Quantum phases classification using quantum machine learning with SHAP-driven feature selection
Year: 2026
Type: article
Abstract: We introduce a novel methodology for the classification of quantum phases within the ANNNI (axial next-nearest-neighbor Ising) model by employing quantum machine learning (QML) techniques in conjunction with the Shapley additive explanations (SHAP) algorithm for effective feature selection. 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’s 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.
Source: Quantum Information Processing
Institution: Universidade Estadual Paulista (Unesp)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,990
OpenAlex ID: W7140118743