发表机构
Université de Sherbrooke（舍布鲁克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个融合机器学习风险预测与量子退火的框架，用于无人机监视路径规划，通过建模为带约束的团队定向问题，在森林火灾预防中实现高效巡视组合优化。
AI 中文摘要
利用中性原子计算机的模拟量子计算正迅速发展为大规模执行组合优化任务的一种有前景的方法。随着技术的成熟，探索利用这种独特信息处理器的真实用例变得合理；其中一个例子是物流管理，其中为车辆分配路线以执行给定任务。自主飞行器（无人机）越来越多地用于对给定区域进行监视。在本文中，我们提出了一种创新框架，该框架整合了经典机器学习预测和量子退火，以根据给定的监视标准找到最佳的巡视组合。机器学习生成的风险图用于寻找可能的监视巡视，而量子辅助启发式算法选择最佳组合，从而生成完整的监视计划。通过将无人机路径的决策问题建模为带有额外约束的团队定向问题（TOP），我们构建了一种使用量子退火的新启发式算法来寻找良好解决方案。我们将此框架应用于森林火灾预防，其中找到最有可能检测到初起火灾的巡视具有明显益处。我们研究了更大的量子资源对启发式性能的影响，并将这些结果与使用经典资源的结果进行了比较。
英文摘要
Analog quantum computing with neutral atom computers is rapidly evolving into a promising way to perform combinatorial optimization tasks at scale. As technology matures, exploring real use cases leveraging such unique information processors becomes warranted; one example is logistics management where vehicles are assigned routes to perform a given task. Autonomous aerial vehicles (drones) are increasingly used for surveillance over a given territory. In this paper, we propose an innovative framework integrating classical machine learning forecasts and quantum annealing to find the best combination of tours for a given surveillance criterion. The risk maps produced by machine learning are used to find possible surveillance tours, while a quantum-assisted heuristic chooses the best combination, producing a full surveillance plan. By modelling our decision problem for drone paths as a Team Orienteering Problem (TOP) with additional constraints, we construct a new heuristic using quantum annealing to find good solutions. We apply this framework to wildfire prevention, where finding tours most likely to detect a starting fire has clear benefits. We study the effect of larger quantum resources on heuristic performance and compare those results to the use of classical resources.
Comments26 pages, 5 figures, 3 tables
