Parameters optimization and real-time calibration of Measurement-Device-Independent Quantum Key Distribution Network based on Back Propagation Artificial Neural Network
arXiv:1812.08388 · doi:10.1364/JOSAB.36.000B92
Abstract
The parameters choosing (such as probabilities of choosing X-basis or Z-basis, intensity of signal state and decoy state, etc.) and system calibrating will be more challenging when the number of users of a measurement-device-independent quantum key distribution(MDI-QKD) network becomes larger. At present, people usually use optimization algorithms to search the best parameters. This method can find the optimized parameters accurately but may cost lots of time and hardware resources. It's a big problem in large scale MDI-QKD network. Here, we present a new method, using Back Propagation Artificial Neural Network(BPNN) to predict, rather than searching the optimized parameters. Compared with optimization algorithms, our BPNN is faster and more lightweight, it can save system resources. Another big problem brought by large scale MDI-QKD network is system recalibration. BPNN can support this work in real time, and it only needs to use some discarded data generated from communication process, rather than require us to add additional devices or scan the system
arXiv comment7 pages, 8 figures
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Cited by in corpus (6)
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- Automated machine learning for secure key rate in discrete-modulated continuous-variable quantum key distribution
- Neural network-based prediction of the secret-key rate of quantum key distribution
- Self-Tuning Transmitter for Quantum Key Distribution Using Machine Intelligence
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