[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81540-en":3,"doc-seo-81540-105":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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},81540,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1782698725881665579",8,"Research & Report","Is Data Efficient Learning Feasible with Quantum Models","Investigating generalization advantages in quantum machine learning (QML) often overlooks how dataset characteristics affect performance. This work introduces a data-generation tool that builds semi-artificial classical datasets tailored to quantum kernel methods (QKMs). Experiments on fully classical datasets show that QKMs can reach comparable error using fewer training examples than classical kernels. The study also transfers a spectral-bias generalization metric to the QML setting, finding strong agreement with empirical results and enabling controlled exploration of dataset complexity for principled quantum advantage.","Is data-efficient learning feasible with quantum  \nmodels?  \nAlona Sakhnenko∗†, Christian B. Mendl†, Jeanette M. Lorenz∗‡  \n∗ Fraunhofer Institute for Cognitive Systems IKS, Munich, Germany  \n†Technical University of Munich, Munich, Germany  \n‡Ludwig-Maximilian University, Munich, Germany  \narXiv :2508 . 19437v2 [ quant-ph] 10 Jul 2026  \nAbstract—The importance of analyzing nontrivial datasets when testing quantum machine learning (QML) models is becoming increasingly prominent in literature, yet a cohesive framework for understanding dataset characteristics remains elusive. In this work, we introduce a data-generation tool that allows to construct semi-artificial classical datasets tailored to quantum kernel methods (QKMs). Using this tool, we show that on fully classical datasets, QKMs can require fewer training examples than classical kernels to reach comparable error, providing clear empirical evidence that data-efficient learning with quantum models is possible on classical data. The main motivation behind this tool is to enable the community to perform controlled studies to figure out which dataset characteristics are particularly fitting for quantum models by tuning the data-generation procedure. Additionally, our study brings a spectral-bias–based generalization metric from classical kernel methods into the QML domain and show that the performance predicted by this metric aligns closely with empirical results, thereby closing an important gap between theory and practice in QML generalization. Our tool paves the way for a systematic exploration of dataset complexities. This could potentially contribute to a deeper understanding of the generalization benefits of QKM models (extendable to abroader family of QML models) and shifts the search for quantum advantage from ad hoc benchmark hunting to principled dataset design.  \nI. INTRODUCTION  \nAside from the speed up opportunities offered by larger quantum computing systems, a potential advantage of quantum machine learning models lies in their generalization benefits over classical models. These efforts often aim to be datasetagnostic, but this frequently results in unpredictable empirical performance. In light of the \"no free lunch\" theorem, which asserts that no single model excels across all datasets, it is sensible to focus more on the qualities of the datasets in conjunction with the capabilities of the models. Recent literature [1, 2] emphasizes the significance of exploring nontrivial datasets for accessing advantage with QML models, which hints at the importance of shifting away from the datasetagnostic approaches commonly used in the field.  \nThis leads to an important questions: (i) what are the nontrivial datasets? (ii) how to characterize the datasets that are trivial for QML models but hard for classical learners? Within the QML community, various approaches have been explored to identify which dataset properties can lead to an advantage when utilizing QML models [3–6] . However, these ideas remain fragmented and lack a unifying framework. One recurring observation across various studies, including  \nthose in classical machine learning, is that the size of the dataset plays a significant role in determining its complexity. Meaning, that larger datasets tend to provide models with more informative patterns, and therefore improving their performance and generalization ability, while smaller (yet complex) datasets remain challenging. This raises a question: is it feasible fora QML model be more data-efficient than their classical counterparts and if so, when?  \nTo address the question, selecting the right analytical tool is key. Making sweeping statements about all QML models is unrealistic, but examining a broad family of models can yield valuable insights. Kernel Methods (KMs) provide a solid theoretical basis for understanding generalization and recent work established a connection [7, 8] between KMsand Deep Learning Models (DLMs), indicating that insights from","cbCaiv08LAW6pwGZ","https://ap.wps.com/l/cbCaiv08LAW6pwGZ","pdf",914205,2,1,10,"English","en",105,"# Introduction\n## Motivation and problem statement\n## Data and model efficiency question\n## Kernel methods as analytical foundation\n## Contributions and paper structure","[{\"question\":\"What does the paper investigate about quantum models?\",\"answer\":\"It examines whether and when QML, specifically quantum kernel methods (QKMs), can be more data-efficient than classical kernel methods, requiring fewer training examples for comparable error.\"},{\"question\":\"How does the proposed tool support the study?\",\"answer\":\"The paper introduces a data-generation tool that constructs semi-artificial classical datasets tailored to quantum kernel methods, allowing controlled experiments on dataset characteristics.\"},{\"question\":\"What does the paper conclude from experiments on classical datasets?\",\"answer\":\"Experiments show that QKMs can achieve comparable generalization error with fewer training examples than standard classical kernels, indicating data-efficient learning is feasible on fully classical data.\"}]",1784174154,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"is-data-efficient-learning-feasible-with-quantum-models","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/is-data-efficient-learning-feasible-with-quantum-models/81540/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-25","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the paper investigate about quantum models?","Question",{"text":75,"@type":76},"It examines whether and when QML, specifically quantum kernel methods (QKMs), can be more data-efficient than classical kernel methods, requiring fewer training examples for comparable error.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed tool support the study?",{"text":80,"@type":76},"The paper introduces a data-generation tool that constructs semi-artificial classical datasets tailored to quantum kernel methods, allowing controlled experiments on dataset characteristics.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the paper conclude from experiments on classical datasets?",{"text":84,"@type":76},"Experiments show that QKMs can achieve comparable generalization error with fewer training examples than standard classical kernels, indicating data-efficient learning is feasible on fully classical data.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":22,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]