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Managing the response to natural disasters effectively can considerablymitigate their devastating impact. This work explores the potential of usingsupervised hybrid quantum machine learning to optimize emergency evacuationplans for cars during natural disasters. The study focuses on earthquakeemergencies and models the problem as a dynamic computational graph where anearthquake damages an area of a city. The residents seek to evacuate the cityby reaching the exit points where traffic congestion occurs. The situation ismodeled as a shortest-path problem on an uncertain and dynamically evolvingmap. We propose a novel hybrid supervised learning approach and test it onhypothetical situations on a concrete city graph. This approach uses a novelquantum feature-wise linear modulation (FiLM) neural network parallel to aclassical FiLM network to imitate Dijkstra's node-wise shortest path algorithmon a deterministic dynamic graph. Adding the quantum neural network in parallelincreases the overall model's expressivity by splitting the dataset's harmonicand non-harmonic features between the quantum and classical components. Thehybrid supervised learning agent is trained on a dataset of Dijkstra's shortestpaths and can successfully learn the navigation task. The hybrid quantumnetwork improves over the purely classical supervised learning approach by 7%in accuracy. We show that the quantum part has a significant contribution of45.(3)% to the prediction and that the network could be executed on anion-based quantum computer. The results demonstrate the potential of supervisedhybrid quantum machine learning in improving emergency evacuation planningduring natural disasters.

A supervised hybrid quantum machine learning solution to the emergency escape routing problem