TY - JOUR
AU - Liang-Ying Chih
AU - Murray Holland
AB - 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.
BT - Physical Review Research
DA - 2021-09
DO - 10.1103/PhysRevResearch.3.033279
N2 - 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.
PY - 2021
EP - 033279
T2 - Physical Review Research
TI - Reinforcement-learning based matterwave interferometer in a shaken optical lattice 
UR - https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.3.033279
VL - 3
ER -