# An Explainable Probabilistic Classifier for Categorical Data Inspired to Quantum Physics Source: [An Explainable Probabilistic Classifier for Categorical Data Inspired to Quantum Physics]() Collection index: [llms.txt]() Data updated: 2026-01-03T15:11:13.972Z Authors: Emanuele Guidotti, Alfio Ferrara Published: 2021-05-26T15:41:30.000Z ## Abstract This paper presents Sparse Tensor Classifier (STC), a supervised classification algorithm for categorical data inspired by the notion of superposition of states in quantum physics. By regarding an observation as a superposition of features, we introduce the concept of wave-particle duality in machine learning and propose a generalized framework that unifies the classical and the quantum probability. We show that STC possesses a wide range of desirable properties not available in most other machine learning methods but it is at the same time exceptionally easy to comprehend and use. Empirical evaluation of STC on structured data and text classification demonstrates that our methodology achieves state-of-the-art performances compared to both standard classifiers and deep learning, at the additional benefit of requiring minimal data pre-processing and hyper-parameter tuning. Moreover, STC provides a native explanation of its predictions both for single instances and for each target label globally. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Text Classification on 20 Newsgroups by-date (test)](): Text Classification; 20 Newsgroups by-date (test); Accuracy: 87.3. - [Classification on Zoo UCI (test)](): Classification; Zoo UCI (test); Precision: 95.3. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()