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The parameters choosing (such as probabilities of choosing X-basis orZ-basis, intensity of signal state and decoy state, etc.) and systemcalibrating will be more challenging when the number of users of ameasurement-device-independent quantum key distribution(MDI-QKD) networkbecomes larger. At present, people usually use optimization algorithms tosearch the best parameters. This method can find the optimized parametersaccurately but may cost lots of time and hardware resources. It's a big problemin large scale MDI-QKD network. Here, we present a new method, using BackPropagation Artificial Neural Network(BPNN) to predict, rather than searchingthe optimized parameters. Compared with optimization algorithms, our BPNN isfaster and more lightweight, it can save system resources. Another big problembrought by large scale MDI-QKD network is system recalibration. BPNN cansupport this work in real time, and it only needs to use some discarded datagenerated from communication process, rather than require us to add additionaldevices or scan the system

journal of the optical society of america. b, optical physics/journal of the optical society of america. b, online

Parameters optimization and real-time calibration of  Measurement-Device-Independent Quantum Key Distribution Network based on Back  Propagation Artificial Neural Network