Quantum-Inspired Optimization Process for Data Imputation
This study proposes a quantum-inspired imputation framework to handle missing clinical data in a diabetes dataset with implausible zeros. It combines Principal Component Analysis (PCA) with quantum rotation optimization, using gradient-free methods like Simulated Annealing to find rotations that preserve statistical fidelity. The method significantly improves distributional accuracy compared to classical and modern machine learning imputation techniques.