Quantum Machine Learning Online Materials and Software Modules for Undergraduate Education

Arizona State University, Scottsdale AZ

Investigators

Abstract

This project aims to serve the national interest by introducing undergraduate students to Quantum Computing (QC), establishing foundations and software tools for workforce development in QC, and implementing these tools in Electrical Engineering and other STEM courses at the undergraduate level. The project plans to develop online content, modules and interactive software to introduce undergraduate students to Quantum computing with emphasis on quantum machine learning (QML). The project team will engage undergraduate students in Electrical and Computer Engineering as well as students from other STEM areas. The project will also include workforce-focused research experiences for undergraduate (REU) students during the summers. The team will also include a research experience for STEM teachers during the summer. The content developed for quantum information systems will be adapted for different groups, including undergraduate students, REU students, and for high school outreach. Grant activities and objectives also include developing a diverse community of users, innovative video-streamed content, interactive software for skill building, and summer training workshops. The materials created will: a) impact several STEM disciplines, b) engage and energize undergraduate students, c) create impactful quantum information science awareness and introductory skills, and d) establish modules and tools for workforce development in quantum information systems. This project is motivated by the national need to develop a workforce in quantum computing with emphasis on QML. The project team will develop and thoroughly assess several QML products for undergraduate courses and training, including widely accessible online materials and interactive software. Materials and modules developed will support senior level elective courses in signal processing and machine learning. The project will introduce undergraduate students to Quantum computing and QML, using application-driven materials and interactive software. Assessment will be handled by the College Research and Evaluation Services Team (CREST). Finally, the project seeks to broaden participation through several strategies including collaborations with minority student chapters and minority serving institutions, and the leveraging of international university collaborations for global dissemination. Specific products include interactive analysis and visualization software tools for quantum machine learning and quantum Fourier transforms. These tools will enable students to understand and experiment with quantum parameters and assess their effect in compelling applications such as voice recognition. The assessment team will evaluate all the modules, their ability to engage students, capabilities in broadening participation, and the overall effectiveness of QML materials and interactive software in workforce development. The project will use a mixed-method assessment process (qualitative and quantitative data collection) to build an understanding of the impact of the use of the quantum computing tools on student learning gains. Assessments will be done through electronic web tools, pre- and post-quizzes, presentations, one-to-one interviews, and ordinary in-class testing. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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.