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Physical Review A

Automatic phase compensation of a continuous-variable quantum-key-distribution system via deep learning

Zhe-Kun Zhang, Weiqi Liu, Jin Qi, Chen He, Peng Huang

Published Jun 30, 2023• 10 citations
Publisher

Abstract

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.

DOI
10.1103/physreva.107.062614

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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.
Quantum Key Distribution (QKD) systems face challenges due to quantum state drift caused by environmental variations. This paper introduces a phase drift compensation algorithm for QKD systems based on Long Short Term Memory (LSTM) networks. This algorithm only learns, predicts, and compensates for phase drift from intermediate data generated during QKD transmission, without the need for additional data acquisition modules such as environmental monitoring. The experimental results show that the algorithm has high accuracy in tracking phase drift. It performs well on the test set and can accurately match actual scanned data. When implemented in practical GHz QKD systems, this algorithm can increase transmission efficiency by 15 % without reducing the bit error rate. In addition, as the algorithm does not rely on environmental monitoring equipment or high-intensity emission light conditions, it enhances the security of the system and reduces potential security risks.
Local local oscillator (LLO) continuous-variable quantum key distribution (CVQKD) systems have gained prominence due to their security advantages with LO generated locally. However, the nonsynchronization of two lasers leads to phase drift, a detrimental phenomenon that restricts excess noise suppression. Prevailing solutions, such as pilot-multiplexing schemes, offer better recovery results than pilot-sequential schemes, but at the expense of increased system complexity and demanding technical requirements. In this work, we introduce an innovative carrier-recovery strategy employing the long-short-term memory (LSTM) neural network, aiming to optimize the recovery performance in the simple self-referenced CVQKD systems with the pilot-sequential scheme. Through temporal modeling, the LSTM network proficiently predicts and compensates for the rapid phase drifts. Experimental validations conducted in both fiber and free-space channels underscore the effectiveness of our method for carrier recovery. In practical CVQKD experiments, our LSTM-based approach yields a near 50% reduction in excess noise, leading to a significant increase in secret key rates when compared with the traditional pilot-sequential method. The simplicity of hardware and operation positions our scheme as a superior alternative for current mainstream pilot-multiplexing CVQKD systems, particularly appealing for on-chip implementation and satellite-to-ground quantum communication applications.
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 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 problems due to the reference pulses transmitted together with quantum signals over the insecure quantum channel. In this paper, we propose 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, and then the phase compensation error is increased. Consequently, the secret key rate is reduced due to the imperfect phase compensation for quantum signals. Based on the noise model of imperfect phase compensation, the practical security of the CVQKD under phase attack is analyzed. Besides, we propose an effective method to detect the intensity of phase attack, in which the deviation of phase compensation error on quantum signals and that on reference signals are monitored in real time. The simulation results show that the security analysis is accurate and the method of phase attack detection is feasible.
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.