Abstract The secret key rate of a continuous-variable quantum key distribution (CV-QKD) system is limited by excess noise. A key issue typical to all modern CV-QKD systems implemented with a reference or pilot signal and an independent local oscillator is controlling the excess noise generated from the frequency and phase noise accrued by the transmitter and receiver. Therefore accurate phase estimation and compensation, so-called carrier recovery, is a critical subsystem of CV-QKD. Here, we explore the implementation of a machine learning framework based on Bayesian inference, namely an unscented Kalman filter (UKF), for estimation of phase noise and compare it to a standard reference method and a previously demonstrated machine learning method. Experimental results obtained over a 20-km fibre-optic link indicate that the UKF can ensure very low excess noise even at low pilot powers. The measurements exhibited low variance and high stability in excess noise over a wide range of pilot signal to noise ratios. This may enable CV-QKD systems with low hardware implementation complexity which can seamlessly work on diverse transmission lines.
Abstract The secret key rate of a continuous-variable quantum key distribution (CV-QKD) system is limited by excess noise. A key issue typical to all modern CV-QKD systems implemented with a reference or pilot signal and an independent local oscillator is controlling the excess noise generated from the frequency and phase noise accrued by the transmitter and receiver. Therefore accurate phase estimation and compensation, so-called carrier recovery, is a critical subsystem of CV-QKD. Here, we explore the implementation of a machine learning framework based on Bayesian inference, namely an unscented Kalman filter (UKF), for estimation of phase noise and compare it to a standard reference method and a previously demonstrated machine learning method. Experimental results obtained over a 20-km fibre-optic link indicate that the UKF can ensure very low excess noise even at low pilot powers. The measurements exhibited low variance and high stability in excess noise over a wide range of pilot signal to noise ratios. This may enable CV-QKD systems with low hardware implementation complexity which can seamlessly work on diverse transmission lines.
Quantum key distribution depending on the principles of quantum mechanics, enables secure data exchange by allowing two users to generate a shared random secret key. As a subtype of quantum key distribution, continuous variable quantum key distribution (CV-QKD) has gained prominence due to its easy integration with traditional coherent optical communication. In practice, the effectiveness of phase compensation significantly impacts both excess noise levels and the generation of secret key rates. Therefore, precise phase compensation, also known as carrier recovery, is important to the overall CV-QKD. Hence, accurate carrier recovery is critical for optimal CV-QKD performance. This work simulates a local local oscillator CV-QKD system and introduces an Unscented Kalman Filter algorithm to enhance phase estimation. The results shows that a secret key rate of 229.5 kbps is achievable over a 300m transmission distance with direct reconciliation.
Continuous-variable quantum key distribution (CV-QKD) has been increasingly studied, which offers the advantage of compatibility with modern coherent optical communication systems. In contrast to CV-QKD with a transmitting local oscillator, the local local oscillator CV-QKD avoids the security vulnerabilities of a local oscillator by generating a local oscillator at the receiver. In practice, the frequency offset of the two lasers introduces extra phase noise, which is generally suppressed by various carrier phase recovery algorithms. However, the accuracy of carrier phase recovery can be influenced by the power of the pilot tone, particularly as the transmission distance increases. To further improve accuracy, we propose a method based on the unscented particle filter algorithm, to increase the accuracy of phase estimation, effectively restore the quantum signal and reduce excess noise. In our work, we demonstrated a local local oscillator CV-QKD experiment with a finite-size block of 1×108 under a transmission distance of 50 km. Through our method, we achieved a secret key rate of 525 kbps, which represents a 28% improvement. These results confirm that our proposed method not only improves the accuracy of carrier phase recovery, but also provides a new approach for future research on algorithms for long-distance CV-QKD. Furthermore, our study improves the phase compensation performance, enabling the orthogonal components of the quantum signal to exhibit enhanced symmetry in phase space.
