In this talk, I will discuss the application of machine learning to the design of an inertial measurement device capable of measuring accelerations and rotations with high-sensitivity. The system this is based on consists of ultracold atoms in an optical lattice potential created by interfering laser beams.
We demonstrate the design of a matter-wave interferometer to measure acceleration in one dimension with high precision. The system we base this on consists of ultracold atoms in an optical lattice potential created by interfering laser beams. Our approach uses reinforcement learning, a branch of machine learning that generates the protocols needed to realize lattice-based analogs of optical components including a beam splitter, a mirror, and a recombiner. The performance of these components is evaluated by comparison with their optical analogs. The interferometer's sensitivity to acceleration is quantitatively evaluated using a Bayesian statistical approach. We find the sensitivity to surpass that of standard Bragg interferometry, demonstrating the future potential for this design methodology.
We theoretically study a new approach to matter-wave interferometer that utilizes ultracold atoms confined in an optical lattice. Through patterned phase modulation of the lattice, the matter wave is split, mirrored, and recombined, resulting in sensitivity to an applied inertial signal as was recently demonstrated experimentally in [LeDesma et al., arXiv:2305.17603 (2023)]. Compared to free-space equivalents, this “shaken lattice” interferometer has the advantage that atoms remain always supported against external forces and perturbations and that by applying different shaking sequences the device could be in principle made sensitive to different signals (inertial, gravitational, etc...) on the fly. In this work, we give a brief overview of this sensing platform and its working principles.
Atom interferometery is an exquisite measurement technique sensitive to inertial forces. However, it is commonly limited to a single sensitive axis, allowing high-precision multidimensional sensing only through subsequent or postcorrected measurements. We report on a novel method for multi-axis inertial sensing based on the correlation of simultaneous light-pulse atom interferometers in 2D array arrangements of Bose-Einstein condensates (BEC). Deploying a scalable 3×3 BEC array spanning 1.6 mm^{2} created using time-averaged optical potentials, we perform measurements of linear acceleration induced by gravity and simultaneously demonstrate sensitivity to angular velocity and acceleration of a rotating reference mirror, as well as gravity gradients and higher-order derivatives. Our Letter enables simple, high-precision multi-axis inertial sensing compatible with high rotation rates, e.g., for inertial navigation in dynamic environments. We finally envision further applications of our method, e.g., 3D in situ measurements and reconstruction of laser beam intensities and wave fronts.