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IEEE Virtual Conference on Communications (VCC)

GNN-based Proactive Entanglement Distribution in Space-Air-Ground Quantum Networks

Mohamed Shaban, Muhammad Ismail, Walid Saad, Don Towsley

Published Nov 4, 2025• 0 citations
Publisher

Abstract

Quantum Internet is essential for unlocking a variety of applications. SPARQ is a space-air-ground quantum network with promising architecture for realizing the quantum Internet. In the early stages of quantum network deployment, limited resources, such as memory units, necessitate efficient management to prevent bottlenecks that can hinder on-demand entanglement distribution. Bottlenecks in quantum networks occur when intermediate repeaters lack the memory needed to accommodate additional entanglement requests. In SPARQ, satellite mobility and fluctuating communication demands lead to continuous changes in bottleneck locations. To address this challenge, in this paper, we propose a proactive entanglement distribution strategy to support on-demand entanglement by predicting and avoiding bottlenecks. Specifically, we propose a graph convolutional neural network (GCNN) framework to predict bottlenecks in SPARQ. The resulting predictions are then integrated into an entanglement distribution policy to proactively avoid bottlenecks, thereby improving the entanglement distribution rate. Simulation results demonstrate the effectiveness of the proposed GCNN model, achieving an accuracy of 97.5% in predicting bottlenecks. The proposed GCNN also improves the accuracy by 6.5% compared to a recurrent neural network (RNN) baseline. This leads to an average improvement of $41.3 \%$ in the entanglement rate when avoiding bottlenecks based on GCNN predictions compared with RNN predictions. The results also show that the proposed approach for anticipating bottlenecks leads to a $51.6 \%$ improvement in the entanglement distribution rate compared to scenarios where bottlenecks are not addressed.

