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Quantum optimal control for sensitive atomic gravimetry

Louis Pagot, Joel Gomes Baptista, Léo Rol, Niranjan Myneni, Franck Correia, Sébastien Merlet, Leonid A. Sidorenkov, Franck Pereira dos Santos

Published Jul 21, 2025• 0 citations
OpenAlex

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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.
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We investigate the sensitivity of gravitational acceleration estimation using squeezed probe states in a quantum metrology framework. In particular, we analyze how the squeezing phase, beyond its amplitude, affects the attainable precision. We show that probes squeezed along the canonical phase-space quadratures can surpass the shot-noise limit only in specific time regimes, whereas position-momentum correlated input states can consistently overcome this limit across all interaction times. Furthermore, we demonstrate that optimal sensitivity can be achieved by combining projective momentum measurements with a time-dependent adjustment of the squeezing phase. Our results highlight the fundamental role of phase-engineered squeezing in quantum gravimetry protocols and provide new insights into the design of optimized sensing strategies.