HPC Meets QC in the Classroom: A Module for Applied Quantum Machine Learning

<p dir="ltr">Quantum accelerated supercomputing, or the inte- gration of quantum computers and quantum emulators with clas- sical 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 outside of computer science and physics who are most familiar with the problem domains and HPC skills.</p><p dir="ltr">In this lightning talk, 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.</p>