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In practical implementation of quantum key distributions (QKD), it requiresefficient, real-time feedback control to maintain system stability when facingdisturbance from either external environment or imperfect internal components.Usually, a "scanning-and-transmitting" program is adopted to compensatephysical parameter variations of devices, which can provide accuratecompensation but may cost plenty of time in stopping and calibrating processes,resulting in reduced efficiency in key transmission. Here we for the firstpropose to employ a well known machine learning model, i.e., the LongShort-Term Memory Network (LSTM), to predict those physical parametervariations in advance and actively perform real-time control on correspondingQKD devices. Experimentally, we take the phase-coding scheme as an example andrun the LSTM model based QKD system for more than 10 days. Experimental resultsshow that we can keep the same level of quantum-bit error rate as thetraditional "scanning-and-transmitting" program by employing our new machinelearning method, but dramatically reducing the scanning time and resulting insignificantly enhanced key transmission efficiency. Furthermore, our presentmachine learning model should also be applicable to any other QKD systems usingany coding scheme or QKD protocols, and thus seems a very promising candidatein large-scale application of quantum communication network in the near future.

Practical phase-modulation stabilization in quantum key distribution via machine learning