Routing is a challenging problem in quantum key distribution (QKD)-secured optical networks (QKD-ONs) and involves the selection of an appropriate route that establishes a secure path between the QKD nodes for secret key distribution. Deep reinforcement learning (DRL) is a promising approach for solving decision-making problems in complex networking environments such as QKD-ONs. By leveraging the capabilities of DRL algorithms, the routing decisions can be optimized to enhance network performance. This paper proposes a DRL-based solution for routing in QKD-ONs that enables the routing agent to learn and adapt to changing network conditions by understanding the networking environment. The performance of the proposed scheme is compared with the baseline schemes on NSFNET in terms of blocking probability. Simulation results indicate that compared to the baseline schemes (shortest path (SP) and hop count (HC)), the proposed DRL-based routing scheme reduces the blocking by 14.31% and 8%, respectively.
Routing is a challenging problem in quantum key distribution (QKD)-secured optical networks (QKD-ONs) and involves the selection of an appropriate route that establishes a secure path between the QKD nodes for secret key distribution. Deep reinforcement learning (DRL) is a promising approach for solving decision-making problems in complex networking environments such as QKD-ONs. By leveraging the capabilities of DRL algorithms, the routing decisions can be optimized to enhance network performance. This paper proposes a DRL-based solution for routing in QKD-ONs that enables the routing agent to learn and adapt to changing network conditions by understanding the networking environment. The performance of the proposed scheme is compared with the baseline schemes on NSFNET in terms of blocking probability. Simulation results indicate that compared to the baseline schemes (shortest path (SP) and hop count (HC)), the proposed DRL-based routing scheme reduces the blocking by 14.31% and 8%, respectively.
Abstract In quantum key distribution‐secured optical networks (QKD‐ONs), constrained network resources limit the success probability of QKD lightpath requests (QLRs). Thus, the selection of an appropriate route and the efficient utilisation of network resources for establishment of QLRs are the essential and challenging problems. This work addresses the routing and resource assignment (RRA) problem in the quantum signal channel of QKD‐ONs. The RRA problem of QKD‐ONs is a complex decision making problem, where appropriate solutions depend on understanding the networking environment. Motivated by the recent advances in deep reinforcement learning (DRL) for complex problems and also because of its capability to learn directly from experiences, DRL is exploited to solve the RRA problem and a DRL‐based RRA scheme is proposed. The proposed scheme learns the optimal policy to select an appropriate route and assigns suitable network resources for establishment of QLRs by using deep neural networks. The performance of the proposed scheme is compared with the deep‐Q network (DQN) method and two baseline schemes, namely, first‐fit (FF) and random‐fit (RF) for two different networks, namely The National Science Foundation Network (NSFNET) and UBN24. Simulation results indicate that the proposed scheme reduces blocking by 7.19%, 10.11%, and 33.50% for NSFNET and 2.47%, 3.20%, and 19.60% for UBN24 and improves resource utilisation up to 3.40%, 4.33%, and 7.18% for NSFNET and 1.34%, 1.96%, and 6.44% for UBN24 as compared with DQN, FF, and RF, respectively.
Abstract Quantum key distribution (QKD) optical networks, relying on the information-theoretic security guaranteed by the fundamental laws of quantum physics, have become a core solution for ensuring data transmission security in critical fields such as finance and energy. However, QKD optical networks have inherent characteristics like severe resource constraints, dynamic changes in network environments. The collaborative optimization of routing, wavelength, and time-slot assignment (RWTA) has thus become a key bottleneck restricting their practical deployment. Traditional algorithms suffer from high complexity and poor adaptability in large-scale dynamic networks, while existing deep reinforcement learning (DRL) schemes insufficiently consider network security states, failing multi-objective co-optimization. Therefore, this paper proposes a multi-objective secure proximal policy optimization algorithm (MOS-PPO) based on DRL to solve the routing, wavelength, and time slots assignment problem in QKD optical networks. The MOS-PPO algorithm expands the state space by introducing a network security state vector that includes quantum bit error rate, risk coefficient, node capacity, and processing capability. It designs a multi-objective reward function, and establishes a security constraint mechanism to achieve the collaborative optimization of blocking probability (BP), resource utilization (RU), and security performance. Simulations on NSFNET and UBN24 topologies show MOS-PPO outperforms DRL-PPO algorithm, Security-aware Load balancing, first fit algorithm, and random fit algorithm. On NSFNET, average BP drops by 20.4%, 22.1%,28.4%, 36.7% and RU rises by 4.0%,3.9%, 22.2%, 31.0%, on UBN24, BP decreases by 9.3%, 11.4%, 18.8%, and 27.4% and RU increases by 3.8%, 21.2%, 10.3%, and 29.5%, with higher stable security margins. This study verifies the high adaptability and multi-objective optimization ability of the MOS-PPO algorithm in dynamic network environments, providing an efficient RWTA solution for the practical deployment of QKD optical networks.
