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SPIE Conference on Quantum and Nonlinear Optics

Phase compensation of continuous variable quantum key distribution based on mean denoising methods

Lifeng Fu, Jinlu Liu, Zhang Chenlin, Tao Zhang, Shao Yun, Bingjie Xu

Published Oct 8, 2021• 2 citations
Publisher

Abstract

The phase compensation with high accuracy is one of the key technologies in continuous variable quantum key distribution (CVQKD) system, which directly influences the secure key rate and transmission distance. However, traditional phase compensation method cannot accurately estimate the phase drift due to the additive noise introduced by coherent detector. In this paper, we propose a new phase compensation method based on mean denoising, where a training sequence is designed for estimating phase drift in the transmitter (Alice) and an average of the multi-points in the training sequence is estimated to remove the influence of additive noise. Simulation results show that the compensation accuracy of the proposed method can reach 0.9932, which is 20% better than that based on traditional method. Our method can significantly reduce the influence of additive noise, and improve the system performance by controlling excess noise in phase compensation process.

DOI
10.1117/12.2601338

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The phase compensation with high accuracy is one of the key technologies in continuous variable quantum key distribution (CVQKD) system, which directly influences the secure key rate and transmission distance. However, traditional phase compensation method cannot accurately estimate the phase drift due to the additive noise introduced by coherent detector. In this paper, we propose a new phase compensation method based on mean denoising, where a training sequence is designed for estimating phase drift in the transmitter (Alice) and an average of the multi-points in the training sequence is estimated to remove the influence of additive noise. Simulation results show that the compensation accuracy of the proposed method can reach 0.9932, which is 20% better than that based on traditional method. Our method can significantly reduce the influence of additive noise, and improve the system performance by controlling excess noise in phase compensation process.
Phase drift extremely limits the secure key rate and secure transmission distance, which is non-negligible in local oscillation continuous variable quantum key distribution (LLO CV-QKD). In order to eliminate the impact caused by phase drift, we analyze the phase noise of the system and propose a phase compensation method based on convolutional neural network (CNN). Moreover, the compensation is performed on the signal according to the estimated value of phase drift before coherent detection. In numerical simulation, we compare the performance of phase compensation methods based on CNN and Kalman filter (KF), and the results show that CNN-based phase compensation has higher accuracy and stability.
In a practical continuous-variable quantum-key-distribution (CVQKD) system, it is vital to accurately evaluate and then compensate for the phase drifts of the signals, so that the involved system can achieve better performance and stability. In this paper, based on the long short-term memory network (LSTM) model, an automatic phase compensation approach of the CVQKD system is proposed. The LSTM model is first trained to predict the phase drift value of the quantum signal relative to the local oscillator over time. Then, the predicted phase drift value can be used by Alice to reconstruct her data. Finally, Alice and Bob can obtain the raw key, so that the CVQKD system can achieve enhancements in terms of performance and stability. The experimental results indicate that the proposed LSTM-based automatic phase compensation algorithm can accurately predict the phase drift value and perform phase compensation instead of real-time phase drift measurement, which improves the performance of the CVQKD system without requiring any additional quantum resources and extra experimental hardware.
The traditional phase compensation method in CVQKD (continuous variable quantum key distribution) usually assumes that the phase drift within a frame is constant. However, in reality, there are usually small phase drifts within a frame, especially in free space. Unperfect compensation of phase drifts can introduce additional phase noise, which can reduce key rate and transmission distance. To address this issue, we propose a two-stage phase compensation scheme that inserts different length phase drift compensation blocks to not only compensate for the average phase drift of a frame but also to compensate for the internal phase drift fluctuations within the frame. This enables more accurate phase drift compensation, particularly useful for scenarios with large and fast phase drift fluctuations.
Phase compensation is a dispensable procedure to reduce the difference between legitimate parties in continuous-variable quantum key distribution (CVQKD) because of the unavoidable phase drift of the quantum signals. However, it is a difficult task to compensate the fast drifted phase accurately. Here, we propose a novel phase compensation scheme based on an optimal iteration algorithm. Analysis shows that this scheme can make the phase compensation reach a higher precision level while simultaneously ensuring the efficiency. When the accuracy is determined, we can minimize the number of iterations by controlling the step-length to increase the algorithm efficiency. Moreover, we can improve the accuracy of phase compensation by means of changing the step-length. This work breaks the bottleneck of accuracy problem in phase compensation and contributes to the performance of the whole CVQKD system.
Continuous-variable quantum key distribution (CVQKD) with a real local oscillator (LO) is confronted with new security issues attributed to the reference pulses transmitted together with quantum signals over the insecure quantum channel. This paper proposes a method of phase attack on reference pulses of the CVQKD with real LOs. Under the phase attack, the phase drifts of reference pulses are manipulated by eavesdroppers so that the phase compensation error is increased. Consequently, the secret key rate is reduced because of the imperfect phase compensation for quantum signals. Based on the noise model of imperfect phase compensation, both the parameters of actual transmittance and actual excess noise are deduced from the phase compensation accuracy, and then they are compared with those estimated by training signals. The simulation results show that the secret key rate calculated from the theoretical parameters is consistent with the one estimated by training signals.
Abstract Although the continuous-variable quantum key distribution (CV-QKD) protocol based on a local local oscillator (LLO) can close all the security loopholes from the transmitted local oscillator (TLO), the phase noise caused by the inaccurate phase reference information limits the performance of the protocol. To reduce the residual phase noise, in this work, we propose a phase estimation and compensation method based on the temporal convolutional neural (TCN) model, where a part of phase information obtained by measuring pilot pulses is employed as the training data and input into the TCN module. With a trained TCN module, the subsequent phase drifts can be more accurately estimated, allowing for better phase compensation and lower phase noise. Numerical analysis shows that the proposed scheme can improve the transmission distance and the secret key rate of the LLO protocol.