Lobe, ElisabethKlein, MaikeKrupka, DanielWinter, CorneliaWohlgemuth, Volker2023-11-292023-11-292023978-3-88579-731-9https://dl.gi.de/handle/20.500.12116/43045Quantum annealers solve Ising problems heuristically. Several standard methods have been established to transform more complex problems into the Ising problem format, which are commonly still applied by hand. In this work, we present our software package quark, automating the full transformation process from an arbitrary discrete optimization problem to the corresponding Ising problem. Based on a parameterized formulation of the original problem, a series of easily reproducible experiments can thus be set up. This allows users to evaluate the suitability of the annealing machines in solving their specific problem without a deeper knowledge about the Ising problem specifics.enIsing ProblemQUBODiscrete OptimizationQuantum AnnealingQuantum ComputingText/Conference Paper10.18420/inf2023_1231617-5468