[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118523-en":3,"doc-seo-118523-105":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},118523,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Quantum Machine Learning for Science and Engineering - UR-044 - Research comparative study","This research compares traditional machine learning algorithms with quantum counterparts by evaluating matched models on selected datasets. Traditional and quantum implementations of SVM, logistic regression, PCA, random forest classifiers, neural networks, and convolutional neural networks (CNN) are trained, tested, and contrasted using metrics such as accuracy, precision, recall, F1, and ROC-AUC. Results show quantum approaches can be advantageous for some cases while performing worse in others, shaping practical understanding of quantum machine learning for science, engineering, and industrial applications.","UR-044  \nQuantum Machine Learning for Science and Engineering  \nAbstract  \nThis research explores the comparative effectiveness of traditional machine learning algorithms and their quantum counterparts. Traditional and quantum implementations of algorithms including Support Vector Machines (SVM), logistic regression, Principal Component Analysis (PCA), random forest classifiers, neural networks, and convolutional neural networks (CNN) are evaluated and contrasted. Findings highlight that quantum algorithms can provide certain clear advantages in some models and data while exhibiting inferior performance in others. By assessing these nuances, this research helps contribute to the understanding of quantum machine learning algorithms and their potential applications for science, engineering, and industrial tasks.  \nIntroduction  \nIn traditional machine learning, we can train a model with a dataset, gradually training it to recognize data so that we can use it to assist us in practical and useful tasks. However, training a traditional model can take a somewhat longtime when used on extremely large datasets.  \nWith the recent development of Quantum computing, it becomes possible to utilize superposition, a property of quantum mechanics, to encode traditional data and process it with quantum circuits, in other words, it allows for the creation of quantum machine learning algorithms. These quantum algorithms, due to the probabilistic nature of qubits, have the potential to allow for speedup in processing or potential improvements in metrics commonly used to evaluate machine learning models such as accuracy, precision, recall, F1 scores, and ROC scores. As such, this research seeks to evaluate that potential  \nResearch Question(s)  \n• How do quantum machine learning algorithms compare to traditional machine learning algorithms?  \n• Can users with sub-optimal hardware make use of quantum machine learning algorithms?  \n• Are quantum machine learning algorithms currently viable for realworld use?  \nProcess Flow  \nFor each model we select a dataset and preprocess it. The traditional and quantum models are then evaluated on the data and the results are collected tobe compared.  \nFig.2 A flowchart showcasing the process for training and evaluating traditional and quantum models  \nResults  \nTraditional Models  \n\n| Model | Dataset | Training Time | Accuracy | Precision | Recall | F1 | ROC-AUC |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| SVM | UCI Skin Segmentation | \u003C1s | 0.96 | 1.00 | 0.93 | 0.97 | 0.97 |\n| LR | UCI Heart Disease | - | 0.79 | - | - | - | - |\n| PCA + LR | UCI Heart Disease | 8ms | 0.79 | 1.00 | 0.31 | 0.47 | 0.65 |\n| Random Forest | UCI Iranian Churn | - | 0.94 | 0.94 | 0.94 | 0.94 | 0.87 |\n| Neural\u003Cbr>Network | UCI Predictive Maintenance | ~1m | 0.99 | 0.50 | 0.50 | 0.50 | - |\n| CNN | MNIST | ~5m | 0.98 | 0.98 | 0.98 | 0.98 | - |\n\nQuantum Models  \n\n| Model | Dataset | Training Time | Accuracy | Precision | Recall | F1 | ROC-AUC |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| SVM | UCI Skin Segmentation | ~18s | 0.96 | 1.00 | 0.93 | 0.97 | 0.97 |\n| LR | UCI Heart Disease | - | 0.60 | - | - | - | - |\n| PCA + LR | UCI Heart Disease | 460s | 0.77 | 1.00 | 0.23 | 0.38 | 0.62 |\n| Random Forest | UCI Iranian Churn | - | 0.83 | 0.78 | 0.83 | 0.79 | 0.54 |\n| Neural\u003Cbr>Network | UCI Predictive Maintenance | ~1m | 0.99 | 0.67 | 0.73 | 0.69 | - |\n| CNN | MNIST | ~5m | 0.90 | 0.90 | 0.90 | 0.90 | - |\n\nFig. 1 ROC curves for the CNN (left) and quantum CNN (right) .  \nMaterials and Methods  \nThe environment used for the implementation was Google Colaboratory, connected to a Python 3 Google Compute Engine backend.  \nConsidering the computational complexity of qubits using traditional hardware, however, it proved to be necessary to also set up a local runtime environment via docker’s Google Colab image. The basic, free Google Compute Engine backend did not have enough processing power to execute some of the quantum algorithms w","cbCaivtW4Bj2aVXJ","https://ap.wps.com/l/cbCaivtW4Bj2aVXJ","pdf",382165,1,"English","en",105,"# Introduction\n## Research Question(s)\n## Process Flow\n## Results\n### Traditional Models\n### Quantum Models\n## Materials and Methods\n## Conclusions\n## Acknowledgments","[{\"question\":\"How do quantum machine learning algorithms compare to traditional machine learning algorithms in this study?\",\"answer\":\"The study finds that most quantum machine learning algorithms are currently worse than traditional counterparts, taking far longer and often yielding significantly poorer results.\"},{\"question\":\"Can users with sub-optimal hardware use quantum machine learning algorithms?\",\"answer\":\"No. Simulating qubits on traditional hardware becomes impractical after roughly 8–10 qubits, limiting access for users with less capable systems.\"},{\"question\":\"Are quantum machine learning algorithms viable for real-world use today?\",\"answer\":\"No. Qubit probabilistic behavior requires measurements several thousand times, and combined with computational complexity, the algorithms are unsuitable for practical deployment.\"}]","Quantum Machine Learning for Science and Engineering - UR-044 - Research comparative study | PDF",1785683978,3,{"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},"quantum-machine-learning-for-science-and-engineering-ur-044-research-comparative-study","",{"@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/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/quantum-machine-learning-for-science-and-engineering-ur-044-research-comparative-study/118523/",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},"How do quantum machine learning algorithms compare to traditional machine learning algorithms in this study?","Question",{"text":73,"@type":74},"The study finds that most quantum machine learning algorithms are currently worse than traditional counterparts, taking far longer and often yielding significantly poorer results.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"Can users with sub-optimal hardware use quantum machine learning algorithms?",{"text":78,"@type":74},"No. Simulating qubits on traditional hardware becomes impractical after roughly 8–10 qubits, limiting access for users with less capable systems.",{"name":80,"@type":71,"acceptedAnswer":81},"Are quantum machine learning algorithms viable for real-world use today?",{"text":82,"@type":74},"No. Qubit probabilistic behavior requires measurements several thousand times, and combined with computational complexity, the algorithms are unsuitable for practical deployment.","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},[91,95,99,103,108,113,118,121,126,129,133],{"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":109,"doc_module":4,"doc_module_name":45,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":119,"slug":120},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"]