[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119962-en":3,"doc-seo-119962-105":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":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},119962,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Quantum Machine Learning for data analysis at LHCb","Machine learning algorithms play a key role in high energy physics, especially in classifying hadronic jets produced at the Large Hadron Collider. With Run 3 bringing higher luminosity and more demanding computational needs, quantum machine learning is presented as a promising direction. The work surveys quantum learning models for LHCb jet tagging, focusing on b vs. b̄ and b vs. c classification, and discusses developments in using entanglement entropy measurements between qubits to extract new information from jet-event data.","IL NUOVO CIMENTO 47 C (2024) 127 DOI 10.1393/ncc/i2024-24127-7  \nColloquia: IFAE 2023  \nQuantum Machine Learning for data analysis at LHCb ( ∗ )  \nA. Gianelle ( 1 ) , D. Lucchesi ( 1 )(2 ) , S. Monaco (2 ) , D. Nicotra (3 )(4 ) , L. Sestini ( 1 )(2 ) and D. Zuliani ( 1 )(2 )  \n(1 ) INFN, Sezione di Padova - Padova, Italy  \n(2 ) Dipartimento di Fisica, Universita` di Padova - Padova, Italy  \n(3 ) Nikhef National Institute for Subatomic Physics - Amsterdam, The Netherlands  \n(4 ) University of Maastricht - Maastricht, The Netherlands  \nreceived 13 February 2024  \nSummary.— Machine learning (ML) algorithms have now become crucial in the field of High Energy Physics (HEP) . An area where the application of such algorithms has proven particularly beneficial is the classification of hadronic jets produced at the Large Hadron Collider (LHC) . Considering the complexity of the tasks in this field and the impending Run 3 data at higher luminosity, it is evident that a step-up in computational power is imperative. One potential candidate comes from the intersection between Quantum Computing (QC) and ML. Quantum Machine Learning (QML) algorithms leverage the intrinsic properties of QC, such as superposition and entanglement, to achieve better performance compared to their classical counterparts. This work provides an overview of these new learning models, with a focus in HEP. Specifically, we present studies of QML applications for the classification of jets produced (b vs. ¯b and b vs. c) at the LHCb experiment. Notably, we discuss recent developments in measuring entanglement entropy between qubits to gain new insights from the jet events data.  \n1.– Heavy Jets at LHCb  \nJets are narrow cones of particles resulting from the strong interactions between quarks in proton-proton collisions. These collisions, occurring at high energies, initiate a cascade of processes leading to the formation of collimated streams of particles.  \nThe LHCb experiment stands out as a fitting tool for the study of jet formation, particularly those originating from heavy quarks. The unique geometry of LHCb in the forward region provides an advantageous and unique point of view for capturing events where heavy quark jets are relevant.  \n(∗ ) IFAE 2023- “Poster” session  \nCreative Commons Attribution 4.0 License ([https://creativecommons.org/licenses/by/4.0](https://creativecommons.org/licenses/by/4.0)) 1  \n2 A. GIANELLE et al.  \nMachine learning (ML) applications currently leverage the intricate structure and correlations among all reconstructed particles to accurately identify the flavor of the originating quark. These ’black box’ implementations provide an ideal platform for this task, as finding an analytical algorithm that encompasses all detected particles is impossible.  \nThe complexity of this task, prompts the introduction of a new computing framework in order to achieve a better performance. Quantum Machine Learning (QML) models emerge as a promising candidate. Given the intricate nature of jet formation and the wealth of information embedded in each collision event, quantum models can amplify theefficiency and precision of jet classification.  \n2.– Quantum Computing for Jets flavour identification  \nQuantum computers possess the capability to accurately evolve a quantum system comprising multiple quantum bits (qubits), gaining a computational advantage by harnessing quantum mechanical phenomena, including entanglement.  \nOperationally, we gain the ability to manipulate the wavefunction of the quantum system—a vector in the Hilbert space. Unlike a classical computer, which may be able to simulate the evolution of such a system up to ∼ 20 qubits, an actual quantum implementation allows us to scale up to the order of hundreds and even thousands of qubits. This scalability enables us to explore a significantly larger solution space, providing the potential to capture more intricate relationships within the embedded input information.  \nQML derives its fundamental concept","cbCaiuvEIZo5QZQn","https://ap.wps.com/l/cbCaiuvEIZo5QZQn","pdf",244857,1,4,"English","en",105,"# Summary\n# Heavy Jets at LHCb\n# Quantum Computing for Jets flavour identification\n# Jets classification\n## b vs. b̄ jet tagging\n## b vs. c jet tagging\n# Entanglement formation in a Quantum Circuit","[{\"question\":\"Why is jet classification important in LHC physics, and what challenge does Run 3 add?\",\"answer\":\"Jet classification helps identify hadronic jets in high energy collisions. Run 3 increases luminosity, making the computational demand higher and requiring more powerful approaches.\"},{\"question\":\"How does quantum machine learning (QML) aim to improve over classical machine learning for LHCb jet tasks?\",\"answer\":\"QML uses quantum computing properties such as superposition and entanglement while drawing on concepts from classical ML. This can, in principle, enhance efficiency and precision for jet tagging.\"},{\"question\":\"What are the main jet tagging studies discussed for LHCb in this document?\",\"answer\":\"The document covers QML applications for b vs. b̄ jet tagging and b vs. c jet tagging, including comparisons with classical methods and discussion of expected improvements after hardware and software upgrades.\"}]","Quantum Machine Learning for data analysis at LHCb | PDF",1785727239,10,{"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},"quantum-machine-learning-for-data-analysis-at-lhcb","",{"@graph":36,"@context":85},[37,53,68],{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/quantum-machine-learning-for-data-analysis-at-lhcb/119962/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"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","2026-08-06","2026-08-03",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},"Why is jet classification important in LHC physics, and what challenge does Run 3 add?","Question",{"text":75,"@type":76},"Jet classification helps identify hadronic jets in high energy collisions. Run 3 increases luminosity, making the computational demand higher and requiring more powerful approaches.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does quantum machine learning (QML) aim to improve over classical machine learning for LHCb jet tasks?",{"text":80,"@type":76},"QML uses quantum computing properties such as superposition and entanglement while drawing on concepts from classical ML. This can, in principle, enhance efficiency and precision for jet tagging.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main jet tagging studies discussed for LHCb in this document?",{"text":84,"@type":76},"The document covers QML applications for b vs. b̄ jet tagging and b vs. c jet tagging, including comparisons with classical methods and discussion of expected improvements after hardware and software upgrades.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":21,"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":29,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":29,"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"]