Satellite communication (SatCom) systems play a vital role in providing global connectivity and enable a wide range of applications, including Internet of Things (IoT) connectivity for remote areas, such as forests and oceans. Two crucial resource allocation challenges in SatCom are beam placement (BP) and frequency assignment (FA) problems, which involve the clique covering (CC) and graph coloring (GC) problems, respectively. Conventional solutions for these problems incur excessive computational cost, which is intractable for classical computers. A promising approach is to formulate these problems using the Ising model, construct their Hamiltonians, and then solve them efficiently by a quantum computer. However, the current quantum computers have very limited hardware and can only handle rather small inputs. To overcome this limitation, we propose a hybrid-quantum-classical-computational pipeline where an efficient hamiltonian reduction method is the key for solving large CC/GC instances. Through experiments on real quantum computers, our reduction method outperforms commercial solutions, allowing quantum annealers to handle significantly larger BP/FA instances while maintaining high probability to achieve feasible solutions and near-optimal performance. Although the inherent hardness of the CC/GC problems cannot be overcome by quantum computing, our research contributes to the early exploration of quantum computing in the context of the complex optimization problems in SatCom systems, particularly in the realm of IoT connectivity for remote areas.
Satellite communication (SatCom) systems play a vital role in providing global connectivity and enable a wide range of applications, including Internet of Things (IoT) connectivity for remote areas, such as forests and oceans. Two crucial resource allocation challenges in SatCom are beam placement (BP) and frequency assignment (FA) problems, which involve the clique covering (CC) and graph coloring (GC) problems, respectively. Conventional solutions for these problems incur excessive computational cost, which is intractable for classical computers. A promising approach is to formulate these problems using the Ising model, construct their Hamiltonians, and then solve them efficiently by a quantum computer. However, the current quantum computers have very limited hardware and can only handle rather small inputs. To overcome this limitation, we propose a hybrid-quantum-classical-computational pipeline where an efficient hamiltonian reduction method is the key for solving large CC/GC instances. Through experiments on real quantum computers, our reduction method outperforms commercial solutions, allowing quantum annealers to handle significantly larger BP/FA instances while maintaining high probability to achieve feasible solutions and near-optimal performance. Although the inherent hardness of the CC/GC problems cannot be overcome by quantum computing, our research contributes to the early exploration of quantum computing in the context of the complex optimization problems in SatCom systems, particularly in the realm of IoT connectivity for remote areas.
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.
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.
Abstract NP-hard optimization problems scale very rapidly with problem size, becoming unsolvable with brute force methods, even with supercomputing resources. Typically, such problems have been approximated with heuristics. However, these methods still take a long time and are not guaranteed to find an optimal solution. Quantum computing offers the possibility of producing significant speed-up and improved solution quality. Current quantum annealing (QA) devices are designed to solve difficult optimization problems, but they are limited by hardware size and qubit connectivity restrictions. We present a novel heterogeneous computing stack that combines QA and classical machine learning, allowing the use of QA on problems larger than the hardware limits of the quantum device. These results represent experiments on a real-world problem represented by the weighted k -clique problem. Through this experiment, we provide insight into the state of quantum machine learning.
Satellite mission planning for Earth observation satellites is a combinatorial optimization problem that consists of selecting the optimal subset of imaging requests, subject to constraints, to be fulfilled during an orbit pass of a satellite. The ever-growing amount of satellites in orbit underscores the need to operate them efficiently, which requires solving many instances of the problem in short periods of time. However, current classical algorithms often fail to find the global optimum or take too long to execute. Here, we approach the problem from a quantum computing point of view, which offers a promising alternative that could lead to significant improvements in solution quality or execution speed in the future. To this end, we study a planning problem with a variety of intricate constraints and discuss methods to encode them for quantum computers. Additionally, we experimentally assess the performance of quantum annealing and the quantum approximate optimization algorithm on a realistic and diverse dataset. Our results identify key aspects like graph connectivity and constraint structure that influence the performance of the methods. We explore the limits of today’s quantum algorithms and hardware, providing bounds on the problems that can be currently solved successfully and showing how the solution degrades as the complexity grows. This work aims to serve as a baseline for further research in the field and establish realistic expectations on current quantum optimization capabilities.
Quantum computing has emerged as a promising computational paradigm with the potential to solve problems that are intractable for classical computers. This paper provides an overview of the current state of quantum computing, including its historical development, hardware advancements, applications, and challenges. Special attention is given to combinatorial optimization, which is considered one of the most promising application domains. The limitations of current quantum systems and future research directions are also discussed. This paper presents a comparative study between a conventional D-Wave-based simulated annealing approach and a proposed hybrid optimization model for solving largescale combinatorial optimization problems. The study focuses on benchmark problems, including AES, TSP gr24, and a real-world load balancing problem, all formulated as QUBO models. The D-Wave approach employs the dwave-neal simulated annealing engine with various parameter settings, while the proposed model introduces a decomposition-based framework combined with adaptive parameter control and iterative refinement. The experimental results demonstrate that the proposed model consistently outperforms the conventional approach in terms of solution quality, achieving significantly lower energy values across all problem instances. In particular, for the TSP gr24 problem, the proposed model achieves near-optimal solutions with substantially reduced computation time. For large-scale problems such as AES and load balancing, the proposed model maintains scalability and robustness, whereas the conventional approach suffers from increased computational cost and performance degradation. Although the proposed method introduces additional computational steps, its hybrid structure effectively mitigates limitations related to local minima, parameter sensitivity, and scalability. These findings indicate that integrating classical optimization techniques with quantum-inspired methods is essential for practical applications. The proposed model provides a promising framework for solving complex real-world optimization problems and highlights the importance of hybrid approaches in advancing quantum annealing technologies.
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.