DOI
10.1109/vcc67261.2025.11351073

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Quantum Internet is essential for unlocking a variety of applications. SPARQ is a space-air-ground quantum network with promising architecture for realizing the quantum Internet. In the early stages of quantum network deployment, limited resources, such as memory units, necessitate efficient management to prevent bottlenecks that can hinder on-demand entanglement distribution. Bottlenecks in quantum networks occur when intermediate repeaters lack the memory needed to accommodate additional entanglement requests. In SPARQ, satellite mobility and fluctuating communication demands lead to continuous changes in bottleneck locations. To address this challenge, in this paper, we propose a proactive entanglement distribution strategy to support on-demand entanglement by predicting and avoiding bottlenecks. Specifically, we propose a graph convolutional neural network (GCNN) framework to predict bottlenecks in SPARQ. The resulting predictions are then integrated into an entanglement distribution policy to proactively avoid bottlenecks, thereby improving the entanglement distribution rate. Simulation results demonstrate the effectiveness of the proposed GCNN model, achieving an accuracy of 97.5% in predicting bottlenecks. The proposed GCNN also improves the accuracy by 6.5% compared to a recurrent neural network (RNN) baseline. This leads to an average improvement of 41.3%41.3 \%41.3% in the entanglement rate when avoiding bottlenecks based on GCNN predictions compared with RNN predictions. The results also show that the proposed approach for anticipating bottlenecks leads to a 51.6%51.6 \%51.6% improvement in the entanglement distribution rate compared to scenarios where bottlenecks are not addressed.
In this article, a space–air–ground quantum (SPARQ) network is developed as a means for providing a seamless on-demand entanglement distribution. The node mobility in SPARQ poses significant challenges to entanglement routing. Existing quantum routing algorithms focus on stationary ground nodes and utilize link distance as an optimality metric, which is unrealistic for dynamic systems, like SPARQ. Moreover, in contrast to the prior art that assumes homogeneous nodes, SPARQ encompasses heterogeneous nodes with different functionalities further complicates the entanglement distribution. To solve the entanglement routing problem, a deep reinforcement learning (RL) framework is proposed and trained using deep Q-network (DQN) on multiple graphs of SPARQ to account for the network dynamics. Subsequently, an entanglement distribution policy, third-party entanglement distribution (TPED), is proposed to establish entanglement between communication parties. A realistic quantum network simulator is designed for performance evaluation. Simulation results show that the TPED policy improves entanglement fidelity by 3% and reduces memory consumption by 50% compared with benchmark. The results also show that the proposed DQN algorithm improves the number of resolved teleportation requests by 39% compared with shortest path baseline and the entanglement fidelity by 2% compared with an RL algorithm that is based on long short-term memory. It also improved entanglement fidelity by 6% and 9% compared with state-of-the-art benchmarks. Moreover, the entanglement fidelity is improved by 15% compared with DQN trained on a snapshot of SPARQ. Additionally, SPARQ enhances the average entanglement fidelity by 23.5% compared with existing networks spanning only space and ground layers.
In this article, we present SPARQ: A SPace- Air-gRound Quantum network that supports longrange quantum teleportation and superdense coding in the quantum Internet. SPARQ integrates satellites (space layer), unmanned aerial vehicles (air layer), and remote end users (ground layer) to perform end-to-end entanglement distribution. We first present the architecture of SPARQ, followed by a discussion of its potential applications as well as the challenges facing SPARQ. Next, we investigate one of SPARQ's challenges, the need for efficient routing algorithms. This challenge is particularly significant for SPARQ because of the presence of three layers with diverse distances and capabilities among the nodes, which makes the existing routing algorithms inefficient. Hence, we propose a routing strategy that adopts entanglement fidelity as a routing metric and specifies optimal end-to-end entangled paths while avoiding intensive resource usage. Experimental results demonstrate the superiority of the SPARQ architecture and the proposed routing algorithm. Specifically, SPARQ achieves up to a 5.5% improvement in entanglement fidelity, a 49% increase in entanglement distribution efficiency, and a 10-fold increase in the average number of served requests. The proposed routing algorithm also achieves up to a 7% improvement in entanglement fidelity and a 9-fold increase in the average number of served requests. Finally, we present the conclusion and future directions.
Quantum networks promise unprecedented capabilities for secure communication and distributed quantum computing through entanglement distribution. However, the fragile nature of quantum entanglement and its exponential decay present significant challenges for efficient resource allocation. This paper introduces SPEP (Strategic Predictive Entanglement Pre-positioning), a novel framework that leverages machine learning techniques to anticipate communication demands and proactively establish entangled links. We implement and evaluate multiple prediction models including LSTM neural networks and ARIMA time series analysis, comparing them against traditional reactive and greedy routing approaches. Through comprehensive simulations across four distinct network topologies with over multiple simulation runs, our SPEP framework demonstrates significant improvement in success rates, 15%15 \%15% reduction in average latency, and 25%25 \%25% better resource utilization compared to baseline methods. The results provide strong evidence that machine learning-driven prediction can significantly enhance quantum network performance, particularly in networks with temporal demand patterns.
This paper addresses the complexities of entanglement distribution in LEO satellite networks, particularly those arising from their dynamic topology. Traditional static and dynamic entanglement distribution methods often result in high entanglement drop rates and reduced end-to-end throughput. We introduce a novel framework that leverages the dynamic nature of LEO satellite networks to enhance entanglement distribution efficiency. Employing a space-time graph model to represent the network's temporal evolution, we propose an entanglement distribution strategy based on path utility, incorporating pointing errors, non-ideal link transmittance for intersatellite links, and atmospheric effects for downlinks. Our approach demonstrates superior performance in reducing entanglement drop rates and improving throughput compared to conventional methods. This study advances the field of quantum communication in satellite networks, offering resilient and efficient entanglement distribution strategies that support practical applications such as distributed computing, quantum multipartite cryptography, and distributed quantum sensing. The findings underscore the potential of integrating dynamic satellite networks with quantum technologies to create a reliable and secure quantum internet.
Spooky Action at a Global Distance −-− Resource-Rate Analysis of a Space-Based Entanglement-Distribution Network for the Quantum Internet
Recent experimental breakthroughs in satellite quantum communications have opened up the possibility of creating a global quantum internet using satellite links. This approach appears to be particularly viable in the near term, due to the lower attenuation of optical signals from satellite to ground, and due to the currently short coherence times of quantum memories. These drawbacks prevent ground-based entanglement distribution using atmospheric or optical-fiber links at high rates over long distances. In this work, we propose a global-scale quantum internet consisting of a constellation of orbiting satellites that provides a continuous on-demand entanglement distribution service to ground stations. The satellites can also function as untrusted nodes for the purpose of long-distance quantum-key distribution. We determine the optimal resource cost of such a network for obtaining continuous global coverage. We also analyze the performance of the network in terms of achievable entanglement-distribution rates and compare these rates to those that can be obtained using ground-based quantum-repeater networks.
Distributing quantum entanglements over long distances is essential for the realization of a global scale quantum Internet. Most of the prior work and proposals assume an on-demand distribution of entanglements which may result in significant network resource under-utilization. In this work, we introduce Quantum Overlay Networks (QONs) for efficient entanglement distribution in quantum networks. When the demand to create end-to-end user entanglements is low, QONs can generate and store maximally entangled Bell pairs (EPR pairs) at specific overlay storage nodes of the network. Later, during peak demands, requests can be served by performing entanglement swaps either over a direct path from the network or over a path using the storage nodes. We solve the link entanglement and storage resource allocation problem in such a QON using a centralized optimization framework. We evaluate the performance of our proposed QON architecture over a wide number of network topologies under various settings using extensive simulation experiments. Our results demonstrate that QONs fare well by a factor of 40% with respect to meeting surge and changing demands compared to traditional non-overlay proposals. QONs also show significant improvement in terms of average entanglement request service delay over non-overlay approaches.