Tremendous progress has been witnessed in artificial intelligence, where neural network backed deep learning systems have been used, with applications in almost every domain. As a representative deep learning framework, Generative Adversarial Network (GAN) has been widely used for generating artificial images, text-to-image or image augmentation across areas of science, arts and video games. However, GANs are computationally expensive, sometimes computationally prohibitive. Furthermore, training GANs may suffer from convergence failure and modal collapse. Aiming at the acceleration of use cases for practical quantum computers, we propose QuGAN, a quantum GAN architecture that provides stable convergence, quantum-states based gradients and significantly reduced parameter sets. The QuGANarchitecture runs both the discriminator and the generator purely on quantum state fidelity and utilizes the swap test on qubits to calculate the values of quantum-based loss functions. Built on quantum layers, QuGAN achieves similar performance with a 94.98% reduction on the parameter set when compared to classical GANs. With the same number of parameters, addition-ally, QuGAN outperforms state-of-the-art quantum based GANsin the literature providing a 48.33% improvement in system performance compared to others attaining less than 0.5% in terms of similarity between generated distributions and original data sets.
Tremendous progress has been witnessed in artificial intelligence, where neural network backed deep learning systems have been used, with applications in almost every domain. As a representative deep learning framework, Generative Adversarial Network (GAN) has been widely used for generating artificial images, text-to-image or image augmentation across areas of science, arts and video games. However, GANs are computationally expensive, sometimes computationally prohibitive. Furthermore, training GANs may suffer from convergence failure and modal collapse. Aiming at the acceleration of use cases for practical quantum computers, we propose QuGAN, a quantum GAN architecture that provides stable convergence, quantum-states based gradients and significantly reduced parameter sets. The QuGANarchitecture runs both the discriminator and the generator purely on quantum state fidelity and utilizes the swap test on qubits to calculate the values of quantum-based loss functions. Built on quantum layers, QuGAN achieves similar performance with a 94.98% reduction on the parameter set when compared to classical GANs. With the same number of parameters, addition-ally, QuGAN outperforms state-of-the-art quantum based GANsin the literature providing a 48.33% improvement in system performance compared to others attaining less than 0.5% in terms of similarity between generated distributions and original data sets.
Tremendous progress has been witnessed in artificial intelligence where neural network backed deep learning systems have been used, with applications in almost every domain. As a representative deep learning framework, Generative Adversarial Network (GAN) has been widely used for generating artificial images, text-to-image or image augmentation across areas of science, arts and video games. However, GANs are computationally expensive, sometimes computationally prohibitive. Furthermore, training GANs may suffer from convergence failure and modal collapse. Aiming at the acceleration of use cases for practical quantum computers, we propose QuGAN, a quantum GAN architecture that provides stable convergence, quantum-states based gradients and significantly reduced parameter sets. The QuGAN architecture runs both the discriminator and the generator purely on quantum state fidelity and utilizes the swap test on qubits to calculate the values of quantum-based loss functions. Built on quantum layers, QuGAN achieves similar performance with a 94.98% reduction on the parameter set when compared to classical GANs. With the same number of parameters, additionally, QuGAN outperforms state-of-the-art quantum based GANs in the literature providing a 48.33% improvement in system performance compared to others attaining less than 0.5% in terms of similarity between generated distributions and original data sets. QuGAN code is released at https://github.com/yingmao/Quantum-Generative-Adversarial-Network
Abstract Generative Adversarial Networks (GANs) have revolutionized Deep learning-based generative models are still challenging to train. This is due to issues such as mode collapse, instability, and non-convergence. This article provides a comprehensive analysis of the challenges faced by classical GANs, offering insights into their computational flow and limitations. Additionally, it explores Quantum Generative Adversarial Networks (QuGANs), detailing their unique architecture and the mechanism of quantum information generation. To further advance the field, this work presents an in-depth analysis of a hybridized model combining classical GANs and QuGANs, leveraging the strengths of both paradigms to enhance learning and stability. Furthermore, a novel algorithm for training quantum–classical GANs is proposed, with a detailed complexity analysis to assess its computational efficiency. The integration of quantum principles into classical frameworks offers a promising direction to mitigate existing GANs limitations while unlocking new possibilities for quantum machine learning. The proposed approach aims to bridge the gap between classical and quantum generative models, improving training dynamics and sample quality. The objective of this investigation is to establish a foundation for improving the efficiency and comprehension of hybrid GANs architectures, which will allow the design of more efficient and durable generative models in quantum computing.
