scholarly journals Machine Learning-Assisted Measurement Device-Independent Quantum Key Distribution on Reference Frame Calibration

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1242
Author(s):  
Sihao Zhang ◽  
Jingyang Liu ◽  
Guigen Zeng ◽  
Chunhui Zhang ◽  
Xingyu Zhou ◽  
...  

In most of the realistic measurement device-independent quantum key distribution (MDI-QKD) systems, efficient, real-time feedback controls are required to maintain system stability when facing disturbance from either external environment or imperfect internal components. Traditionally, people either use a “scanning-and-transmitting” program or insert an extra device to make a phase reference frame calibration for a stable high-visibility interference, resulting in higher system complexity and lower transmission efficiency. In this work, we build a machine learning-assisted MDI-QKD system, where a machine learning model—the long short-term memory (LSTM) network—is for the first time to apply onto the MDI-QKD system for reference frame calibrations. In this machine learning-assisted MDI-QKD system, one can predict out the phase drift between the two users in advance, and actively perform real-time phase compensations, dramatically increasing the key transmission efficiency. Furthermore, we carry out corresponding experimental demonstration over 100 km and 250 km commercial standard single-mode fibers, verifying the effectiveness of the approach.

Author(s):  
Qin Dong ◽  
Guoqi Huang ◽  
Wei Cui ◽  
Rong-zhen Jiao

Abstract The satellite-based measurement-device-independent quantum key distribution can promote the realization of quantum communication networks. Under the condition of the limited data set, it is necessary to optimize all parameters. For communication networks, real-time prediction and optimization are also indispensable. With the development of machine learning, cross-combination with machine learning has also become the mainstream of parameter optimization in various disciplines. This paper discusses the asymmetric MDI-QKD based on the satellite in the case of statistical fluctuations and uses the local search algorithm (LSA) to achieve full parameter optimization under the condition of considering the probability of sending the signal. Compared with fixed related parameters, the key rate is increased by an order of magnitude. On this basis, random forest is used to predict the high-precision optimal parameters, thereby eliminating the simulation and iteration required by the search method to meet the real-time optimization of the future QKD network.


2020 ◽  
Vol 45 (9) ◽  
pp. 2624
Author(s):  
Donghwa Lee ◽  
Seongjin Hong ◽  
Young-Wook Cho ◽  
Hyang-Tag Lim ◽  
Sang-Wook Han ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document