Liang-Ying Chih

Abstract A remarkable characteristic of quantum computing is the potential for reliable computation despite faulty qubits. This can be achieved through quantum error correction, which is typically implemented by repeatedly applying static syndrome checks, permitting correction of logical information. Recently, the development of time-dynamic approaches to error correction has enabled different codes and implementations that do not rely on static syndrome measurements.

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.