Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2209.13993 · Quantum Science and Technology

Towards a scalable discrete quantum generative adversarial neural network

Smit Chaudhary, Patrick Huembeli, Ian MacCormack, Taylor L. Patti, Jean Kossaifi, Alexey Galda

Published Mar 16, 2023• 14 citations
arXivHTMLPDF

Abstract

Abstract Quantum generative adversarial networks (QGANs) have been studied in the context of quantum machine learning for several years, but there has not been yet a proposal for a fully QGAN with both, a quantum generator and discriminator. We introduce a fully QGAN intended for use with binary data. The architecture incorporates several features found in other classical and quantum machine learning models, which up to this point had not been used in conjunction. In particular, we incorporate classical noise reuploading in the generator, auxiliary qubits in the discriminator to enhance expressivity, and a direct connection between the generator and discriminator circuits, obviating the need to access the generator’s probability distribution. We show that, as separate components, the generator and discriminator perform as desired. We empirically demonstrate the expressive power of our model on both synthetic data as well as low energy states of an Ising model. Our demonstrations suggest that the model is not only capable of reproducing discrete training data, but also of potentially generalizing from it.

DOI
10.1088/2058-9565/acc4e4

Related papers

Quantum Generative Adversarial Networks (QGANs) for Data Augmentation

Jincheng Zhang

This paper investigates the application of Quantum Generative Adversarial Networks (QGANs) to data augmentation for machine learning. Traditional Generative Adversarial Networks (GANs) rely on classical neural networks, while this research explores leveraging quantum circuits as generators within a QGAN architecture. The core idea is to train a quantum generator to produce synthetic data samples, supplementing existing datasets with novel instances. The discriminator remains a classical neural network, tasked with distinguishing between real and generated data. We present a theoretical framework for this approach, outlining the mathematical formulation and key components. Specifically, we introduce the notation (x, s_1, s_2, ..., s_K) to represent data samples, generator states, and relevant parameters. The objective function for the QGAN is defined as minimizing the Jensen-Shannon divergence between the real and generated data distributions. The effectiveness of this approach is discussed, highlighting potential advantages and challenges. This work contributes to the emerging field of quantum machine learning and offers a novel strategy for improving the performance of machine learning models, particularly in scenarios with limited training data.

•Aug 31, 2026•0 citations

Quantum Generative Adversarial Networks for Synthetic Data Generation

Jincheng Zhang

This paper investigates the application of Quantum Generative Adversarial Networks (QGANs) for the generation of synthetic data. Traditional Generative Adversarial Networks (GANs) often face challenges in producing high-quality synthetic data, especially when dealing with complex data distributions. We propose a novel framework utilizing quantum circuits to construct both the generator and discriminator within a QGAN architecture. The core innovation lies in harnessing quantum superposition and interference during the adversarial training process to potentially improve the realism and diversity of the generated synthetic data. Our approach introduces a theoretical foundation and a conceptual design for QGANs, paving the way for future research and development in this emerging field. The primary goal is to demonstrate the potential benefits of incorporating quantum mechanical principles into the GAN framework, leading to enhanced synthetic data generation capabilities. The performance of a QGAN is expected to be superior to a classical GAN due to the unique properties of quantum systems.

•Sep 2, 2026•0 citations

Modeling Neuronal Activity with Quantum Generative Adversarial Networks

Vinicius Hernandes, Eliška Greplová

Understanding the information processing in neuronal networks relies on the development of computational models that accurately reproduce their activity data. Machine learning techniques have shown promising results in generating synthetic neuronal data, but interpretability remains an issue due to a large number of parameters requiring fitting. Quantum machine learning models, particularly quantum generative learning, are emerging as more compact alternatives that offer similar outcomes. This study presents an efficient framework for generating synthetic neuronal data using a Quantum Generative Adversarial Network (QGAN), with a quantum generator and a classical discriminator. We tested the proposed framework for the minimal case of two neurons, considering the case of single time-steps. Preliminary results demonstrate the QGAN's capability to achieve reliable outcomes with a reduced number of trainable parameters, scaling efficiently for increasing neuronal network sizes. The model effectively captures spiking frequencies of real data, although further refinement is required to incorporate temporal correlations for more extended time-steps. Despite certain limitations, this study lays the foundation for future advancements in using quantum adversarial generative networks to model neuronal activity. The promising potential of QGANs in this domain highlights the possibility of gaining valuable insights into the functioning of complex biological systems through quantum-inspired computational methods.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 17, 2023•1 citation

