Deploying quantum networks involves the placement of quantum repeaters, which generate entangled qubits for quantum computers to perform quantum teleportation. Besides placing repeaters on ground devices, there have been efforts towards the use of satellites or drones as repeaters. This paper considers the scenario of using drones due to their cost-efficiency and support for flexible network formation. While several issues in this scenario, such as enhancing network connectivity, have been studied, this paper addresses a new problem: how to minimize the number of drones placed in the air to cover all quantum computers on the ground and make the entire quantum network connected. Given that this problem is NP-hard, we propose a suboptimal but polynomial-time approach for it. Our approach consists of two stages. Stage 1 aims to minimize the number of drones needed to cover the ground computers, and Stage 2 aims to minimize the number of drones needed for connecting the drones returned by Stage 1. Both stages use algorithms of no more than quadratic time complexity. We conduct experiments to determine the best algorithm for the setting of quantum networks from several high-performing existing algorithms and design optimization techniques for existing algorithms. Our experiments confirm that our approach reduces the number of drones placed effectively.
Deploying quantum networks involves the placement of quantum repeaters, which generate entangled qubits for quantum computers to perform quantum teleportation. Besides placing repeaters on ground devices, there have been efforts towards the use of satellites or drones as repeaters. This paper considers the scenario of using drones due to their cost-efficiency and support for flexible network formation. While several issues in this scenario, such as enhancing network connectivity, have been studied, this paper addresses a new problem: how to minimize the number of drones placed in the air to cover all quantum computers on the ground and make the entire quantum network connected. Given that this problem is NP-hard, we propose a suboptimal but polynomial-time approach for it. Our approach consists of two stages. Stage 1 aims to minimize the number of drones needed to cover the ground computers, and Stage 2 aims to minimize the number of drones needed for connecting the drones returned by Stage 1. Both stages use algorithms of no more than quadratic time complexity. We conduct experiments to determine the best algorithm for the setting of quantum networks from several high-performing existing algorithms and design optimization techniques for existing algorithms. Our experiments confirm that our approach reduces the number of drones placed effectively.
Quantum networks rely on repeaters to distribute entangled qubit pairs for applications such as quantum key distribution and distributed quantum computing. Minimizing the number of repeaters—while meeting connectivity and range constraints—is central to making deployments practical at scale. This thesis unifies three complementary lines of work toward that goal for terrestrial and non-terrestrial quantum networks. Two-Step Linear Programming (TSLP) is a scalable optimization framework that decomposes complex problems into two sequential linear programming steps to reduce computational complexity. First, a TSLP approach for repeater placement on existing infrastructure (terrestrial networks) is presented. Integer Linear Programming (ILP) is a mathematical modeling technique where variables are restricted to be integers to find the best possible outcome in a set of requirements. This work contributes a scalable optimization framework that addresses the computational limitations of traditional ILP models. While existing ILP solutions face O(n2) complexity, the proposed TSLP approach reduces this to O(n) variables. Simulation results on synthetic and real-world topologies show that TSLP dramatically decreases running time while achieving higher node connectivity through a strategic, slight increase in the number of repeaters. This advances research by enabling the design of large-scale fiber-based quantum networks that were previously computationally inaccessible. Second, a drone-based (non-terrestrial) repeater network for serving ground stations is studied. A key contribution of this work is the development of a polynomial-time, two- stage algorithm that decouples coverage and connectivity requirements. Stage 1 optimizes the coverage of ground terminals, while Stage 2 ensures the network backbone is fully connected. Third, the thesis addresses the need for fault tolerance in error-prone quantum net- works by extending the 1-connected drone model to achieve 2-connectedness. The Delaunay Triangulation with pruning, Steiner points, and pruning again (DT-PSP) is a geometry-aware heuristic that minimizes the number of required drone-based repeaters by leveraging geometric properties. The major contribution here is the introduction of the DT-PSP approach based on Delaunay Triangulation and Steiner node placement. Collectively, the proposed algorithms improve network connectivity and reduce deployment cost across diverse quantum-network settings. By bridging the gap between theoretical optimality and practical scalability, this PhD work provides a comprehensive toolkit for the real-world deployment of future large-scale quantum networks.
