Abstract

Quantum key distribution (QKD) can help distant agents to share unconditional secret keys, and the achievable secret key rate can be enhanced with the help of decoy-state protocol. To implement QKD experimentally, the agents are supposed to accurately transmit a number of different intensity pulses with the LiNbO3 based Mach-Zehnder (LNMZ) intensity modulator. However, the bias drift of LNMZ intensity modulator may affect the performance of a QKD system. In this letter, we reveal a simple RC circuit model to demonstrate the bias drift in the LNMZ intensity modulator. And based on the model, we propose a multi-step bias stable scheme to control the bias working point. Experimental result shows that our scheme can eliminate the bias drift of at arbitrary working point within a long time range. Besides, there is no need of any feedback mechanisms in the scheme. This means our scheme will not lead to any increasement in system complexity, making it more suitable for a QKD system.

Details

Title
Arbitrary bias control of LiNbO3 based Mach-Zehnder intensity modulators for QKD system
Author
Teng, Jun 1 ; Wang, Shuang 2 ; Yin, Zhen-Qiang 2 ; Chen, Wei 2 ; Fan-Yuan, Guan-Jie 1 ; Guo, Guang-Can 2 ; Han, Zheng-Fu 2 

 University of Science and Technology of China, CAS Key Laboratory of Quantum Information, Hefei, P.R. China (GRID:grid.59053.3a) (ISNI:0000 0001 2167 9639); University of Science and Technology of China, CAS Center for Excellence in Quantum Information and Quantum Physics, Hefei, P.R. China (GRID:grid.59053.3a) (ISNI:0000 0001 2167 9639) 
 University of Science and Technology of China, CAS Key Laboratory of Quantum Information, Hefei, P.R. China (GRID:grid.59053.3a) (ISNI:0000 0001 2167 9639); University of Science and Technology of China, CAS Center for Excellence in Quantum Information and Quantum Physics, Hefei, P.R. China (GRID:grid.59053.3a) (ISNI:0000 0001 2167 9639); University of Science and Technology of China, Hefei National Laboratory, Hefei, P.R. China (GRID:grid.59053.3a) (ISNI:0000 0001 2167 9639) 
Pages
33
Publication year
2023
Publication date
Dec 2023
Publisher
Springer Nature B.V.
e-ISSN
21960763
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2862003116
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.