Generative Adversarial Networks (GANs) are generative models that function as a minimax game, in which a generative network and a discriminative network compete against each other with the goal of creating data that convincingly resembles the real sample. With the advent of Quantum Computing and the development of Quantum Machine Learning (QML) models, Quantum Generative Adversarial Networks (QuGANs) have been increasingly studied due to the possible advantages this new type of architecture can offer, especially regarding performance improvements, scalability, and the exploration of new applications. In this context, this study's guiding question is: how were QuGANs developed between the years 2018 and 2025? To answer this question, the general objective of this work is to conduct a systematic literature review of QuGANs during the proposed period. Using a systematic literature review as the methodological basis, articles published and available on the online platforms Lens, Scopus, and Web of Science were selected, and the Rayyan tool was employed to identify duplicate works and those that did not specifically address QuGANs. As a result, a prevalence of hybrid models was observed, in which the developed architecture integrates quantum and classical characteristics in a complementary manner. Regarding the type of application, approaches involving theoretical foundations and image generation stand out as the most common. Other application areas are also explored (chemistry and pharmaceuticals, quantum error correction, high-energy physics, experimental implementation, medical applications, cloud computing, anomaly detection, telecommunications, biometrics, finance, physics and simulations, security and cryptography, software engineering, noise in QuGANs, and survey), demonstrating the broad potential of QuGANs across various research fields and industrial sectors.
Quantum mechanics studies nature and its behavior at the scale of atoms and subatomic particles. By applying quantum mechanics, a lot of problems can be solved in a more convenient way thanks to its special quantum properties, such as superposition and entanglement. In the current noisy intermediate-scale quantum era, quantum mechanics finds its use in various fields of life. Following this trend, researchers seek to augment machine learning in a quantum way. The generative adversarial network (GAN), an important machine learning invention that excellently solves generative tasks, has also been extended with quantum versions. Since the first publication of a quantum GAN (QuGAN) in 2018, many QuGAN proposals have been suggested. A QuGAN may have a fully quantum or a hybrid quantum–classical architecture, which may need additional data processing in the quantum–classical interface. Similarly to classical GANs, QuGANs are trained using a loss function in the form of max likelihood, Wasserstein distance, or total variation. The gradients of the loss function can be calculated by applying the parameter-shift method or a linear combination of unitaries in order to update the parameters of the networks. In this paper, we review recent advances in quantum GANs. We discuss the structures, optimization, and network evaluation strategies of QuGANs. Different variants of quantum GANs are presented in detail.
Tremendous progress has been witnessed in artificial intelligence within the domain of neural network backed deep learning systems and its applications. As we approach the post Moore’s Law era, the limit of semiconductor fabrication technology along with a rapid increase in data generation rates have lead to an impending growing challenge of tackling newer and more modern machine learning problems. In parallel, quantum computing has exhibited rapid development in recent years. Due to the potential of a quantum speedup, quantum based learning applications have become an area of significant interest, in hopes that we can leverage quantum systems to solve classical problems. In this work, we propose a quantum deep learning architecture; we demonstrate our quantum neural network architecture on tasks ranging from binary and multi-class classification to generative modelling. Powered by a modified quantum differentiation function along with a hybrid quantum-classic design, our architecture encodes the data with a reduced number of qubits and generates a quantum circuit, loading it onto a quantum platform where the model learns the optimal states iteratively. We conduct intensive experiments on both the local computing environment and IBM-Q quantum platform. The evaluation results demonstrate that our architecture is able to outperform Tensorflow-Quantum by up to 12.51% and 11.71% for a comparable classic deep neural network on the task of classification trained with the same network settings. Furthermore, our GAN architecture runs the discriminator and the generator purely on quantum hardware and utilizes the swap test on qubits to calculate the values of loss functions. In comparing our quantum GAN, we note our architecture is able to achieve similar performance with 98.5% reduction on the parameter set when compared to classical GANs. With the same number of parameters, additionally, QuGAN outperforms other quantum based GANs in the literature for up to 125.0% in terms of similarity between generated distributions and original data sets.
Hybrid Quantum Generative Adversarial Networks (QGANs) represent one of the most promising convergences between quantum computation and deep learning. This survey consolidates developments in hybrid QGANs from 2021 to 2025 and highlights the emergence of the Quantum Implicit Neural Representation GAN (QINR-QGAN) as a state-of-the-art model. We examine architectural innovations, hybrid-layer mechanisms, quantum feature encodings, and training dynamics. Chronological analysis shows that advances such as data re-uploading circuits, strongly entangling layers, and implicit neural representations have significantly improved generative performance, convergence, and interpretability. Comparative experiments demonstrate up to 18 % improvement in FID scores and 22 % higher entropy in generated data. This paper also discusses future directions, including quantum transformers, NISQ deployment, and cross-domain applications, positioning QINR-QGAN as a scalable bridge between quantum and classical AI.