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IEEE Communications Magazine

SPARQ: Toward Efficient Space-Air-Ground Quantum Networks

Mohamed Shaban, Muhammad Ismail, Walid Saad

Published Dec 10, 2025• 2 citations
Publisher

Abstract

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.

DOI
10.1109/mcom.001.2500240

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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.
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.
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.
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.
In the era of 6G and beyond, space-aerial-terrestrial quantum networks (SATQNs) are poised to advance the development of a global-scale quantum Internet. These networks leverage free space optical satellite and aerial quantum networks to complement optical fiber-based terrestrial quantum networks to enable the distribution of high-fidelity quantum entanglement over long distances. However, establishing multi-hop end-to-end quantum entanglement remains highly challenging, not only due to time-varying link conditions and structural heterogeneity inherent in SATQNs, but also because noise in quantum channels and imperfections in quantum operations can degrade the quality of entanglement. To address this challenge, we formulate an optimization problem that maximizes SATQN throughput by jointly optimizing routing path selection and entanglement generation rates (PS-EGR) while ensuring high entanglement fidelity. The resulting problem is a mixed-integer linear programming (MILP) formulation, which is NP-hard. We propose a Benders’ decomposition (BD)-based approach to solve this problem efficiently. Specifically, the MILP is decomposed into a master problem for binary routing path selection and a subproblem for continuous entanglement generation rate optimization. Numerical results validate the effectiveness of the proposed PS-EGR scheme, offering critical insights into the optimization and deployment of SATQNs.
This article envisions the concept of a ``Quantum Internet in the Sky", aiming to establish ubiquitous quantum communication links among distant nodes via free-space optical channels. Our key focus is on deploying quantum communication terminals on non-terrestrial platforms, specifically unmanned aerial vehicles and satellites, at various altitudes. By highlighting the unique characteristics of these platforms compared to terrestrial counterparts, we address inherent challenges and discuss potential solutions through meticulous system designs and analyses of typical non-terrestrial quantum communication scenarios. Finally, we illuminate the path forward by proposing essential future directions that underscore the integration of high-dimensional multipartite quantum communications with sensing, computing, and intelligence for multiple users en route to realizing a fully operational Quantum Internet.
This paper investigates the design of a regional Quantum Network in Tennessee (QNTN) that will connect three quantum local area networks in different cities. We explore two approaches for achieving this interconnection: deploying a satellite constellation in the space layer and employing high-altitude platforms (HAPs) in the aerial layer. Our comparison reveals that a space-ground architecture that uses 108 satellites provides 55.17% coverage of the day and handles 57.75% of entanglement distribution requests with an average fidelity of 0.96. In contrast, the air-ground architecture delivers full-day coverage, fulfills 100% of requests, and achieves a higher average fidelity of 0.98. However, HAPs face significant challenges such as limited operational time, sensitivity to vibrations and weather conditions, and the need for continuous maintenance. This paper contributes to the understanding of optimal architecture for regional quantum networks, highlighting the trade-offs between satellite-based and air-ground approaches.