[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118955-en":3,"doc-seo-118955-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},118955,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Fitting a Collider in a Quantum Computer - Tackling the Challenges of Quantum Machine Learning for Big Datasets - Abstract","Current quantum systems face notable constraints when processing large, high-dimensional datasets common in high energy physics. This paper investigates feature and data prototype selection methods to address the challenge. A grid search is used to train and benchmark quantum machine learning models against classical shallow methods on both reduced and complete datasets. Results show quantum algorithm performance can be comparable to classical approaches even for large datasets, with Sequential Backward Selection outperforming in specific cases but being more unstable. Variability is linked to discrete variables, motivating Principal Component analysis–transformed data for quantum machine learning in the HEP setting.","arXiv :2211 .03233v4 [hep-ph] 6 Dec 2023  \nFitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets  \nMiguel Caçador Peixoto1 , Nuno Filipe Castro1, 2 , Miguel Crispim Romão1, 3  \n,  \nMaria Gabriela Jordão Oliveira1 and Inês Ochoa4  \n1 LIP – Laboratório de Instrumentação e Física Experimental de Partículas, Escola de Ciências, Campus de Gualtar, Universidade do Minho, 4701-057 Braga, Portugal  \n2 Departamento de Física, Escola de Ciências, Campus de Gualtar, Universidade do Minho, 4701-057 Braga, Portugal  \n3 Department of Physics and Astronomy, University of Southampton, SO17 1BJ Southampton, United Kingdom  \n4 LIP – Laboratório de Instrumentação e Física Experimental de Partículas, Av. Prof. Gama Pinto, 2, 1649-003 Lisboa, Portugal  \nDecember 8, 2023  \nAbstract  \nCurrent quantum systems have significant limitations affecting the processing of large datasets with high dimensionality, typical of high energy physics. In the present paper, feature and data prototype selection techniques were studied to tackle this challenge. A grid search was performed and quantum machine learning models were trained and benchmarked against classical shallow machine learning methods, trained both in the reduced and the complete datasets. The performance of the quantum algorithms was found to be comparable to the classical ones, even when using large datasets. Sequential Backward Selection and Principal Component Analysis techniques were used for feature’s selection and while the former can produce the better quantum machine learning models in specific cases, it is more unstable. Additionally, we show that such variability in the results is caused by the use of discrete variables, highlighting the suitability of Principal Component analysis transformed data for quantum machine learning applications in the high energy physics context.  \n1 Introduction  \nThe Standard Model of Particle Physics (SM) provides a remarkable description of the fundamental constituents of matter and their interactions, being in excellent agreement with the collider data accumulated so far. Nonetheless, there are still important open questions, unaddressed by the SM, such as gravity, dark matter, dark energy, or the matter-antimatter asymmetry in the universe [1], motivating a comprehensive search program for new physics phenomena beyond the SM (BSM) at the Large Hadron Collider (LHC) at CERN.  \nThe search for BSM phenomena at colliders poses specific challenges in data processing and analysis, given the extremely large datasets involved and the low signal to background ratios expected. In this context, the analysis of the collision data obtained by the LHC experiments often relies on machine learning (ML), a field in computer science that can harness large amounts of data to train generalizable algorithms for a variety of applications [2, 3], such as classification tasks. These techniques have shown an outstanding ability to find correlations in high-dimensional parameter spaces to discriminate between  \npotential signal and background processes. They are known to scale with data, and usually rely on a large number of learnable parameters to achieve their remarkable performance.  \nIn order to train these large models, classical1 machine learning (CML) takes advantage of hardware accelerators, such as graphics processing units (GPUs), for efficient, parallel, and fast matrix multiplications. On the other hand, a new class of hardware is becoming available, with the advent of noisy intermediate-scale quantum (NISQ) computing devices. This accelerated the development of new quantum algorithms targeted at exploiting the capacity and feasibility of this new technology for ML applications.  \nQuantum machine learning (QML) is an emerging research field aiming to use quantum circuits to tackle ML tasks. One of the motivations for using this new technology in high energy physics (HEP) relates to the intrinsic properties of quan","cbCaisEGE4AUzPaa","https://ap.wps.com/l/cbCaisEGE4AUzPaa","pdf",869002,1,30,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does the paper address in high energy physics?\",\"answer\":\"It addresses limitations of current quantum systems when processing large, high-dimensional datasets typical of collider experiments.\"},{\"question\":\"How did the authors compare quantum and classical machine learning approaches?\",\"answer\":\"They trained quantum machine learning models and benchmarked them against classical shallow machine learning methods using both reduced and complete datasets, with a grid search for selection.\"},{\"question\":\"Why can quantum model performance vary, and what method helps?\",\"answer\":\"The paper attributes variability to the use of discrete variables; it finds that Principal Component analysis–transformed data is more suitable for quantum machine learning in this context.\"}]","Fitting a Collider in a Quantum Computer - Tackling the Challenges of Quantum Machine Learning for Big Datasets - Abstract | PDF",1785721191,76,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"fitting-a-collider-in-a-quantum-computer-tackling-the-challenges-of-quantum-machine-learning-for-big-datasets-abstract","",{"@graph":36,"@context":86},[37,54,69],{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/fitting-a-collider-in-a-quantum-computer-tackling-the-challenges-of-quantum-machine-learning-for-big-datasets-abstract/118955/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the paper address in high energy physics?","Question",{"text":76,"@type":77},"It addresses limitations of current quantum systems when processing large, high-dimensional datasets typical of collider experiments.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How did the authors compare quantum and classical machine learning approaches?",{"text":81,"@type":77},"They trained quantum machine learning models and benchmarked them against classical shallow machine learning methods using both reduced and complete datasets, with a grid search for selection.",{"name":83,"@type":74,"acceptedAnswer":84},"Why can quantum model performance vary, and what method helps?",{"text":85,"@type":77},"The paper attributes variability to the use of discrete variables; it finds that Principal Component analysis–transformed data is more suitable for quantum machine learning in this context.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":21,"slug":122},"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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]