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Improving the performance of quantum sensors using quantum control

Quantum control provides a pathway to extract more - and more useful - information from quantum sensors. In this presentation we will highlight a series of experimental and numerical studies demonstrating how quantum control can augment the performance of atomic and solid-state devices for gravimetry, precision navigation, and magnetometry. We begin with demonstrations that a class of cloud-compute-enabled AI agents can be used to autonomously optimize cold-atom loading and BEC production without user intervention. Next, we focus on the application of robust control to mitigate platform noise in fielded atom interferometers. We show 10X improvements interferometer visibility under realistic noise conditions, and also demonstrate up to 1000X improvements in sensitivity under transverse acceleration. Finally, we present novel operating techniques for magnetometry in cluttered environments, in which Slepian window functions are employed to suppress out-of-band sensitivity.