Applications Optimization Innovation Center Ulm


We solve mission planning problems using three quantum algorithms and create an interface between a classical planning system and quantum computers.

We consider three challenges from space operations and implement them in a scalable way for quantum computers: In addition to on-call duty planning, this involves planning ground station contacts for satellite constellations and the best possible recording planning for an earth observation satellite while adhering to the technical barriers. In addition to the development of the quantum algorithms, the focus is on the operationalization and evaluation of the algorithms. In addition, we are researching further possible applications of quantum computers at the German Space Operations Center (GSOC).


There are already a large number of providers who provide NISQ systems or quantum annealers using web APIs. In order to verify their usability for real challenges and to make the German Space Operations Center quantum future-proof, we want to solve selected planning problems from operational space operations using such quantum computers.

In addition, we will analyze to what extent this can be implemented more efficiently, faster or more scalably than with classic solutions. Mission planning problems in particular are very suitable for this, as they are often of a generic nature.

By implementing the relevant services and interfaces, we get a good template for future implementation of further challenges. Moreover, many such problems can be solved with multiple algorithms. This is a good basis for comparisons between classical algorithms and different quantum algorithms.


Our main task is to implement real operational problems from mission planning using quantum algorithms. We will also optimize solutions in terms of their qubit and gate usage and integrate them into the operational workflow of satellite operations.

Since the capacity of current quantum computers is limited, we try to increase the size of solvable problems with the implemented algorithms. This happens, for example, through hybrid approaches, improved problem encoding and circuit optimization. We can often also optimize generic algorithms for specific problems.

In addition, we also want to integrate these methods into operations and the associated requirements. To do this, we define suitable interfaces, create services and enable connection to existing APIs. Finally, we show the use of the developed algorithms in an end-to-end demonstration.

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