论文标题
在光学晶格中使用超电原子旋转传感的增强学习
Reinforcement Learning for Rotation Sensing with Ultracold Atoms in an Optical Lattice
论文作者
论文摘要
在本文中，我们研究了一种增强学习方法，以在光学晶格中设计陀螺仪，以旋转旋转。我们的方法不是基于传统的原子干涉法，即分裂，反射和重组波功能组件。取而代之的是，学习代理人被分配了生成晶格摇动序列的任务，该序列优化了陀螺仪对端到端设计理念中旋转信号的敏感性。结果是一种与熟悉的Mach-Zehnder型干涉仪完全不同的干扰装置。在同一总审讯时间中，端到端的设计与传统的Bragg干涉仪相比，灵敏度提高了20倍。
In this paper, we investigate a design approach of reinforcement learning to engineer a gyroscope in an optical lattice for the inertial sensing of rotations. Our methodology is not based on traditional atom interferometry, that is, splitting, reflecting, and recombining wavefunction components. Instead, the learning agent is assigned the task of generating lattice shaking sequences that optimize the sensitivity of the gyroscope to rotational signals in an end-to-end design philosophy. What results is an interference device that is completely distinct from the familiar Mach-Zehnder-type interferometer. For the same total interrogation time, the end-to-end design leads to a 20-fold improvement in sensitivity over traditional Bragg interferometry.
