicate that our approach is an appealing alternative to the conventional photon summation method. The Bayesian real-time estimation readout was experimentally realized by combining a high-performance acquisition and processing hardware, and the Rabi oscillation experiments divulged that the signal-to-noise ratio of our approach was improved by 28.6%. Therefore, it is anticipated that the employed Bayesian estimation readout will effectively present superior sensing capabilities of the NV ensemble, and foster the further development of compact and scalable quantum sensors and consequently novel quantum information processing devices on a monolithic platform.