Quantum computing: new potentials for automated machine learning

Quantum computing makes it possible to advance computationally intensive technologies such as machine learning (ML). In the "AutoQML" project, eight partners from research and industry are therefore developing solutions that combine quantum computing and ML. An open source platform is intended to enable developers to use quantum machine learning algorithms without in-depth specialist knowledge. The Fraunhofer Institute for Manufacturing Engineering and Automation IPA is playing a key role in the project and is contributing its expertise in quantum computing and conventional ML methods. How can companies exploit the potential of digitalization and remain competitive? The use of technologies such as artificial intelligence (AI) can help companies to benefit as much as possible from the digital transformation. ML in particular 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.

New approach: quantum computing takes machine learning to a new level

The automated machine learning (AutoML) approach counteracts these challenges and makes it easier for specialists to use AI. In particular, the choice of specific ML algorithms is automated. This means that users have to spend less time dealing with and learning about 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. The joint project "AutoQML" is based on 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 developers with simplified access to conventional and quantum ML algorithms via an open source platform. In addition to Fraunhofer, the companies GFT Integrated Systems, USU Software AG, IAV GmbH Ingenieursgesellschaft Auto und Verkehr, KEB Automation KG, TRUMPF Werkzeug-maschinen GmbH + Co KG and Zeppelin GmbH 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.

The best of both worlds: Software library for hybrid total solutions

The project consortium will integrate components of quantum computing into current machine learning solution approaches in order to be able to use the performance, speed and complexity advantages of quantum algorithms in an industrial context. In the so-called AutoQML-Developer Suite - a software library - developed quantum ML 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 project will run for three years. Further market dissemination by the corporate partners will enable the transfer of research-based high-technology to a broad industrial environment with the aim of significantly strengthening Germany as an industrial location. The project is funded by the German Federal Ministry of Economics and Climate Protection. More information: http://www.autoqml.ai/

This article originally appeared on m-q.ch - https://www.m-q.ch/de/quantencomputing-neue-potenziale-fuer-automatisiertes-maschinelles-lernen/