论文标题
神经网络辅助$^{171} $ yb $^{+} $量子磁力计
A Neural Network Assisted $^{171}$Yb$^{+}$ Quantum Magnetometer
论文作者
论文摘要
当在广泛的参数中暴露于目标场时，多功能磁力计必须提供可读的响应。在这项工作中，我们通过实验表明，$^{171} $ yb $^{+} $的组合具有足够训练的神经网络的原子传感器，使得可以在不同的挑战性场景中调查目标字段。特别是，我们表征了存在大射击噪声的射频（RF）字段，包括通过单发测量进行连续数据采集的极限情况。此外，通过合并神经网络，我们将原子磁力计的工作状态大大扩展到场景中，其中RF驱动驱动的响应超出了其标准谐波行为。我们的结果表明，在一般量子传感任务的数据处理阶段整合神经网络的好处，以破译传感器响应中包含的信息。
A versatile magnetometer must deliver a readable response when exposed to target fields in a wide range of parameters. In this work, we experimentally demonstrate that the combination of $^{171}$Yb$^{+}$ atomic sensors with adequately trained neural networks enables to investigate target fields in distinct challenging scenarios. In particular, we characterize radio frequency (RF) fields in the presence of large shot noise, including the limit case of continuous data acquisition via single-shot measurements. Furthermore, by incorporating neural networks we significantly extend the working regime of atomic magnetometers into scenarios in which the RF driving induces responses beyond their standard harmonic behavior. Our results indicate the benefits to integrate neural networks at the data processing stage of general quantum sensing tasks to decipher the information contained in the sensor responses.
