Recurrent neural network approach to quantum signal: coherent state restoration for continuous-variable quantum key distribution
In continuous-variable quantum key distribution (CV-QKD), weak signal carrying information transmits from Alice to Bob; during this process it is easily influenced by unknown noise which reduces signal-to-noise ratio, and strongly impacts reliability and stability of the communication. Recurrent quantum neural network (RQNN) is an artificial neural network model which can perform stochastic filtering without any prior knowledge of the signal and noise. In this paper, a modified RQNN algorithm with expectation maximization algorithm is proposed to process the signal in CV-QKD, which follows the basic rule of quantum mechanics. After RQNN, noise power decreases about 15 dBm, coherent signal recognition rate of RQNN is 96%, quantum bit error rate (QBER) drops to 4%, which is 6.9% lower than original QBER, and channel capacity is notably enlarged.
KeywordsCV-QKD RQNN Neural network Information identification Signal processing
This work was supported by National Natural Science Foundation of China (NSFC) (61177072) and Key Research and Development Program of Shandong Province (CN) (2017GGX201010).
- 13.Rupp, M.: Asymptotic equivalent analysis of the LMS algorithm under linearly filtered processes. EURASIP J. Adv. Sig. Pr. 1, 1–16 (2016)Google Scholar
- 16.Dawes, R.L.: Quantum neurodynamics: neural stochastic filtering with the Schroedinger equation. IJCNN Proc. 1, 133–140 (1992)Google Scholar
- 18.Behera, L., Sundaram, B.: Stochastic filtering and speech enhancement using a recurrent quantum neural network. In: International Conference on Intelligent Sensing & Information Processing, Chennai, India, 4–7 January 2004Google Scholar
- 20.Gandhi, V, McGinnity, T.M.: Quantum neural network based surface EMG signal filtering for control of robotic hand. In: International Joint Conference on Neural Networks, Dallas, TX, 4–9 August 2013Google Scholar
- 23.Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. ICPR 2, 28–31 (2004)Google Scholar
- 28.Lvovsky, A.I.: Continuous-variable optical quantum state tomography. Rev. Mod. Phys. 1, 299–332 (2005)Google Scholar