Decomposition-Based QAOA for Maximum Coverage Location Problem in Satellite Constellation Design

• URL: http://arxiv.org/abs/2607.08102v1
• Date: Thu, 09 Jul 2026 04:39:44 GMT
• Title: Decomposition-Based QAOA for Maximum Coverage Location Problem in Satellite Constellation Design
• Abstract summary: maximizing coverage while utilizing limited orbital resources is one of the critical design challenges for complex optimization problems.<n>The maximal covering location problem (MCLP), serves as a base for orbital coverage modeling, is NP-hard and computationally intractable for large-constellation instances.<n>This paper proposes a scalable quantum optimization framework for MCLP in satellite constellation design.
• Score: 4.190898032627423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An increase in earth observation missions has increased the demand of efficient design and optimization of satellite constellations. Maximizing coverage of the target while effectively utilizing the limited orbital resources is one of the critical design challenges for complex combinatorial optimization problems. The maximal covering location problem (MCLP), serves as a base for orbital coverage modeling, is NP-hard and computationally intractable for large-constellation instances. Using heuristics, metaheuristics, and mixed-integer linear programming, classical solvers have achieved optimal or near-optimal results, yet their scalability is limited as the problem size increases. Quantum computing advancements, including the quantum approximate optimization algorithms, offer a potential solution to NP-hard combinatorial optimization problems. Current quantum hardware limitations, such as low qubit counts and circuit depth, restrict solutions for small-scale instance problems. To address this challenge, this paper proposes a scalable quantum optimization framework for MCLP in satellite constellation design. A decomposition-based quantum methodology is proposed, in which large MCLP instances are partitioned into subgraphs by classical decomposition, optimized independently via quantum optimization circuits, and combined using quantum reconstruction strategies. Computational results across different constellation sizes reveal better scalability in less time while maintaining competitive coverage performance compared to classical solvers.

Related papers

• A Hybrid Classical-Quantum Approach for Multi-Constrained Location Optimization Problem [0.19573380763700712]
  The Maximal Covering Location Problem (MCLP) aims to determine the optimal facility placements that maximize total coverage.<n>It is characterized by both equality and inequality constraints, which ensure correct coverage but significantly increase the complexity of exploring the solution space as instance size grows.<n>In this work, the MCLP is formulated as a Quadratic Unconstrained Binary Optimization model, where constraint embedding plays a critical role in solution quality.
  arXiv  Detail & Related papers  (2026-07-08T19:44:55Z)
• A NISQ-Aware Hybrid Quantum-Classical Framework for Scalable Combinatorial Optimization [8.152791871486682]
  We propose a hybrid quantum-classical optimization framework that reformulates large-scale optimization as a resource-bounded distribution evolution process.<n>Instead of directly optimizing individual solutions, the proposed framework operates on a probabilistic representation of the solution space.<n>Extensive experiments on benchmark and synthetic datasets demonstrate that the proposed framework consistently outperforms classical and quantum-inspired baselines.
  arXiv  Detail & Related papers  (2026-05-30T05:17:07Z)
• EQE-QAOA: An Equivalence-Preserving Qubit Efficient Framework for Combinatorial Optimization [54.05451096499336]
  Existing techniques rely on qubit reduction at the cost of information loss, leading to degraded computational performance.<n>We propose the Equivalence-preserving Qubit Efficient QAOA, which significantly reduces the required number of qubits without degrading performance.<n>We show that it is broadly applicable to large-scale optimization problems, excluding only unconstrained problems with completely independent variables.
  arXiv  Detail & Related papers  (2026-04-20T13:57:49Z)
• Hot-Starting Quantum Portfolio Optimization [39.916647837440316]
  Combinatorial optimization with a smooth and convex objective function arises naturally in applications such as discrete mean-variance portfolio optimization.<n>We introduce a novel approach that restricts the search space to discrete solutions in the vicinity of the continuous optimum by constructing a compact Hilbert space.<n> Experiments on software solvers and a D-Wave Advantage quantum annealer demonstrate that our method outperforms state-of-the-art techniques.
  arXiv  Detail & Related papers  (2025-10-13T08:47:43Z)
• Efficient QAOA Architecture for Solving Multi-Constrained Optimization Problems [3.757262277494307]
  This paper proposes a novel combination of constraint encoding methods for the Quantum Approximate Optimization Ansatz.<n>One-hot constraints are enforced through $XY$-mixers that restrict the search space to the feasible sub-space naturally.<n>Since $XY$-mixers restrict the search space, specific state vector entries are always zero and can be omitted from the simulation, saving valuable memory and computing resources.
  arXiv  Detail & Related papers  (2025-06-03T17:46:53Z)
• SCOOP: A Quantum-Computing Framework for Constrained Combinatorial Optimization [0.0]
  We present SCOOP, a novel framework for solving constrained optimization problems.<n>SCOOP transforms a constrained problem into an unconstrained counterpart, forming SCOOP problem twins.<n>We demonstrate the framework on three NP-hard problems, Minimum Dominating Set, Minimum Maximal Matching, and Minimum Set Cover.
  arXiv  Detail & Related papers  (2025-04-15T06:17:23Z)
• Quantum Optimization Benchmarking Library - The Intractable Decathlon [26.228146982868846]
  We present ten optimization problem classes that are difficult for existing classical algorithms.<n>The individual properties of the problem classes vary in terms of objective and variable type, coefficient ranges, and density.<n>We introduce the Quantum Optimization Benchmarking Library (QOBLIB) where the problem instances and solution track records can be found.
  arXiv  Detail & Related papers  (2025-04-04T18:00:00Z)
• Distributed Quantum Approximate Optimization Algorithm on a Quantum-Centric Supercomputing Architecture [1.953969470387522]
  Quantum approximate optimization algorithm (QAOA) has shown promise in solving optimization problems by providing quantum speedup on gate-based quantum computing systems.<n>However, QAOA faces challenges for high-dimensional problems due to the large number of qubits required and the complexity of deep circuits.<n>We present a distributed QAOA (DQAOA) which decomposes a large computational workload into smaller tasks that require fewer qubits and shallower circuits.
  arXiv  Detail & Related papers  (2024-07-29T17:42:25Z)
• Multiobjective variational quantum optimization for constrained problems: an application to Cash Management [45.82374977939355]
  We introduce a new method for solving optimization problems with challenging constraints using variational quantum algorithms. We test our proposal on a real-world problem with great relevance in finance: the Cash Management problem. Our empirical results show a significant improvement in terms of the cost of the achieved solutions, but especially in the avoidance of local minima.
  arXiv  Detail & Related papers  (2023-02-08T17:09:20Z)
• A Hybrid Quantum-Classical Algorithm for Robust Fitting [47.42391857319388]
  We propose a hybrid quantum-classical algorithm for robust fitting. Our core contribution is a novel robust fitting formulation that solves a sequence of integer programs. We present results obtained using an actual quantum computer.
  arXiv  Detail & Related papers  (2022-01-25T05:59:24Z)
• Scaling Quantum Approximate Optimization on Near-term Hardware [49.94954584453379]
  We quantify scaling of the expected resource requirements by optimized circuits for hardware architectures with varying levels of connectivity. We show the number of measurements, and hence total time to synthesizing solution, grows exponentially in problem size and problem graph degree. These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
  arXiv  Detail & Related papers  (2022-01-06T21:02:30Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.