arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2302.06310quant-phphysics.app-ph
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基于贝叶斯估计的氮空位中心自旋的有效实时读出

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

Jixing Zhang, Tianzheng Liu, Sigang Xia, Guodong Bian, Pengcheng Fan, Mingxin Li, Sixian Wang, Xiangyun Li, Chen Zhang, Shaoda Zhang, Heng Yuan

摘要:

在这项工作中,为了提高自旋读出效率的氮空位(NV)中心,提出了一种实时贝叶斯估计算法,该算法结合先验概率分布和荧光似然函数建立的NV中心动力学模型的实施。理论上读出方差的Cramer-Rao下限的超越和在模拟中读出效率的提高表明,我们的方法是一个有吸引力的替代传统的光子求和方法。结合高性能的采集处理硬件,实验实现了贝叶斯实时估计读出,Rabi振荡实验表明,该方法的信噪比提高了28.6%.因此,预期所采用的贝叶斯估计读出将有效地呈现NV系综的优越感测能力,并促进紧凑且可扩展的量子传感器的进一步发展,并因此促进单片平台上的新型量子信息处理装置的进一步发展。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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