标题： 量子计算在飞机载荷优化中的应用 显示英文标题

标题： Quantum Computing for Optimizing Aircraft Loading

摘要： 飞机装载优化问题是一个计算上非常困难的问题，已知的最佳经典算法随着对象数量的增加呈指数级增长。 我们提出了一种基于 QAOA 算法多角度变体的方法（Multi-Angle Layered Variational Quantum Algorithm, MAL-VQA），旨在与标准 QAOA 算法相比，减少量子电路中的两量子比特门的数量，从而使量子优化算法能够在近期的离子阱量子处理单元（QPU）上运行。 我们还描述了一种新型的成本函数实现方法，能够处理多种不同类型的不等式约束，而无需在量子电路中引入松弛变量，从而使得具有复杂约束的大规模问题可以在具有低量子比特计数的近期 QPU 上表示。 我们在 IonQ QPU Aria 和 Forte 上执行的不同飞机装载问题实例上展示了该算法的性能。我们的实验获得了从 12 个量子比特到 28 个量子比特的所有研究问题实例的最优解。 这表明了随着近期量子硬件的改进，该方法有望扩展到显著更大的问题规模，并且证明了量子算法在应对不同问题实例的初始猜测和约束变化时的鲁棒性。 显示英文摘要

摘要： The aircraft loading optimization problem is a computationally hard problem with the best known classical algorithm scaling exponentially with the number of objects. We propose a quantum approach based on a multi-angle variant of the QAOA algorithm (Multi-Angle Layered Variational Quantum Algorithm (MAL-VQA)) designed to utilize a smaller number of two qubit gates in the quantum circuit as compared to the standard QAOA algorithm so that the quantum optimization algorithm can be run on near-term ion-trap quantum processing units (QPU). We also describe a novel cost function implementation that can handle many different types of inequality constraints without the overhead of introducing slack variables in the quantum circuit so that larger problems with complex constraints may be represented on near-term QPUs which have low qubit counts. We demonstrate the performance of the algorithm on different instances of the aircraft loading problem by execution on IonQ QPUs Aria and Forte. Our experiments obtain the optimal solutions for all the problem instances studied ranging from 12 qubits to 28 qubits. This shows the potential scalability of the method to significantly larger problem sizes with the improvement of quantum hardware in the near future as well as the robustness of the quantum algorithm against varying initial guesses and varying constraints of different problem instances.