[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-detail-118522-en":59,"doc-seo-118522-105":80},{"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":5,"data":60},{"doc_id":61,"user_id":62,"nickname":63,"user_avatar":64,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":66,"doc_content":67,"file_id":68,"file_url":69,"file_type":70,"file_size":71,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":72,"language_code":73,"site_id":74,"html_lang":73,"table_of_contents":75,"faqs":76,"seo_title":77,"seo_description":66,"update_tm":78,"read_time":79},118522,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136","GRM-109 - Quantum Machine Learning for Science and Engineering Research","Quantum Machine Learning (QML) is investigated for practical problem-solving across real-world application areas. Classical models including SVM, Neural Networks, Logistic Regression, and Naive Bayes are systematically compared with QML variants such as QSVM, QNN, QLR, QCL, and hybrid quantum architectures. The workflow covers dataset selection, quantum-compatible preprocessing with normalization/encoding and occasional PCA, quantum-circuit construction, training with quantum and hybrid optimizers, and performance evaluation in PennyLane and Qiskit. Experiments target tasks like fraud detection, quality prediction, patient flow analysis, energy-efficiency estimation, and predictive maintenance.","GRM-109  \nQUANTUM MACHINE LEARNING FOR SCIENCE AND ENGINEERING  \nRESEARCH  \nAbstract  \nThis research project aims to understand and explore the practical applications of Quantum Machine Learning (QML) in solving real-world challenges. By comparing classical machine learning models, such as Support Vector Machines (SVM), Neural Networks, Logistic Regression, and Naive Bayes, with their quantum counterparts. Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), Quantum Logistic Regression (QLR), Quantum Circuit Learning (QCL), and Hybrid Quantum Models, we gain hands-on experience in advanced machine learning techniques. The project covers diverse domains, including cybersecurity, healthcare, industrial engineering, energy management, and supply chain optimization. Each part of the project involves working with real-world datasets, preprocessing, parameter tuning (like qubit settings), and performance evaluation using platforms such as PennyLane and Qiskit. Through this project, we not only learn about the theoretical foundations of QML but also develop practical skills in applying quantum models to high-dimensional and complex data for tasks like fraud detection, quality prediction, patient flow analysis, energy efficiency estimation, and predictive maintenance.  \nIntroduction  \nQuantum Machine Learning (QML) signifies a transformative advancement in data-driven problem-solving by leveraging the computational advantages of quantum computing in conjunction with the predictive capabilities of machine learning. This project provides rigorous practical experience and research-  \n1 .Selected Dataset -We began by choosing real-world datasets that are suitable for quantum analysis and align with practical applications.  \n2 . Preprocessed Data - Cleaned the data, handled missing values, and transformed it into a quantumcompatible format using normalization and encoding techniques. Used PCA as well for certain models.  \n3 . Built Quantum Circuit - Designed and implemented quantum models like QSVM and QNN, tailoring the circuits to match the structure of each dataset.  \n4 . Trained Model - Using quantum and hybrid optimizers, we trained the models to learn patterns from the data effectively.  \n5 . Evaluated Model - We measured the accuracy and other key metrics of the quantum models and compared them against classical counterparts to assess performance.  \n6 . Interpreted Results -Finally, we analyzed the outcomes to draw meaningful insights, highlighting where quantum models performed better or showed potential.  \nAcross various experiments and datasets, Quantum Machine Learning (QML) models consistently demonstrated superior performance compared to classical models. In multiple settings—including neural networks, Naive Bayes, and Random Forest comparisons—hybrid or pure QML models achieved higher or competitive accuracies, with some exceeding 96% .These results highlight the growing potential of QML to enhance predictive accuracy and reliability in real-  \nResults  \noriented exposure to emerging quantum technologies through the implementation of advanced models, including Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), Quantum Logistic Regression (QLR), Quantum Circuit Learning (QCL), and Hybrid QuantumClassical architectures. The application of these models spans a broad spectrum of high-impact domains such as healthcare