Quick Navigation
Topics
Trapped Ion Quantum Computing
Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad
arXiv
Authors: Simon Templier, Pierrick Cheiney, Quentin d'Armagnac de Castanet, Baptiste Gouraud, Henri Porte, Fabien Napolitano, Philippe Bouyer, Baptiste Battelier, Brynle Barrett
Year
2022
Paper ID
58921
Status
Preprint
Abstract Read
~2 min
Abstract Words
147
Citations
N/A
Abstract
Robust and accurate acceleration tracking remains a challenge in many fields. For geophysics and economic geology, precise gravity mapping requires onboard sensors combined with accurate positioning and navigation systems. Cold-atom-based quantum inertial sensors can potentially provide such high-precision instruments. However, current scalar instruments require precise alignment with vector quantities. Here, we present the first hybrid three-axis accelerometer exploiting the quantum advantage to measure the full acceleration vector by combining three orthogonal atom interferometer measurements with a classical navigation-grade accelerometer triad. Its ultra-low bias permits tracking the acceleration vector over long timescales - yielding a 50-fold improvement in stability $6 times 10-8 g$ over our classical accelerometers. We record the acceleration vector at a high data rate (1 kHz), with absolute magnitude accuracy below 10 μg, and pointing accuracy of 4 μrad. This paves the way toward future strapdown applications with quantum sensors and highlights their potential as future inertial navigation units.
Why This Paper Matters
- This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
- It adds a 2022 reference point for readers tracking recent quantum research.
- Robust and accurate acceleration tracking remains a challenge in many fields.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.