Quantum Key Distribution (QKD) networks guarantee information-theoretical security of exchanged keys, but key rates are still limited. This makes efficient and adaptive routing a critical challenge, especially in meshed topologies without quantum repeaters. Conventional shortest path routing approaches struggle to cope with dynamic key store filling levels and changes in network topologies, which leads to load imbalance and blocked connections. In this work, we propose an adaptive routing framework based on Deep Reinforcement Learning (DRL) for hop-wise end-to-end routing in unknown meshed QKD networks. The agent leverages Graph Attention Networks (GATs) to process the network states of varying topologies, enabling generalization across previously unseen meshed networks without topology-specific retraining. The agent is trained on random graphs with 10 to 20 nodes and learns a routing policy that explicitly balances key consumption across the network by utilizing a reward function that is based on the entropy of key store filling levels. We evaluate the proposed approach on the 14-node NSFNET topology under time-varying traffic demands. Simulation results demonstrate that the DRL-based routing significantly outperforms hop-based and weighted shortest path benchmarks, achieving up to a 18.7% increase in mean key store filling levels while completely avoiding key store depletion. These results highlight the potential of graph-based DRL methods for scalable, adaptive, and resource-efficient routing in future QKD networks.
Resource constraints in quantum key distribution-secured optical networks (QKD-ONs) frequently prevent successful quantum light-path request (QLR) fulfillment. This work introduces a routing and resource assignment (RRA) strategy prioritizing minimal transmission distance to address this limitation. The proposed scheme employs a metric evaluating fiber length to determine optimal quantum signal paths. By minimizing cumulative (photon) travel distance, the proposed approach improves resource efficiency. The proposed distance-aware deep reinforcement learning (DA-DRL) based scheme’s performance is evaluated against first-fit (FF) and random-fit (RF) methods, across two network topologies NSFNET and UBN24. Simulation results show that, for NSFNET, the proposed approach scheme reduces blocking by 6.18% and 9.18% and for UBN24, by 2.71% and 3.41%, when compared to FF and RF respectively.
Due to the unavailability of quantum repeaters, end-to-end secret keys need to be relayed in a hop-by-hop manner using so called trusted nodes in long-distance QKD networks. Therefore, a sufficient number of secret keys is required between all QKD devices along the path until the destination node is reached. As key generation rates are typically limited to a few kbps depending on link lengths, constraint-based routing algorithms considering key availability levels are desirable. We show that centralized routing based on Deep Reinforcement Learning (DRL) combined with Graph Neural Networks (GNNs) is able to reduce blockings and enhance load-balancing compared to conventional shortest path algorithms. Additionally, because of the training on random topologies, the agent is able to act in topologies unseen during training.
Quantum routing deals with identifying a set of quantum repeaters to use to create entanglement between distant endpoints. Previous approaches proposed shortest-path and linear programming methods to find a solution to this problem. While the shortest path approach results in suboptimal performance, linear programming takes too long to find a solution as the network size and constraints increase. In this paper, we apply Deep Q-Reinforcement Learning (DQRL) to optimize routing in quantum networks both in terms of execution time and performance. The proposed Quantum Routing Algorithm (QuRA) first chooses which request to schedule among all requests. It then determines which route to take for the selected request. Since the number of all possible routes can be very high, we developed a hop-by-hop decision making model to lower the complexity while still attaining high performance. Experiments show that the proposed QuRA outperforms existing solutions by up to 90% in success rate and up to 79% in execution time. These results highlight QuRA as a scalable and effective solution for intelligent routing in quantum networks.