Decisions and the description of decision behavior rely heavily on probability theory. Still there are irrationalities, paradoxes and fallacies when applying models like rational choice theory or expected utility theory. These models are based on classical probability (founded on Kolmogorov’s axioms). Quantum probability is an alternative way to assign probabilities to events. Recent work (see overview in Ashtiani 2015) showed progress in cognition, decision making and judgment and reasoning by testing if quantum probability models could explain paradoxes and fallacies better than using classical probability. To our knowledge fit was evaluated but of-sample prediction quality was not tested. We wanted to find out if it is possible to do predictions with quantum probability models. We found that quantum probability models can be used to calculate predictions values in order to evaluate the in-sample and out-of-sample quality of quantum probability models given a specific dataset. Some of the results indicate that quantum models are better than classical models and that further exploration is needed. In order to do that, we’ll develop a new model based on quantum probability and evaluate it using the Choice Prediction Competition 2015.