Quantum memories are a critical component of entanglement-based quantum networks, enabling the storage and synchronisation of quantum states across dynamic links. However, current quantum memories have significantly lower capacity than the rate at which entanglement can be generated, making memory saturation a key bottleneck that reduces network efficiency and hinders the scaling of quantum networks. This problem is especially pronounced in dynamic satellite-based quantum networks, where short visibility windows constrain link availability. In this paper, we present a support entanglement-swapping algorithm that utilises leftover entanglement in quantum memories, thereby alleviating memory saturation and increasing network connectivity. Our algorithm combines two mathematical concepts, line graphs and maximum-cardinality matching, to select independent entanglement swap pairs without sharing any entanglement between concurrent swaps. This property ensures that the resulting changes to the network remain local and mutually independent, making the algorithm easy to integrate alongside any existing routing schemes without requiring network-wide coordination. We evaluate the algorithm through simulations on both static fibre-based networks and dynamic satellite networks. Across most configurations, our algorithm increases both the mean and the total number of entanglements shared between end nodes, while also increasing the network's long-range connectivity. The 'SwapWithToUse' algorithm variant consistently provides the greatest improvements, with gains increasing as entanglement-generation rate increases.
Quantum memories are a critical component of entanglement-based quantum networks, enabling the storage and synchronisation of quantum states across dynamic links. However, current quantum memories have significantly lower capacity than the rate at which entanglement can be generated, making memory saturation a key bottleneck that reduces network efficiency and hinders the scaling of quantum networks. This problem is especially pronounced in dynamic satellite-based quantum networks, where short visibility windows constrain link availability. In this paper, we present a support entanglement-swapping algorithm that utilises leftover entanglement in quantum memories, thereby alleviating memory saturation and increasing network connectivity. Our algorithm combines two mathematical concepts, line graphs and maximum-cardinality matching, to select independent entanglement swap pairs without sharing any entanglement between concurrent swaps. This property ensures that the resulting changes to the network remain local and mutually independent, making the algorithm easy to integrate alongside any existing routing schemes without requiring network-wide coordination. We evaluate the algorithm through simulations on both static fibre-based networks and dynamic satellite networks. Across most configurations, our algorithm increases both the mean and the total number of entanglements shared between end nodes, while also increasing the network's long-range connectivity. The 'SwapWithToUse' algorithm variant consistently provides the greatest improvements, with gains increasing as entanglement-generation rate increases.
The quantum network is designed to connect numerous quantum nodes and support various ground-breaking quantum applications. Most of these applications require communicating parties to share entangled pairs. Therefore, entanglement routing, a technology distributing entangled pairs between distant quantum nodes, plays a vital role in realizing quantum networks’ capability. However, due to the limitation of quantum memory size and quantum decoherence, the entangled pairs shared by adjacent quantum nodes can hardly satisfy concurrent entanglement routing requests, thus leading to severe network congestion. In this paper, we propose a novel congestion mitigation (CM) scheme to tackle such bottleneck problems. The basic idea of CM is to “recycle” idle link-level entanglement resources from well-resourced links to bottleneck links utilizing a unique enabling technology of quantum networks, called entanglement swapping. CM can increase the capacity of each bottleneck link, thus overcoming resource limitations to improve resource utilization and network throughput. To complete our work, we also propose a swapping-based entanglement routing design, including path selection and resource allocation algorithms. Extensive simulations show that our design can significantly alleviate network congestion and improve the request service rate of quantum networks compared to the traditional entanglement routing designs.
The expansion of satellite-based quantum networks requires adaptive routing mechanisms that can sustain entanglement under dynamic orbital and atmospheric conditions. Conventional schemes, often tailored to static or idealised topologies, fail to capture the combined effects of orbital motion, fading, and trust variability in inter-satellite links. This work proposes an \textit{adaptive entanglement-aware routing framework} that jointly accounts for orbital geometry, atmospheric attenuation, and multi-parameter link evaluation. The routing metric integrates fidelity, trust, and key-rate weights to maintain connectivity and mitigate loss from turbulence and fading. Monte Carlo simulations across multiple orbital densities (ρ=10−6~km−3) and environmental regimes, standard atmosphere, strong turbulence, and clear-sky LEO show up to a 275\% improvement in key generation rate and a 15\% increase in effective entanglement fidelity over existing adaptive methods. The framework achieves sub-linear path-length scaling with network size and remains robust for fading variances up to σfade=0.1, demonstrating strong potential for future global quantum constellations.
