Abstract Future quantum networks will have to support different applications on shared infrastructure, like fidelity-critical quantum key distribution (QKD) and latency-sensitive distributed quantum computing (DQC). The existing approaches for entanglement routing either optimise for a single application, or rely on sequential resource allocation, which results in inefficient resource utilisation, unfairness and degraded performance when multiple applications compete for shared network resources. In this paper, we propose AMARQ, a multi-objective deep Q-network based routing framework to jointly serve heterogeneous QKD and DQC traffic on shared quantum infrastructure. The agent makes adaptive and application-aware routing decisions to dynamically balance the rigid fidelity and latency requirements of QKD and the high-throughput and elastic rate requirements of DQC. The reward function combines a successful-routing throughput term with a per-edge fidelity penalty and an end-of-episode failure penalty; per-flow latency is enforced as a QoS constraint, and inter-application fairness is enforced by a dedicated admission-control loop that reserves a dynamic QKD qubit pool. We assess the performance of AMARQ on grid topologies with 25 to 625 nodes. AMARQ attains a request success ratio above 85%, which is up to 32 percentage points higher than ESDI and 39 percentage points higher than Cicconetti, while keeping the end-to-end fidelity above 0.80 and the inference latency below 90 ms for all configurations.
Abstract Future quantum networks will have to support different applications on shared infrastructure, like fidelity-critical quantum key distribution (QKD) and latency-sensitive distributed quantum computing (DQC). The existing approaches for entanglement routing either optimise for a single application, or rely on sequential resource allocation, which results in inefficient resource utilisation, unfairness and degraded performance when multiple applications compete for shared network resources. In this paper, we propose AMARQ, a multi-objective deep Q-network based routing framework to jointly serve heterogeneous QKD and DQC traffic on shared quantum infrastructure. The agent makes adaptive and application-aware routing decisions to dynamically balance the rigid fidelity and latency requirements of QKD and the high-throughput and elastic rate requirements of DQC. The reward function combines a successful-routing throughput term with a per-edge fidelity penalty and an end-of-episode failure penalty; per-flow latency is enforced as a QoS constraint, and inter-application fairness is enforced by a dedicated admission-control loop that reserves a dynamic QKD qubit pool. We assess the performance of AMARQ on grid topologies with 25 to 625 nodes. AMARQ attains a request success ratio above 85%, which is up to 32 percentage points higher than ESDI and 39 percentage points higher than Cicconetti, while keeping the end-to-end fidelity above 0.80 and the inference latency below 90 ms for all configurations.
Distributed quantum computing (DQC) connects multiple quantum processing units (QPUs) via quantum interconnects to overcome the qubit-count and connectivity limitations of individual devices. A critical challenge in this paradigm is routing entanglement resources efficiently across the quantum interconnect network so that inter-QPU quantum gates can be executed with high fidelity and minimal resource waste. We propose the Entanglement-Aware Adaptive Routing (EAAR) framework, which jointly optimizes path selection, entanglement purification scheduling, and congestion-aware load balancing for DQC workloads. EAAR models the quantum network as a fidelity-weighted directed graph and transforms the maximum-fidelity path problem into an additive shortest-path problem. An adaptive purification scheduler targets bottleneck links when end-to-end fidelity falls below application-defined thresholds, and a dynamic congestion penalty steers traffic away from over-utilized entanglement buffers. We evaluate EAAR in simulation across grid, ring, random-regular, and small-world topologies ranging from 4 to 24 QPU nodes. Results demonstrate that EAAR improves average remote-gate fidelity by up to 10.3% and reduces entanglement consumption by up to 44.8% compared to greedy baselines, while maintaining competitive latency with shortest-path routing. These findings confirm that holistic, entanglement-aware routing is essential for practical, high-fidelity distributed quantum computation.
Abstract Entanglement-based quantum networks require intelligent routing mechanisms that not only maximize fidelity and connectivity but also ensure service differentiation under varying traffic conditions. In this work, we propose a novel QoS-aware traffic modeling and scheduling framework integrated into a reinforcement learning-based quantum routing agent. Unlike prior approaches that assume deterministic or uniform request arrival patterns, our model introduces stochastic and bursty traffic scenarios using Erlang and ON/OFF distributions to emulate realistic quantum workloads. Each entanglement request is assigned a set of QoS attributes, including fidelity requirements, time-to-live constraints, and priority levels. We then extend the reward function of the agent to consider deadline compliance and resource efficiency in addition to fidelity. Through simulation, we compare the proposed method against state-of-the-art baselines, including DQRA and Proactive RL, under both best-effort and QoS-prioritized scheduling modes. Results demonstrate that our approach maintains higher success rates and lower latency for high-priority requests, while preserving entanglement fidelity and efficient resource allocation across varying network loads. This study highlights the importance of integrating QoS mechanisms into quantum routing to support scalable and differentiated quantum services
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.