Thanks to the applications such as Quantum Key Distribution and Distributed Quantum Computing, the deployment of quantum networks is gaining great momentum. A major component in quantum networks is repeaters, which are essential for reducing the error rate of qubit transmission for long-distance links. However, repeaters are expensive devices, so minimizing the number of repeaters placed in a quantum network while satisfying performance requirements becomes an important problem. Existing solutions typically solve this problem optimally by formulating an Integer Linear Program (ILP). However, the number of variables in their ILPs is O ( n 2 ) , where n is the number of nodes in a network. This incurs infeasible running time when the network scale is large. To overcome this drawback, this paper proposes to solve the repeater placement problem by two steps, with each step using a linear program of a much smaller scale with O ( n ) variables. Although this solution is not optimal, it dramatically reduces the time complexity, making it practical for large-scale networks. Moreover, it constructs networks that have higher node connectivity than those by existing solutions, since it deploys slightly more number of repeaters into networks. Our extensive experiments on both synthetic and real-world network topologies verified our claims.
The rapid advancement of quantum physical devices is transforming the quantum Internet from theory to practice. To achieve the envisioned full-time and all-location coverage, mobile quantum networks using drones have been proposed as a critical supplement to existing fiber and satellite quantum links, especially in scenarios where traditional optical links are unavailable. Although experiments in drone-based entanglement distribution have been successfully conducted, the field currently lacks a systematic network architecture and a dedicated protocol stack to integrate drone nodes and coordinate complex network functions efficiently. To address this gap, we propose a comprehensive design framework for drone-based quantum entanglement networks. First, we introduce a hierarchical architecture consisting of a quantum backbone relay network and quantum local area access networks. Second, for the local networks, we develop a clustering model based on the classical-quantum combinatorial metric to dynamically select and manage cluster heads. Third, we design a layered protocol stack that modularizes the end-to-end quantum information transmission process into well-defined functional layers. Subsequently, based on this architecture and protocol stack, we present the integrated design of a quantum drone node. Finally, we evaluate the designed network architecture. The results demonstrate that the proposed framework can significantly reduce the network overhead and improve the end-to-end entanglement distribution rate. Our work lays the foundation for the scalable development of mobile quantum networks.
Quantum internet needs to ensure timely provision of entangled qubits to be used in tasks that involve distributed quantum computing or sensing. This has been addressed by a top-down approach of optimized quantum networks [21], in which entanglement is distributed prior to receiving tasks. Upon the task arrival, the desired entanglement state is attained with local operations and classical communication. The pre-distribution of entanglements should aim to minimize the amount of qubits used, as this decreases the risk of decoherence and thus degradation of the entangled state. The optimized quantum networks consider a multi-hop optical network and in this work we are supplementing it with Satellite-aided Entanglement Distribution (SED). The motivation is that satellites can shortcut the topology and place the entanglement at two nodes not directly connected through the optical network. We devise an algorithm for strategic placement of entanglements with SED, which results in a decrease in the number of qubits used in pre-distribution of entanglement. The numerical results show that SED can significantly enhance the performance of small quantum networks, while Entanglement-sharing Constraints (EC) are critical for large networks.
Quantum internet needs to ensure timely provision of entangled qubits to be used in tasks that involve distributed quantum computing or sensing. This has been addressed by a top-down approach of optimized quantum networks [arXiv:2107.10275], in which entanglement is distributed prior to receiving tasks. Upon the task arrival, the desired entanglement state is attained with local operations and classical communication. The pre-distribution of entanglements should aim to minimize the amount of qubits used, as this decreases the risk of decoherence and thus degradation of the entangled state. The optimized quantum networks consider a multi-hop optical network and in this work we are supplementing it with Satellite-aided Entanglement Distribution (SED). The motivation is that satellites can shortcut the topology and place the entanglement at two nodes not directly connected through the optical network. We devise an algorithm for strategic placement of entanglements with SED, which results in a decrease in the number of qubits used in pre-distribution of entanglement. The numerical results show that SED can significantly enhance the performance of small quantum networks, while Entanglement-sharing Constraints (EC) are critical for large networks.
Quantum networking has been attracting growing attention in both academia and industry during recent years. Although many routing protocols have been proposed to optimize the network layer of quantum networks, another key networklayer problem is under-explored: the topology design problem. Existing work is limited to using structure topologies such as a ring or grid, using randomly generated topologies, or using existing Internet backbone infrastructure. Whether these choices are desired for quantum networks is not fully explained. We propose the quantum network topology design problem, for which a set of end users are given and we need to compute a set of repeaters and their locations and connectivities while ensuring the network performance, such as throughput, is optimized under the same infrastructure cost (e.g., number of repeaters). We present the methodology of designing topologies based on the unique characteristics of entanglement distribution networks and propose an algorithm called TriPlace to compute a topology for a given set of end users in different locations of a geographic region. The simulation results show that TriPlace incurs a lower infrastructure cost than all existing solutions while achieving similar throughput performance as the best of these solutions. We believe this work will start a new research problem for quantum networks and attract more future work.