# Quantum Generative Adversarial Networks: Bridging Classical and Quantum Realms

arXiv: https://arxiv.org/abs/2312.09939 · HTML: https://mlsanity.com/paper/2312.09939/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Quantum generative adversarial networks](https://mlsanity.com/paper/1804.08641/) (2018)
- [Experimental Quantum Generative Adversarial Networks for Image Generation](https://mlsanity.com/paper/2010.06201/) (2020)
- [Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation](https://mlsanity.com/paper/2212.11614/) (2022)
- [Learning quantum data with the quantum Earth Mover's distance](https://mlsanity.com/paper/2101.03037/) (2021)
- [Generalization Metrics for Practical Quantum Advantage in Generative Models](https://mlsanity.com/paper/2201.08770/) (2022)

## Built on by (most cited first, 6 of 6)

- [QGAN-based data augmentation for hybrid quantum-classical neural networks](https://mlsanity.com/paper/2505.24780/) (2025)
- [Quantum-Enhanced Generative Adversarial Networks: Comparative Analysis of Classical and Hybrid Quantum-Classical Generative Adversarial Networks](https://mlsanity.com/paper/2508.09209/) (2025)
- [Do We Really Need Quantum Machine Learning?: A Multidimensional Empirical Study](https://mlsanity.com/paper/2605.27923/) (2026)
- [Quantum-Enhanced Generative Models for Rare Event Prediction](https://mlsanity.com/paper/2511.02042/) (2025)
- [Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions](https://mlsanity.com/paper/2512.15286/) (2025)
- [Towards quantum machine learning for assessing the resilience of post-quantum cryptography](https://mlsanity.com/paper/2607.13722/) (2026)

## Abstract (arXiv)

In this pioneering research paper, we present a groundbreaking exploration into the synergistic fusion of classical and quantum computing paradigms within the realm of Generative Adversarial Networks (GANs). Our objective is to seamlessly integrate quantum computational elements into the conventional GAN architecture, thereby unlocking novel pathways for enhanced training processes. Drawing inspiration from the inherent capabilities of quantum bits (qubits), we delve into the incorporation of quantum data representation methodologies within the GAN framework. By capitalizing on the unique quantum features, we aim to accelerate the training process of GANs, offering a fresh perspective on the optimization of generative models. Our investigation deals with theoretical considerations and evaluates the potential quantum advantages that may manifest in terms of training efficiency and generative quality. We confront the challenges inherent in the quantum-classical amalgamation, addressing issues related to quantum hardware constraints, error correction mechanisms, and scalability considerations. This research is positioned at the forefront of quantum-enhanced machine learning, presenting a critical stride towards harnessing the computational power of quantum systems to expedite the training of Generative Adversarial Networks. Through our comprehensive examination of the interface between classical and quantum realms, we aim to uncover transformative insights that will propel the field forward, fostering innovation and advancing the frontier of quantum machine learning.