Algorithmic Bias Mitigation via Quantum Bias

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper explores the potential of quantum computing to address algorithmic bias in machine learning models. Current machine learning systems often exhibit systematic biases due to the data they are trained on, leading to unfair or discriminatory outcomes. We propose a novel algorithmic approach utilizing quantum entanglement to actively mitigate this bias by manipulating quantum states to counter correlated noise. The core mechanism involves the design of a quantum stabilizer that monitors and corrects correlated noise within the model, offering a fundamentally different approach than traditional bias mitigation techniques. We outline the theoretical framework, the proposed quantum algorithm, and preliminary experimental results demonstrating the effectiveness of this approach.

DOI: 10.5281/zenodo.22245119

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