  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.
