Quantum-Inspired Optimization Process for Data Imputation

• URL: http://arxiv.org/abs/2505.04841v2
• Date: Sun, 11 May 2025 00:20:39 GMT
• Title: Quantum-Inspired Optimization Process for Data Imputation
• Authors: Nishikanta Mohanty, Bikash K. Behera, Badshah Mukherjee, Christopher Ferrie,
• Abstract summary: This study introduces a novel quantum-inspired imputation framework evaluated on the UCI Diabetes dataset.<n>The method integrates Principal Component Analysis (PCA) with quantum-assisted rotations, optimized through gradient-free classicals.<n>It reconstructs missing values while preserving statistical fidelity.
• Score: 1.5186937600119894
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Data imputation is a critical step in data pre-processing, particularly for datasets with missing or unreliable values. This study introduces a novel quantum-inspired imputation framework evaluated on the UCI Diabetes dataset, which contains biologically implausible missing values across several clinical features. The method integrates Principal Component Analysis (PCA) with quantum-assisted rotations, optimized through gradient-free classical optimizers -COBYLA, Simulated Annealing, and Differential Evolution to reconstruct missing values while preserving statistical fidelity. Reconstructed values are constrained within +/-2 standard deviations of original feature distributions, avoiding unrealistic clustering around central tendencies. This approach achieves a substantial and statistically significant improvement, including an average reduction of over 85% in Wasserstein distance and Kolmogorov-Smirnov test p-values between 0.18 and 0.22, compared to p-values > 0.99 in classical methods such as Mean, KNN, and MICE. The method also eliminates zero-value artifacts and enhances the realism and variability of imputed data. By combining quantum-inspired transformations with a scalable classical framework, this methodology provides a robust solution for imputation tasks in domains such as healthcare and AI pipelines, where data quality and integrity are crucial.

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