[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-291225-105":59,"doc-detail-291225-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","lartpc-hit-based-topology-classification-with-quantum-machine-learning-and-symmetry-considerations","LArTPC hit-based topology classification with quantum machine learning and symmetry considerations","","A new quantum machine learning approach separates tracklike and showerlike event topologies in liquid argon time projection chamber (LArTPC) neutrino experiments by converting detector energy deposits into 2D images for pixel-level classification. Using simulated MicroBooNE data and a custom dataset, the method learns topology from local image patches with convolution-based and quantum-enhanced quanvolutional neural networks. Symmetry extensions beyond translation include rotational symmetry, showing mixed gains: quantum models can outperform classical ones at similar parameter counts, though larger classical models with far more parameters perform better.",{"@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/lartpc-hit-based-topology-classification-with-quantum-machine-learning-and-symmetry-considerations/291225/",{"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/lartpc-hit-based-topology-classification-with-quantum-machine-learning-and-symmetry-considerations/291225.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How are LArTPC events represented for the proposed topology classification?","Question",{"text":113,"@type":114},"Event energy deposits are treated as 2D images derived from detector readout and calorimetric information, enabling pixel-level analysis.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What learning models are used to classify tracklike versus showerlike topologies?",{"text":118,"@type":114},"Convolution-based learning models are used, including quantum-enhanced quanvolutional neural network architectures.",{"name":120,"@type":111,"acceptedAnswer":121},"How does incorporating rotational symmetry affect performance?",{"text":122,"@type":114},"Rotational symmetry is beneficial only in a small number of cases, and its impact still needs further exploration.","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},291225,1789643801,{"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":145,"preview_page_count":4},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","LArTPC hit-based topology classification with quantum machine learning  \nand symmetry considerations  \nCallum Duffy, 1 Marcin Jastrzebski, 1 Stefano Vergani, 1,* Leigh H. Whitehead,2 Ryan Cross,3 Andrew Blake ,4  \nSarah Malik, 1 and John Marshall3  \n1Department of Physics and Astronomy, University College London, London, United Kingdom 2Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom  \n3Department of Physics, University of Warwick, Coventry, United Kingdom  \n4Physics Department, Lancaster University, Lancaster, United Kingdom  \n(Received 22 May 2025; accepted 2 September 2025; published 12 November 2025)  \nWe present a new approach to separate tracklike and showerlike topologies in liquid argon time projection chamber (LArTPC) experiments for neutrino physics using quantum machine learning. Effective reconstruction of neutrino events in LArTPCs requires accurate and granular information about the energy deposited in the detector. These energy deposits can be viewed as 2D images. Simulated data from the MicroBooNE experiment and a simple custom dataset are used to perform pixel-level classification of the underlying particle topology. Images of the events have been studied by creating small patches around each pixel to characterize its topology based on its immediate neighborhood. This classification is achieved using convolution-based learning models, including quantum-enhanced architectures known as quanvolutional neural networks. The quanvolutional networks are extended to symmetries beyond translation. Rotational symmetry has been incorporated into a subset of the models. This study demonstrates that quantum-enhanced models perform better than their classical counterparts with a comparable number of parameters, but are outperformed by classical models with two orders of magnitude more parameters. The inclusion of rotation symmetry appears beneficial only in a small number of cases and remains to be explored further. Possible future use of quantum machine learning in the reconstruction phase is discussed, with emphasis on future LArTPC experiments such as Deep Underground Neutrino Experiment (DUNE) -far detector (FD) .  \nDOI: 10.1103/byy5-zk73  \nI. INTRODUCTION  \nLiquid argon time projection chamber (LArTPC) detectors are commonly used in neutrino physics and represent the current state of the art of in the field. Although there are differences in the design, they normally comprise a cryostat filled with high-purity liquid argon kept at 87 K, a system to apply an internal electric field, a readout mechanism made with wires, and a photon detection system. In LArTPCs, charged particles interact with argon nuclei, liberating electrons through ionization. These electrons drift in an electric field toward readout wires, where they are detected. Combining the wire number with the time of detection, especially if the scintillation light is used as a trigger, gives spatial and time information in a 2D view.  \n*Contact author: [s.vergani@ucl.ac.uk](s.vergani@ucl.ac.uk)  \nPublished by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.  \nCalorimetric information is added to each 2D view. The interactions of neutrinos with the argon nuclei are inferred by the presence of charged particles produced in the interaction emerging from a common vertex. An example of a 2D image produced by a neutrino interaction in a LArTPC experiment is shown in Fig. 1. The Micro Booster Neutrino Experiment (MicroBooNE) [1] employed an 85 metric tons LArTPC detector to perform precision physics measurements and collected five years of data from 2016 to 2021. The experiment’s primary scientific goals were to solve the puzzle of the MiniBooNE low energy excess [2], to measure different neutrino cross sections, and to search for astrophysical p","cbCaioGG755eedX5","https://ap.wps.com/l/cbCaioGG755eedX5","pdf",2031172,15,"English","# Introduction\n## LArTPC reconstruction context\n## 2D image representation of energy deposits\n# Proposed method\n## Pixel-level classification from local patches\n## Convolution-based learning and quantum-enhanced models\n## Symmetry extensions beyond translation","[{\"question\":\"How are LArTPC events represented for the proposed topology classification?\",\"answer\":\"Event energy deposits are treated as 2D images derived from detector readout and calorimetric information, enabling pixel-level analysis.\"},{\"question\":\"What learning models are used to classify tracklike versus showerlike topologies?\",\"answer\":\"Convolution-based learning models are used, including quantum-enhanced quanvolutional neural network architectures.\"},{\"question\":\"How does incorporating rotational symmetry affect performance?\",\"answer\":\"Rotational symmetry is beneficial only in a small number of cases, and its impact still needs further exploration.\"}]","LArTPC hit-based topology classification with quantum machine learning and symmetry considerations | PDF",38]