Active feedback control method for quantum communication system based on machine learning

Abstract

An active feedback control method for a quantum communication system based on machine learning is disclosed. In the transmission process of a quantum key distribution system, the present invention uses a pre-trained double-layer LSTM network to predict, according to a real-time ambient temperature, humidity and laser light intensity fluctuation, as well as voltage changes in the past moment, a zero-phase voltage value of a phase modulator at a receiving end at the next moment, and updates the network at a fixed time interval, so that the LSTM network can accurately predict for a long time, ensuring that the quantum key distribution system operates stably and efficiently for a long time. The present invention greatly improves the transmission efficiency of the quantum key distribution system by method of active prediction and feedback control. The present invention is not limited to being applied to quantum key distribution systems or phase encoding systems, and also applicable to quantum key distribution systems or quantum communication networks based on other encoding methods.