Quantum-Inspired Noise Reduction in Machine Learning

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Noise within training datasets poses a substantial challenge to the effectiveness of machine learning models. Traditional methods for noise reduction often rely on assumptions about the nature of the noise, which may not always hold true. This paper proposes a novel approach to noise reduction inspired by quantum noise cancellation techniques. The core idea is to leverage a learned "reference" signal to actively suppress noise while preserving the integrity of the underlying data. We formulate this approach mathematically, defining key parameters and processes. The algorithm operates by estimating the noise covariance matrix, constructing a quantum-inspired reference signal, and then employing a projection-based method to remove the noise. We demonstrate the effectiveness of this method through theoretical analysis and present a mathematical framework for its implementation. The results indicate a significant improvement in model performance when trained on noisy data, highlighting the potential of quantum-inspired techniques in machine learning. Specifically, we explore the impact of different reference signal learning strategies and the influence of the noise covariance matrix estimation on the overall noise reduction performance. The approach offers a flexible and adaptive solution for data cleaning, particularly in scenarios where the characteristics of the noise are unknown.

DOI: 10.5281/zenodo.22270737

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