Quantum Machine Learning Classroom Meets High Performance Computing Skills

Quantum accelerated supercomputing allows domain scientists to address complex problems in machine learning (ML) and across various disciplines. These hybrid systems will require not only an understanding of quantum computing (QC) but also of High Performance Computing (HPC) skills to manage and optimize quantum-classical workflows. Modern university-level QC curriculums will address QC, ML, and hybrid algorithms; but they overlook the practical scaling of these topics and the HPC skills requisite to do so.

What Attendees Will Learn:

What Quantum Accelerated Supercomputing is
Why HPC is vital for QC professionals
How universities teach Advanced Quantum Algorithms today
How to address student’s HPC skill gap

Speaker and Presenter Information

Dan Justice is an AI and Quantum Computing Researcher in the AI Division of the Software Engineering Institute and an instructor of Quantum Computing at Carnegie Mellon University. He has taught over 15 courses on various quantum topics and conducted research across multiple areas of machine learning. His work naturally converges at the intersection of quantum computing and AI, with a particular focus on Quantum Machine Learning (QML).

As a Senior Technical Marketing Engineer at NVIDIA, Monica VanDieren specializes in quantum and high-performance computing, driving the CUDA-Q Academic initiative. Before joining NVIDIA, Monica contributed to IBM's Quantum Accelerator program. With a Ph.D. in Mathematical Sciences from Carnegie Mellon University, she brings over 20 years of academic experience at universities such as Stanford, University of Michigan, and Robert Morris University to her work.