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

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guang Ping He
Keyword(s):  
Quantum Key Distribution ◽  
Key Distribution ◽  
Measurement Device ◽  
Joint Measurement ◽  
Measurement Device Independent

scholarly journals Measurement-device-independent quantum key distribution with leaky sources

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Weilong Wang ◽  
Kiyoshi Tamaki ◽  
Marcos Curty
Keyword(s):  
Quantum Key Distribution ◽  
Communication Systems ◽  
Signal Transmission ◽  
Information Leakage ◽  
Key Distribution ◽  
Time Frame ◽  
Measurement Device ◽  
Decoy State ◽  
Unrealistic Assumption ◽  
Measurement Device Independent

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.

Polarization-Encoding-Based Measurement-Device-Independent Quantum Key Distribution with a Single Untrusted Source

2016 ◽  
Vol 33 (10) ◽  
pp. 100301 ◽  
Author(s):  
Chuan-Qi Liu ◽  
Chang-Hua Zhu ◽  
Lian-Hui Wang ◽  
Lin-Xi Zhang ◽  
Chang-Xing Pei
Keyword(s):  
Quantum Key Distribution ◽  
Key Distribution ◽  
Measurement Device ◽  
Polarization Encoding ◽  
Measurement Device Independent

scholarly journals Long-Distance Free-Space Measurement-Device-Independent Quantum Key Distribution

2020 ◽  
Vol 125 (26) ◽  
Author(s):  
Yuan Cao ◽  
Yu-Huai Li ◽  
Kui-Xing Yang ◽  
Yang-Fan Jiang ◽  
Shuang-Lin Li ◽  
...  
Keyword(s):  
Free Space ◽  
Quantum Key Distribution ◽  
Key Distribution ◽  
Long Distance ◽  
Measurement Device ◽  
Space Measurement ◽  
Measurement Device Independent

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 ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Reference Frame ◽  
Quantum Key Distribution ◽  
Short Term Memory ◽  
Key Distribution ◽  
Transmission Efficiency ◽  
System Stability ◽  
Measurement Device ◽  
Measurement Device Independent

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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