Cornerstones for an Undergraduate Quantum Computing Program: Creating an Active Learning Curriculum

About This Grant

This project aims to serve the national interest by developing instructional materials that enhance student learning in three areas: introductory quantum computing (QC), quantum machine learning (QML), and QC applied to quantum chemistry (QCChem). This project falls under IUSE:EDU NSF 23-510 Track 1: Engaged Student Learning, Level 1. QC is significant because it has the potential to solve large-scale problems more efficiently than conventional computing, which could be transformative in areas such as drug discovery, artificial intelligence, financial modeling, and digital security. This project has the potential to enhance the economic competitiveness of the US by training quantum-aware and quantum-proficient personnel for the future workforce. In teaching undergraduate QC, there is a need for “active learning” methods, as opposed to traditional lectures, to get students to be more engaged in the classroom by solving problems. Students could also benefit from learning about real-world applications of QC. This project aims to develop and evaluate new course materials for introductory QC, QML, and QCChem at a level that can be understood by undergraduates majoring in various STEM disciplines. While learning QC is the primary goal, students may also be strengthened in basic STEM subjects, including programming, artificial intelligence, molecular structure, and quantum mechanics. This discipline forms the basis for much of science and technology in the 20th and 21st centuries. The project may enhance the infrastructure for US education by helping faculty from other institutions develop educational programs in QC in their institutions. There is an urgent need to train the next generation of workers in quantum information science and technology. Primarily, undergraduate institutions are well-suited to develop QC curriculum materials and can help expand the quantum workforce by enabling students from multiple educational pathways to study QC. The goal of this project is to develop active learning instructional materials for introductory QC, QML, and QCChem targeted at undergraduates majoring in physics, engineering, computer science, data science, and chemistry. Active learning pedagogies are recognized for enhancing student engagement, critical thinking skills, and retention in STEM fields. The project aims to develop a broader range of course materials and formally evaluate approaches to enhance teaching methods for students. In addition, to help undergraduates appreciate the utility of QC, the project plans to develop learning modules for QML and QCChem. Machine learning uncovers patterns in large datasets and enables predictions to be made. It is one of the most significant technological developments of the 21st century. Quantum chemistry is a branch of theoretical chemistry that focuses on understanding the electronic structure of molecules and chemical reactions using quantum mechanics. It is critical today for industrial research in drug discovery and the development of new materials. Due to the computationally intensive nature of machine learning and quantum chemistry, researchers are exploring the potential of QC to enhance the performance of these algorithms. The objective is to provide undergraduates with a solid foundation in QML and QCChem, as well as an understanding of the exciting potential that arises when QC is combined with algorithms designed for conventional computers. The intention is to make course materials available on a website and accessible to working scientists, engineers, and faculty from other institutions. Faculty-development workshops offered through this project could enable the PIs to help external faculty implement QC modules in their home institutions. The NSF IUSE: EDU 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 efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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.