  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.
