  Bayesian networks are powerful tools for probabilistic analysis and have been
widely used in machine learning and data science. Unlike the time-consuming
parameter training process of neural networks, Bayes classifiers constructed on
Bayesian networks can make decisions based solely on statistical data from
samples. In this paper, we focus on constructing quantum Bayes classifiers
(QBCs). We design both a naive QBC and three semi-naive QBCs (SN-QBCs). These
QBCs are then applied to image classification tasks. To reduce computational
complexity, we employ a local feature sampling method to extract a limited
number of feature attributes from an image. These attributes serve as nodes of
the Bayesian networks to generate the QBCs. We simulate these QBCs on the
MindQuantum platform and evaluate their performance on the MNIST and
Fashion-MNIST datasets. Our results demonstrate that these QBCs achieve good
classification accuracies even with a limited number of attributes. The
classification accuracies of QBCs on the MNIST dataset surpass those of
classical Bayesian networks and quantum neural networks that utilize all
available feature attributes.
