Efficient and scalable quantum key scheduling remains a critical challenge in trusted-relay Quantum Key Distribution (QKD) networks due to imbalanced key resource utilization, dynamic key consumption, and topology-induced congestion. This paper presents a Q-learning-based adaptive routing framework designed to optimize quantum key delivery in dynamic QKD networks. The model formulates routing as a Markov Decision Process, with a compact state representation that combines the current node, destination node, and discretized key occupancy levels. Reward function is designed to jointly penalize resource imbalance and rapid key depletion while promoting traversal through links with sustainable key generation, guiding the agent toward balanced and congestion-aware decisions. Compared to Dijkstra, the Q-learning scheduler achieves significantly lower delivery latency, more effective key resource utilization, and greater reliability: maintaining 45.8 vs 118.5 hops in average delay under heavy load, sustaining 74.5\% vs 61.5\% in average key utilization, and reducing the failure ratio to 7.2\% vs 41.5\%, collectively confirming its advantages in scalability, congestion resilience, and resource-efficient decision making.
Efficient and scalable quantum key scheduling remains a critical challenge in trusted-relay Quantum Key Distribution (QKD) networks due to imbalanced key resource utilization, dynamic key consumption, and topology-induced congestion. This paper presents a Q-learning-based adaptive routing framework designed to optimize quantum key delivery in dynamic QKD networks. The model formulates routing as a Markov Decision Process, with a compact state representation that combines the current node, destination node, and discretized key occupancy levels. Reward function is designed to jointly penalize resource imbalance and rapid key depletion while promoting traversal through links with sustainable key generation, guiding the agent toward balanced and congestion-aware decisions. Compared to Dijkstra, the Q-learning scheduler achieves significantly lower delivery latency, more effective key resource utilization, and greater reliability: maintaining 45.8 vs 118.5 hops in average delay under heavy load, sustaining 74.5% vs 61.5% in average key utilization, and reducing the failure ratio to 7.2% vs 41.5%, collectively confirming its advantages in scalability, congestion resilience, and resource-efficient decision making.
Trusted relay quantum key distribution networks (TR-QKDNs) have emerged as one of the most practical solutions for implementing large-scale QKDNs. The development of efficient routing algorithms is crucial to ensure adaptability to diverse network parameters and topologies. However, current approaches suffer from multiple limitations, including insufficient consideration of key influencing factors, excessive reliance on manually configured parameters, and scalability bottlenecks caused by the exponential complexity of classical algorithms. In this paper, we propose a quantum reinforcement learning-based routing algorithm, named hybrid quantum deep deterministic policy gradient (HQ-DDPG) that integrates a custom-designed quantum neural network (QNN) with the deep deterministic policy gradient (DDPG), to intelligently balance multiple influencing factors and dynamically optimize routing decisions in TR-QKDNs. To reduce computational complexity, we further utilize a quantum single-source shortest path (QSSP) algorithm to compute the optimal routing path. Training results demonstrate that the proposed HQ-DDPG outperforms DDPG in terms of performance metrics, achieving nearly double the training convergence speed while reducing resource requirements by approximately 45 times and exhibiting superior network expressiveness. In typical network topology tests, the proposed algorithm consistently maintains a quantum key delivery ratio above 91.35% under high-load demand, significantly surpassing both DDPG and optimized link state routing (OLSR). Finally, leveraging distributed quantum computing, the proposed QNN enables efficient solutions for large-scale TR-QKDN problems with fewer quantum resources.
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.
Quantum secure communication networks based on quantum key distribution (QKD) technology are gradually improving, which can provide remote and networked key services for multiple users. This paper proposes a key distribution solution for latency and resource optimization in trusted relay QKD networks. In this scheme, the key distribution process between trusted relay nodes is presented as a joint optimization problem by considering the constraints of service delay, bandwidth, path resource consumption, quality loss and remaining quantum key quantity. In this paper, we treat key distribution as a bi-objective minimization problem, maintaining a trade-off between service delay and path resource consumption. Specifically, we propose a dynamic key distribution framework based on linear programming and particle swarm optimization. Simulation results show that the key distribution path selection in this scheme can be adjusted according to the service delay, key quality, and path resource consumption. Compared with the Random and customized OSPF policies, this scheme has higher performance. More specifically, the solution reduced average service latency by 27% while reducing path resource consumption by 560%.
Quantum key distribution (QKD) networks promise information-theoretic security for multiple nodes by leveraging the fundamental laws of quantum mechanics. In practice, QKD networks require dedicated routing protocols to coordinate secure key distribution among distributed nodes. However, most existing routing protocols operate under the assumption that all relay nodes are honest and fully trustworthy, an assumption that may not hold in realistic scenarios. Malicious nodes may tamper with routing updates, causing inconsistent key-state views or divergent routing plans across the network. Such inconsistencies increase routing failure rates and lead to severe wastage of valuable secret keys. To address these challenges, we propose a distributed routing framework that combines two key components: (i) Causal Consistency Key-State Update, which prevents malicious nodes from propagating inconsistent key states and routing plans; and (ii) Trust-Aware Multi-path Flow Optimization, which incorporates trust metrics derived from discrepancies in reported states into the path-selection objective, penalizing suspicious links and filtering fabricated demands. Across 50-node topologies with up to 30% malicious relays and under all three attack modes, our protocol sustains a high demand completion ratio (DCR) (mean 0.90, range 0.81-0.98) while keeping key utilization low (16.6 keys per demand), decisively outperforming the baselines-Multi-Path Planned (DCR 0.48, 30.8 keys per demand) and OSPF (DCR ≤0.12, 296 keys per demand; max 1601). These results highlight that our framework balances reliability and efficiency, providing a practical and resilient foundation for secure QKD networking in adversarial environments.
Since the QKD network can overcome the distance limitation and expand the point-to-point QKD system to a multi-user key distribution system, some testing QKD networks have been built. However, all of this previous research seldom focused on the routing mechanism of QKD network in detail. Therefore, this paper focuses on the routing issue in trust relaying QKD network, builds a model of the trust relaying QKD network and proposes a secret-key-aware routing method. In our method, a dynamic model for the residual local key is proposed to forecast the residual local key quantity of each QKD link more accurately, and the cost of QKD link and relaying path are defined by multiple affecting factors, e.g. the generation, consumption rate and the local key depletion index. The proposed method is implemented and evaluated in a simulation environment. The simulation results show that our routing method can increase the success rate of key exchange, make all the QKD links participate key exchange with almost equal opportunity to achieve load balance, and trade off the local key generation and consumption of each QKD link. Therefore, our proposed method can contribute to effectively improve the holistic performance of the trust relaying QKD network.
With the development of quantum computing and computer science, the risk of traditional cryptographic algorithms being cracked is increasing. Therefore, quantum key distribution (QKD) technology based on quantum physical properties has been developed to ensure the security of information communication. Large-scale QKD networks can be built using trusted relay technology, but the hops encryption and decryption process of trusted relay will consume extra keys, which makes it particularly important to choose a reasonable path to reduce the consumption of extra keys during key distribution. The existing routing algorithms mainly focus on the number of remaining keys and hops, but ignore the congestion information of the network, resulting in a high key exchange failure rate and an increase in key consumption. In view of this background, we propose a load balancing routing (LBR) algorithm, which can realize congestion control in QKD network by dynamically detecting link state with SDN controller. Simulation results show that LBR improves key request success rate by more than 10% and key utilization efficiency by about 5% compared with existing methods.