Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp25-cday-program.php
Document Type
Event
Start Date
15-4-2025 4:00 PM
Description
This 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 Deep Neural Networks (QDNN), and Hybrid Quantum Models, we gain hands-on experience in advanced machine learning techniques. The project cover diverse domains including cybersecurity, healthcare, industrial engineering, energy management, and supply chain optimization. Each part of 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.
Included in
GRM-109 Quantum Machine Learning For Science And Engineering Research
https://www.kennesaw.edu/ccse/events/computing-showcase/sp25-cday-program.php
This 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 Deep Neural Networks (QDNN), and Hybrid Quantum Models, we gain hands-on experience in advanced machine learning techniques. The project cover diverse domains including cybersecurity, healthcare, industrial engineering, energy management, and supply chain optimization. Each part of 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.
