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Near-Optimal Entanglement Distribution in Satellite-Assisted Quantum Networks

Wan-Ting Ho, Lifeng Chen, Jing-Jhih Du, Jian-Jhih Kuo, Ming-Jer Tsai

Published Dec 8, 2025• 0 citations
Publisher

Abstract

Satellite-assisted quantum network (SQN) is emerging as a promising solution to overcome the distance limitations of ground-based fiber quantum network (QN). However, each satellite and ground station has a limited number of transmitters and receivers, respectively, and the Entanglement Distribution Rate (EDR) decreases with the distance between satellites and ground stations, highlighting the need for effective resource allocation to serve requests in the SQN. In this paper, we present a novel optimization problem, termed ESOP, which aims to maximize the total EDR in the network while simultaneously considering the resource capacities of both satellites and ground stations, as well as the fidelity requirements of individual requests. To solve ESOP, we propose a (2 + ϵ)-approximation algorithm, AESOP, which combines a greedy approach with a tailored local search. Simulation results show that AESOP achieves up to 64% improvement in total EDR compared to the existing method.

DOI
10.1109/globecom59602.2025.11432709

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Satellite-assisted quantum network (SQN) is emerging as a promising solution to overcome the distance limitations of ground-based fiber quantum network (QN). However, each satellite and ground station has a limited number of transmitters and receivers, respectively, and the Entanglement Distribution Rate (EDR) decreases with the distance between satellites and ground stations, highlighting the need for effective resource allocation to serve requests in the SQN. In this paper, we present a novel optimization problem, termed ESOP, which aims to maximize the total EDR in the network while simultaneously considering the resource capacities of both satellites and ground stations, as well as the fidelity requirements of individual requests. To solve ESOP, we propose a (2 + ϵ)-approximation algorithm, AESOP, which combines a greedy approach with a tailored local search. Simulation results show that AESOP achieves up to 64% improvement in total EDR compared to the existing method.
Satellite-based quantum networks are emerging as a promising solution for the development of a global quantum internet in the near future. The ability to leverage the advantageous lower attenuation of optical signals from satellites to ground presents an exciting opportunity to establish a robust and secure quantum communication infrastructure on a global scale. By utilizing a constellation of satellites, it becomes feasible to continuously distribute high-fidelity quantum entanglements among ground stations over long distances, overcoming the limitations of traditional terrestrial-based quantum communication systems. In this article, we first provide a brief survey of existing solutions for satellite-based entanglement distribution, highlighting the various approaches and technologies that have been employed in this rapidly evolving field. We then delve into a formulated optimal entanglement distribution problem, aiming to optimize the distribution of quantum entanglement resources across the satellite network to maximize efficiency and reliability. This problem is addressed through a detailed exploration of several different methodologies and algorithms, each tailored to specific operational settings and constraints. Our experimental results confirm the efficiency of these approaches and provide valuable insights into their practical implementation and performance. Finally, we identify several key directions for further study and development in the realm of satellite-based quantum networks.
The advancement of satellite-based quantum networks shows promise in transforming global communication infrastructure by establishing a secure and reliable quantum Internet. These networks use optical signals from satellites to ground stations to distribute high-fidelity quantum entanglements over long distances, overcoming the limitations of traditional terrestrial systems. However, the complexity of satellite-based entanglement distribution and terrestrial quantum swapping in the integrated network requires joint optimization with satellite assignment, resource allocation, and path selection. To address this challenge, we introduce a hybrid quantum-classical algorithm to solve the optimization problem by leveraging the strengths of both quantum and classical computing. The original problem is decomposed into a master problem and several subproblems using Dantzig-Wolfe decomposition and linearization techniques. Through experiments, this study demonstrates the effectiveness and reliability of the proposed methods in optimizing large-scale networks and managing qubit usage compared to the classical optimization techniques. The findings provide valuable insights for designing and implementing satellite-based entanglement distribution in quantum networks, paving the way for a secure global quantum communication infrastructure.
Satellite-based quantum networks leverage the advantageous properties of optical signals from satellites to ground stations, to enable the distribution of high-fidelity quantum entanglements over vast distances, thus circumventing the limitations of traditional terrestrial-based systems. However, the satellite-based entanglement distribution coupled with terrestrial quantum swapping in the space-terrestrial integrated network becomes very complex when considering the joint optimization with satellite assignment, resource allocation, and path selections. To tackle this issue, we introduce a hybrid quantum-classical Dantzig-Wolfe decomposition technique by leveraging the strengths of both quantum and classical computing, to solve the joint optimization problem. Through a series of experiments, the paper demonstrates the efficiency and robustness of the proposed methods in addressing large-scale network optimization and balancing qubit usage. The insights generated by this work offer valuable guidance for the design and implementation of satellite-based entanglement distribution for quantum networks, thereby laying the groundwork for the realization of a secure quantum communication infrastructure on a global scale.
Recent technological advancements in satellite based quantum communication has made it a promising technology for realizing global scale quantum networks. Due to better loss distance scaling compared to ground based fiber communication, satellite quantum communication can distribute high quality quantum entanglements among ground stations that are geographically separated at very long distances. This work focuses on optimal distribution of bipartite entanglements to a set of pair of ground stations using a constellation of orbiting satellites. In particular, we characterize the optimal satellite-to-ground station transmission scheduling policy with respect to the aggregate entanglement distribution rate subject to various resource constraints at the satellites and ground stations. We cast the optimal transmission scheduling problem as an integer linear programming problem and solve it efficiently for some specific scenarios. Our framework can also be used as a benchmark tool to measure the performance of other potential transmission scheduling policies.
Quantum satellite networks offer a promising solution for achieving long-distance quantum communication by enabling entanglement distribution across global scales. This work formulates and solves the quantum satellite network scheduling problem by optimizing satellite-to-ground station pair assignments under realistic system and environmental constraints. Our framework accounts for limited satellite and ground station resources, fairness, entanglement fidelity thresholds, and real world non-idealities including atmospheric losses, weather and background noise. In addition, we incorporate the complexities of multi-satellite relays enabled via inter-satellite links. We propose an integer linear programming (ILP) based optimization framework that supports multiple scheduling objectives, allowing us to analyze tradeoffs between maximizing total entanglement distribution rate and ensuring fairness across ground station pairs. Our framework can also be used as a benchmark tool to measure the performance of other potential transmission scheduling policies.
Quantum satellite networks enable entanglement distribution between ground stations, but existing single-satellite solutions relying solely on satellite-to-ground downlinks cannot effectively support long-distance entanglement distribution. To overcome this limitation, we propose a hybrid approach,Entanglement Distribution using Hybrid Links(ED-HL), which integrates bothentanglement distribution using a single satellite(ED-SS) andentanglement distribution assisted by inter-satellite links(ED-IS), where satellites are equipped with quantum memories. The key challenge in ED-HL is to effectively coordinate satellites to distribute entanglement to ground stations via inter-satellite links and downlinks.We formulate this entanglement distribution problem as an integer programming problem, establish its submodularity, monotonicity and non-negativity properties, and design a greedy algorithm for its solution. Simulations based on real-world constellation data show that ED-HL significantly extends the entanglement distribution distance compared to single-satellite approaches.