Robust quantum computing for practical applications
Quantum computing has the potential to massively and sustainably change a wide range of industries and enable numerous new applications. These include, for example, simulations of new materials for more efficient solar cells and batteries, as well as data-intensive applications for artificial intelligence (AI) and cybersecurity.
Mr. Roscher, you work in an absolutely trendy area, quantum computing. What is that and how can society be practically advanced by it?
Simply put, quantum computers are X times faster in terms of processing speed for very specific tasks than today’s computers. In other words: In the future, quantum computers will solve problems within seconds, for which today’s computers would need centuries. This enables numerous new applications. These include solutions for complex optimization problems e.g. in logistics, finance and insurance, but also allows better medical diagnostics.
In which areas do you advance technology development with your daily work?
In the context of developing software applications for quantum computing, Fraunhofer IKS focuses its expertise in safety to the research field of “reliable and robust quantum computing”. As already mentioned, quantum computing offers many possibilities for innovative solutions, but it can only deliver a real added value, if its calculations are reliable and safe. In addition to pure computing power, the robustness of quantum computing is therefore a basic prerequisite for its successful use in practice.
What specific influence do microelectronics have on your work?
Regardless of the specific quantum computing project, microelectronics is in every device we work with in our daily lives. Without the constantly increasing computing power at all levels, the broad application of machine learning would not be possible at all. The success of artificial intelligence is therefore also a success of microelectronics.
And what are the biggest challenges you are currently addressing?
The use of neural networks in safety-critical environments is very challenging because even small changes to the input, such as an image, can lead to a different result of the calculations. Therefore, a proof of quality for the calculations of neural networks is required. For this purpose, certain algorithms are used, which currently only work for relatively simple networks. With the size and complexity of a neural network, the computation time needed, to verify the results, increases massively. For this reason, Fraunhofer IKS is investigating how the optimization validation and verification of autonomous, networked systems can be improved with the help of quantum computing.
Karsten Roscher is working at the Fraunhofer Institute for Cognitive Systems IKS (then Fraunhofer ESK) since 2010. He is heading the Department of Certifiable Artificial Intelligence since 2020. Karsten Roscher studied computer science and electrical engineering with a focus on multimedia systems, computer networks and robot vision at Ilmenau University of Technology. He is currently researching on the reliable determination of uncertainties as well as methods to ensure robustness and tractability for machine learning.