Parameter optimization and real-time calibration of a measurement-device-independent quantum key distribution network based on a back propagation artificial neural network
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Abstract
Selection of parameters (e.g., the probability of choosing an X-basis or Z-basis, the intensity of signal state and decoy state, etc.) and system calibrating are more challenging when the number of users of a measurement-device-independent quantum key distribution (MDI-QKD) network increases. At present, optimization algorithms are usually employed when searching for the best parameters. This method can find the optimized parameters accurately, but it may take a lot of time and hardware resources. This is a big problem in a large-scale MDI-QKD network. Here, we present, to the best of our knowledge, a new method, using a back propagation artificial neural network (BPNN) to predict, rather than search, the optimized parameters. Compared to optimization algorithms, our BPNN is faster and more lightweight, and it can save system resources. Another big problem brought by large-scale MDI-QKD networks is system recalibration. BPNN can support this work in real time, and it only needs to use some discarded data generated from the communication process, rather than adding additional devices or scanning the system.