Projektarbeit (laufend)

Fan Yang

Toward Acceleration in hybrid Classical, Quantum, and AI Computation

As classical computing approaches its limits, large-scale scientific simu-lations and complex engineering computations are becoming prohibitively time-consuming.To overcome these computational bottlenecks, next-generation computational paradigmsare required.
This project explores an integrated approach to accelerate computational workflows using two complementary technologies: Hybrid Classical-Quantum Computing, where quantum algorithms are implemented within the XACC framework to build a practical hybrid workflow, establishing a robust foundation for scalable quantum-accelerated applications despite modest immediate speedups; and AI-based Surrogate Modeling, which involves training AI models to approximate outputs of computationally intensive simulations, enabling near-instantaneous predictions and drastically reducing classical computational demands. By combining these approaches, the project aims to explore the feasibility of a hybrid, heterogeneous computing paradigm, leveraging existing HPC, quantum computing, and AI-driven simulation frameworks for large-scale problems.