Abstract Quantum communication plays a key role in enabling distributed Noisy Intermediate-Scale Quantum (NISQ) systems; however, practical quantum networks are often affected by link fluctuations, limited quantum resources, and entanglement degradation. These factors make reliable routing and scheduling difficult, particularly in large-scale and dynamically changing environments. Classical methods such as Historical Weighted Fidelity Routing (HWFR) and Dynamic Multi-Priority Quantum Scheduling (DMPQS) generally perform well only when network conditions remain stable, and their performance degrades when entanglement success rates vary or relay nodes become congested. A major challenge therefore lies in operating under uncertain and time-varying fidelity conditions. Most existing approaches focus on either routing or scheduling in isolation and do not explicitly consider future fidelity variations, which can lead to inefficient resource usage and increased end-to-end latency. In this work, we propose a Deep Reinforcement Learning-based Quantum Communication Optimization Framework (DRL-QCOF) that jointly addresses routing and scheduling. The framework employs a Bi-LSTM-based Quantum Fidelity Prediction Network (QFPN) to estimate future link fidelity F_"pred" (t)from time-series features including link length, noise coefficient, entanglement success probability, and relay utilization. These predictions are then used by a Deep Reinforcement Learning-based Adaptive Quantum Scheduling Agent (DRAQS-Agent), implemented using an Actor–Critic architecture with a continuous reward formulation, to guide routing and scheduling decisions across distributed nodes. Simulation results indicate that the proposed approach improves the average link fidelity to a maximum of 0.88, compared to 0.78–0.86 achieved by existing methods. The end-to-end latency is reduced from 104 ms to 83 ms, while baseline schemes remain above 110 ms under the same conditions. In addition, the average response time is reduced to approximately 147 ms, resource utilization increases to 71–83%, and the success ratio improves from 93% to 97%, resulting in improved end-to-end reliability. The cumulative reward stabilizes at around 16.0, with convergence observed within 300 training episodes, and the achieved throughput reaches 118–121 entangled pairs per second, confirming the effectiveness of the proposed framework in dynamic NISQ network scenarios.
Future quantum networks will enable the interconnection of multiple users distributed across vast geographic distances. Due to these large separations and limited physical resources, communication will often rely on multi-path routing strategies, where physical resources are distributed across channels of varying lengths and delivered to end users. Efficient long-distance quantum communication therefore requires optimizing the allocation of these resources across available paths. In this work, we introduce a performance-oriented approach to quantum network routing by extending the classical concept of Quality of Service (QoS) to the context of multi-path quantum resources distribution. Unlike prior models that consider entanglement generation or quantum memory coherence in isolation, we investigate the interplay between path assignment strategies, coherence time constraints, and quantum error correction (QEC), and how they jointly impact end-to-end communication fidelity. We analyze both unencoded and encoded transmissions over aggregated network paths, quantifying the effects of resource allocation on transmission success. Our findings show that fidelity cannot be optimized independently of memory lifetimes, and that while QEC can enhance performance under specific conditions, it also imposes additional constraints depending on network topology and path-length asymmetries. This work provides a foundation for developing QoS-aware quantum routing protocols that balance fidelity, throughput, and memory utilization -- key considerations for near-term quantum repeater networks.
Abstract The innovative dynamics of communication networks are being challenged unprecedentedly by rising data traffic, dynamically evolving topologies, and the increased complexity of distributed intelligent applications. Typically, routing algorithms are inadequate for adjusting in actual time to address network congestion, changes in computational needs and heterogeneity and real-time. In this paper, we propose a new hybrid quantum-augmented routing model that synergistically integrates quantum reinforcement learning with physics-motivated network modeling by reasoning in terms of a space‒time continuum. Here, the phenomenon of network congestion is simulated as a curvature on the fabric of space-time, and the computation facilities on nodes are envisioned as a gravitational source, which directs the flows of data. The most essential innovation is the development of an artificial intelligence awareness layer (AIAL), that functions with quantum-aid learning agents proactively weighing and choosing the routing paths in live environments. The quantum agents query the multiple parallel paths in SQL via quantum superposition and quantum parallelism to enhance convergence and global route optimization. The hybrid structure is built upon features of the local adaptability of quantum-enhanced Q-learning and global contextual knowledge of deep Q-networks (DQNs), which are constantly adjusted to changes in network metrics, such as the load, latency, and availability of nodes. Large simulations demonstrate that this structure has a reward/cost ratio of 138.16, scales well during states of extreme congestion and is structurally efficient in all cases, with higher resiliency and faster convergence of the framework than those of OSPF, the EIGRP, the IS-IS and the RIP. Although this is a positive finding, it is reached under certain simulation conditions and it is not directly applicable in real-life network with diverse topologies and traffic patterns. The findings corroborate that coupling quantum reinforcement learning with physics-inspired routing will make full use of the adaptive optimal routing discoverable in uncertain situations, changing network conditions in an autonomous manner. This study establishes a basis for a new generation of cognitively adaptive, self-optimizing networks to best meet the needs of 6G, edge computing and quantum-aware distributed infrastructures.