We, the QAIMS lab lab at the Aerospace Faculty of TU Delft, participated as finalists in the Airbus/BMW Quantum Computing Challenge 2024. Stacking sequence retrieval, a complex combinatorial task within a bi-level optimization framework, is crucial for designing laminated composites that meet aerospace requirements for weight, strength, and stiffness. This document presents the scientifically relevant sections of our submission, which builds on our prior research on applying quantum computation to this challenging design problem. For the competition, we expanded our previous work in several significant ways. First, we incorporated a full set of manufacturing constraints into our algorithmic framework, including those previously established theoretically but not yet demonstrated, thereby aligning our approach more closely with real-world manufacturing demands. We implemented the F-VQE algorithm, which enhances the probability shaping of optimal solutions, improving on simpler variational quantum algorithms. Our approach also demonstrates flexibility by accommodating diverse objectives as well as finer ply-angle increments alongside the previously demonstrated conventional ply angles. Scalability was tested using the DMRG algorithm, which, despite limitations in entanglement representation, enabled simulations with up to 200 plies. Results were directly compared to conventional stacking sequence retrieval algorithms with DMRG showing high competitiveness. Given DMRG's limited entanglement capabilities, it serves as a conservative baseline, suggesting potential for even greater performance on fully realized quantum systems. This document serves to make our competition results publicly available as we prepare a formal publication on these findings and their implications for aerospace materials design optimization.

翻译：我们，代尔夫特理工大学航空航天学院QAIMS实验室，作为决赛入围者参与了2024年空中客车/宝马量子计算挑战赛。层合板序列检索是一个双层优化框架内的复杂组合任务，对于设计满足航空航天领域重量、强度和刚度要求的层合复合材料至关重要。本文档展示了我们提交材料中具有科学价值的部分，该工作基于我们先前将量子计算应用于这一挑战性设计问题的研究。为此次竞赛，我们在多个重要方面拓展了先前的工作。首先，我们将一套完整的制造约束纳入算法框架，包括那些先前已建立理论但尚未验证的约束，从而使我们的方法更贴近实际制造需求。我们实现了F-VQE算法，该算法增强了对最优解的概率塑造能力，改进了更简单的变分量子算法。我们的方法还展现出灵活性，能够适应多样化的目标函数，并在先前已论证的传统铺层角度之外，兼容更精细的铺层角度增量。我们使用DMRG算法测试了可扩展性，该算法尽管在纠缠表示方面存在局限，但实现了多达200层铺层的模拟。我们将结果与传统层合板序列检索算法进行了直接比较，显示DMRG具有很高的竞争力。鉴于DMRG有限的纠缠处理能力，它可作为一个保守的性能基准，暗示在完全实现的量子系统上可能获得更优的性能。本文档旨在公开我们的竞赛成果，同时我们正在就这些发现及其对航空航天材料设计优化的意义准备正式出版物。