Internet Based Event - 10Th June - Quantum Computing
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| About This Webcast
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
Who should attend:
- Leaders in Emerging Technologies
- Quantum Computing Educators
- Advanced Computing Specialists
- Cross-Disciplinary Students
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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).
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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.
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Sports Analytics & Machine Learning:
Quantum Machine Learning (QML) could supercharge sports analytics, helping teams process and model massive datasets — e.g., player movement, game strategies, injury prediction — more efficiently than classical ML alone.
Biomechanics and Wearables:
High-performance computing (HPC) is already used in sports biomechanics, such as motion capture and muscle simulation. Quantum computing could eventually model such systems in real-time with higher accuracy.
Game Strategy Optimization:
Teams in sports like football or basketball run complex simulations to optimize strategy. QML might one day optimize those simulations much faster, even in-game or between matches.
Talent Scouting and Performance Prediction:
Scouting decisions could be improved using predictive models that evaluate long-term potential, adjusting for complex variables. QML may make these predictions more accurate and scalable.
eSports and Simulation-based Sports:
In eSports, where real-time decisions and analytics are paramount, quantum-classical systems could simulate outcomes or optimize strategies more effectively.
🎯 Who Should Be Interested (From a Sports Context)?
Sports data scientists looking to future-proof their skill sets.
Athletic performance researchers using simulations and modeling.
Teams or orgs investing in AI/ML-based scouting or strategy tools.
Students in sports science or data analytics aiming to explore cutting-edge technologies applicable to their field.