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Advanced Quantum Technologies

Dual Discriminators Quantum Generation Adversarial Network Based on Quantum Convolutional Neural Network

Li‐Hua Gong, Yu‐Qi Chen, Shun Zhou, Qing‐Wei Zeng

Published Jun 25, 2025• 36 citations
Publisher

Abstract

Abstract As a crucial component in quantum machine learning, quantum generative adversarial networks play a significant role in generating discrete distributions. However, due to issues such as the vanishing gradient and mode collapse, the results generated by quantum generative adversarial models are of suboptimal quality sometimes. Given the robust image feature extraction capabilities of quantum convolutional neural networks, a hybrid quantum convolutional neural network model is incorporated into the quantum generative adversarial network as a discriminator. Different from the traditional multi‐layer linear structure, this discriminator adopts a parallel structure. This parallel structure can analyze both the local and the global features of an image simultaneously. It can promptly detect the defects in the global distribution of generated data, prompting the generator to explore more data patterns and avoid falling into mode collapse. The feasibility of this solution is verified through generation experiments on the handwritten dataset, Fashion‐MNIST dataset, and CIFAR‐100 dataset. The experimental results show that the FID (Fréchet Inception Distance) scores of the generated results on these three datasets reach 14, 20, and 17 respectively, fully demonstrating the performance of this image generation algorithm.

DOI
10.1002/qute.202500224

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This study presents an investigation of the Dual Discriminator Hybrid Quantum Generative Adversarial Network (DDHQ-GAN), a framework designed to enhance the performance of conventional Generative Adversarial Networks (GANs) through the incorporation of a hybrid quantum discriminator. The proposed DDHQ-GAN architecture comprises three primary components: a generator and two discriminators. The research evaluates the efficacy of the DDHQ-GAN in comparison with existing GAN variants, employing the Fréchet Inception Distance (FID) as a quantitative metric to assess image generation quality. The study further examines the interplay between the structural configurations of parameterized quantum circuits, classical neural network architectures, and model hyperparameters, using the MNIST dataset as the experimental benchmark. Empirical results demonstrate that the DDHQ-GAN achieves superior performance, reflected by lower FID scores, while incurring only a marginal increase in the number of parameters and quantum computational resources.
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.
Abstract Quantum machine learning as a field has emerged rather quickly thanks to developments in quantum computing. Of these developments, the one of primary interest is the Quantum Generative Adversarial Network or QGAN which is an enhancement of the familiar GAN to use quantum computation necessary for producing synthetic images. To sum up, based on different types of experiments, QGANs outperformed classical GANs, especially in cases with images such as MNIST and Fashion MNIST datasets. Nevertheless, their capabilities are not fully comprehensible due to existing constraints in quantum systems technology, especially in the NISQ era. In this regard, the current study undertakes a proposed research direction that focuses on improving the resolution of grayscale images that have been produced from the “optdigits” dataset, which contains handwritten digit images. Our work then contrasts this with prior work in terms of FID scores, loss function values, runtime, and the resolution of the images. Further, we extend the work by carrying out the proposed methodology on the FMNIST dataset and provide results to corroborate the efficacy of the proposed technique, besides enabling comparison on the same platform with prior works.
Quantum Machine Learning has received a lot of attention recently in the research community due to its potential to provide computational advantages in areas including training neural networks and big data analytics. In this paper, we focus on generative approaches using quantum machine learning. Specifically, we train QuantumClassical Hybrid Generative Adversarial Networks (qGANs) for imaging applications. We use the MNIST and Fashion-MNIST datasets in our qGAN simulations, and we present examples that generate small-scale grayscale images of the zero digit and shirts classes. In our research, we used Xanadu's PennyLane quantum programming software to implement and evaluate our algorithms. Challenges in our study included long computational times and large memory requirements for training the qGAN. However, our results revealed that our proposed hybrid model achieves a higher average PSNR, a lower FID score, but a lower Inception score than a classical GAN.
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 presents a novel framework for Quantum Generative Adversarial Networks (QGANs) that integrates Quantum Kernel Learning into the discriminator architecture. By replacing the classical discriminator with a Quantum Kernel Discriminator (QKD), the proposed model leverages high-dimensional Hilbert space mappings to enhance separability between real and generated data. The quantum discriminator employs Pauli Feature Maps and Fidelity Quantum Kernels to evaluate sample similarity in the quantum space, leading to improved training stability and reduced mode collapse. Empirical results on the MNIST dataset demonstrate faster convergence, higher fooling scores, and improved sample diversity compared to classical GANs. Additionally, the application of Quantum Kernel Methods to the Iris dataset highlights the generalization capabilities of the QKD approach. These findings establish the potential of hybrid quantum-classical architectures in advancing generative modeling and open new avenues for applying quantum machine learning in low-data and high-complexity domains.