The increasing number of Low Earth Orbit (LEO) satellites, driven by lower manufacturing and launch costs, is proving invaluable for Earth observation missions and low-latency internet connectivity. However, as the number of satellites increases, the number of communication links to maintain also rises, making the management of this vast network increasingly challenging and highlighting the need for clustering satellites into efficient groups as a promising solution. This paper formulates the clustering of LEO satellites as a coalition structure generation (CSG) problem and leverages quantum annealing to solve it. We represent the satellite network as a graph and obtain the optimal partitions using a hybrid quantum-classical algorithm called GCS-Q. The algorithm follows a top-down approach by iteratively splitting the graph at each step using a quadratic unconstrained binary optimization (QUBO) formulation. To evaluate our approach, we utilize real-world three-line element set (TLE/3LE) data for Starlink satellites from Celestrak. Our experiments, conducted using the D-Wave Advantage annealer and the state-of-the-art solver Gurobi, demonstrate that the quantum annealer significantly outperforms classical methods in terms of runtime while maintaining the solution quality. The performance achieved with quantum annealers surpasses the capabilities of classical computers, highlighting the transformative potential of quantum computing in optimizing the management of large-scale satellite networks.
The increasing number of Low Earth Orbit (LEO) satellites, driven by lower manufacturing and launch costs, is proving invaluable for Earth observation missions and low-latency internet connectivity. However, as the number of satellites increases, the number of communication links to maintain also rises, making the management of this vast network increasingly challenging and highlighting the need for clustering satellites into efficient groups as a promising solution. This paper formulates the clustering of LEO satellites as a coalition structure generation (CSG) problem and leverages quantum annealing to solve it. We represent the satellite network as a graph and obtain the optimal partitions using a hybrid quantum-classical algorithm called GCS-Q. The algorithm follows a top-down approach by iteratively splitting the graph at each step using a quadratic unconstrained binary optimization (QUBO) formulation. To evaluate our approach, we utilize real-world three-line element set (TLE/3LE) data for Starlink satellites from Celestrak. Our experiments, conducted using the D-Wave Advantage annealer and the state-of-the-art solver Gurobi, demonstrate that the quantum annealer significantly outperforms classical methods in terms of runtime while maintaining the solution quality. The performance achieved with quantum annealers surpasses the capabilities of classical computers, highlighting the transformative potential of quantum computing in optimizing the management of large-scale satellite networks.
Recent studies have shown promising results indicating the potential of Noisy Intermediate Scale Quantum (NISQ) devices. In this work, Low-earth Orbit (LEO) satellite network design problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, and Quantum Annealing (QA) is utilized to solve the resulting problem with real world setting. Compare to classical approaches, the experimental results indicate improvements in terms of network path length and number of satellite hops in network path that amount to network latency. Further, an iterative post-processing method, Statistical Qubit Freezing (SQF), which freezes initial states of qubits and reduces the size of the problem in each annealing cycle, is proposed and evaluated. Solution found with SQF indicates that SQF in fact allows the system to reach lower energy state.
Beam Placement (BP) is a well-known problem in Low-Earth Orbit (LEO) satellite communication (SatCom) systems, which can be modelled as an NP-hard clique cover problem. Recently, quantum computing has emerged as a novel technology which revolutionizes how to solve challenging optimization problems by formulating Quadratic Unconstrained Binary Optimization (QUBO), then preparing Hamiltonians as inputs for quantum computers. In this paper, we study how to use quantum computing to solve BP problems. However, due to limited hardware resources, existing quantum computers are unable to tackle large optimization spaces. Therefore, we propose an efficient Hamiltonian Reduction method that allows quantum processors to solve large BP instances encountered in LEO systems. We conduct our simulations on real quantum computers (D-Wave Advantage) using a real dataset of vessel locations in the US. Numerical results show that our algorithm outperforms commercialized solutions of D-Wave by allowing existing quantum annealers to solve 17.5 times larger BP instances while maintaining high solution quality. Although quantum computing cannot theoretically overcome the hardness of BP problems, this work contributes early efforts to applying quantum computing in satellite optimization problems, especially applications formulated as clique cover/graph coloring problems.
