Quantum decision tree classifier

Manuel Pegalájar Cuéllar, L. G. B. Ruiz, M. C. Pegalajar

Avery M. Leider, Gio Abou Jaoude, Abigail Strobel +1 more

Saeide Hajighasemi

In this paper, we propose a post-hoc confidence- aware explanation layer for Quantum Logic Decision Trees (QLDTs) in binary classification. The goal is to improve the decision-level interpretability of QLDT+ predictions without modifying the underlying decision mechanism. The proposed layer translates class-labelled QLDT+ paths into linguistic IF– THEN rules and attaches an object-level explanation score to each prediction. This score is computed as a transparent aggregation of three interpretable components: rule-path acti- vation, empirical path reliability, and the normalized distance from the QLDT+ decision threshold. The resulting value is not intended to estimate a posterior class probability; rather, it serves as an explanation-based confidence indicator for decision support. Since the layer is applied only after the QLDT+ prediction has been made, the predicted labels remain fully determined by the original CQQL evaluation score and the global threshold. Experiments on the Banknote Authentication and Diabetes datasets show that correctly classified instances receive higher average explanation confidence than incorrectly classified ones, while near-threshold cases receive lower confi- dence values. Selective-prediction, component-wise ablation, and weight-sensitivity analyses further indicate that the proposed score provides useful information about decision stability and reliability while preserving the original QLDT+ classifier.

Emmanuel Zambrini Cruzeiro, Christine De Mol, Serge Massar +1 more

Riyaaz Uddien Shaik, Aiswarya Unni, Weiping Zeng

A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for pseudo-labelling of samples. Here, a PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral dataset is prepared by quantum-based pseudo-labelling and 11 different machine learning algorithms viz., support vector machine (SVM), K-nearest neighbour (KNN), random forest (RF), light gradient boosting machine (LGBM), XGBoost, support vector classifier (SVC) + decision tree (DT), RF + SVC, RF + DT, XGBoost + SVC, XGBoost + DT, and XGBoost + RF with this dataset are evaluated. An accuracy of 86% was obtained for the classification of pine trees using the hybrid XGBoost + decision tree technique.

Edward Grant, Marcello Benedetti, Cao, S +5 more