A Post-Hoc Explanation Layer for Quantum Logic Decision Trees Constructed with QLDT+

We propose a post-hoc explanation layer for binary Quantum Logic Decision Tree classifiers constructed with QLDT+. The layer does not modify the fitted tree, the decision threshold, or the predicted label. It translates the class-labelled QLDT+ paths into IF–THEN rules and attaches an explanation-based confidence score to each prediction. This score is the equal-weight average of three quantities: rule support, the activation mass of the contributing rules of the predicted class; path reliability, a class-specific reliability derived from the training evidence ratio; and the normalized distance of the QLDT+ score from the decision threshold. In a stratified 5-fold cross-validation on the Banknote and Diabetes datasets, which was repeated with ten random seeds, correct predictions received a higher mean score than incorrect ones in every run (mean difference 0.18 and 0.14), and near-threshold decisions received lower scores. In selective prediction, the error decreased as coverage decreased; on Diabetes, however, the decisions retained at 40% coverage or less were all predicted as class 0. The layer is therefore best read as a decomposable explanation of a fixed QLDT+ decision rather than as a calibrated probability or an optimized selective-prediction score.