Pondicherry University

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researchsquare.com Nov 10, 2026
Hybrid quantum medical models get two-level explanations: exact attribution over 8 semantic dims, and CNOT pruning that raises accuracy under noise
Hybrid quantum-classical medical models can predict but cannot say which semantic factors or which quantum gates drove a decision. FQXAI compresses inputs into an eight-dimensional proxy-supervised semantic bottleneck, turns it into IF-THEN rationales through Gaussian fuzzy membership states, and computes exact Shapley values over all 256 semantic coalitions without Monte Carlo sampling. On MIT-BIH Arrhythmia, PneumoniaMNIST and PathMNIST the models reached accuracies of 0.890, 0.768 and 0.726, and under 5% depolarizing noise SVQX-guided pruning cut CNOT counts from 14 to 11, 12 and 10 while raising accuracy by 2.20, 1.74 and 1.51 percentage points.
Hybrid quantum-classical medical models can predict but cannot say which semantic factors or which quantum gates drove a decision. FQXAI compresses inputs into an eight-dimensional proxy-supervised semantic bottleneck, turns it into IF-THEN rationales through Gaussian fuzzy membership states, and computes exact Shapley values over all 256 semantic coalitions without Monte Carlo sampling. On MIT-BIH Arrhythmia, PneumoniaMNIST and PathMNIST the models reached accuracies of 0.890, 0.768 and 0.726, and under 5% depolarizing noise SVQX-guided pruning cut CNOT counts from 14 to 11, 12 and 10 while raising accuracy by 2.20, 1.74 and 1.51 percentage points.
Pondicherry University
Ankit Raj（First author, Corresponding author）
FQXAI Quantum Machine Learning Explainable AI SHAP concept bottleneck models
Natural Sciences and Applied Technology Sep 30, 2026
Fuzzy-rank feature selection plus H2O AutoML ensembles reach up to 95.1% accuracy and 98.1% AUC on two public cervical cancer datasets
The work introduces an interpretable Fuzzy Rank-H2O AutoML framework in which a Fuzzy Rank Feature Selection (FRFS) algorithm picks predictors by combining statistical significance, information gain, clinical importance, and uncertainty, H2O AutoML then automatically builds ensemble models, and SHAP and LIME supply global and patient-level explanations; evaluated on two public cervical cancer datasets with stratified five-fold cross-validation where SMOTE is applied only to training folds to avoid information leakage, it reports a highest accuracy of 95.1% and AUC of 98.1%, outperforming traditional machine learning models and the baseline H2O AutoML framework.
The work introduces an interpretable Fuzzy Rank-H2O AutoML framework in which a Fuzzy Rank Feature Selection (FRFS) algorithm picks predictors by combining statistical significance, information gain, clinical importance, and uncertainty, H2O AutoML then automatically builds ensemble models, and SHAP and LIME supply global and patient-level explanations; evaluated on two public cervical cancer datasets with stratified five-fold cross-validation where SMOTE is applied only to training folds to avoid information leakage, it reports a highest accuracy of 95.1% and AUC of 98.1%, outperforming traditional machine learning models and the baseline H2O AutoML framework.
Pondicherry University
Saranya Aranganathan（First author）
Cervical Cancer and HPV Research Endometrial and Cervical Cancer Treatments AI in cancer detection

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Hybrid quantum medical models get two-level explanations: exact attribution over 8 semantic dims, and CNOT pruning that raises accuracy under noise

Fuzzy-rank feature selection plus H2O AutoML ensembles reach up to 95.1% accuracy and 98.1% AUC on two public cervical cancer datasets