Quantum-Enhanced Algorithmic Bias Mitigation (QAEB)

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces QAEB, a novel algorithmic approach to mitigate bias in machine learning models. Traditional bias mitigation techniques often involve post-processing, which can be computationally expensive and may not fully address the underlying causes of bias. QAEB leverages quantum phenomena, specifically quantum interference, to proactively shield data points from influencing model decision-making. We detail the core mechanism, the underlying theoretical framework, and preliminary experimental results demonstrating the algorithm's effectiveness in reducing bias in a specific machine learning scenario. The paper concludes with a discussion of future research directions and potential applications.

DOI: 10.5281/zenodo.22491756

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