TAQO-PAM

Quantum Algorithms for Optimising Planning and Control in Industrial Production

The increasing mass production of individualised goods and the complex logistics required within modern factories demand the solution of extensive optimisation problems in real time. Classical computers cannot solve such problems sufficiently well. This project therefore aims to design hybrid, quantum-classical algorithms. The algorithms will enable the soon to be available quantum computers with a few tens of qubits to contribute to solving these problems. This is done by integrating customised quantum processors (QPUs) into existing scenarios and by extending existing methods of factory automation and production planning.

Algorithms for Solving Industrial Problems

By focussing on local data processing directly in the company instead of using external cloud services, the need to share basic knowledge and production runtime data with third parties is avoided. In addition, there are no delays caused by data transfers during time-critical calculations. Based on the assumption that suitable customised QPUs will be available in the medium term, the project addresses the lack of quantum algorithms for optimising manufacturing tasks, the lack of integration of quantum computing into industrial processes and the accessibility of the technology for users to whom the results should be made available without in-depth quantum mechanical and quantum computing knowledge.

By systematically transferring real-world problems to methods that combine the advantages of quantum algorithms with the advantages of classical algorithms, the aim is to successfully solve industrially utilisable applications. One focus here is also on the interaction with artificial intelligence algorithms, for example in the field of quantum reinforcement learning. In the future, the algorithms developed in the project can also be executed and expanded on more powerful quantum computers so that even more complex optimisations of production processes will be possible, further increasing the productivity and competitiveness of companies.