Quantum Computing: New Potentials for Automated Machine Learning
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About the project
Strong association between research and industry
The joint project »AutoQML« pursues two main goals: First, the newly developed AutoQML approach is to raise machine learning to a new level. For this purpose, quantum machine learning algorithms (QML algorithms) will be newly developed. On the other hand, the already existing AutoML approach will be significantly improved with quantum computing, because certain problems can be solved faster with the help of quantum computing than with conventional algorithms.
Based on use cases from the automotive and production sector, new tools, components, methods and algorithms for machine learning with quantum computers will be developed. The tools and methods developed in the project will be integrated into the PlanQK platform as an open source solution and thus made available to (quantum) developers. In addition, new quantum computing software for automated machine learning will be developed, in particular for hybrid QML algorithms using quantum computing.
In the project, components of quantum computing will be integrated into current machine learning approaches to exploit the performance, speed and complexity advantages of quantum algorithms in an industrial context. In the so-called AutoQML-Developer Suite – a software library – developed QML components and methods will be brought together in the form of a toolbox and made available to developers in an open source platform. This will enable users to apply machine learning and quantum machine learning and to develop hybrid overall solutions.
The approach of the project »AutoQML« simply explained (in german)