Machine learning designed optical lattice atom interferometer

Victor E. Colussi; Justin Copenhaver; Maximilian Seifert; Michael Perlin; Murray Holland

doi:10.1117/12.3003353

13 March 2024 Machine learning designed optical lattice atom interferometer

Victor E. Colussi, Justin Copenhaver, Maximilian Seifert, Michael Perlin, Murray Holland

Proceedings Volume 12912, Quantum Sensing, Imaging, and Precision Metrology II; 129120J (2024) https://doi.org/10.1117/12.3003353
Event: SPIE Quantum West, 2024, San Francisco, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Victor E. Colussi, Justin Copenhaver, Maximilian Seifert, Michael Perlin, and Murray Holland "Machine learning designed optical lattice atom interferometer", Proc. SPIE 12912, Quantum Sensing, Imaging, and Precision Metrology II, 129120J (13 March 2024); https://doi.org/10.1117/12.3003353

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available