Quantum generative adversarial networks with dual generators

Quangong Ma, Chaolong Hao, Nianwen Si +1 more

Abstract Quantum generative adversarial networks (QGANs) have demonstrated strong capabilities in tasks like synthetic data generation and detecting anomalies. Recent developments have increasingly integrated traditional machine learning techniques to boost the performance of QGANs. Motivated by this progress, we propose an innovative QGAN architecture that incorporates a classical learning component and employs a dual-generator design. Our approach improves upon the traditional hybrid quantum–classical GAN structure and introduces a redesigned loss function tailored for the new model. Experiments on multiple datasets indicate that our method surpasses previous techniques in image generation quality, achieving a 1.38% average reduction in Fréchet inception distance scores compared to the current state-of-the-art, and improvements of 6.52%, 0.36%, and 0.38% in structural similarity index, cosine similarity, and peak signal-to-noise ratio metrics, respectively. Additionally, our architecture supports the generation of larger images (up to 78 × 78 ), as verified on the CelebA dataset. Simulations conducted in noisy conditions further confirm the robustness and effectiveness of both the proposed architecture and loss function.

Machine Learning Science and Technology•Sep 25, 2025•1 citation

Quantum Generative Adversarial Networks for Synthetic Data Augmentation

Jincheng Zhang

This paper explores the application of Quantum Generative Adversarial Networks (QGANs) for synthetic data augmentation, aiming to surpass the limitations of classical Generative Adversarial Networks (GANs). We investigate the potential of utilizing quantum circuits to implement the generator and discriminator components of a GAN, capitalizing on quantum phenomena like superposition and interference to enhance the data generation process. Our research demonstrates that QGANs, in principle, can produce synthetic data with improved quality and diversity, particularly when dealing with complex and high-dimensional datasets. We present a theoretical framework and analysis, highlighting the core claim that QGANs offer a promising avenue for generating more realistic and varied synthetic data. The novelty of this work lies in the exploration of quantum-enhanced GANs for data augmentation, a field with significant potential for advancing machine learning applications. The core mechanism focuses on harnessing quantum mechanics to refine the generative process.

•Sep 5, 2026•0 citations

On the generalization limits of quantum generative adversarial networks with pure state generators

Jasmin Frkatovic, Akash Malemath, Ivan Kankeu +7 more

We investigate the capabilities of quantum generative adversarial networks (QGANs) in image generations tasks. Our analysis centers on fully quantum implementations of both the generator and discriminator. Through extensive numerical testing of current main architectures, we find that QGANs struggle to generalize across datasets, converging on merely the average representation of the training data. When the output of the generator is a pure-state, we analytically derive a lower bound for the discriminator quality given by the fidelity between the pure-state output of the generator and the target data distribution, thereby providing a theoretical explanation for the limitations observed in current models. Our findings reveal fundamental challenges in the generalization capabilities of existing quantum generative models. While our analysis focuses on QGANs, the results carry broader implications for the performance of related quantum generative models.

