This paper implements the Transferable Belief Model (TBM) on quantum circuits, demonstrating that it offers a more concise and efficient alternative to Bayesian approaches within the framework of quantum computing. TBM, a semantic interpretation of Dempster-Shafer theory that enables reasoning and decision-making in uncertain and incomplete environments, offers a unique semantics for handling uncertain testimony. Despite the inherent computational complexity, we propose a novel belief transfer approach that leverages the unique characteristics of quantum computing, offering a new perspective on the fundamental information representation of quantum AI models. This suggests that belief functions are more appropriate than Bayesian approaches for handling uncertainty in quantum circuits.
