Platform and Ecosystem for Quantum Assisted Artificial Intelligence
Jan. 01, 2020 to Dec. 31, 2023
Successful use of artificial intelligence (AI) requires deep knowledge and technical experience in using complementary technologies and concepts. With the rapid development of AI methods, the barriers to entry for small and medium-sized enterprises wishing to use AI in their products are also rising. Similarly, this applies to innovative approaches to combining AI and quantum computing, so-called quantum-assisted artificial intelligence (QKI).
In the PlanQK project, a web-based knowledge platform for QKI is being developed. The goal is to create a technical basis for promoting knowledge and technology exchange between quantum and AI experts, developers, and potential users of QKI. Researchers will use the platform to collect and discuss specific QKI algorithms, prepare them for execution on quantum computers, and validate them in industrial use cases. In addition, the platform provides a way to deploy datasets for training QKI models in public or in protected areas. Implementations and applications uploaded to the platform can be run on different quantum resources and analyzed using specific metrics.
Together with 18 partners and funded by the German Federal Ministry for Economic Affairs and Energy (BMWi), the scientists of the Data Analytics Center (DANA) of Fraunhofer FOKUS are working on the development of AI-based solutions for use cases from the three areas:
- "Forecasting and classification," e.g., forecasting and classification of anomaly and fraud detection in the financial sector
- "Maintenance and detection," e.g., industrial and public transportation maintenance; QKI for assisted COVID-19 reporting; road condition maintenance
- "Planning and optimization", e.g., optimizing production lines in industry, networking and controlling industrial components, and warehouse optimization
In addition, Fraunhofer FOKUS is extending the PlanQK platform to include semantic management of knowledge so that it is also available in a machine-readable and interpretable form for AI-based functions such as semantic search or question-answering systems.