Reinforcement Learning for Rotation Sensing with Ultracold Atoms in an Optical Lattice

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.