{"ID":2839631,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.15566","arxiv_id":"2511.15566","title":"Constraint-preserving quantum algorithm for the multi-frequency antenna placement problem","abstract":"Quantum algorithms for combinatorial optimization typically encode constraints as soft penalties within the objective function, which can reduce efficiency and scalability compared to state-of-the-art classical methods that instead exploit constraints to guide the search toward high-quality solutions. Although solving this issue for an arbitrary problem is inherently a hard task, we address this challenge for a specific problem in the field of telecommunications, the multi-frequency antenna placement problem, by introducing a constraint-preserving quantum adiabatic algorithm (QAA). To this aim, we construct a quantum circuit that prepares an initial state comprising an equal superposition of all feasible solutions, and define a custom mixer that preserves both the one-hot encoding constraint for vertex coloring and the cardinality constraint on the number of antennas. This scheme can be extended to a broader range of applications characterized by similar constraints. We first benchmark the performance of this quantum algorithm against a basic version of QAA, demonstrating superior performance in terms of feasibility and success probability. We then apply this algorithm to large problem sizes with hundreds of variables using a constraint-aware decomposition method based on the SPLIT framework. Our results indicate competitive performance against other large-scale classical approaches, such as branch-and-bound and simulated annealing. This work supports previous claims that constraint-aware algorithms are crucial for the practical and efficient application of quantum methods in industrial settings.","short_abstract":"Quantum algorithms for combinatorial optimization typically encode constraints as soft penalties within the objective function, which can reduce efficiency and scalability compared to state-of-the-art classical methods that instead exploit constraints to guide the search toward high-quality solutions. Although solving...","url_abs":"https://arxiv.org/abs/2511.15566","url_pdf":"https://arxiv.org/pdf/2511.15566v1","authors":"[\"Matteo Vandelli\",\"Francesco Ferrari\",\"Daniele Dragoni\"]","published":"2025-11-19T15:59:20Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"math.OC\"]","methods":"[]","has_code":false}