Applications and Projects

The Fraunhofer IIS technology enables an extremely fast, AI-based search and evaluation of similar game scenes in large sports tracking databases. As a result, relevant game situations can be found and automatically analyzed in less than a second in order to optimize game tactics and decisions based on data.

DARCII explores the use of artificial intelligence and federated learning to analyze GNSS interference across distributed and mobile sensor nodes. Different interference patterns are detected and characterized as individual fingerprints. The goal is to identify sources of interference more reliably and improve the resilience of satellite-based navigation.

Satellite navigation is an essential part of our critical infrastructure. The DARCY project develops an intelligent sensor network that detects, assesses, and localizes disruptions to GNSS signals. By combining machine learning and crowdsourcing, measurement data is aggregated to reliably identify sources of interference and increase the resilience of satellite-based navigation systems.

VerQueRD investigates how quantum computing could impact the security of today’s encryption methods. Using quantum-based, quantum-inspired and hybrid approaches, the project assesses potential risks and provides a basis for the transition to quantum-secure encryption

Attraqt’em explores the potential of quantum computing for intelligent and efficient energy planning. The project focuses on hybrid optimization methods that combine classical approaches with quantum algorithms to address complex challenges in energy system modeling.

QuaST develops software and services to make quantum computing more accessible for industrial applications. The project focuses on efficient solutions for complex optimization problems in areas such as logistics, production and business forecasting.

BayQS explores how quantum computing can be harnessed for practical applications. Its research focuses on areas including imaging, sensor technology, logistics, mobile communications and radio-based localization.

Bench-QC develops a universal benchmarking framework to compare the performance of quantum computers with classical computers for industrial applications. The aim is to reliably assess when quantum computing can offer advantages over conventional solutions.

QACI develops solutions to make quantum computing usable for science and industry. The focus is on optimized quantum algorithms, efficient compilation methods, and practical applications for today’s and future quantum computers.

In the field of radio-based localization, Fraunhofer IIS is developing machine learning-based methods to determine positions more reliably and accurately. The use of deep neural networks and hybrid sensor fusion is intended to improve or, in some cases, replace traditional localization methods in dynamic environments.