Managing the response to natural disasters effectively can considerably mitigate their devastating impact. This work explores the potential of using supervised hybrid quantum machine learning to optimize emergency evacuation plans for cars during natural disasters. The study focuses on earthquake emergencies and models the problem as a dynamic computational graph where an earthquake damages an area of a city. The residents seek to evacuate the city by reaching the exit points where traffic congestion occurs. The situation is modeled as a shortest-path problem on an uncertain and dynamically evolving map. We propose a novel hybrid supervised learning approach and test it on hypothetical situations on a concrete city graph. This approach uses a novel quantum feature-wise linear modulation (FiLM) neural network parallel to a classical FiLM network to imitate Dijkstra's node-wise shortest path algorithm on a deterministic dynamic graph. Adding the quantum neural network in parallel increases the overall model's expressivity by splitting the dataset's harmonic and non-harmonic features between the quantum and classical components. The hybrid supervised learning agent is trained on a dataset of Dijkstra's shortest paths and can successfully learn the navigation task. The hybrid quantum network improves over the purely classical supervised learning approach by 7% in accuracy. We show that the quantum part has a significant contribution of 45.(3)% to the prediction and that the network could be executed on an ion-based quantum computer. The results demonstrate the potential of supervised hybrid quantum machine learning in improving emergency evacuation planning during natural disasters.

翻译：有效管理自然灾害响应可以显著减轻其破坏性影响。本研究探索了利用有监督混合量子机器学习优化自然灾害期间汽车紧急疏散方案的潜力。研究聚焦于地震紧急情况，将问题建模为动态计算图，其中地震破坏城市某区域。居民试图通过到达出口点（此处发生交通拥堵）来疏散城市。该情境被建模为在不确定且动态演变地图上的最短路径问题。我们提出了一种新颖的有监督混合学习方法，并在具体城市图的假设情境中进行了测试。该方法使用一种新颖的量子特征线性调制（FiLM）神经网络，与经典FiLM网络并行运行，以在确定性动态图上模拟Dijkstra的节点级最短路径算法。通过并行添加量子神经网络，将数据集的谐波与非谐波特征在量子与经典组件之间拆分，从而增强整体模型的表达能力。该有监督混合学习智能体在Dijkstra最短路径数据集上训练，能够成功学习导航任务。与纯经典有监督学习方法相比，混合量子网络在准确率上提升了7%。我们证明，量子部分对预测有显著贡献，占比45.3%，并且该网络可在基于离子的量子计算机上执行。结果展示了有监督混合量子机器学习在改善自然灾害期间紧急疏散规划方面的潜力。