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The study is centered on two eminent quantum algorithms: quantum support vector machines (QSVM) and variational quantum classifiers (VQC). Our findings demonstrate that both QSVM and VQC attain notable predictive accuracy when confined to merely 5 or 6 critical features, indicating that an optimized subset of observables can maintain robust performance with reduced computational cost. In examining the relationship between accuracy and the number of features, we demonstrated that the incorporation of supplementary features may even negatively impact the model\u2019s accuracy. The proposed framework underscores the potential of interdisciplinary strategies for the analysis of intricate quantum systems, furthering progress in the application of quantum machine learning to recognize patterns in quantum information science.<\/jats:p>","DOI":"10.1007\/s11128-026-05132-1","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T06:30:44Z","timestamp":1774247444000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantum phases classification using quantum machine learning with SHAP-driven feature selection"],"prefix":"10.1007","volume":"25","author":[{"given":"Giovanni S.","family":"Franco","sequence":"first","affiliation":[]},{"given":"Felipe","family":"Mahlow","sequence":"additional","affiliation":[]},{"given":"Pedro M.","family":"Prado","sequence":"additional","affiliation":[]},{"given":"Guilherme E. 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