Performance improvement of discrete-modulation continuous-variable quantum key distribution by using the machine-learning-based detector

2018 ◽  
Author(s):  
Jiawei Li ◽  
Duan Huang ◽  
Cailang Xie ◽  
Ling Zhang ◽  
Ying Guo
Keyword(s):  
Machine Learning ◽  
Performance Improvement ◽  
Quantum Key Distribution ◽  
Key Distribution ◽  
Continuous Variable

Discrete-modulated continuous-variable quantum key distribution with a machine-learning-based detector

2018 ◽  
Vol 57 (06) ◽  
pp. 1 ◽  
Author(s):  
Jiawei Li ◽  
Ying Guo ◽  
Xudong Wang ◽  
Cailang Xie ◽  
Ling Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Key Distribution ◽  
Key Distribution ◽  
Continuous Variable

scholarly journals Secure Continuous-Variable Quantum Key Distribution with Machine Learning

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 511
Author(s):  
Duan Huang ◽  
Susu Liu ◽  
Ling Zhang
Keyword(s):  
Machine Learning ◽  
Quantum Key Distribution ◽  
Key Distribution ◽  
Continuous Variable ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The Common ◽  
Practical Security

Quantum key distribution (QKD) offers information-theoretical security, while real systems are thought not to promise practical security effectively. In the practical continuous-variable (CV) QKD system, the deviations between realistic devices and idealized models might introduce vulnerabilities for eavesdroppers and stressors for two parties. However, the common quantum hacking strategies and countermeasures inevitably increase the complexity of practical CV systems. Machine-learning techniques are utilized to explore how to perceive practical imperfections. Here, we review recent works on secure CVQKD systems with machine learning, where the methods for detections and attacks were studied.

scholarly journals Performance Improvement of Underwater Continuous-Variable Quantum Key Distribution via Photon Subtraction

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 1011 ◽  
Author(s):  
Qingquan Peng ◽  
Guojun Chen ◽  
Xuan Li ◽  
Qin Liao ◽  
Ying Guo
Keyword(s):  
Performance Improvement ◽  
Quantum Key Distribution ◽  
Secure Communication ◽  
Key Distribution ◽  
Continuous Variable ◽  
Transmission Performance ◽  
Transmission Distance ◽  
Optical Attenuation ◽  
Photon Subtraction ◽  
Simulation Results

Considering the ocean water’s optical attenuation is significantly larger than that of Fiber Channel, we propose an approach to enhance the security of underwater continuous-variable quantum key distribution (CVQKD). In particular, the photon subtraction operation is performed at the emitter to enhance quantum entanglement, thereby improving the underwater transmission performance of the CVQKD. Simulation results show that the photon subtraction operation can effectively improve the performance of CVQKD in terms of underwater transmission distance. We also compare the performance of the proposed protocol in different water qualities, which shows the advantage of our protocol against water deterioration. Therefore, we provide a suitable scheme for establishing secure communication between submarine and submarine vehicles.

scholarly journals Machine learning aided carrier recovery in continuous-variable quantum key distribution

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Hou-Man Chin ◽  
Nitin Jain ◽  
Darko Zibar ◽  
Ulrik L. Andersen ◽  
Tobias Gehring
Keyword(s):  
Machine Learning ◽  
Phase Noise ◽  
Quantum Key Distribution ◽  
Unscented Kalman Filter ◽  
Key Distribution ◽  
Continuous Variable ◽  
Excess Noise ◽  
Carrier Recovery ◽  
Pilot Signal ◽  
Wide Range

AbstractThe secret key rate of a continuous-variable quantum key distribution (CV-QKD) system is limited by excess noise. A key issue typical to all modern CV-QKD systems implemented with a reference or pilot signal and an independent local oscillator is controlling the excess noise generated from the frequency and phase noise accrued by the transmitter and receiver. Therefore accurate phase estimation and compensation, so-called carrier recovery, is a critical subsystem of CV-QKD. Here, we explore the implementation of a machine learning framework based on Bayesian inference, namely an unscented Kalman filter (UKF), for estimation of phase noise and compare it to a standard reference method and a previously demonstrated machine learning method. Experimental results obtained over a 20-km fibre-optic link indicate that the UKF can ensure very low excess noise even at low pilot powers. The measurements exhibited low variance and high stability in excess noise over a wide range of pilot signal to noise ratios. This may enable CV-QKD systems with low hardware implementation complexity which can seamlessly work on diverse transmission lines.

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