Continuous-variable quantum key distribution (CV-QKD) holds promise for enhancing security in communication networks. However, obtaining a higher secure key rate poses challenges, particularly in reliable phase estimation. So, it is very necessary for CV-QKD implementations with independent local oscillator (LO) to employ carrier recovery along with precise phase estimation. Our methodology combines extended Kalman filters (EKF) with recurrent neural networks (RNNs) to enhance the accuracy of phase recovery for locally generated LO signals. Using numerical simulations, we evaluate the achievable secret key rates for different transmission distances and line widths. The proposed method achieves a phase error of approximately 1 × 10–4, leading to positive secure key rates for distances up to 40 km. This method of phase tracking solves the problem and is effective in real-time deployment of CV-QKD in communication networks.
Quantum key distribution (QKD) enables information-theoretic-secure secret key exchange thanks to the laws of quantum mechanics. Since it can easily be incorporated into existing telecom networks, continuous-variable QKD (CV QKD) using coherent states and coherent detection has recently emerged as a promising candidate. However, the secret key rate and link distance in CV QKD systems are still limited by the imperfections of used components. In this work, through simulations, we study the use of different digital filtering techniques for optical phase recovery, aided by pilot symbols, to mitigate the phase noise induced by the coherent mixing of two free-running lasers in a CV QKD system. Moving average, Butterworth and unscented Kalman filters are evaluated for different phase drift conditions. Results show that phase recovery from small amplitude pilot symbols, along with further digital filtering, can considerably reduce phase noise and measurement error, while increasing the secret key rate and link distance of the CV QKD system.
Accurately estimating phase is crucial in continuous-variable quantum key distribution systems, directly impacting the final secret key rate. In previous systems that utilize the local local oscillator, phase estimation is closely tied to the amplitude and signal-to-noise ratio (SNR) of the pilot signal. As SNR decreases, so does the accuracy of phase estimation, leading to increased excess noise and a potential loss of the system's secret key rate. The SNR of the pilot signal is constrained by the optical source power, which tends to approach 0 dB, particularly over long distances. Enhancing the accuracy of phase estimation under conditions of low SNR for pilot signals is highly challenging, especially in long-distance quantum key distribution. Our paper proposes two digital filter methods: the unscented Kalman filtering (UKF) and the Savitzky-Golay filtering (SGF) for phase estimation, which effectively mitigates the effects of additive noise. The approach applies to digital systems and therefore holds particular advantages in systems based on discrete-modulation (DM) protocols. Through simulations and practical implementation based on DM protocols, we find that the digital filter scheme is capable of accurately estimating phase, especially in scenarios with low SNR of the pilot signal. The conventional method fails to achieve an effective secret key rate, whereas the digital-filter-based scheme achieves a secret key rate of almost 30 kbps over a 20 km fiber link with a repetition frequency of 5 MHz under conditions where the SNR of the pilot signal is around 0 dB.
Local local oscillator (LLO) continuous-variable quantum key distribution (CVQKD) schemes have been extensively studied for their enhanced security and simplified system implementation. However, such schemes typically require numerous pilot signals to compensate for phase drift caused by two independent lasers, which significantly increases system overhead and limits the achievable secret key rate. In this work, we propose an enhanced-rate CVQKD scheme, which aims to enhance the secret key rate by achieving high-accuracy phase compensation based on a particle filter (PF) algorithm under low system overhead. Specifically, to counter the loss in phase estimation accuracy due to the reduced number of pilot signals, we introduce a PF-based carrier phase recovery algorithm, complemented by an exponentially weighted phase prediction method to improve phase estimation accuracy. This approach effectively suppresses excess noise under limited pilot information and improves phase compensation precision. Experimental results demonstrate that, compared to the traditional scheme with a 1:1 signal-to-pilot number ratio, our method achieves its maximum performance with a signal-to-pilot number ratio of 3:1, while maintaining phase compensation accuracy virtually unchanged, reducing system overhead by 50% and achieving a 147% improvement in the secret key rate, reaching 235 kbps in a 30 km standard fiber link.