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Quantum-Inspired Data Augmentation for Computer Vision

Jincheng Zhang

Published Sep 2, 2026• 0 citations
Publisher

Abstract

This paper proposes a novel data augmentation technique inspired by quantum mechanics, specifically leveraging the principles of superposition and quantum random access memory (QRAM) simulation. Traditional data augmentation methods often rely on simple transformations like rotations, flips, and color jittering, which may not fully capture the inherent variability within a dataset. Our approach aims to generate more diverse and realistic augmented data by simulating a QRAM, effectively creating multiple correlated versions of training images simultaneously. The core idea is to represent each training image as a superposition of multiple states, allowing us to explore a vastly larger solution space than conventional methods. We quantify the augmentation process using metrics related to image entropy and feature space coverage. Preliminary simulations indicate that this quantum-inspired augmentation can significantly improve the robustness and generalization performance of computer vision models compared to standard augmentation techniques. Further research is planned to explore the practical implementation of this concept and its scalability.

DOI
10.5281/zenodo.22248988

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