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Title:
ACTIVE FEEDBACK CONTROL METHOD FOR MACHINE LEARNING-BASED QUANTUM COMMUNICATION SYSTEM
Document Type and Number:
WIPO Patent Application WO/2020/238224
Kind Code:
A1
Abstract:
An active feedback control method for a machine learning-based quantum communication system. According to the present invention, in the transmission process of a quantum key distribution system, a double-layer LSTM network completed pre-training is used to predict a zero phase voltage value of a phase modulator at a receiving end at the next moment according to the real-time temperature in the external environment, the humidity, the laser light intensity fluctuation, and the change in the voltage at the past moment, and the network is updated at a fixed time interval so that the LSTM network can accurately predict for a long time, thereby ensuring the long-time efficient and stable operation of the quantum key distribution system. The present invention greatly improves the transmission efficiency of the quantum key distribution system by means of active prediction and feedback control methods. The present invention is not only applied to a quantum key distribution system or a phase encoding system, but also applicable to a quantum key distribution system or a quantum communication network based on other encoding modes.

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Inventors:
WANG QIN (CN)
LIU JINGYANG (CN)
Application Number:
PCT/CN2020/070401
Publication Date:
December 03, 2020
Filing Date:
January 06, 2020
Export Citation:
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Assignee:
UNIV NANJING POSTS & TELECOMMUNICATIONS (CN)
International Classes:
H04L9/08; G06N3/04; H04B10/70
Domestic Patent References:
WO2006124209A12006-11-23
Foreign References:
CN110365473A2019-10-22
CN107612688A2018-01-19
CN105490805A2016-04-13
Other References:
LIU, WEIQI ET AL.: "Integrating Machine Learning to Achieve an Automatic Parameter Prediction for Practical Continuous-Variable Quantum Key Distribution", PHYSICAL REVIEW A 97, 022316 (2018), 12 February 2018 (2018-02-12), XP055762825, DOI: 20200326172217A
LIU, JINGYANG ET AL.: "Practical Phase-Modulation Stabilization in Quantum Key Distribution via Machine Learning", PHYSICAL REVIEW APPLIED 12, 014059 (2019), 30 July 2019 (2019-07-30), XP081382191, DOI: 20200326184816PX
Attorney, Agent or Firm:
NANJING ZHENGLIAN INTELLECTUAL PROPERTY AGENCY CO., LTD. (CN)
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