# FQXAI

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.