Quantum computing has the potential to change how computationally demanding simulation problems are solved. PN 8 explored how quantum computers could be used as accelerators within heterogeneous computing systems and which parts of established simulation algorithms might benefit from quantum-based approaches.
Rather than transferring complete simulation codes to quantum hardware, the research focused on identifying suitable subproblems and developing hybrid quantum–classical algorithms. Particular attention was given to numerical methods for partial differential equations, optimization, machine learning and stochastic problems, while also considering the limitations of current noisy quantum systems.
Research Focus
The research addressed three closely connected questions:
- Quantum acceleration: How could quantum computers be integrated as accelerators into heterogeneous simulation and computing environments?
- Suitable problem classes: Which simulation problems or subproblems offered the greatest potential for quantum acceleration?
- Hybrid algorithms: Which components of established simulation methods could be replaced or redesigned to exploit quantum computing efficiently?
Projects
| Simulating stochastic processes with quantum devices | Christian Holm (Wolfgang Nowak) |
| Quantum computing for modular iterative solvers | Miriam Schulte (Dominik Göddeke) |
| Quantum-enhanced and data-integrated PDE solution methods | Tim Ricken (Andrea Beck) |
Associated Projects
| Robust quantum algorithms using Lipschitz bounds | Julian Berberich |
Project Network Coordinators
Miriam Schulte
Prof. Dr. rer. nat.Simulation of Large Systems | Dean of Studies Simulation Technology
Johannes Kästner
Prof. Dr.Computational Chemistry