SatQNet: Satellite-assisted Quantum Network Entanglement Routing Using Directed Line Graph Neural Networks
Authors: Tobias Meuser, Jannis Weil, Aninda Lahiri, Marius Paraschiv
Organizations: Communication Networks Lab, Technical University of Darmstadt · Institute of Communication Technology, Leibniz University Hannover · Quantum Information Group, IMDEA Networks
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 Existing routing methods often rely on global topology information that quickly becomes outdated due to delays in the classical control plane, while decentralized methods typically act on incomplete local information.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.
Figures & tables
Fig. 1 : Simplified example of a satellite-assisted quantum network. The quantum repeaters are depicted as nodes, connected via fiber links. Dedicated ground stations (satellite dishes) have both fiber and satellite connections. Link colors represent entanglement generation probability, with blue indicating higher probability (shorter links) transitioning to red for lower probability (longer links).
Parameter
Meaning
Ground-to-satellite
Inter-satellite
RE
Earth radius
6371km
6371km
hs
Satellite altitude
500 – 1200km (LEO)
500 – 600km
λ
Optical wavelength
850nm (e.g. Micius downlink)
850nm or 1550nm
DT
Tx aperture diameter
0.30m (satellite) [ 63 , 64 ]
0.10m (typical ISL terminal) [ 65 ]
DR
Rx aperture diameter
1.0m (ground) [ 63 , 64 ]
0.10m
θdiv
Full beam divergence
∼10μrad (GS/SG) [ 63 , 64 ]
20 – 40μrad (SS) [ 65 ]
TABLE I : Representative parameter values for ground-to-satellite and inter-satellite links. Values are consistent with reported satellite quantum key distribution (QKD) demonstrations and free-space optical (FSO) link budgets [ 63 , 64 , 65 , 66 , 67 ] .
Fig. 2 : Visualization of path-planning in satellite-assisted quantum networks. The quantum repeaters are depicted as the nodes of the graph, which are connected via fiber. Dedicated ground stations have both fiber and satellite connections and serve as hubs between the topologies. If elementary links are available between repeaters, each blue wavy line represents one of these links. The agent is represented as an orange rectangle, which moves across the network during planning. The planned path is depicted as an orange line. Once the path is planned, the elementary links are consumed to create an end-to-end entanglement.
Fig. 5 : Influence of satellite parameters on the performance of the approaches.
Fig. 6 : Connectivity of Klagenfurt to other European cities.
Future quantum networks will make use of entanglement to perform numerous tasks, such as sending quantum information over long distances, distributed quantum computing, and quantum sensing. In general, these tasks will need to be performed simultaneously in various regions of a network, while minimizing resources and latency. We will thus require policies for scheduling link-level entanglement resources, and using the link-level entanglement to create various forms of multipartite entanglement required for every task. In this work, we address this problem using reinforcement learning. We formulate a Markov Decision Process for the problem and use double deep Q-networks (DQN) with Message Passing Neural Networks (MPNNs), experience replay buffers, and curriculum training to obtain policies. The key physical parameter is the probability of link-level entanglement generation, i.e., the link activation probability. We show that our policies maintain 100% success for up to 71% lower link activation probability than the baseline heuristics for a set of physically relevant network topologies. We then examine an additional constraint where experiment (task) placements are restricted to specific hardware types and demonstrate a similar advantage in performance over heuristics, with our policy maintaining at least an 80% success rate for up to a 59% lower link activation probability. Finally, we explore methods to interpret the learned policy by defining metrics enabling conclusions to be drawn about the model's behavior and by tasking a large language model (LLM) to derive a novel heuristic given example actions taken by the DQN-trained policy. We find that the LLM heuristic performs similarly to the DQN-trained policy in performance, indicating a promising method for interpretable policy extraction for large quantum networks, where direct training becomes computationally expensive.
Leon Rode, Sumeet Khatri, Supartha Podder
Department of Computer Science, Stony Brook University · Department of Computer Science and Center for Quantum Information Science and Engineering, Virginia Tech
We study an adversarial bandit problem for entanglement-based quantum-network routing over a modest graph corpus. Alice selects an end-to-end repeater route for an Ekert-91 protocol (E91) representing her move, while Eve selects an attack surface, either edge intercept--resend or repeater memory degradation. Payoffs are drawn from cached SeQUeNCe-simulated E91 transcripts, and Alice accepts a turn when the finite-sample statistic violates the Clauser-Horne-Shimony-Holt (CHSH) bound. Performing adversarial co-learning across 50 structured topologies, we find that learned retention tracks a full-matrix minimax reference closely (Pearson r=0.99): under a one-surface Eve action model, bottleneck families have zero retention, while non-bottleneck families follow a 1−1/N coverage principle. We then fit decision-tree explanation models to graph-, attack-, and route-level topology-corpus targets and report their faithfulness. Finally, we construct prompt records for local language models to summarize the tree evidence, resulting in an open-source explanation workflow for quantum-repeater network games.
Brennan Bell, Inti Gabriel Mendoza Estrada, Andreas Trügler +1
RFI-IRFOS and TU Graz, Graz, Austria · openmaind FlexCo and TU Graz, Graz, Austria · Know Center Research GmbH and University of Graz, Graz, Austria +1
Due to fundamental limitations on terrestrial quantum links, satellites have received considerable attention for their potential as entanglement generation sources in a global quantum internet. In this work, we focus on the problem of designing a constellation of satellites for such a quantum network. We find satellite inclination angles and satellite cluster allocations to achieve maximal entanglement generation rates to fixed sets of globally distributed ground stations. Exploring two black-box optimization frameworks: a Bayesian Optimization (BO) approach and a Genetic Algorithm (GA) approach, we find comparable results, indicating their effectiveness for this optimization task. While GA and BO often perform remarkably similar, BO often converges more efficiently, while later growth noted in GAs is indicative of less susceptibility towards local maxima. In either case, they offer substantial improvements over naive approaches that maximize coverage with respect to ground station placement.