Quantum key distribution (QKD) is a cutting-edge technology that guarantees unbreakable security. Multi-band transmission across the O+E+S+C+L bands offers a viable solution for the coexistence of quantum and classical signals over existing fiber infrastructure. However, the secure key rate (SKR) achievable in quantum channels (QChs) is influenced by variations in classical traffic load and its spectrum usage patterns. To support dynamic and time-varying classical traffic, it is essential to estimate the achievable SKR for each QCh in real-time, enabling the selection of the optimal frequency that maximizes SKR. Conventional methods rely on solving complex integral noise equations to estimate SKR, but their computational complexity makes them unsuitable for real-time operations. In this letter, we propose a machine learning (ML) algorithm to evaluate the SKR of QChs, taking into account the time-varying behavior of classical traffic, and to select the optimal frequency for QChs. We implement three ML algorithms across various fiber intervals, all of which estimate the optimal frequency for QChs with 99% accuracy and perform computations in an average of 0.09 seconds— significantly faster than the conventional method, which has a mean computation time of 637 seconds.
Quantum key distribution (QKD) is a cutting-edge technology that guarantees unbreakable security. Multi-band transmission across the O+E+S+C+L bands offers a viable solution for the coexistence of quantum and classical signals over existing fiber infrastructure. However, the secure key rate (SKR) achievable in quantum channels (QChs) is influenced by variations in classical traffic load and its spectrum usage patterns. To support dynamic and time-varying classical traffic, it is essential to estimate the achievable SKR for each QCh in real-time, enabling the selection of the optimal frequency that maximizes SKR. Conventional methods rely on solving complex integral noise equations to estimate SKR, but their computational complexity makes them unsuitable for real-time operations. In this letter, we propose a machine learning (ML) algorithm to evaluate the SKR of QChs, taking into account the time-varying behavior of classical traffic, and to select the optimal frequency for QChs. We implement three ML algorithms across various fiber intervals, all of which estimate the optimal frequency for QChs with 99% accuracy and perform computations in an average of 0.09 seconds— significantly faster than the conventional method, which has a mean computation time of 637 seconds.
In this letter, we propose to implement quantum key distribution (QKD) by using a multi-band strategy. We present a thorough noise analysis of quantum-classical coexistence while incorporating all relevant and precise noise parameters. Our research demonstrates that utilizing the entire extended C-band for quantum and transmitting classical channels (CChs) over the extended L-band with bandwidth of 75 GHz and power of 0 dBm yields no quantum communication beyond 13 km. To address the problem, we utilize the C+L-band to serve CChs and other bands (e.g., O, E, S) for quantum channels (QChs), while taking into account impairments over the quantum one. Results indicate that, while C+L-band is fully loaded, wavelengths of 1381 nm and below (1260-1381 nm) are appropriate for quantum signals, with 1381 nm offering the highest key rate (3.08 Mbit/sec) over a fiber length of 70 km. Finally, to explore an additional operational scenario when the network is not fully loaded, we focused on determining the optimal policy for assigning CChs. The results reveal the importance of prioritizing the maximization of the gap between the CChs and QChs. In addition, the channel assignment pattern of CChs affects the performance of QChs transmitted at other bands (O, E, and S).
Integrating quantum key distribution (QKD) with existing optical networks is highly desired to reduce the deployment costs and achieve efficient resource utilization, and some point-to-point transmitting experiments have verified its feasibility. Nevertheless, there are still many problems in the realistic scenario where QKD coexists with dynamic data traffics. On the one hand, the conventional static channel allocation schemes cannot guarantee the quality of quantum channels in the presence of the time-varying noises. On the other hand, considering the complex noise generation caused by dynamic classical data traffics with variable characters, it is challenging to achieve online high-performance quantum channel assignments. To address these problems, we propose a machine learning based noise-suppressing channel allocation (ML-NSCA) scheme. In this scheme, the LightGBM based ML framework is trained to predict the optimal channel allocations with lowest noise impacts, according to which, the quantum channels are periodically reallocated to guarantee high secure key rate. To improve the accuracy and scalability of the ML framework, we also optimize the method of feature extraction during the training process. The performance evaluation results indicate that the proposed scheme can effectively resist the dynamic noise impacts in the realistic optical networks and obtain higher secure key rate with less operation complexity than the previous schemes.
Classical-quantum coexistence enables cost-effective transmission of data and quantum signals over the same fiber-optic channel. Nevertheless, weak quantum-key distribution (QKD) signals are susceptible to non-linear interference generated from the classical traffic, primarily spontaneous Raman scattering (SpRS) and four-wave-mixing (FWM), as well as to unfiltered noise. In QKD protocols, increased channel loss and excess noise both reduce the secret key rates (SKRs), as illustrated in this work for the two-state BB84 and Gaussian-modulated coherent-states (GMCS) protocols. In this study, we derive closed-form expressions for evaluating the accumulated interference power from coexisting classical signals in a quantum frequency channel. Our model enables effective design of classical-quantum systems in single-mode fibers (SMFs), capturing the evolution of interference arising from the relevant physical phenomena. We utilize the model to examine frequency allocation in multiband transmission systems, demonstrating that, contrary to common practice of allocating QKD channels in the O-band, increased SKR is achieved by placing quantum channels in the upper E-/lower S-band across the relevant scenarios.