Mega low Earth orbit (LEO) satellite constellations are a key infrastructure for 6G global connectivity, but their high node density, fast orbital motion, and continuously changing inter-satellite topology make routing difficult. Clustering is widely used to reduce routing complexity by partitioning a constellation into routing domains. This paper evaluates, side by side, two quantum-ready clustering approaches for mega LEO constellations. The first is qc-kmeans, a hybrid quantum–classical compressive K-means algorithm that selects cluster centroids by solving small quadratic unconstrained binary optimization (QUBO) problems with the quantum approximate optimization algorithm (QAOA). The second, RS-QUBO, partitions the inter-satellite link (ISL) graph directly with a routing- and stability-aware QUBO that rewards compact, balanced routing domains and penalizes changes between consecutive snapshots. Both are compared with classical baselines under identical inputs, with every trial recorded. For qc-kmeans, selecting the best of repeated trials suggests better cluster quality than K-means, but over all trials the two are practically identical on a 10,687-satellite Starlink snapshot and on a synthetic benchmark; an ablation shows that the outcome is determined by the classical K-means seeding and refinement, while QAOA accounts for most of the runtime. For RS-QUBO, on a real, irregular 1454-satellite Starlink sub-network with time-varying links and 3% random link failures, it is the only method of the main comparison that keeps all routes inside their routing domains at every tested number of domains (0% routing failures, compared with 39–71% for K-means, 16–44% for a grid tiling, and 2–22% for recomputed METIS), with a delay stretch of 1.08–1.18 over the routed pairs and 4–11% of satellites changing domain between snapshots. A component ablation attributes the absence of failures to adapting the partition to the current links and repairing disconnected domains, which also removes the failures of recomputed METIS; the compactness objective lowers the delay stretch relative to cut-based partitioning in the recomputed pipelines at 8 and 32 domains but not at 16. Domain changes are costly for routing: under a link-state update model, RS-QUBO needs 0.6–1.2 times the update load of flat routing, mostly to announce satellites that change domain, whereas an incremental refinement of a static METIS partition is also free of routing failures and needs 0.3–0.4 times the flat load. QAOA solves 6–12-satellite instances of this QUBO better than random sampling in ideal simulation, but the benefit becomes marginal under an ibm_torino noise model, and the compiled circuits require up to 960 two-qubit gates. We conclude that, in the evaluated conditions, the benefit of quantum-ready clustering for satellite routing comes from the problem formulation and the classical pipeline around it, solved classically, and that domain stability matters as much for routing as domain quality; QAOA is demonstrated at small scale on current hardware and noise models.
The formation of energy communities is pivotal for advancing decentralized and sustainable energy management. Within this context, Coalition Structure Generation (CSG) emerges as a promising framework. The complexity of CSG grows rapidly with the number of agents, making classical solvers impractical for even moderate sizes. This suggests CSG as an ideal candidate for benchmarking quantum algorithms against classical ones. Facing ongoing challenges in attaining computational quantum advantage for exact optimization, we pivot our focus to benchmarking quantum and classical solvers for approximate optimization. Approximate optimization is particularly critical for industrial use cases requiring real-time optimization, where finding high-quality solutions quickly is often more valuable than achieving exact solutions more slowly. Our findings indicate that quantum annealing (QA) on DWave can achieve solutions of comparable quality to our best classical solver, but with more favorable runtime scaling, showcasing an advantage. This advantage is observed when compared to solvers, such as Tabu search, simulated annealing, and the state-of-the-art solver Gurobi, in finding approximate solutions for energy community formation involving over 100 agents. DWave also surpasses 1-round QAOA on IBM hardware. Our findings represent the largest benchmark of quantum approximate optimizations for a real-world dense model beyond the hardware's native topology, where D-Wave demonstrates a scaling advantage.
Abstract The formation of energy communities is pivotal for advancing decentralized and sustainable energy management. Within this context, coalition structure generation (CSG) emerges as a promising framework. The complexity of CSG grows rapidly with the number of agents, making classical solvers impractical for even moderate sizes. This suggests CSG as an ideal candidate for benchmarking quantum algorithms against classical ones. Facing ongoing challenges in attaining computational quantum advantage for exact optimization, we pivot our focus to benchmarking quantum and classical solvers for approximate optimization. Approximate optimization is particularly critical for industrial use cases requiring real-time optimization, where finding high-quality solutions quickly is often more valuable than achieving exact solutions more slowly. Our findings indicate that quantum annealing (QA) on DWave can achieve solutions of comparable quality to our best classical solver, but with more favorable runtime scaling, showcasing an advantage. This advantage is observed when compared to solvers, such as Tabu search, simulated annealing, and the state-of-the-art solver Gurobi in finding approximate solutions for energy community formation involving over 100 agents. DWave also surpasses 1-round QAOA on IBM hardware. Our findings represent the largest benchmark of quantum approximate optimizations for a real-world dense model beyond the hardware’s native topology, where D-Wave demonstrates a scaling advantage.
Abstract The scheduling complexity of Agile Earth Observation Satellites (AEOSs) increases significantly as Earth Observation missions progress. This makes traditional optimization techniques less effective, restricting their application to small-scale and open-loop scheduling problems. In this paper, we investigate the potential of quantum solvers to address the closed-loop scheduling problem for a formation of AEOSs, overcoming the limitations of classical optimization techniques. To this end, we formulate the scheduling problem as a novel Quadratic Unconstrained Binary Optimization (QUBO) problem, i.e., a formulation specifically designed for quantum solvers. Moreover, penalty functions are introduced to minimize mission energy consumption and reduce deviations between the original and rescheduled solutions. The formulated QUBO problem is implemented on a D-Wave quantum annealer for a daily and large-scale scheduling scenario. The obtained results demonstrate significant improvements in computational efficiency and solution quality compared to traditional methods like Simulated Annealing and Tabu Search, highlighting the potential of quantum solvers in optimizing complex scheduling tasks for AEOS formations.