Quantum Key Distribution Networks (QKDNs) rely on strategic trusted node deployment to overcome distance constraints and enable large-scale quantum-secured communication. We introduce OptiNode, a novel algorithm that determines, using Machine Learning (ML) techniques, the optimal placement of trusted nodes within QKDNs. Leveraging an analytical framework and Bayesian-optimized k-means clustering, OptiNode balances operational feasibility with communicational efficiency using only fixed node positions, which are to be connected, and their expected secure traffic as inputs. An empirical evaluation confirms the algorithm's effectiveness in generating deployable topologies. Our work paves the way for building efficient and high performance QKDN.
Quantum Key Distribution Networks (QKDNs) rely on strategic trusted node deployment to overcome distance constraints and enable large-scale quantum-secured communication. We introduce OptiNode, a novel algorithm that determines, using Machine Learning (ML) techniques, the optimal placement of trusted nodes within QKDNs. Leveraging an analytical framework and Bayesian-optimized k-means clustering, OptiNode balances operational feasibility with communicational efficiency using only fixed node positions, which are to be connected, and their expected secure traffic as inputs. An empirical evaluation confirms the algorithm's effectiveness in generating deployable topologies. Our work paves the way for building efficient and high performance QKDN.
The technological maturity of Quantum Key Distribution (QKD) products have generated high demand for secure key distribution in transmitting sensitive data. To provide this service, long-haul network operators are exploring strategies for network-wide deployment of QKD devices while utilizing existing infrastructure. One approach is to place trusted nodes at existing in-line amplifier huts in the network. In this study, we present a near-optimal fiber span aggregation algorithm, which effectively minimizes the number of trusted nodes between Points-of-Presences (PoPs) in the network. In this study, we evaluate six different topological QKD network deployments and eight different technological QKD network deployments in terms of the total secure key rate, as well as the number of QKD fiber pairs deployed. Our results indicate that the span aggregation algorithm increases the distance between each trusted node by up to 20%, thereby reducing the overall number of trusted nodes. Capacity planning for various topologies and technology deployments on simulated Secure Application Entity (SAE) requests is undertaken. Our proposed capacity planning shows that a baseline deployment with span aggregation using a QKD device with a cut-off distance of 140 km can save up to 3 times the number of QKD deployed fibers as compared to a minimum spanning tree-based low-cost deployment.
A reliable secure communication can be given between two remote parties by key sharing, quantum key distribution (QKD) is widely concentrated as the information in QKD is safeguarded by the laws of quantum physics. There are many techniques that deal with quantum key distribution network (QKDN), however, only few of them use machine learning (ML) and soft computing techniques to improve QKDN. ML can analyze data and improve itself through model training without having to be programmed manually. There has been a lot of progress in both the hardware and software of ML technologies. Given ML’s advantageous features, it can help improve and resolve issues in QKDN, facilitating its commercialization. The proposed work provides a detailed understanding of role of each layer of QKDN, addressing the limitations of each layer, and suggesting a framework to improve the performance metrics for various applications of QKDN by applying machine learning techniques, such as support vector machine and decision tree algorithms.
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 (QKD) revolutionizes secure communication, offering unbreakable cryptography against quantum computers’ rising computational power. This paper explores the placement of trusted nodes to enhance the efficiency of quantum communication networks. Two practical scenarios are investigated: the multi-band (MB) setting, utilizing different wavelength bands for QKD and classical systems, and the multifiber (MF) solution, employing separate fibers for QKD and classical communication. The study optimizes secret key rate (SKR) per quantum transmitter and receiver (Q-TRx) in metro quantum-optical networks. A heuristic algorithm, considering noise sources and fiber attenuation, is proposed for routing, modulation, spectrum, and time assignment. The simulations conducted on various network topologies reveal optimal QKD trusted node distances (QTND) and wavelengths place. The simulation results indicate that the optimal QTND value for the MB-scenario and MF-scenario is 20 and 25 km, respectively. Compared to scenarios without QKD trusted node placement along the 40 km link, both the MB-scenario and MF-scenario exhibit 40% and 15% improvements, respectively, in SKR/QTRx relative to their optimal QTND values.
Purpose The purpose of optimal protocol prediction and the benefits offered by quantum key distribution (QKD), including unbreakable security, there is a growing interest in the practical realization of quantum communication. Realization of the optimal protocol predictor in quantum key distribution is a critical step toward commercialization of QKD. Design/methodology/approach The proposed work designs a machine learning model such as K-nearest neighbor algorithm, convolutional neural networks, decision tree (DT), support vector machine and random forest (RF) for optimal protocol selector for quantum key distribution network (QKDN). Findings Because of the effectiveness of machine learning methods in predicting effective solutions using data, these models will be the best optimal protocol selectors for achieving high efficiency for QKDN. The results show that the best machine learning method for predicting optimal protocol in QKD is the RF algorithm. It also validates the effectiveness of machine learning in optimal protocol selection. Originality/value The proposed work was done using algorithms like the local search algorithm or exhaustive traversal, however the major downside of using these algorithms is that it takes a very long time to revert back results, which is unacceptable for commercial systems. Hence, machine learning methods are proposed to see the effectiveness of prediction for achieving high efficiency.
Quantum Key Distribution (QKD) enables two distant users to exchange a secret key with information-theoretic security, based on the fundamental laws of quantum physics. While it is arguably the most mature application of quantum cryptography, it has inherent limitations in the achievable distance and the scalability to large-scale infrastructures. While the applicability of QKD can be readily increased with the use of intermediary trusted nodes, this adds additional privacy requirements on third parties. In this work, we present an efficient scheme leveraging a trusted node with lower privacy requirements thanks to the use of post-quantum cryptographic techniques, and implement it on a deployed fiber optic quantum communication network in the Paris area.