Quantum-Inspired Generative Adversarial Networks for Symbolic Data

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Generative Adversarial Networks (GANs) have demonstrated remarkable success in generating realistic images and other continuous data. However, their application to symbolic data, such as code, mathematical formulas, and logical expressions, has proven challenging. The inherent discrete nature of symbolic data, coupled with the typical stochastic nature of GAN training, leads to difficulties in capturing the complex relationships and structural constraints present in these domains. This paper proposes a novel approach leveraging quantum-inspired concepts – superposition and entanglement – within a GAN architecture to address these limitations. We introduce Quantum-Inspired Generative Adversarial Networks (Q-GANs) designed specifically for generating symbolic data. The Q-GAN incorporates layers inspired by quantum mechanics to explore a broader range of potential symbolic representations during the generation process, aiming to improve the quality and complexity of the generated outputs. We demonstrate the potential of this approach through theoretical analysis and a conceptual framework, outlining key components and training strategies. The core contribution lies in adapting quantum mechanical principles to overcome the inherent challenges of GANs when dealing with symbolic data.

DOI: 10.5281/zenodo.22633967

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