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.

采用监督混合量子机器学习优化自然灾害期间汽车紧急疏散计划的潜力研究。研究聚焦于地震紧急情况，将问题建模为一个动态计算图，其中地震会破坏城市的某个区域，居民试图通过到达交通堵塞的出口点来撤离城市。该研究提出了一种新颖的混合监督学习方法，并在具体城市图上对假设情景进行了测试。该方法使用一种新型的量子特征线性调制（FiLM）神经网络，并行于经典 FiLM 网络，以模仿确定动态图上的 Dijkstra 最短路径算法，从而增加了整个模型的表达能力。混合监督学习代理根据 Dijkstra 最短路径的数据集进行训练，能够成功学习导航任务。量子部分相对于纯经典监督学习方法提高了 7% 的准确性，并且该网络在预测中有 45.3% 的重要贡献度，可以在基于离子的量子计算机上执行。结果显示，监督混合量子机器学习有助于改善自然灾害期间的紧急疏散计划。

一个监督的混合量子机器学习应用于紧急逃生路径问题的解决方案

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

We investigate the exploration of an unknown environment when no reward
function is provided. Building on the incremental exploration setting
introduced by Lim and Auer [1], we define the objective of learning the set of
$\epsilon$-optimal goal-conditioned policies attaining all states that are
incrementally reachable within $L$ steps (in expectation) from a reference
state $s_0$. In this paper, we introduce a novel model-based approach that
interleaves discovering new states from $s_0$ and improving the accuracy of a
model estimate that is used to compute goal-conditioned policies to reach newly
discovered states. The resulting algorithm, DisCo, achieves a sample complexity
scaling as $\tilde{O}(L^5 S_{L+\epsilon} \Gamma_{L+\epsilon} A \epsilon^{-2})$,
where $A$ is the number of actions, $S_{L+\epsilon}$ is the number of states
that are incrementally reachable from $s_0$ in $L+\epsilon$ steps, and
$\Gamma_{L+\epsilon}$ is the branching factor of the dynamics over such states.
This improves over the algorithm proposed in [1] in both $\epsilon$ and $L$ at
the cost of an extra $\Gamma_{L+\epsilon}$ factor, which is small in most
environments of interest. Furthermore, DisCo is the first algorithm that can
return an $\epsilon/c_{\min}$-optimal policy for any cost-sensitive
shortest-path problem defined on the $L$-reachable states with minimum cost
$c_{\min}$. Finally, we report preliminary empirical results confirming our
theoretical findings.

DisCo 算法采用新颖的基于模型的方法，通过同时发现新的状态和提高计算目标策略的模型估计的准确性来达到对未知环境进行探索的目的，并在任何有成本限制的最短路径问题中返回一个接近于最优解的策略。