[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117946-en":3,"doc-seo-117946-105":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},117946,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Quantum Machine Learning in Climate Change and Sustainability - a Review","Climate change and global sustainability are critical challenges, demanding innovative solutions that combine cutting-edge technologies with scientific insight. Quantum machine learning (QML) leverages quantum computing to tackle complex problems across climate research. This work surveys literature applying QML to climate change and sustainability, reviewing methods that may speed up decarbonization in energy systems, climate data forecasting, climate monitoring, and hazardous event prediction. The review also addresses challenges, current limitations, and opportunities for future research to advance QML-enabled approaches.","Quantum Machine Learning in Climate Change and Sustainability: a Review  \nAmal Nammouchi 1 , Andreas Kassler2,3 , Andreas Theorachis4  \nKarlstad University, Computer Science Department, 65635 Karlstad, Sweden 1,2  \nKarlstad University, Electrical Engineering Department,  \n65635 Karlstad, Sweden4  \nDeggendorf Institute of Technology, Department of Applied Computer Science,  \n94469 Deggendorf, Germany3  \n[amal.nammouchi@kau.se](amal.nammouchi@kau.se1)[1](amal.nammouchi@kau.se1) , [andreas.kassler@kau.se](andreas.kassler@kau.se2)[2](andreas.kassler@kau.se2), andreas.kassler@th-deg.de3 , [andreas.theocharis@kau.se](andreas.theocharis@kau.se4)[4](andreas.theocharis@kau.se4)  \narXiv :2310 .09162v1 [ cs .LG] 13 Oct 2023  \nAbstract  \nClimate change and its impact on global sustainability are critical challenges, demanding innovative solutions that combine cutting-edge technologies and scientific insights. Quantum machine learning (QML) has emerged as a promising paradigm that harnesses the power of quantum computing to address complex problems in various domains including climate change and sustainability. In this work, we survey existing literature that applies quantum machine learning to solve climate change and sustainability-related problems. We review promising QML methodologies that have the potential to accelerate decarbonization including energy systems, climate data forecasting, climate monitoring, and hazardous events predictions. We discuss the challengesand current limitations of quantum machine learning approaches and provide an overview of potential opportunities and future work to leverage QML-based methods in the important area of climate change research.  \nIntroduction and Background  \nClimate change and global sustainability present pressing challenges, necessitating innovative solutions for managing complex distributed systems such as energy systems. While classical machine learning techniques have been applied to several problems in this area, Quantum machine learning (QML) offers a promising approach to overcome classical machine learning (ML) limitations in climate change research by leveraging quantum computing (Singh et al. 2021) . This section introduces the need for significant actions to face climate change, most importantly, by introducing new cutting-edge technologies such as quantum machine learning (QML) (Wittek 2014) to help accelerate the CO2-free transition. We present a brief overview of quantum machine learning fundamentals introducing quantum computing concepts and quantum neural network paradigms. The paper highlights the motivation and advantages of using QML to address challenges related to mitigation and adaptation of climate change applications.  \nCopyright © 2023, Association for the Advancement of Artificial Intelligence ([www.aaai.org](www.aaai.org)). All rights reserved.  \nRelation between QML and Climate Change  \nThe urgency to address climate change-related issues has reached a critical juncture, demanding immediate and innovative actions. With the planet experiencing unprecedented shifts in weather patterns, historically recorded highest temperatures and heat waves, rising sea levels, and ecological disruptions, the imperative to combat climate change has never been more evident. To effectively navigate this global challenge and expedite the transition to a sustainable future, harnessing cutting-edge technologies such as QML is an important step. Quantum machine learning presents a significant opportunity to better understand complex climate dynamics. Because QML can process and analyze intricate data sets at an unparalleled speed, better insights into climate models would be a significant advantage in enhancing predictive accuracy which allows for more informed decisionmaking. As the climate crisis accelerates, integrating quantum machine learning into our efforts not only underscores our commitment to innovative problem-solving but also offers a powerful tool to drive the rapid change","cbCaisHGt8TtaX8q","https://ap.wps.com/l/cbCaisHGt8TtaX8q","pdf",303670,1,9,"English","en",105,"# Abstract\n# Introduction and Background\n# Relation between QML and Climate Change\n# A brief Overview of Quantum Machine Learning Fundamentals\n## Quantum computing basics: qubits and Hilbert space\n## Superposition and entanglement","[{\"question\":\"What problems does the review focus on in climate change and sustainability using QML?\",\"answer\":\"The review focuses on applying quantum machine learning to climate change and sustainability tasks, particularly energy systems, climate data forecasting, climate monitoring, and hazardous event prediction.\"},{\"question\":\"How does QML aim to improve climate-related decision-making?\",\"answer\":\"The document argues that QML can analyze intricate climate data sets faster than classical approaches, potentially improving predictive accuracy and enabling more informed decision-making.\"},{\"question\":\"What are the key quantum computing concepts introduced as fundamentals for QML?\",\"answer\":\"The paper introduces qubits and Hilbert space, then explains superposition and entanglement as core principles behind quantum computation and quantum machine learning approaches.\"}]","Quantum Machine Learning in Climate Change and Sustainability - 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