arXiv:2508.09844•Scientific Reports•Jun 9, 2026•2 citations
Abstract Quantum generative adversarial networks (QGANs) have been studied in the context of quantum machine learning for several years, but there has not been yet a proposal for a fully QGAN with both, a quantum generator and discriminator. We introduce a fully QGAN intended for use with binary data. The architecture incorporates several features found in other classical and quantum machine learning models, which up to this point had not been used in conjunction. In particular, we incorporate classical noise reuploading in the generator, auxiliary qubits in the discriminator to enhance expressivity, and a direct connection between the generator and discriminator circuits, obviating the need to access the generator’s probability distribution. We show that, as separate components, the generator and discriminator perform as desired. We empirically demonstrate the expressive power of our model on both synthetic data as well as low energy states of an Ising model. Our demonstrations suggest that the model is not only capable of reproducing discrete training data, but also of potentially generalizing from it.
This paper investigates the application of Quantum Generative Adversarial Networks (QGANs) to data augmentation for machine learning. Traditional Generative Adversarial Networks (GANs) rely on classical neural networks, while this research explores leveraging quantum circuits as generators within a QGAN architecture. The core idea is to train a quantum generator to produce synthetic data samples, supplementing existing datasets with novel instances. The discriminator remains a classical neural network, tasked with distinguishing between real and generated data. We present a theoretical framework for this approach, outlining the mathematical formulation and key components. Specifically, we introduce the notation (x, s_1, s_2, ..., s_K) to represent data samples, generator states, and relevant parameters. The objective function for the QGAN is defined as minimizing the Jensen-Shannon divergence between the real and generated data distributions. The effectiveness of this approach is discussed, highlighting potential advantages and challenges. This work contributes to the emerging field of quantum machine learning and offers a novel strategy for improving the performance of machine learning models, particularly in scenarios with limited training data.
This paper investigates the application of Quantum Generative Adversarial Networks (QGANs) for the generation of synthetic data. Traditional Generative Adversarial Networks (GANs) often face challenges in producing high-quality synthetic data, especially when dealing with complex data distributions. We propose a novel framework utilizing quantum circuits to construct both the generator and discriminator within a QGAN architecture. The core innovation lies in harnessing quantum superposition and interference during the adversarial training process to potentially improve the realism and diversity of the generated synthetic data. Our approach introduces a theoretical foundation and a conceptual design for QGANs, paving the way for future research and development in this emerging field. The primary goal is to demonstrate the potential benefits of incorporating quantum mechanical principles into the GAN framework, leading to enhanced synthetic data generation capabilities. The performance of a QGAN is expected to be superior to a classical GAN due to the unique properties of quantum systems.
Understanding the information processing in neuronal networks relies on the development of computational models that accurately reproduce their activity data. Machine learning techniques have shown promising results in generating synthetic neuronal data, but interpretability remains an issue due to a large number of parameters requiring fitting. Quantum machine learning models, particularly quantum generative learning, are emerging as more compact alternatives that offer similar outcomes. This study presents an efficient framework for generating synthetic neuronal data using a Quantum Generative Adversarial Network (QGAN), with a quantum generator and a classical discriminator. We tested the proposed framework for the minimal case of two neurons, considering the case of single time-steps. Preliminary results demonstrate the QGAN's capability to achieve reliable outcomes with a reduced number of trainable parameters, scaling efficiently for increasing neuronal network sizes. The model effectively captures spiking frequencies of real data, although further refinement is required to incorporate temporal correlations for more extended time-steps. Despite certain limitations, this study lays the foundation for future advancements in using quantum adversarial generative networks to model neuronal activity. The promising potential of QGANs in this domain highlights the possibility of gaining valuable insights into the functioning of complex biological systems through quantum-inspired computational methods.
Abstract Quantum generative adversarial networks (QGANs) have demonstrated strong capabilities in tasks like synthetic data generation and detecting anomalies. Recent developments have increasingly integrated traditional machine learning techniques to boost the performance of QGANs. Motivated by this progress, we propose an innovative QGAN architecture that incorporates a classical learning component and employs a dual-generator design. Our approach improves upon the traditional hybrid quantum–classical GAN structure and introduces a redesigned loss function tailored for the new model. Experiments on multiple datasets indicate that our method surpasses previous techniques in image generation quality, achieving a 1.38% average reduction in Fréchet inception distance scores compared to the current state-of-the-art, and improvements of 6.52%, 0.36%, and 0.38% in structural similarity index, cosine similarity, and peak signal-to-noise ratio metrics, respectively. Additionally, our architecture supports the generation of larger images (up to 78 × 78 ), as verified on the CelebA dataset. Simulations conducted in noisy conditions further confirm the robustness and effectiveness of both the proposed architecture and loss function.
This paper explores the application of Quantum Generative Adversarial Networks (QGANs) for synthetic data augmentation, aiming to surpass the limitations of classical Generative Adversarial Networks (GANs). We investigate the potential of utilizing quantum circuits to implement the generator and discriminator components of a GAN, capitalizing on quantum phenomena like superposition and interference to enhance the data generation process. Our research demonstrates that QGANs, in principle, can produce synthetic data with improved quality and diversity, particularly when dealing with complex and high-dimensional datasets. We present a theoretical framework and analysis, highlighting the core claim that QGANs offer a promising avenue for generating more realistic and varied synthetic data. The novelty of this work lies in the exploration of quantum-enhanced GANs for data augmentation, a field with significant potential for advancing machine learning applications. The core mechanism focuses on harnessing quantum mechanics to refine the generative process.
We investigate the capabilities of quantum generative adversarial networks (QGANs) in image generations tasks. Our analysis centers on fully quantum implementations of both the generator and discriminator. Through extensive numerical testing of current main architectures, we find that QGANs struggle to generalize across datasets, converging on merely the average representation of the training data. When the output of the generator is a pure-state, we analytically derive a lower bound for the discriminator quality given by the fidelity between the pure-state output of the generator and the target data distribution, thereby providing a theoretical explanation for the limitations observed in current models. Our findings reveal fundamental challenges in the generalization capabilities of existing quantum generative models. While our analysis focuses on QGANs, the results carry broader implications for the performance of related quantum generative models.