[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118936-en":3,"doc-seo-118936-105":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":4,"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},118936,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Quantum-Assisted Simulation - A Framework for Developing Machine Learning Models in Quantum Computing","Machine Learning models learn from historical data to classify new, unseen inputs, yet traditional computing often cannot process Big Data within practical time limits. Quantum Computing offers a different information-processing paradigm, enabling quantum algorithms to process classical data potentially exponentially faster. By mapping Quantum Machine Learning (QML) algorithms into the quantum-mechanical domain, the work targets faster data processing, lower resource requirements, and improved accuracy and efficiency. It surveys QC and ML, reviews QML algorithms and hardware limits, provides a simulation setup procedure, then compares classical and quantum approaches using a quantum simulator, highlighting performance differences.","arXiv :2311 . 10363v2 [ quant-ph] 31 Oct 2024  \nQuantum-Assisted Simulation: A Framework for Developing Machine Learning Models in Quantum Computing  \nMinati Rath∗1 and Hema Date2  \n1 Department of Analytics and Decision Science, IIM Mumbai, India,  \n2 Department of Analytics and Decision Science, IIM Mumbai, India  \nNovember 1, 2024  \nAbstract  \nMachine Learning (ML) models are trained using historical data to classify new, unseen data.  \nHowever, traditional computing resources often struggle to handle the immense amount of data, commonly known as Big Data, within a reasonable time frame. Quantum Computing (QC) provides a novel approach to information processing, offering the potential to process classical data exponentially faster than classical computing through quantum algorithms. By mapping Quantum Machine Learning (QML) algorithms into the quantum mechanical domain, we can potentially achieve exponential improvements in data processing speed, reduced resource requirements, and enhanced accuracy and efficiency.  \nIn this article, we delve into both the QC and ML fields, exploring the interplay of ideas between them, as well as the current capabilities and limitations of hardware. We investigate the history of quantum computing, examine existing QML algorithms, and present a simplified procedure for setting up simulations of QML algorithms, making it accessible and understandable for readers.  \nFurthermore, we conduct simulations on a dataset using both traditional machine learning and quantum machine learning approaches. We then compare their respective performances by utilizing a quantum simulator.  \nQuantum Machine Learning, QML, Quantum Computing, Machine Learning, Artificial Intelligence, Big Data, Quantum Optimization  \n1 Introduction  \nData is becoming the driving force behind business growth. Technologies have evolved and are generating huge volume of data from businesses. While it is easy to transmit data securely and seamlessly over internet, storing and retrieving is simple. However, processing them to get meaningful information is a mammoth of task in terms of processing power. Automation and internet are the largest contributors in data generation. Artificial intelligence, machine learning, optimisation and simulation models need voluminous data for training models, requiring state-of-the-art high-performance computing. The computational efforts required to solve any problem depends on size of data and complexity of the equation sets to solve. Some real time simulations need few  \n∗ Corresponding author: Minati Rath  \ngiga bytes of data for one instance of execution. One such physics simulation done by National Aeronautics and Space Administration (NASA) is simulation of retro propulsion of a Mars lander using NASA’s Fully Unstructured Navier-Stokes 3-Dimensional (FUN3D) computational fluid dynamics library [1] . 150 Tera Bytes (TB)s of data is used for visualization of the simulation.  \nClassical computing systems work on the principles of binary bits to solve problems whereas Quantum computation is an entirely new way of information processing. Qubits have the capability tobe in quantum states apart from being in binary states.  \nHence, it has become key area to study how quantum computing as a technology is evolving and how machine learning and artificial intelligence models can benefit from them; along with special attention towards error correction and stability of systems and models.  \nThe rest of the article is organised as follows.  \n1. Section 2 describes classical computing; it’s limitations and bottlenecks.  \n2. In Section 3, developmental history and key features of Quantum computers; along with formulation of quantum algorithms are presented.  \n3. Section 4 illustrates different supervised and unsupervised machine learning models.  \n4. In Section 5, we presented research set up for integrating machine learning models into quantum domain; along with challenges impacting quantum machine learningQML","cbCaip5bAPW3I758","https://ap.wps.com/l/cbCaip5bAPW3I758","pdf",654316,1,24,"English","en",105,"# Introduction\n## Classical computing and its limitations\n# Quantum computing overview\n## Qubits and quantum algorithm formulation\n# Machine learning models\n## Supervised and unsupervised approaches\n# Quantum-assisted simulation framework\n## Integrating ML into the quantum domain\n## Simulation setup and challenges\n# Conclusion","[{\"question\":\"Why are traditional computing resources insufficient for training large machine learning models?\",\"answer\":\"Traditional systems struggle with the scale of Big Data within reasonable time frames. Processing requirements grow with data size and problem complexity, creating computational bottlenecks.\"},{\"question\":\"How does quantum computing potentially improve machine learning performance?\",\"answer\":\"Quantum algorithms may process classical data exponentially faster by mapping Quantum Machine Learning (QML) methods into the quantum mechanical domain. This can reduce resource needs and improve accuracy and efficiency.\"},{\"question\":\"What is compared in the simulations using a quantum simulator?\",\"answer\":\"The document runs simulations on a dataset using both traditional machine learning and quantum machine learning approaches, then compares their performances to evaluate differences.\"}]","Quantum-Assisted Simulation - A Framework for Developing Machine Learning Models in Quantum Computing | PDF",1785721091,60,{"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-assisted-simulation-a-framework-for-developing-machine-learning-models-in-quantum-computing","",{"@graph":36,"@context":85},[37,54,68],{"@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":53},"https://docshare.wps.com/document/quantum-assisted-simulation-a-framework-for-developing-machine-learning-models-in-quantum-computing/118936/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are traditional computing resources insufficient for training large machine learning models?","Question",{"text":75,"@type":76},"Traditional systems struggle with the scale of Big Data within reasonable time frames. Processing requirements grow with data size and problem complexity, creating computational bottlenecks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does quantum computing potentially improve machine learning performance?",{"text":80,"@type":76},"Quantum algorithms may process classical data exponentially faster by mapping Quantum Machine Learning (QML) methods into the quantum mechanical domain. This can reduce resource needs and improve accuracy and efficiency.",{"name":82,"@type":73,"acceptedAnswer":83},"What is compared in the simulations using a quantum simulator?",{"text":84,"@type":76},"The document runs simulations on a dataset using both traditional machine learning and quantum machine learning approaches, then compares their performances to evaluate differences.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"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":53,"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":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]