Status: Available

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Status: Available

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Status: In Progress

Title: MC-QLDT: A Multi-class Quantum-inspired Logic Classifier
Description: Quantum‑inspired logic decision trees (QLDT) have been shown to effectively capture curved decision boundaries while maintaining interpretability. However, they are limited to binary classification tasks. In many practical application domains, such as medical diagnosis, data naturally span multiple classes. This work proposes MC‑QLDT, an extension that generalizes the QLDT framework to multi‑class problems. We investigate several strategies and we evaluate the resulting models on benchmark datasets against classical decision trees and random forests, with a focus on both predictive accuracy and interpretability. We expect this work to offer a practical and interpretable solution for multi‑class classification tasks where decision boundaries are non‑linear and transparent reasoning is essential for user trust and adoption.