发表机构
Quantum Computing Solutions, Leonardo S.p.A.; Leonardo Hypercomputing Continuum, Leonardo S.p.A.（量子计算解决方案，莱昂纳多股份公司; 莱昂纳多超算连续体，莱昂纳多股份公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对应急响应中无人机通信桥的部署优化问题，将其建模为IQP与QUBO问题，对比经典求解器与量子算法，为时间敏感场景提供实用解并探索量子计算的应用潜力。
AI 中文摘要
我们提出了一个组合优化问题，用于部署配备5G天线的无人机，以协助自然灾害受灾地区的救援行动。我们的目标是优化无人机的部署位置，在给定候选站点间的飞行自组织网络中提供覆盖。该公式旨在最大化信号覆盖、最小化干扰，同时确保网络连通性。为缓解干扰影响，我们采用多频率方案。我们将此问题建模为整数二次规划（IQP）问题。我们通过CPLEX求解器获得数值解，并对该问题在实际网络配置中的可扩展性进行初步分析。我们的发现表明，随着站点数量增加，求解时间（TTS）呈显著指数增长，这在紧急、时间敏感的场景中构成关键挑战。为解决此问题，可通过对求解器设置时间限制来生成近似次优解。尽管这些解并非最优，但在大多数情况下能保持连通性，在解质量与计算时间之间提供了实用的权衡，计算时间仍处于无人机实时重新部署的可行范围内。考虑到经典求解器在这些场景中的局限性，我们探索量子计算作为有前景的替代方案。具体而言，我们将问题重新建模为二次无约束二进制优化（QUBO）问题，适用于大多数量子算法。通过高性能计算模拟，我们证明量子绝热算法（QAA）能准确求解小规模实例，为量子计算未来应用于灾难响应中大规模、时间关键型优化问题铺平道路。
英文摘要
We present a combinatorial optimization problem for the strategic deployment of UAVs equipped with 5G antennas to assist rescue operations in regions hit by natural disasters. Our goal is to optimize the placement of UAVs to provide coverage in flying ad-hoc networks among given candidate sites. Our formulation aims to maximize signal coverage and minimize interference while ensuring network connectivity. To mitigate interference effects, we incorporate the use of multiple frequencies. We formulate this problem as an integer quadratic program (IQP). We present numerical solutions obtained via the CPLEX solver and conduct a preliminary analysis of the problem's scalability in realistic network configurations. Our findings reveal a significant exponential increase in Time-to-Solution (TTS) as the number of sites grows, which poses a critical challenge in urgent, time-sensitive scenarios. To address this issue, approximate suboptimal solutions can be produced by enforcing a time limit on the solver. Although these solutions are not optimal, they preserve connectivity in most cases, providing a practical trade-off between solution quality and computational times that remain within feasible limits for real-time UAV redeployment. Recognizing the limitations of classical solvers in these contexts, we explore quantum computing as a promising alternative. Specifically, we reformulate the problem as a quadratic unconstrained binary optimization (QUBO) problem, suitable for most quantum algorithms. Through high-performance computing emulation, we show that the quantum adiabatic algorithm (QAA) can accurately solve small-scale instances, paving the way for future application of quantum computing to large-scale, time-critical optimization problems in disaster response.
Comments12 pages, 11 figures
