# Noise robustness and experimental demonstration of a quantum generative adversarial network for continuous distributions

arXiv: https://arxiv.org/abs/2006.01976 · HTML: https://mlsanity.com/paper/2006.01976/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

## Built on by (most cited first, 11 of 11)

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- [Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics](https://mlsanity.com/paper/2103.15470/) (2021)
- [Power of Quantum Generative Learning](https://mlsanity.com/paper/2205.04730/) (2022)
- [F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits](https://mlsanity.com/paper/2110.04253/) (2021)
- [Quantum Deep Learning for Mutant COVID-19 Strain Prediction](https://mlsanity.com/paper/2203.03556/) (2022)
- [Expressibility-Enhancing Strategies for Quantum Neural Networks](https://mlsanity.com/paper/2211.12670/) (2022)
- [Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs](https://mlsanity.com/paper/2501.08678/) (2025)
- [Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning](https://mlsanity.com/paper/2602.00048/) (2026)
- [Topology-Guided Quantum GANs for Constrained Graph Generation](https://mlsanity.com/paper/2512.10582/) (2025)

## Abstract (arXiv)

The potential advantage of machine learning in quantum computers is a topic of intense discussion in the literature. Theoretical, numerical and experimental explorations will most likely be required to understand its power. There has been different algorithms proposed to exploit the probabilistic nature of variational quantum circuits for generative modelling. In this paper, we employ a hybrid architecture for quantum generative adversarial networks (QGANs) and study their robustness in the presence of noise. We devise a simple way of adding different types of noise to the quantum generator circuit, and numerically simulate the noisy hybrid quantum generative adversarial networks (HQGANs) to learn continuous probability distributions, and show that the performance of HQGANs remain unaffected. We also investigate the effect of different parameters on the training time to reduce the computational scaling of the algorithm and simplify its deployment on a quantum computer. We then perform the training on Rigetti's Aspen-4-2Q-A quantum processing unit, and present the results from the training. Our results pave the way for experimental exploration of different quantum machine learning algorithms on noisy intermediate scale quantum devices.