Objective

We implement concepts of neuromorphic quantum computing for optimisation and prediction tasks with use cases from aerodynamics and aeroelastics.

It is probably a basic human need to want to gain knowledge about future events in order to be able to prepare for them at an early stage. Accordingly, time series analysis has long been concerned with the development of prediction algorithms. Artificial intelligence has greatly improved such prediction methods. The next step is now to incorporate quantum computing. Quantum reservoir computing (QRC), in which neuromorphic computing concepts are combined with quantum computing, appears to be a very promising approach for improving the prediction of complex dynamic systems.

In the NeMoQc project, optimal setups of the QRC are now to be developed for prediction and optimisation tasks.

Motivation

Precise predictions of complex systems are very important in many technical applications (rocket engines, satellite control, power networks, etc.), as this gives you enough time to act proactively and influence the system behaviour in good time. But good predictions are also of great importance in many areas of everyday life. Just think of the precise prediction of (extreme) weather events, including droughts and the associated risk of food insecurity. Concepts of neuromorphic quantum computing appear to have the potential to make predictions and optimisations better and much more efficiently than before. This should make it possible to better solve technical and even urgent socio-economic issues.

Challenge

In the NeMoQc project, we want to better understand quantum reservoir computing (QRC) approaches and apply them in a suitable way. The following questions are in the foreground: Is there a best-suited quantum reservoir for QRC that combines (minimal) size and technical feasibility with statistically valid prediction and optimisation results for real data that are superior to conventional methods? How large is the quantum reservoir required for this? Previous results suggest that quantum systems with very few qubits already deliver promising results, which makes the technical feasibility of QRC in the NISQ era appear easier. What do the most suitable quantum reservoirs look like? Can the shape of the optimal quantum reservoir found be explained? Is there a universal best quantum reservoir or are there problem-specific optimal solutions? The answers to all these questions harbour enormous potential for innovation, which is to be exploited within the scope of this project.