Quantum Machine Learning for Cybersecurity and Science & Engineering Data
Disciplines
Cybersecurity | Numerical Analysis and Scientific Computing | Other Computer Sciences
Abstract (300 words maximum)
Classical Machine Learning (CML) involves training software models to make predictions or generative content utilizing datasets. In contrast, quantum computing involves the utilization of computers that consist of quantum bits which process more information than standard computers. Due to their performance, they can produce enhanced solutions to complex problems. Quantum Machine Learning (QML) combines the power of quantum computers alongside CML to address problems more efficiently and effectively than CML. This study will be examining various datasets from cybersecurity and engineering fields, where having quicker and more efficient solutions can lead to improved accuracy and performance. This was conducted by implementing the datasets into different models of QML and comparing the results to CML. The models of QML include the Quantum Neural Network model, the Quantum Random Forest Classifier model, and the Quantum Support Vector Machine Learning model. This study aims to evaluate the effectiveness of the QML in providing more accurate and faster solutions compared to the traditional CML method.
Academic department under which the project should be listed
CCSE - Computer Science
Primary Investigator (PI) Name
Dr. Yong Shi
Quantum Machine Learning for Cybersecurity and Science & Engineering Data
Classical Machine Learning (CML) involves training software models to make predictions or generative content utilizing datasets. In contrast, quantum computing involves the utilization of computers that consist of quantum bits which process more information than standard computers. Due to their performance, they can produce enhanced solutions to complex problems. Quantum Machine Learning (QML) combines the power of quantum computers alongside CML to address problems more efficiently and effectively than CML. This study will be examining various datasets from cybersecurity and engineering fields, where having quicker and more efficient solutions can lead to improved accuracy and performance. This was conducted by implementing the datasets into different models of QML and comparing the results to CML. The models of QML include the Quantum Neural Network model, the Quantum Random Forest Classifier model, and the Quantum Support Vector Machine Learning model. This study aims to evaluate the effectiveness of the QML in providing more accurate and faster solutions compared to the traditional CML method.