Machine learning (ML) already plays a major role in the digitalization strategy of many companies and enables more efficient processes and new business models, among other things. However, there is often a lack of specialists. As a result, the implementation of ML solutions is still often associated with a high workload. From data acquisition and the selection of suitable algorithms to the optimization of training, detailed expertise in ML is required.

The approach of automated machine learning (AutoML) counteracts these challenges and makes it easier for developers to use artificial intelligence. In particular, the choice of specific ML algorithms is automated. This means that users have to spend less time familiarizing themselves with ML and can concentrate more on their actual processes. In this context, quantum computing marks a breakthrough into a new technological era, as it can significantly improve the AutoML approach. In addition, quantum computing offers the computing power often required for AutoML.

Taking quantum computing to a new level?

The joint project "AutoQML" addresses this innovation and pursues two main objectives: Firstly, the new AutoQML approach is being developed. This will be expanded to include newly developed quantum ML algorithms. Secondly, quantum computing takes the AutoML approach to a new level, as certain problems can be solved faster with quantum computing than with conventional algorithms.

Led by the Fraunhofer Institute for Industrial Engineering IAO, the project provides simplified access to conventional and quantum ML algorithms via an open source platform. In addition to the Fraunhofer Institute for Manufacturing Engineering and Automation IPA, the companies GFT Integrated Systems, USU Software, IAV, KEB Automation, Trumpf Werkzeugmaschinen and Zeppelin are also involved in the project. The solutions developed are being tested on the basis of specific use cases from the automotive and production sectors.

Software library for complete hybrid solutions

The project consortium will integrate quantum computing components into current machine learning approaches in order to be able to utilize the performance, speed and complexity advantages of quantum algorithms in an industrial context. In the AutoQML Developer Suite - a software library - developed quantum ML components and methods are to be brought together in the form of a toolbox and made available on an open source platform. This should enable users to use machine learning and quantum machine learning and develop hybrid overall solutions.

The project will run for three years. Further market dissemination by the corporate partners will enable the transfer of research-related high technology to a broad industrial environment with the aim of significantly strengthening Germany as an industrial location. The project is funded by the Federal Ministry of Economics and Climate Protection (BMWK).