The Fraunhofer IKS examines the potential of quantum computing with an application-driven focus and researches robust and reliable quantum-assisted solutions for use cases in the field of machine learning, combinatorial optimization and simulation.
The quantum algorithms of today are hybrid, and combine classical and quantum computation parts. A practical quantum advantage can realize the following in multiple directions:
- Hybrid quantum-classical machine learning offers superior generalization capabilities and requires less training data.
- Quantum-assisted solutions to combinatorial optimization problems may offer better heuristics than presently available heuristics. This advantage is now being realized through quantum-inspired algorithms.
- The simulation of quantum mechanical systems through the use of quantum computing is expected to lead to more accurate solutions than presently possible – which is essential for drug design, for example.
- Quantum-inspired methods for various problems mimic specific quantum effects on classical computers, and are now running on a productive basis.
QC is an emerging technology. We can help you to get ahead of the curve, and offer a scientifically excellent, application-driven method for starting to explore quantum technologies in your area.