  This article deals with the problem of the uncertainty in rule-based systems
(RBS), but from the perspective of quantum computing (QC). In this work we
first remember the characteristics of Quantum Rule-Based Systems (QRBS), a
concept defined in a previous article by one of the authors of this paper, and
we introduce the problem of quantum uncertainty. We assume that the subjective
uncertainty that affects the facts of classical RBSs can be treated as a direct
consequence of the probabilistic nature of quantum mechanics (QM), and we also
assume that the uncertainty associated with a given hypothesis is a consequence
of the propagation of the imprecision through the inferential circuits of RBSs.
This article does not intend to contribute anything new to the QM field: it is
a work of artificial intelligence (AI) that uses QC techniques to solve the
problem of uncertainty in RBSs. Bearing the above arguments in mind a quantum
model is proposed. This model has been applied to a problem already defined by
one of the authors of this work in a previous publication and which is briefly
described in this article. Then the model is generalized, and it is thoroughly
evaluated. The results obtained show that QC is a valid, effective and
efficient method to deal with the inherent uncertainty of RBSs
