ProT204 – Big Data Processing in the Era of Quantum Computing

Quantum computing is rapidly transitioning from experimental systems to practical tools that complement modern high-performance computing. This tutorial introduces attendees to the emerging landscape of hybrid quantum–classical systems, where GPUs, CPUs, and quantum processors work together to accelerate data-intensive workloads.

Using NVIDIA CUDA-Q, we demonstrate how computational pipelines can integrate quantum devices with classical HPC resources, enabling new possibilities for optimization, simulation, and machine learning.

This hands-on, application-focused session bridges the gap between modern Big Data workflows and the growing capabilities of quantum computing. Participants will learn how hybrid systems operate, how quantum programs are executed, and how classical accelerators such as GPUs complement quantum processors.

By the end of the tutorial, attendees will understand:

• The basics of quantum computation and quantum circuits
• How hybrid pipelines are orchestrated across CPUs, GPUs, and QPUs
• Where quantum techniques may offer advantages in Big Data contexts
• How to build and run simple quantum programs with CUDA-Q

This tutorial is ideal for:

• Big Data researchers exploring advanced compute paradigms
• Engineers interested in hybrid quantum–classical workflows
• Students and newcomers who want a guided introduction to quantum computing
• Practitioners already familiar with HPC or GPU computing who want to understand quantum integration

No prior experience with quantum computing is required.

1. Welcome & Motivation – Why quantum + HPC matters today
2. Quantum Computing Essentials – Qubits, gates, circuits
3. Quantum Hardware Today – Understanding NISQ devices
4. Hybrid Workflows – Merging CPUs, GPUs, and QPUs
5. CUDA-Q Programming – Building and running basic hybrid jobs
6. Case Study – Example applications in data-driven workloads
7. Q&A and Discussion

Shuwen Kan – Fordham University

Shuwen Kan is a Ph.D. candidate in the Department of Computer and Information Science at Fordham University in New York City. Her reseaech interests lie at the intersection of quantum computing systems, compiler design, and leveraging machine learning tools for system optimization and design.

Ying Mao – Fordham University

Dr.Ying Mao is a tenured Associate Professor in the Department of Computer and Information Science at Fordham University in New York City.is His research interests mainly focus on computing systems and applications, including quantum-based systems, quantum-classical co-optimizations, quantum hardware-software co-design, quantum learning systems, cloud virtualization, resource management and system visualization.

Materials will be made available before and after the session:

• Slide deck (PDF)
• Introductory quantum programming examples
• CUDA-Q demo scripts
• Links to additional resources

All materials will be posted here.

Date: December 9 (Tuesday), 2025

Time: 16:30–18:00

Location: Auditorium – IEEE Big Data 2025, Macau SAR, China

For questions or further information, please contact:

Shuwen Kan
Fordham University
Email: sk107@fordham.edu