About QLindA

Motivation

In recent years, the latest advances in artificial intelligence (AI) have made it possible for AI systems to independently learn to play games such as chess or Go better than any human or computer before. The key technology for this is called reinforcement learning and is now also used for adaptive control in industrial environments. The current rapid increase in the capacity of quantum computers opens up the possibility of using quantum computers in AI systems and offers the potential for groundbreaking performance improvements that could trigger a technological revolution with implications for a wide range of applications.

Goals and Approach

The project aims to combine the latest advances in quantum computing and artificial intelligence, in particular in reinforcement learning (RL), and make them technically usable. Based on existing scientific contributions, the project will investigate how RL can be implemented on quantum computers (QRL) in order to solve a variety of relevant problems from industrial applications: RL-based control optimization in the process industry, the use of distributed automation systems in the smart factory and optimization in production planning.

Innovation and Prospects

The fundamentally different approach to classical algorithm design, which is coupled to the hardware, requires research into the transferability of classical approaches to quantum algorithms even before error-corrected quantum computers become available. The project will develop novel algorithms, create a benchmark for evaluating the methods and a library to make them usable for industrial applications, and investigate the possibilities and potential as well as existing limitations.

Learn more about QLindA on the Fraunhofer website.