Collaborative Research: CyberTraining: Pilot: Broadening CI Workforce Development for Quantum-Based Machine Learning Research in Science and Engineering

Quantum-based machine learning (QML) applies quantum computing (QC) to machine learning (ML), which revolutionizes computational tasks by leveraging the unique properties of quantum mechanics, leading to more efficient solutions in science and engineering (S&E). However, there is a shortage of QML research workforce for S&E. In addition, QML is absent in most colleges' curricula, and there is a need for hands-on QML training materials.

To meet these challenges, Kennesaw State University and Florida Agricultural and Mechanical University (HBCU) will collaboratively build QML research, education capacity, and workforce in S&E.

In this project, the investigators will develop open-source, hands-on QML training materials on a dedicated open repository, including nine transferable modules with hands-on labs that cover key concepts of QC and QML in computer science (CS) and their applications in industrial engineering (IE), integrate the modules into existing CS and IE courses, and host faculty workshops and student training camps to train faculty and students with the developed hands-on QML training modules. Through these efforts, the project aims to (1) train and empower S&E faculty and students with QML skills and enhance their real-world research capability with advanced quantum cyberinfrastructure and (2) build diverse and multidisciplinary communities of collaborative QML research in S&E.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.