Accelerated quantum supercomputing allows domain scientists to address complex problems across various disciplines. These distributed 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 and of Artificial Intelligence (AI) to address challenges in advancing quantum computing technology, including error correction and calibration, and control. Modern university-level QC curriculums address QC and hybrid algorithms, but they overlook the practical scaling of these topics and the HPC and AI skills requisite to do so.
What Attendees Will Learn:
- What Accelerated Quantum Supercomputing is
- Why HPC and AI is vital for QC professionals
- How universities teach Advanced Quantum Algorithms today
- How to address students’ HPC skill gap
Who should attend:
- Leaders in Emerging Technologies
- Quantum Computing Educators
- Advanced Computing Specialists
- Cross-Disciplinary Students
About the Speakers
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.
