Reinforcement learning for rotation sensing with ultracold atoms in an optical lattice

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. Ins...

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Main Authors: Liang-Ying Chih, Murray Holland
Format: Article
Language:English
Published: American Physical Society 2024-11-01
Series:Physical Review Research
Online Access:http://doi.org/10.1103/PhysRevResearch.6.043191
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author Liang-Ying Chih
Murray Holland
author_facet Liang-Ying Chih
Murray Holland
author_sort Liang-Ying Chih
collection DOAJ
description 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. What results is an interference device that is completely distinct from the familiar Mach-Zehnder-type interferometer. For the same total interrogation time, the end-to-end design leads to a twentyfold improvement in sensitivity over traditional Bragg interferometry.
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spelling doaj-art-b76ee8531e2d4387b4edfbc2b330ecf02025-08-20T02:23:35ZengAmerican Physical SocietyPhysical Review Research2643-15642024-11-016404319110.1103/PhysRevResearch.6.043191Reinforcement learning for rotation sensing with ultracold atoms in an optical latticeLiang-Ying ChihMurray HollandIn 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. What results is an interference device that is completely distinct from the familiar Mach-Zehnder-type interferometer. For the same total interrogation time, the end-to-end design leads to a twentyfold improvement in sensitivity over traditional Bragg interferometry.http://doi.org/10.1103/PhysRevResearch.6.043191
spellingShingle Liang-Ying Chih
Murray Holland
Reinforcement learning for rotation sensing with ultracold atoms in an optical lattice
Physical Review Research
title Reinforcement learning for rotation sensing with ultracold atoms in an optical lattice
title_full Reinforcement learning for rotation sensing with ultracold atoms in an optical lattice
title_fullStr Reinforcement learning for rotation sensing with ultracold atoms in an optical lattice
title_full_unstemmed Reinforcement learning for rotation sensing with ultracold atoms in an optical lattice
title_short Reinforcement learning for rotation sensing with ultracold atoms in an optical lattice
title_sort reinforcement learning for rotation sensing with ultracold atoms in an optical lattice
url http://doi.org/10.1103/PhysRevResearch.6.043191
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