Hybrid quantum optimization is applicable to school aggregation as a proof of concept, with classical optimization currently advantaged¶

Claim · All claims
Evidence · 1 study · design r2 · q1
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q1 i? The model can be represented and evaluated within a hybrid quantum-classical optimization framework, providing a proof-of-concept assessment rather than a computational advantage over classical methods. → Alessia Ciacco 2026

Evidence¶

Alessia Ciacco 2026¶

Alessia Ciacco, Luigi Di Puglia Pugliese, Francesca Guerriero. (2026). School network reorganization under educational and spatial constraints using classical and quantum optimization. https://arxiv.org/abs/2608.05427 doi:10.48550/arxiv.2608.05427

q1 · i? · design · r2

Computational experiments solved the formulation with Gurobi as an exact classical benchmark and via D-Wave's hybrid CQM solver, with each instance solved five times to assess stochastic variability. The article reports a proof-of-concept assessment, not a quantum advantage.

"The computational experiments provide a proof-of-concept assessment of the applicability of hybrid quantum optimization to the considered school aggregation problem, while also highlighting the current computational advantage of classical optimization and the limitations of the tested benchmark instances."

Discussion¶

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