scholarly journals Measurement-device-independent quantum key distribution with classical Bob and no joint measurement

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guang Ping He
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Weilong Wang ◽  
Kiyoshi Tamaki ◽  
Marcos Curty

AbstractMeasurement-device-independent quantum key distribution (MDI-QKD) can remove all detection side-channels from quantum communication systems. The security proofs require, however, that certain assumptions on the sources are satisfied. This includes, for instance, the requirement that there is no information leakage from the transmitters of the senders, which unfortunately is very difficult to guarantee in practice. In this paper we relax this unrealistic assumption by presenting a general formalism to prove the security of MDI-QKD with leaky sources. With this formalism, we analyze the finite-key security of two prominent MDI-QKD schemes—a symmetric three-intensity decoy-state MDI-QKD protocol and a four-intensity decoy-state MDI-QKD protocol—and determine their robustness against information leakage from both the intensity modulator and the phase modulator of the transmitters. Our work shows that MDI-QKD is feasible within a reasonable time frame of signal transmission given that the sources are sufficiently isolated. Thus, it provides an essential reference for experimentalists to ensure the security of implementations of MDI-QKD in the presence of information leakage.


2020 ◽  
Vol 125 (26) ◽  
Author(s):  
Yuan Cao ◽  
Yu-Huai Li ◽  
Kui-Xing Yang ◽  
Yang-Fan Jiang ◽  
Shuang-Lin Li ◽  
...  

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.


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