Parameter estimation of continuous variable quantum key distribution system via artificial neural networks

2021 ◽  
Author(s):  
Hao Luo ◽  
Yi-jun Wang ◽  
Wei Ye ◽  
Hai Zhong ◽  
Yi-yu Mao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parameter Estimation ◽  
Artificial Neural Networks ◽  
Distribution System ◽  
Quantum Key Distribution ◽  
Key Distribution ◽  
Continuous Variable ◽  
Artificial Neural

scholarly journals Demonstration of high-speed and low-complexity continuous variable quantum key distribution system with local local oscillator

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shengjun Ren ◽  
Shuai Yang ◽  
Adrian Wonfor ◽  
Ian White ◽  
Richard Penty
Keyword(s):  
Distribution System ◽  
Repetition Rate ◽  
Quantum Key Distribution ◽  
Optimization Technique ◽  
Key Distribution ◽  
Local Oscillator ◽  
Continuous Variable ◽  
Noise Model ◽  
System Level ◽  
Excess Noise

AbstractWe present an experimental demonstration of the feasibility of the first 20 + Mb/s Gaussian modulated coherent state continuous variable quantum key distribution system with a locally generated local oscillator at the receiver (LLO-CVQKD). To increase the signal repetition rate, and hence the potential secure key rate, we equip our system with high-performance, wideband devices and design the components to support high repetition rate operation. We have successfully trialed the signal repetition rate as high as 500 MHz. To reduce the system complexity and correct for any phase shift during transmission, reference pulses are interleaved with quantum signals at Alice. Customized monitoring software has been developed, allowing all parameters to be controlled in real-time without any physical setup modification. We introduce a system-level noise model analysis at high bandwidth and propose a new ‘combined-optimization’ technique to optimize system parameters simultaneously to high precision. We use the measured excess noise, to predict that the system is capable of realizing a record 26.9 Mb/s key generation in the asymptotic regime over a 15 km signal mode fibre. We further demonstrate the potential for an even faster implementation.

Artificial Neural Networks to Improve Current Harmonics Identification and Compensation

2010 ◽  
pp. 256-290 ◽  
Author(s):  
Patrice Wira ◽  
Djaffar Ould Abdeslam ◽  
Jean Mercklé
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Digital Signal Processor ◽  
Power Distribution ◽  
Distribution System ◽  
Control Laws ◽  
Identification Schemes ◽  
Learning Capabilities ◽  
Artificial Neural ◽  
The One

Artificial Neural Networks (ANNs) have demonstrated very interesting properties in adaptive identification schemes and control laws. In this work, they are employed for the on-line control strategy of an Active Power Filter (APF) in order to improve its performance. Indeed, neural-based approaches are synthesized to design adaptive and efficient harmonic identification schemes. The proposed neural approaches are employed for compensating for the changing harmonic distortions introduced in a power distribution system by unknown nonlinear loads. The implementation of the ANNs has been optimized on a digital signal processor for real-time experiments. The feasibility of the implementation has been validated and the neural compensation schemes exhibit good performances compared to conventional approaches. By their learning capabilities, ANNs are able to take into account time-varying parameters such as voltage sags and harmonic content changes, and thus appreciably improve the performance of the APF compared to the one obtained with traditional compensating methods.

Digital Self-Coherent Continuous Variable Quantum Key Distribution System

2020 ◽  
Author(s):  
Tobias A. Eriksson ◽  
Ruben S. Luís ◽  
Kadir Gümüş ◽  
Georg Rademacher ◽  
Benjamin J. Puttnam ◽  
...  
Keyword(s):  
Distribution System ◽  
Quantum Key Distribution ◽  
Key Distribution ◽  
Continuous Variable
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