Algorithmic Bias Mitigation via Quantum Amplitude Amplification

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Abstract: This paper explores the potential of quantum amplitude amplification (QAA) to address algorithmic bias in machine learning datasets. The inherent biases present within large datasets necessitate innovative mitigation strategies, and quantum computing offers a novel pathway to address this challenge. We propose a framework for leveraging quantum algorithms to directly correct subtle biases, potentially surpassing existing post-processing techniques. The core mechanism centers around utilizing quantum superposition to amplify subtle variations in data, effectively 'correcting' the underlying data distribution. The research will outline a theoretical foundation for QAA's application to bias detection and correction within machine learning models, examining its potential impact on fairness and accuracy.

DOI: 10.5281/zenodo.22155790

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