  A classical decision tree is completely based on splitting measures, which
utilize the occurrence of random events in correspondence to its class labels
in order to optimally segregate datasets. However, the splitting measures are
based on greedy strategy, which leads to construction of an imbalanced tree and
hence decreases the prediction accuracy of the classical decision tree
algorithm. An intriguing approach is to utilize the foundational aspects of
quantum computing for enhancing decision tree algorithm. Therefore, in this
work, we propose to use fidelity as a quantum splitting criterion to construct
an efficient and balanced quantum decision tree. For this, we construct a
quantum state using the occurrence of random events in a feature and its
corresponding class. The quantum state is further utilized to compute fidelity
for determining the splitting attribute among all features. Using numerical
analysis, our results clearly demonstrate that the proposed algorithm
cooperatively ensures the construction of a balanced tree. We further compared
the efficiency of our proposed quantum splitting criterion to different
classical splitting criteria on balanced and imbalanced datasets. Our
simulation results show that the proposed splitting criterion exceeds all
classical splitting criteria for all possible evaluation metrics.