Quantum networks are expected to become a key enabler for interconnecting quantum devices. In contrast to classical communication networks, however, information transfer in quantum networks is usually restricted to short distances due to physical constraints of entanglement distribution. Satellites can extend entanglement distribution over long distances, but routing in such networks is challenging because satellite motion and stochastic link generation create a highly dynamic quantum topology. Existing routing methods often rely on global topology information that quickly becomes outdated due to delays in the classical control plane, while decentralized methods typically act on incomplete local information. We propose SatQNet, a reinforcement learning approach for entanglement routing in satellite-assisted quantum networks that can be decentralized at runtime. Its key innovation is an edge-centric directed line graph neural network that performs local message passing on directed edge embeddings, enabling it to better capture link properties in high-degree and time-varying topologies. By exchanging messages with neighboring repeaters, SatQNet learns a local graph representation at runtime that supports agents in establishing high-fidelity end-to-end entanglements. Trained on random graphs, SatQNet outperforms heuristic and learning-based approaches across diverse settings, including a real-world European backbone topology, and generalizes to unseen topologies without retraining.
Quantum Information networks (QIN) aim to interconnect quantum systems via quantum repeaters through end-to-end entanglement of qubits. The integration of both satellites and ground segments is essential to provide global connectivity, even over large distances. In this paper, we study the problem of entanglement routing in large-scale quantum networks -i.e., with a large number of nodes, with a hybrid space-ground architecture. We decompose the routing problem into two subcomponents: request scheduling and path selection, and structure our approach around two phases: a proactive and a reactive step. We design and implement an entanglement routing strategy in an emulated European quantum network, first in a custom Python-based simulator for performance forecasting, and then into the NetSquid quantum network simulator. Our work thus provides a tool allowing for a comparison between terrestrial and satellite segments in terms of successful end-to-end entanglement in a large-scale network and shows that both segments are required for optimal service. Our results indicate that, within the emulated European topology, terrestrial networks can support end-toend entanglement for approximately 35% of user pairs -the remaining pairs are too distant and suffer from prohibitive losses. In contrast, the satellite-based network achieves connectivity for up to 91% of user pairs, although some pairs remain unreachable due to a lack of simultaneous satellite visibility. However, while satellites can outperform terrestrial networks in total photon distribution, they may not be suitable for lowlatency requests if out of visibility, making ground networks essential. This highlights the complementary roles of space and terrestrial segments in quantum networking.
Space-based quantum information networks require simulation tools that capture orbital dynamics, probabilistic quantum operations, and classical coordination. We introduce QNETSIM, a modular discrete-event simulator for the design and performance evaluation of satellite and hybrid space-ground quantum networks. The framework integrates orbital geometry, free-space optical and fiber channels, quantum memories, entanglement generation, and Bell-state measurements within a common event-driven architecture. Physical models account for optical loss, atmospheric turbulence, detector imperfections, finite memory capacity, and storage-induced decoherence. Space-time routing and segmented nesting support entanglement distribution across changing topologies, while a software-defined control plane coordinates generation, storage, and swapping. Configurable scenarios encompass onboard repeaters, satellite constellations, ground-based repeaters, and direct satellite distribution. Through explicit interfaces between physical models, network protocols, and orchestration, QNETSIM links component characteristics and operational policies to entanglement fidelity, throughput, latency, availability, and memory utilization, enabling systematic exploration of architectural and resource-management trade-offs in future space-based quantum information networks.
Entanglement generation and swapping is a difficult process due to probabilistic nature of quantum mechanics. To overcome this issue, existing quantum routing algorithms try to create entanglement on multiple paths between source and destination. Although it is possible to save entangled qubits on unused links using quantum memories, the quantum routing algorithms discard them and try creating new entanglement in each time slot. In this work, we leverage the longevity of entanglement and introduce two enhancements to improve the performance of existing routing algorithms: (i) The generation and caching of entanglements across multiple time slots, and (ii) the proactively executing entanglement swapping for frequently utilized links, increasing the success rate of future requests. Since entanglement caching reduces available quantum memory on quantum repeaters, we apply reinforcement learning to identify the optimal set of segments that are likely to be used in the future. Through comprehensive simulations, we demonstrate that caching unused entanglements lead to 10 - 15% improvement on the performance of quantum routing algorithms REPS and SEER. Complementing the caching with reinforcement learning based proactively entanglement swapping leads to further performance improvements. Specifically, using Q-Learning to decide on which segments to proactively swap leads to 52.55% and 41.79% improvement for REPS and SEER algorithms, respectively. Deep Q-Learning outperforms Q-Learning with 61.43% and 48.27% improvements rates.