[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124637-en":3,"doc-seo-124637-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124637,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",6,"Technology","A supervised hybrid quantum machine learning solution to the emergency escape routing problem","This work investigates supervised hybrid quantum machine learning for optimizing car emergency evacuation plans during earthquakes. The evacuation task is modeled as a shortest-path problem on an uncertain, dynamically evolving city graph where road conditions change after earthquake damage and traffic congestion forms near exit points. A hybrid supervised approach is proposed using a feature-wise linear modulation (FiLM) quantum neural network in parallel with a classical FiLM network to imitate node-wise Dijkstra on a deterministic dynamic graph. The quantum-enhanced model improves accuracy by 7%, with the quantum component contributing 45.3% to prediction, and is designed for execution on an ion-based quantum computer.","arXiv :2307 . 15682v1 [ quant-ph] 28 Jul 2023  \nA supervised hybrid quantum machine learning solution to the emergency escape routing problem  \nNathan Haboury, 1 Mo Kordzanganeh, 1 Sebastian Schmitt,2 Ayush Joshi, 1 Igor Tokarev, 1 Lukas Abdallah, 1 Andrii Kurkin, 1 Basil Kyriacou, 1 and Alexey Melnikov 1  \n1 Terra Quantum AG, Kornhausstrasse 25, 9000 St. Gallen, Switzerland  \n2 Honda Research Institute Europe GmbH, Carl-Legien-Straße 30, 63073 Offenbach am Main, Germany  \nManaging 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.  \nI. INTRODUCTION  \nNatural disasters like earthquakes can result in devastating effects, including loss of life and property damage [1, 2] . Emergency evacuation procedures are critical in such scenarios, and optimizing these procedures is essential for saving lives [3] . One of the most common modes of transportation during emergency evacuations is cars, and it is important to ensure that the routes taken by these vehicles are safe and efficient. The standard road network, however, can be heavily affected by earthquakes through dynamic effects like land deformation, collapsing buildings or debris [4–8] . Such effects can be modelled and applied in traffic simulation using sophisticated probabilistic models [9, 10] . Using such models, a complete solution for medical rescue, including route planning, which considers collapsed buildings, was proposed in [11] . This study, however, excludes the consideration of traffic capability or capacity due to the challenges involved in obtaining post-earthquake travel data. The central challenge of optimization-based methods [12–16] is the complexity of large-scale problems, in particular on evolving (dynamic) networks [17] . Dijkstra’s algorithm effectively finds the optimal path on a static graph, and while algorithms like A* [18] might offer faster alternatives, Dijkstra’s is the only one with an optimality guarantee [19] . However, this algorithm struggles to find the shortest path in an evolving and uncertain situation. Therefore, it is necessary to adapt the  \nalgorithm for graphs with dynamically changing edge weights by rerunning it every time the graph is modified. We refer to this as the node-wise Dijkstra’s algorithm. Furthermore, Dijkstra’s algorithm (node-wise or otherwise) requires global knowledge of the graph. This would require ","cbCaioDP6x1W7Kux","https://ap.wps.com/l/cbCaioDP6x1W7Kux","pdf",2998220,1,15,"English","en",105,"# Introduction\n## Problem setup and motivation\n## Limitations of optimization and global information\n## Hybrid quantum machine learning approach\n# Supervised learning framework\n## Training data and node-wise Dijkstra decisions\n## Dynamic environment and traffic accumulation\n# Model description and results\n## Hybrid FiLM quantum-classical architecture\n## Accuracy gains and quantum contribution\n## Potential QPU execution","[{\"question\":\"How is the emergency evacuation routing problem modeled in the study?\",\"answer\":\"The city is represented as an uncertain, dynamically evolving graph after earthquake damage, and residents evacuate by reaching exit points where traffic congestion occurs. The task is formulated as a shortest-path problem on this evolving map.\"},{\"question\":\"What is the core idea of the proposed hybrid quantum machine learning method?\",\"answer\":\"The method uses a hybrid supervised learning setup with a quantum FiLM neural network running in parallel to a classical FiLM network. Together they mimic node-wise Dijkstra’s path quality while relying only on local information.\"},{\"question\":\"What performance improvement does the hybrid quantum model achieve?\",\"answer\":\"Compared with purely classical supervised learning, the hybrid quantum model improves accuracy by 7%. The quantum part contributes 45.3% to the prediction.\"}]","A supervised hybrid quantum machine learning solution to the emergency escape routing problem | PDF",1785893450,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-supervised-hybrid-quantum-machine-learning-solution-to-the-emergency-escape-routing-problem","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-supervised-hybrid-quantum-machine-learning-solution-to-the-emergency-escape-routing-problem/124637/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How is the emergency evacuation routing problem modeled in the study?","Question",{"text":75,"@type":76},"The city is represented as an uncertain, dynamically evolving graph after earthquake damage, and residents evacuate by reaching exit points where traffic congestion occurs. The task is formulated as a shortest-path problem on this evolving map.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the core idea of the proposed hybrid quantum machine learning method?",{"text":80,"@type":76},"The method uses a hybrid supervised learning setup with a quantum FiLM neural network running in parallel to a classical FiLM network. Together they mimic node-wise Dijkstra’s path quality while relying only on local information.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance improvement does the hybrid quantum model achieve?",{"text":84,"@type":76},"Compared with purely classical supervised learning, the hybrid quantum model improves accuracy by 7%. 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