Efficient packing of items into bins is a common daily task. Known as Bin Packing Problem, it has been intensively studied in the field of artificial intelligence, thanks to the wide interest from industry and logistics. Since decades, many variants have been proposed, with the three-dimensional Bin Packing Problem as the closest one to real-world use cases. We introduce a hybrid quantum-classical framework for solving real-world three-dimensional Bin Packing Problems (Q4RealBPP), considering different realistic characteristics, such as: i) package and bin dimensions, ii) overweight restrictions, iii) affinities among item categories and iv) preferences for item ordering. Q4RealBPP permits the solving of real-world oriented instances of 3dBPP, contemplating restrictions well appreciated by industrial and logistics sectors.

翻译：将物品高效装入箱中是日常常见任务，即装箱问题。得益于工业与物流领域的广泛关注，该问题在人工智能领域已被深入研究。数十年来，研究者提出了众多变体，其中三维装箱问题最贴近实际应用场景。我们提出一种混合量子-经典框架（Q4RealBPP）来解决实际三维装箱问题，该框架考虑了多种现实特征，包括：i）包裹与箱体尺寸，ii）超重限制，iii）物品类别间的相容性，以及iv）物品排序偏好。Q4RealBPP能够解决面向实际场景的三维装箱问题实例，所纳入的约束条件深受工业与物流领域的认可。