Armin Wolf

Armin Wolf, Cristian Grozea

Obtaining Quadratic Unconstrained Binary Optimisation models for various optimisation problems, in order to solve those on physical quantum computers (such as the the DWave annealers) is nowadays a lengthy and tedious process that requires one to remodel all problem variables as binary variables and squeeze the target function and the constraints into a single quadratic polynomial into these new variables. We report here on the basis of our automatic converter from MiniZinc to QUBO, which is able to process a large set of constraint optimisation and constraint satisfaction problems and turn them into equivalent QUBOs, effectively optimising the whole process.

Patricia Bickert, Cristian Grozea, Ronny Hans et al.

We propose and compare Constraint Programming (CP) and Quantum Annealing (QA) approaches for rolling stock assignment optimisation considering necessary maintenance tasks. In the CP approach, we model the problem with an Alldifferent constraint, extensions of the Element constraint, and logical implications, among others. For the QA approach, we develop a quadratic unconstrained binary optimisation (QUBO) model. For evaluation, we use data sets based on real data from Deutsche Bahn and run the QA approach on real quantum computers from D-Wave. Classical computers are used to evaluate the CP approach as well as tabu search for the QUBO model. At the current development stage of the physical quantum annealers, we find that both approaches tend to produce comparable results.