Quantum Machine Learning

Machine learning is known to provide solutions for data analysis and interpretation, and it is used in various fields such as computer vision, malware detection and drug discovery. However, traditional machine learning approaches are incapable of successfully extracting useful information from large data set, as it requires tremendous time and resources while being performed on traditional computers. Quantum computing is a new type of qubit-enabled computing paradigm based on the quantum properties such as superposition, interface and entanglement for data process and other tasks. It can be used to work on problems traditional supercomputers would not be able to handle efficiently. Quantum computing can collaborate with machine learning for faster computation and more accurate data analysis, and quantum machine learning (QML) has gained a lot of attention from both academia and industry recently.

One of the most important applications of QML is for cybersecurity. This project will begin with research of quantum computing and machine learning, then followed by the development of a system that uses the TensorFlow Quantum and PennyLane packages and applies quantum machine learning (QML) algorithms to process various security and malicious data sets and compares the performance with classical machine learning (CML) algorithms.

We propose the development of a system that uses the TensorFlow Quantum and PennyLane packages and applies quantum machine learning (QML) algorithms to process various security and malicious data sets and compares the performance with classical machine learning (CML) algorithms. The data sets for our modules include: