[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290786-105":59,"doc-detail-290786-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","potential-of-quantum-scientific-machine-learning-applied-to-weather-modelling","Potential of quantum scientific machine learning applied to weather modelling","","Quantum scientific machine learning is explored as an approach to address the computational and predictive challenges of weather modelling. The study uses parameterised quantum circuits as learning models, comparing supervised learning from weather data with physics-informed solving of atmospheric dynamics equations. A quantum model is trained to reproduce global stream function dynamics at 4° resolution, supported by classical and quantum architecture choices. The barotropic vorticity equation (BVE) is then solved using differentiable quantum circuits under suitable boundary conditions, enabling accurate prediction of future dynamics from an artificial initial state, with remaining challenges acknowledged.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/potential-of-quantum-scientific-machine-learning-applied-to-weather-modelling/290786/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/potential-of-quantum-scientific-machine-learning-applied-to-weather-modelling/290786.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-07","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How does the paper use parameterised quantum circuits for weather modelling?","Question",{"text":113,"@type":114},"It treats parameterised quantum circuits as machine learning models and studies two paradigms: supervised learning from weather data and physics-informed solving of the atmospheric dynamics equations.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What is the barotropic vorticity equation (BVE) used for in the study?",{"text":118,"@type":114},"The BVE is introduced as the atmospheric model, described as a third-order PDE in its stream function formulation, and then solved using differentiable quantum circuits under appropriate boundary conditions.",{"name":120,"@type":111,"acceptedAnswer":121},"What kind of prediction performance is demonstrated?",{"text":122,"@type":114},"The trained quantum model is used to predict unseen future dynamics with high accuracy, demonstrated for both global stream function dynamics at 4° resolution and BVE evolution from an artificial initial weather state.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290786,1789642567,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":36,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","arXiv :2404 .08737v1 [ quant-ph] 12 Apr 2024  \nPotential of quantum scientific machine learning applied to weather modelling  \nBen Jaderberg, Antonio A. Gentile, Atiyo Ghosh, and Vincent E. Elfving  \nPASQAL, 7 rue L´eonard de Vinci, 91300 Massy, France  \nCaitlin Jones and Davide Vodola  \nBASF Digital solutions, Next Generation Computing,  \nPfalzgrafenstr. 1, 67061 Ludwigshafen, Germany  \nJohn Manobianco  \nBASF Corporation, APM/DS, 2 TW Alexander Drive,  \nResearch Triangle Park, North Carolina 27709, USA  \nHorst Weiss  \nBASF SE, Next Generation Computing, Pfalzgrafenstr. 1, 67061 Ludwigshafen, Germany  \nIn this work we explore how quantum scientific machine learning can be used to tackle the challenge of weather modelling. Using parameterised quantum circuits as machine learning models, we consider two paradigms: supervised learning from weather data and physics-informed solving of the underlying equations of atmospheric dynamics. In the first case, we demonstrate how a quantum model can be trained to accurately reproduce real-world global stream function dynamics at a resolution of 4° . We detail a number of problem-specific classical and quantum architecture choices used to achieve this result. Subsequently, we introduce the barotropic vorticity equation (BVE) as our model of the atmosphere, which is a 3rd order partial differential equation (PDE) in its stream function formulation. Using the differentiable quantum circuits algorithm, we successfully solve the BVE under appropriate boundary conditions and use the trained model to predict unseen future dynamics to high accuracy given an artificial initial weather state. Whilst challenges remain, our results mark an advancement in terms of the complexity of PDEs solved with quantum scientific machine learning.  \nI. INTRODUCTION  \nThe ability to forecast the weather is of great importance to many areas of modern society including agriculture, transportation and the mitigation of extreme weather events. Accurate prediction of near and medium term weather remains difficult owing to the complex, nonlinear and dynamic behaviour of atmospheric physics. Simulating such processes incurs a staggering computational cost. For example, the Integrated Forecasting System (IFS) used by the European Centre for MediumRange Weather Forecasts (ECMWF) computes numerical simulations on a 1-million CPU core supercomputer every 6 hours [1, 2] . These state-of-the-art spectral element methods (SEMs) involve computations that alternate between real-space and spectral representations at every time step [3, 4], as discretised in a semi-Lagrangian scheme [5] where the grid computation often relies upon a finite element method [6] . However, significant open problems remain with these methods, including maintaining an appropriate grid resolution, avoiding numerical instabilities and bottlenecks imposed by data transfer when transposing from real to spectral spaces [7] . This ultimately leads to error in predictions [8], particularly at time scales beyond 1 day [9, 10] . Crucially, it is not clear yet how much further these numerical methods will need to scale to solve these issues, nor if the required computational power to do so will be available. Therefore,  \nmethods which might offer reductions in computational cost or improved long-time accuracy are highly desirable.  \nRecently, machine learning (ML) has demonstrated great potential as a solution. ML methods differ from traditional methods as they make predictions based on weather patterns that are present in the data sets used to train them. Rather than repeatedly running expensive simulations, a larger one-off cost is invested in the training of a ML model, with the idea that after training the model can make predictions at low cost. When large amounts of historical data are used as a data set [11], the predictions of such models may even outperform IFSin accuracy [12, 13] . However, open questions remain around whether such models generalise to regi","cbCainWilKZqNrQV","https://ap.wps.com/l/cbCainWilKZqNrQV","pdf",5051605,16,"English","# Introduction\n## Weather forecasting challenges and computational costs\n## Machine learning approaches and generalisation limits\n## Scientific machine learning and physics-informed learning\n## Quantum machine learning motivation and algorithmic advantages","[{\"question\":\"How does the paper use parameterised quantum circuits for weather modelling?\",\"answer\":\"It treats parameterised quantum circuits as machine learning models and studies two paradigms: supervised learning from weather data and physics-informed solving of the atmospheric dynamics equations.\"},{\"question\":\"What is the barotropic vorticity equation (BVE) used for in the study?\",\"answer\":\"The BVE is introduced as the atmospheric model, described as a third-order PDE in its stream function formulation, and then solved using differentiable quantum circuits under appropriate boundary conditions.\"},{\"question\":\"What kind of prediction performance is demonstrated?\",\"answer\":\"The trained quantum model is used to predict unseen future dynamics with high accuracy, demonstrated for both global stream function dynamics at 4° resolution and BVE evolution from an artificial initial weather state.\"}]","Potential of quantum scientific machine learning applied to weather modelling | PDF"]