Versatile Atomic Magnetometry Assisted by Bayesian Inference
Versatile Atomic Magnetometry Assisted by Bayesian Inference
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DOI：
10.1103/physrevapplied.16.024044
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发表时间：
2020-03
影响因子：
4.6
通讯作者：
R. Puebla;Y. Ban;J. Haase;M. Plenio;M. Paternostro;J. Casanova
中科院分区：
文献类型：
--
作者：
R. Puebla;Y. Ban;J. Haase;M. Plenio;M. Paternostro;J. Casanova
量子传感器通常将外场转换为周期响应，然后通过在傅里叶空间中进行分析来确定其频率。这允许对表征外部信号的参数进行线性推断。然而，在实践中，量子传感器只能在一个狭窄的振幅和频率范围内检测场。偏离这个范围，以及存在显著的噪声源和较短的检测时间，导致传感器的响应和目标场之间的线性关系的损失，从而限制了传感器的工作状态。在这里，我们通过贝叶斯推理方法来解决这些挑战，该方法可以容忍与传感器期望的周期响应的强烈偏差，并且即使在非常有限的测量数量下也能够提供可靠的估计。我们演示了$^{171}$Yb$^{+}$捕获离子量子传感器的方法，但强调了该方法对不同系统的一般适用性。
Quantum sensors typically translate external fields into a periodic response whose frequency is then determined by analyses performed in Fourier space. This allows for a linear inference of the parameters that characterize external signals. In practice, however, quantum sensors are able to detect fields only in a narrow range of amplitudes and frequencies. A departure from this range, as well as the presence of significant noise sources and short detection times, lead to a loss of the linear relationship between the response of the sensor and the target field, thus limiting the working regime of the sensor. Here we address these challenges by means of a Bayesian inference approach that is tolerant to strong deviations from desired periodic responses of the sensor and is able to provide reliable estimates even with a very limited number of measurements. We demonstrate our method for an $^{171}$Yb$^{+}$ trapped-ion quantum sensor but stress the general applicability of this approach to different systems.