Quantum Key Distribution (QKD) offers theoretically unbreakable security by leveraging quantum mechanics. However, practical implementation is challenged by environmental vulnerabilities, noise, and hardware imperfections. Recently, Machine Learning (ML) has emerged as a powerful tool to address these limitations and enhance the real-world viability of QKD systems. In this survey, we review ML techniques applied to improve QKD security and performance across five applications. First, parameter optimization, covering signal calibration, polarization alignment, phase stabilization, modulation state tuning, and post-processing enhancements to maximize secure key generation and minimize error rates. Second, attack detection, where ML models identify and classify quantum threats such as photon-number-splitting and Trojan-horse attacks. Third, protocol selection, leveraging ML to dynamically choose QKD protocols based on operational conditions. Fourth, key performance prediction of core metrics such as Secret Key Rate (SKR) and Quantum Bit Error Rate (QBER). Finally, quantum network management, optimizing large-scale QKD deployments through intelligent routing, node management, and resource allocation. Performance improvements are evaluated using accuracy, reduced QBER, and increased SKR. While ML shows significant potential for finance, government, and defense applications, challenges remain in scalability, computational demands, and real-world testing. Ongoing work should focus on lightweight, generalizable models and standardized benchmarks for practical ML-enhanced QKD deployment.
Quantum key distribution over optical networks has been noted as a state-of-the-art approach to next-generation secure communication. We propose two approaches to manage the signal for quantum key exchange between two trusted nodes; in the first approach, the quantum channel (QCh) uses distinct fiber whereas the classic channel (CCh) and public interaction channel (PICh) use another fiber to be transmitted on C-band (C+L band), and in the second approach CCh and PICh are transmitted over C+L band and QCh only uses O-band, both routed on the same fiber. In this context, two variations of the initial approach have been put forth, namely scenario 1 and scenario 2. In scenario 1, the CChs are positioned within the extended C-band, while in scenario 2, they find their place within the extended C+L-band. Concerning the secondary approach, denoted as scenario 3, the CChs+PIChs and QChs are situated in the extended C+L-band and low-loss O-band with consideration of noises over QChs, respectively. We propose a heuristic resource allocation with a time-division multiplexing technique in quan-tum resource allocation to compare the resource utilization of the mentioned approaches. The simulation outcomes demonstrate the advantages of adopting scenario 3 in relation to fiber deployment. This is highlighted by an 86 % (34 %) enhancement in fiber efficiency when compared to scenario 1 (2), all while employing an equal count of quantum transmitters and receivers over the initial eight years. As per the specifications of scenario 3, during the concluding year, there has been a notable 44 % surge in quantum transmitter and receiver utilization for both scenarios 1 and 2. This upsurge is accompanied by a substantial reduction in fiber utilization, amounting to a 117 % (34 %) reduction concerning scenario 1 (2).
Quantum key distribution (QKD) is considered as a promising solution for secure communications. Nevertheless, challenges such as the elevated cost of dark-fibers used for QKD, quantum transceivers (Q-TRx), and security issues with QKD trusted nodes impede its widespread deployment. This paper explores a multi-band elastic optical network (MB-EON) for implementing QKD on the existing fiber infrastructure, augmenting network capacity without the need for new fiber installations. The careful transmission of classical and quantum channels in MB-EON necessitates impairment-aware algorithms to shield quantum channels from potential disruptions caused by classical channels. To tackle this, we conduct an in-depth analysis of various noise sources to pinpoint the optimal key transfer scenario. Our study further includes calculations for the upper bound of the public interaction channel (PICh) bit rate in the BB84-Decoy protocol and the secret key rate (SKR). Furthermore, a time division multiplexing technique is proposed to allocate bit-rate of each Q-TRx and PICh-TRx among received demands and grooming them as a one connection. Furthermore, we delve into the QKD trusted node placement method as a robust solution for long-distance QKD, effectively minimizing Q-TRx usage and selecting less QKD trusted nodes to bolster network security by proposing the hot-links algorithm (HLA). Finally, three routing, modulation format, spectrum, and time slot assignment (RMSTA) algorithms are proposed and compared. The simulation results reveal that the proposed HLA trusted node placement method with flexible routing of classical channels and QKD channels demonstrates significant improvements in key transfer efficiency, network security, and blocking probability compared to the fixed-distance trusted node placement with joint routing of CChs and QKD channels.