analytics, supply chain optimization, cybersecurity threat detection, industrial process improvement, and intelligent energy systems. By systematically comparing quantum and classical methodologies, this work fosters analytical rigor, enhances critical thinking, and equips with the skills necessary to contribute substantively to the future of quantum-enhanced data science and intelligent systems. Moreover, this project lays a strong foundation for future research and innovation, empowering us to explore novel quantum algorithms and contribute to the evolution of next-generation c","cbCaivL6cY6eBuhx","https://ap.wps.com/l/cbCaivL6cY6eBuhx","pdf",716029,"English","en",105,"# Abstract\n# Introduction\n# Selected Dataset\n## Preprocessed Data\n## Built Quantum Circuit\n## Trained Model\n## Evaluated Model\n## Interpreted Results\n# Research Question(s)\n## Materials and Methods\n# Results\n# Conclusions","[{\"question\":\"What is the main goal of this Quantum Machine Learning research project?\",\"answer\":\"To understand and explore practical applications of Quantum Machine Learning by comparing QML models with classical machine learning models on real-world challenges.\"},{\"question\":\"Which classical and quantum models are compared?\",\"answer\":\"Classical models include SVM, Neural Networks, Logistic Regression, and Naive Bayes. Quantum and hybrid models include QSVM, QNN, QLR, QCL, and hybrid quantum-classical architectures.\"},{\"question\":\"How are datasets prepared and models implemented?\",\"answer\":\"Real-world datasets are selected and cleaned, missing values are handled, and features are normalized and encoded into quantum-compatible formats, with PCA used for some models. Classical implementations use Python libraries (NumPy, Pandas, scikit-learn), while quantum implementations use PennyLane and Qiskit in Google Colab.\"}]","GRM-109 - Quantum Machine Learning for Science and Engineering Research | PDF",1785683977,3,{"code":4,"msg":81,"data":82},"ok",{"site_id":74,"language":73,"slug":83,"title":65,"keywords":84,"description":66,"schema_data":85,"social_meta":138,"head_meta":140,"extra_data":142,"updated_unix":78},"grm-109-quantum-machine-learning-for-science-and-engineering-research","",{"@graph":86,"@context":137},[87,100,120],{"@type":88,"itemListElement":89},"BreadcrumbList",[90,94,96,98],{"item":91,"name":92,"@type":93,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":95,"name":9,"@type":93,"position":14},"https://docshare.wps.com/document/",{"item":97,"name":40,"@type":93,"position":79},"https://docshare.wps.com/document/research-report/",{"item":99,"name":65,"@type":93,"position":19},"https://docshare.wps.com/document/grm-109-quantum-machine-learning-for-science-and-engineering-research/118522/",{"url":99,"name":65,"@type":101,"image":102,"author":107,"headline":65,"publisher":109,"fileFormat":112,"inLanguage":73,"description":66,"dateModified":113,"datePublished":114,"encodingFormat":112,"isAccessibleForFree":115,"interactionStatistic":116},"DigitalDocument",{"url":103,"@type":104,"width":105,"height":106},"https://docshare.wps.com/thumbnails/grm-109-quantum-machine-learning-for-science-and-engineering-research/118522.png","ImageObject",300,407,{"name":63,"@type":108},"Person",{"url":91,"name":110,"@type":111},"DocShare","Organization","application/pdf","2026-09-19","2026-08-02",true,{"@type":117,"interactionType":118,"userInteractionCount":19},"InteractionCounter",{"@type":119},"ViewAction",{"@type":121,"mainEntity":122},"FAQPage",[123,129,133],{"name":124,"@type":125,"acceptedAnswer":126},"What is the main goal of this Quantum Machine Learning research project?","Question",{"text":127,"@type":128},"To understand and explore practical applications of Quantum Machine Learning by comparing QML models with classical machine learning models on real-world challenges.","Answer",{"name":130,"@type":125,"acceptedAnswer":131},"Which classical and quantum models are compared?",{"text":132,"@type":128},"Classical models include SVM, Neural Networks, Logistic Regression, and Naive Bayes. Quantum and hybrid models include QSVM, QNN, QLR, QCL, and hybrid quantum-classical architectures.",{"name":134,"@type":125,"acceptedAnswer":135},"How are datasets prepared and models implemented?",{"text":136,"@type":128},"Real-world datasets are selected and cleaned, missing values are handled, and features are normalized and encoded into quantum-compatible formats, with PCA used for some models. Classical implementations use Python libraries (NumPy, Pandas, scikit-learn), while quantum implementations use PennyLane and Qiskit in Google Colab.","https://schema.org",{"og:url":99,"og:type":139,"og:title":65,"og:site_name":110,"og:description":66},"article",{"robots":141,"canonical":99},"index,follow",{"doc_id":61,"site_id":74}]