Quantum GANs

Objectives

The objectives of this project is to develop a quantum GAN using parameterized quantum circuits in order to be able to compare both classical and quantum models and study training stability, sample quality or convergence behavior.

This project is part of a broader exploration of Quantum Computing and Quantum Machine Learning.

Background

Generative Adversarial Networks consist of two competing models:

• Generator (G): Generates synthetic data
• Discriminator (D): Distinguishes real data from generated data

The objective function is describe as follows:

$$\min_{G} \max_{D} V(D, G) = \mathbb{E}_{x \sim p_{data}} [\log D(x)] + \mathbb{E}_{z \sim p_{z}} [\log(1 - D(G(z)))]$$

One objective of G is to minimize V, whereas another objective of G is to maximize V.

In this project, an hybrid architecture is opted for, as mentionned in most of research papers. Specifically, the generator is based on a variational quantum algorithm and the discriminator is a classic CNN, even though, both generator and discriminator could rely on quantum algorithms, leveraging quantum superposition and entanglement.

Structure

.
├── data/
│   └── dataset.py
│
├── qgan/              
│   ├── generator.py
│   ├── discriminator.py
│   └── vqc.py
│
├── experiments/
├── main.ipynb
├── LICENSE
└── README.md

References

• J. Jäger et al., Scaling Quantum Machine Learning without Tricks: High-Resolution and Diverse Image Generation [2026]
• Riofrio et al., A Characterization of Quantum Generative Models [2024]
• Benedetti et al., Parameterized quantum circuits as machine learning models [2019]
• I. Goodfellow et al., Generative Adversarial Networks [2014]
• S. Lloyd et al., Quantum Generative Adversarial Learning [2018]
• M. Cerezo et al. Variational Quantum Algorithms [2021]
• J.McClean et al. Barren plateus in quantum neural networke training landscapes

This list is not exhaustive, is subject to change.

License

This project is licensed under the APACHE 2.0 License.