Beginnings: Building Educational Growth for Industry (BEGIN) Learning—Quantum Literacy, Quantum AI, and Quantum Machine Learning

Ina Solutions Inc., Fairfax VA

Investigators

Abstract

Currently, quantum computation is becoming essential in new and emerging technologies, science, and business. To build a broad base of quantum literacy, knowledge, and confidence, the Beginnings: Building Educational Growth for Industry (BEGIN) Learning—Quantum Literacy, Quantum AI, and Quantum Machine Learning project will engage women and other historically underrepresented students in learning the importance of quantum applications. This accelerated, 6-week experiential learning will include quantum machine learning, quantum AI, and quantum literacy through hands-on experience, mentoring, and building community of quantum trained technicians. Through cross-sector partnerships with non-profits, minority business enterprises, and HBCUs—and building connections into industry, government, and academia—the project will enhance student skill sets in this emerging and novel technology, thereby strengthening community engagement, identity, and belonging. Building career path, enhancing technical skills, and expanding networking opportunities, this project serves to anchor and increase diverse participation in the next generation of quantum literate workforce. The Beginnings: Building Educational Growth for Industry (BEGIN) Learning—Quantum Literacy, Quantum AI, and Quantum Machine Learning is an accelerated, 6-week experiential learning project that focuses on training women and historically underrepresented groups to become skilled technicians at an entry level, thereby enhancing their workforce skills. This project serves as a micro-credential certification whereby participants gain hands-on skills through an online virtual classroom. Specifically, our expected outcomes will comprise successfully completing training in technical course materials including exploration of quantum mechanics, quantum circuits, introduction to Python coding, classic machine learning versus quantum machine learning, and quantum generative adversarial networks, among other topics. Through this comprehensive student-training approach, the project will develop a unique, national model to train populations previously excluded from or unaware of opportunities in emerging technologies. Taking a whole-student approach, the project employs the convergence of technical skills with community engagement through a peer-to-peer cohort model. The students will further benefit by being introduced to networking skill-building exercises through virtual and in-person opportunities. 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.