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.
@article{arxiv.2302.06310,
title = {Efficient real-time spin readout of nitrogen-vacancy centers based on Bayesian estimation},
author = {Jixing Zhang and Tianzheng Liu and Sigang Xia and Guodong Bian and Pengcheng Fan and Mingxin Li and Sixian Wang and Xiangyun Li and Chen Zhang and Shaoda Zhang and Heng Yuan},
journal= {arXiv preprint arXiv:2302.06310},
year = {2023}
}