Quantum-Inspired Diffusion Processes for Data Generation

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Abstract: Quantum-inspired diffusion processes offer a novel approach to data generation, leveraging the principles of quantum mechanics to create highly realistic and diverse synthetic data for training machine learning models. This paper explores the design of such processes, focusing on incorporating subtle quantum fluctuations to enhance data realism and robustness. The core mechanism centers on mimicking quantum phenomena within the diffusion algorithm, yielding synthetic data with unprecedented levels of complexity and variation. The research investigates the potential benefits of this approach and outlines a framework for future development.

DOI: 10.5281/zenodo.22610664

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