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arXiv:2606.31534

Machine Learning based Optimization of CV-QKD Under Practical Constraints

Svitlana Matsenko, Amirhossein Ghazisaeidi, Marcin Jarzyna, Mateusz Kucharczyk, Mikkel Schmidt, Konrad Banaszek, Darko Zibar

quant-phSubmitted Jun 30, 2026• Announced Jul 1, 2026• 0 citations
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Abstract

Practical hardware limitations, including finite transmitter and receiver filter lengths as well as the finite resolution of digital-to-analog and analog-to-digital converters, lead to mode mismatch and degrade the performance of continuous-variable quantum key distribution systems. To address this, we develop a machine learning-based end-to-end optimization framework that jointly optimizes transmitter pulse shaping and receiver matched filtering. The approach employs reinforcement learning under realistic hardware constraints, including a limited number of filter taps, finite digital-to-analog and analog-to-digital converter resolution, analog low-pass filtering, and the optimal mean photon number. By mitigating mode mismatch and accounting for implementation constraints, the proposed method improves overall system performance. Simulation results demonstrate enhanced secure key rates compared to conventional approaches, demonstrating the effectiveness of the proposed framework.

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Advanced Quantum Technologies•Jun 11, 2025•5 citations
Practical hardware limitations, including finite transmitter and receiver filter lengths as well as the finite resolution of digital-to-analog and analog-to-digital converters, lead to mode mismatch and degrade the performance of continuous-variable quantum key distribution systems. To address this, we develop a machine learning-based end-to-end optimization framework that jointly optimizes transmitter pulse shaping and receiver matched filtering. The approach employs reinforcement learning under realistic hardware constraints, including a limited number of filter taps, finite digital-to-analog and analog-to-digital converter resolution, analog low-pass filtering, and the optimal mean photon number. By mitigating mode mismatch and accounting for implementation constraints, the proposed method improves overall system performance. Simulation results demonstrate enhanced secure key rates compared to conventional approaches, demonstrating the effectiveness of the proposed framework.
Continuous-variable quantum key distribution (CV-QKD) enables two remote parties to establish information-theoretically secure keys and offers high practical feasibility due to its compatibility with mature coherent optical communication technologies. However, as CV-QKD systems progress toward digital implementations, device nonidealities drive the optical field from a single-mode to a continuous-mode region, thereby underscoring the mismatch between theoretical models and practical systems. Here, we introduce temporal modes to construct an entanglement-based scheme that more accurately captures device nonidealities and develop a corresponding secret key rate calculation method applicable to continuous-mode scenarios. We demonstrate that optimizing the pulse-shaping format can significantly improve performance under detector-bandwidth-limited conditions. Experimental results also confirm that the proposed model effectively describes the impact of sampling-time deviations. We further analyze a linear weighted-reconstruction digital signal processing method,which improves the secret key rate by approximately 50% in a 30-km fiber experiment without requiring additional hardware, demonstrating a substantial performance enhancement at metropolitan distances. The proposed theoretical framework accommodates a broader range of experimental conditions and can guide the optimization of digital CV-QKD systems.
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 (QKD) guarantees information-theoretic security by the laws of physics, but deployment at scale requires compact, manufacturable photonic terminals. Continuous-variable QKD (CV-QKD) is well suited for this transition through telecom-compatible, room-temperature coherent detection. However, unifying full on-chip core terminal integration, room-temperature operation, high loss tolerance, and composable end-to-end security in long-distance QKD remains a key bottleneck. Here we report a fully integrated CV-QKD platform in which two hybrid III--V/Si3_33​N4_44​ integrated lasers, a silicon transmitter, and a silicon coherent receiver implement the core terminal functions, operating with a local local oscillator (LLO) over fibre links of 25--150 km. A Bayesian machine-learning algorithm maintains robust phase lock throughout the long records required for composable security, consistently outperforming the conventional unscented Kalman filter, while rate-matched multidimensional reconciliation approaches the Shannon limit. The system certifies a composable finite-size secret-key rate of 29.3 kbps at 100 km from a 140-billion-symbol block, with 12.9 kbps at 125 km under finite-size analysis and 9.17 kbps at 150 km under asymptotic analysis. By establishing the longest finite-size and asymptotic reaches and the highest secret-key rate per symbol reported for integrated CV-QKD, this work advances the development of practical chip-based quantum networks.
Abstract In the continuous‐variable quantum key distribution (CV‐QKD) system, the secret key rate and transmission distance are important factors to evaluate the quality of the system. Numerical methods are commonly employed to calculate the secret key rate of QKD, but they often face challenges to find the optimal parameter to enhance the secret key rate. In this study, a machine learning based parameter optimization approach is proposed for the Gaussian‐modulated CV‐QKD system. The proposed approach adopts the genetic algorithm backpropagation (GA‐BP) based approach to enhance the secret key rate by optimize parameters of the CV‐QKD system. Furthermore, under certain assumptions, some theoretical results are established, which enables us to identify the maximum secret key rate under a given GA‐BP network. Based on these results, a bisection based method is proposed that can effectively find the optimal modulation variance and the corresponding maximum secret key rate. Finally, the simulation results show that the proposed approach can achieve a higher secret key rate than the conventional methods under the numerical settings.