[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116924-en":3,"doc-seo-116924-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"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},116924,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",6,"Technology","Quantum Machine Learning Applied to Cybersecurity","This project delves into the nascent field of quantum machine learning (QML), exploring its application to cybersecurity challenges. QML leverages quantum computing to potentially achieve superior performance over classical machine learning algorithms. The research investigates several quantum models, including quantum naïve Bayes, quantum neural networks, and quantum support vector machines, trained to address specific cybersecurity problems. To benchmark their effectiveness, these quantum models are rigorously compared against their classical counterparts using metrics such as performance time, accuracy, precision, and F1 scores. While the initial results indicate that the quantum models, as designed for quantum hardware, exhibited poorer performance on classical systems compared to their classical implementations, this experiment provided invaluable insights into optimizing QML algorithms. The findings contribute to a better understanding of how to enhance the performance and practical application of these cutting-edge algorithms, paving the way for future advancements in quantum-enhanced cybersecurity solutions. The project aimed to optimize quantum models by analyzing their comparative performance against classical models.","UR-363  \nQuantum Machine Learning in Cybersecurity  \nINTRO/ABSTRACT  \nQuantum machine learning is a very new field of machine learning that utilizes quantum computing techniques to achieve a higher level of performance. This project explores several quantum models trained to solve cybersecurityrelated problems that suit those respective models. The project compares the performances of these quantum models to their classical counterparts to find ways to better understand and optimize quantum machine learning algorithms.  \nMETHODS  \nThe models tested include quantum naïve bayes, quantum neural network, and quantum SVM. The classical versions of these models were also implemented in order to compare their performance with that of the quantum versions. The models were evaluated on their performance time, accuracy, precision, and f1 scores. Based on how the quantum models compared to the classic models, the quantum models were then optimized to gain knowledge on how to better optimize quantum models.  \nRESULTS  \nThe quantum models consistently had a poorer performance compared to their classical implementations. This was expected due to the quantum models being designed to take advantage of quantum computers. However, this experiment allowed for an opportunity to learn how to better optimize these algorithms on classical computers.  \nREFERENCES  \nBorujeni, S. E., Nannapaneni, S., Nguyen, N. H., Behrman, E. C., & Steck, J. E. (2021, April  \n12) . Quantum Circuit representation of Bayesian Networks. [arXiv.org](arXiv.org).  \nAuthors: Anna Destito, Joseph Ragsdale, Adam Waugh Advisors: Dr. Yong Shi, PHD, Professor Sharon Perry  \n\n| Quantum Machine Learning Models For Testing, Viability, Performance, and Optimization Techniques |  |  |  |\n| --- | --- | --- | --- |\n| \u003Cbr> |  |  |  |\n|  |  | Scan this QR code to view the project website and view step-by-step implementation explanations. |  |","cbCaidBpvwdZFCaf","https://ap.wps.com/l/cbCaidBpvwdZFCaf","pdf",429467,1,"English","en",105,"# INTRO/ABSTRACT\n# METHODS\n# RESULTS\n# REFERENCES","[{\"question\":\"What is quantum machine learning?\",\"answer\":\"Quantum machine learning is a new field that uses quantum computing techniques to improve machine learning performance.\"},{\"question\":\"Which quantum models were tested in this project?\",\"answer\":\"The project tested quantum naïve Bayes, quantum neural networks, and quantum support vector machines.\"},{\"question\":\"What was the performance of the quantum models compared to their classical counterparts?\",\"answer\":\"The quantum models consistently showed poorer performance compared to their classical implementations on classical computers, as expected since they are designed for quantum computers.\"}]","Quantum Machine Learning Applied to Cybersecurity | 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