Algorithmic Quantum Bias for Machine Learning

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum computing promises revolutionary advancements across numerous fields, and machine learning is no exception. However, current machine learning algorithms often exhibit biases stemming from the data they are trained on, leading to unfair or discriminatory outcomes. This paper investigates the potential of utilizing quantum algorithms to mitigate these biases, focusing on a novel approach employing quantum error correction and superposition to explore alternative solutions. We argue that this methodology offers a fundamentally new avenue for bias reduction, potentially identifying and neutralizing inherent biases within the learning process. The paper will present a conceptual framework, outlining the core mechanism and its potential benefits, while acknowledging the significant challenges and limitations inherent in this rapidly evolving field.

DOI: 10.5281/zenodo.22324620

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