[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117071-en":3,"doc-seo-117071-105":29,"detail-sidebar-cat-0-en-105":89},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117071,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","Tensor Networks for (Quantum) Machine Learning","This document explores the application of tensor networks in machine learning, particularly focusing on quantum machine learning (QML). It outlines the fundamental concepts of machine learning, including data-driven models and the role of weight tensors. The document proposes tensor networks as an ansatz for these weight tensors, and by mapping isometric tensor nodes to quantum gates, it provides a quantum ansatz. For Near-term Intermediate-Scale Quantum (NISQ) devices, a 2-qubit ansatz is required, and mid-circuit measurements and resets are introduced for qubit-efficient ordering. The document highlights an approach to QML using tensor network-inspired algorithms, which enhances the problem-solving capacity of quantum computers, especially when analyzing time series data by leveraging periodic features to reduce encoding effort. This approach is applied to estimate operating parameters from the fluttering of plane wings, specifically in the context of remote sensing and analysis of wing instabilities. Hybrid circuits with direct latent space and the QuTeNet framework are mentioned, along with a review of quantum tensor networks. Visualizations include the loss landscape of a quantum MPS classifier circuit and a surface plot illustrating a machine learning function. Ongoing projects discussed include exploring QTN applications in machine learning, such as encoding hyper-spectral images and classifying wing instabilities, joining quantum simulation and machine learning with Tensor Networks (TNs) through QuTeNet, and a PhD project on optimizing QTN machine learning models.","From classical to quantum tensor network machine learning  \na) Machine learning is a data driven approach where a model function 􀝂 is trained to map a datum 􀝔 to a desired result 􀝈  \nb) The model f(􀝔) consists of a data mapping Φ(􀝔) anda weight tensor 􀜹  \nc) Tensor networks are anansatz for 􀜹  \nd) Mapping isometric tensor nodes to quantum gates provides a quantum ansatz  \ne) For NISQ devices a 2-qubit ansatz is required  \nf) Mid-circuit measurements and resets provide a qubit efficient ordering  \nImage: Rieser et al.  \nAnalysis of wing  \nLearning  \n DLR Institute for AI Safety and Security   \nAn approach to quantum machine learning using tensor network inspired algorithms.  \nIn remote sensing, a typical  \ninstabilities  \nLoss landscape of a quantum MPS classifier circuit. Image: Lautaro Hickmann  \nWhen analysing time series, one can make use of periodic features in the data to reduce the encoding effort on the quantum computer. We investigate quantum  \napproaches inspired by tensor networks for this task. This approach significantly increases the size of problems a quantum  \nWe apply this approach to estimate operating parameters from fluttering of plane wings.  \nHybrid circuit with direct latent space  \nQuTeNet:  \nQTN Review:","cbCaik8vtWSKUzTF","https://ap.wps.com/l/cbCaik8vtWSKUzTF","pdf",1703860,1,"English","en",105,"# From classical to quantum tensor network machine learning\n## Analysis of wing instabilities\n## Loss landscape of a quantum MPS classifier circuit\n## Hybrid circuit with direct latent space\n## QuTeNet:\n## QTN Review:\n## Ongoing projects","[{\"question\":\"What is the role of tensor networks in this approach to machine learning?\",\"answer\":\"Tensor networks are proposed as an ansatz for the weight tensor in machine learning models, which maps data to desired results. Mapping these tensor nodes to quantum gates provides a specific quantum ansatz for quantum machine learning.\"},{\"question\":\"How can tensor networks benefit the analysis of time series data on quantum computers?\",\"answer\":\"By analyzing time series data with tensor network-inspired quantum approaches, periodic features in the data can be used to reduce the encoding effort on the quantum computer. This significantly increases the size of problems that can be tackled.\"},{\"question\":\"What is the practical application of this QML approach discussed in the document?\",\"answer\":\"The document mentions applying this approach to estimate operating parameters from the fluttering of plane wings, specifically in the context of remote sensing and analyzing wing instabilities.\"}]","Tensor Networks for (Quantum) Machine Learning | PDF",1785673545,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":27},"tensor-networks-for-quantum-machine-learning","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/technology/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/tensor-networks-for-quantum-machine-learning/117071/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What is the role of tensor networks in this approach to machine learning?","Question",{"text":73,"@type":74},"Tensor networks are proposed as an ansatz for the weight tensor in machine learning models, which maps data to desired results. Mapping these tensor nodes to quantum gates provides a specific quantum ansatz for quantum machine learning.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How can tensor networks benefit the analysis of time series data on quantum computers?",{"text":78,"@type":74},"By analyzing time series data with tensor network-inspired quantum approaches, periodic features in the data can be used to reduce the encoding effort on the quantum computer. This significantly increases the size of problems that can be tackled.",{"name":80,"@type":71,"acceptedAnswer":81},"What is the practical application of this QML approach discussed in the document?",{"text":82,"@type":74},"The document mentions applying this approach to estimate operating parameters from the fluttering of plane wings, specifically in the context of remote sensing and analyzing wing instabilities.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":90},[91,95,99,103,108,111,116,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":92,"show_sort_weight":93,"slug":94},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":96,"show_sort_weight":97,"slug":98},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":100,"show_sort_weight":101,"slug":102},"Exam",70,"exam",{"id":104,"doc_module":4,"doc_module_name":45,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":109,"slug":110},50,"technology",{"id":112,"doc_module":4,"doc_module_name":45,"category_name":113,"show_sort_weight":114,"slug":115},7,"Healthcare",40,"healthcare",{"id":117,"doc_module":4,"doc_module_name":45,"category_name":118,"show_sort_weight":119,"slug":120},8,"Research & Report",30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":104,"slug":136},19,"General","general"]