Quick Navigation
Topics
Quantum Communication Networks
Quantum Compilation Routing Architecture
Photonic Quantum Computing
Quantum Machine Learning
Efficient and Robust Spatial-to-Fiber Coupling forMultimode Quantum Networks via CascadedAdaptive Feedback Control
arXiv
Authors: Ya Li, WanRu Wang, Weizhe Qiao, Qizhou Wu, Changqing Niu, Xiaolong Zou, Youxing Chen, Xin Guo
Year
2025
Paper ID
51313
Status
Preprint
Abstract Read
~2 min
Abstract Words
87
Citations
N/A
Abstract
Duan-Lukin-Cirac-Zoller (DLCZ)-based multimodequantum networks rely on efficient spatial-to-fiber coupling, yetenvironmental perturbations compromise this performance. Wedevelop a cascaded adaptive feedback control system integratedinto the quantum entanglement source preparation path.Leveraging a power-feedback hillclimbing algorithm, itdynamically regulates piezoelectric-actuated mirrors to achieveautonomous multi-dimensional beam alignment, Experimentsshow it rapidly boosts single-mode fiber (SMF) coupling efficieneyto over 70% within 20 seconds and entering the most efficient andstable transmission state after 75 seconds.Importantly, it enhancesthe stability of the atom-photon interfacecritical for quantumlight-matter interactionsproviding a practical framework forefficient, robust spatial light transmission in scalable quantumnetworks.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2025 reference point for readers tracking recent quantum research.
- Duan-Lukin-Cirac-Zoller (DLCZ)-based multimodequantum networks rely on efficient spatial-to-fiber coupling, yetenvironmental perturbations compromise this performance.
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.