Presentation
HPC meets QC in the Classroom: A Module for Applied Quantum Machine Learning
DescriptionQuantum accelerated supercomputing, or the integration of quantum computers and quantum emulators with classical supercomputers, allows domain scientists to address complex problems across various disciplines. To design and implement effective hybrid algorithms at scale, practitioners require not only an understanding of quantum computing (QC) and the problem domains, but also the High Performance Computing (HPC) skills to optimize quantum-classical workflows. Current QC curriculum largely overlooks the practical integration and scaling of hybrid algorithms, and often university quantum computing courses do not attract students who are most familiar with the problem domains.
We describe the pedagogical motivation for a module that addresses both of these shortcomings. We survey existing HPC and QC educational literature to create an integrated HPC+QC competency. This informs the design of an educational module, pilot-tested in a master's level applied machine learning course, which introduces students to QC concepts through a hybrid neural network example.
We describe the pedagogical motivation for a module that addresses both of these shortcomings. We survey existing HPC and QC educational literature to create an integrated HPC+QC competency. This informs the design of an educational module, pilot-tested in a master's level applied machine learning course, which introduces students to QC concepts through a hybrid neural network example.