In this work, we propose IQGAN, a quantum Generative Adversarial Network (GAN) framework for multiqubit image synthesis that can be efficiently implemented on Noisy Intermediate Scale Quantum (NISQ) devices. We investigate the reasons for the inferior generative performance of current quantum GANs in our preliminary study and conclude that an adjustable input encoder is the key to ensuring high-quality data synthesis. We then propose the IQGAN architecture featuring a trainable multiqubit quantum encoder that effectively embeds classical data into quantum states. Furthermore, we propose a compact quantum generator that significantly reduces the design cost and circuit depth on NISQ devices. Experimental results on both IBM quantum processors and quantum simulators demonstrated that IQGAN outperforms state-of-the-art quantum GANs in qualitative and quantitative evaluation of the generated samples, model convergence, and quantum computing cost.
In this work, we propose IQGAN, a quantum Generative Adversarial Network (GAN) framework for multiqubit image synthesis that can be efficiently implemented on Noisy Intermediate Scale Quantum (NISQ) devices. We investigate the reasons for the inferior generative performance of current quantum GANs in our preliminary study and conclude that an adjustable input encoder is the key to ensuring high-quality data synthesis. We then propose the IQGAN architecture featuring a trainable multiqubit quantum encoder that effectively embeds classical data into quantum states. Furthermore, we propose a compact quantum generator that significantly reduces the design cost and circuit depth on NISQ devices. Experimental results on both IBM quantum processors and quantum simulators demonstrated that IQGAN outperforms state-of-the-art quantum GANs in qualitative and quantitative evaluation of the generated samples, model convergence, and quantum computing cost.
Quantum Generative Adversarial Networks (QGANs) represent a useful development in quantum machine learning, using the particular properties of quantum mechanics to solve the challenges of data analysis and modeling. This paper brings up a general analysis of five QGAN architectures, focusing on their evolution, strengths, weaknesses, and limitations in noisy intermediate-scale quantum (NISQ) devices. Primary methods like Entangling Quantum GAN (EQ-GAN) and Quantum state fidelity (QuGAN) concentrate on stability, convergence, and robust performance on small-scale datasets such as 2 × 2 grayscale images. Intermediate models such as Image Quantum GAN (IQGAN) and Experimental Quantum GAN (EXQGAN) provide new ideas like trainable encoders and patch-based sub-generators that are scalable to 8 × 8 datasets with increasing noise resilience. The most advanced method is Parameterized Quantum Wasserstein GAN (PQWGAN), which uses a hybrid quantum-classical structure to obtain high-resolution image processing for 28 × 28 grayscale datasets while trying to maintain parameter efficiency. This study explores, analyzes, and summarizes critical problems of QGANs, including accuracy, convergence, parameter efficiency, image quality, performance metrics, and training stability under noisy conditions. In addition, developing QGANs can generate and train parameters in quantum approximation optimization algorithms. One of the useful applications of QGAN is generating medical datasets that can generate medical images from limited datasets to train specific medical models for the recognition of diseases.
The development of Generative Adversarial Networks (GANs) revolutionized the creation of synthetic data and signified the start of a revolutionary age in artificial intelligence. GANs have been used over the years in a variety of domains, including video production, natural language processing, and image synthesis. However, issues with classical GANs include computational efficiency, limited scalability for high-dimensional data and significant processing costs. Quantum GANs (QGANs) have been developed to overcome these drawbacks by integrating adversarial training with the concepts of quantum computing. In order to improve efficiency and stability, QGANs make use of quantum superposition and entanglement. This makes them an effective choice for producing high-quality synthetic data. By using noisy intermediate scale quantum (NISQ) devices for effective, noise-tolerant computations, quantum GANs (QGANs) allow for high-dimensional data synthesis using fewer resources than traditional techniques. Among their many uses, QGANs are particularly good at picture synthesis, making use of quantum circuits to produce realistic images quickly. This research studies the architecture of QGANs, particularly quantum circuit designs, implementation strategies for QGANs, and a comparison of several performance metrics.
Quantum Generative Adversarial Networks (QGANs) includes the principles of quantum computing and adversarial learning networks, giving the advantages of generative quality, parallelism, and state-space exploration. This paper presents a comparative analysis of FID between QGANs and Wasserstein GANs (WGANs), a variant of classical GAN for AI-based image generation. These two models are evaluated using MNIST data set for Fréchet Inception Distance (FID) and Inception Score (IS). Further, Structural Similarity Index Measure (SSIM), convergence time and robustness to mode collapse is also estimated. The Experimental results show that QGANs offer faster convergence and improved detail retention for images with low-dimensional quantum-enhanced simulations. QGAN implementations are constrained by the limitations of Noisy Intermediate-Scale Quantum (NISQ) hardware, the outcome suggests promising future potential for QGANs in scalable AI-based image generation.
With the speedy advancement of hardware, quantum machine learning has garnered widespread attention. Generative Adversarial Networks (GANs) are widely adopted quantum machine learning frameworks used for providing highstandard audio content, images, and videos. Utilizing quantum computing for image generation has been found to offer several potential advantages over non-quantum technologies. Current quantum Generative Adversarial Networks (QGANs) still face challenges, such as the inability to generate high-quality images. In the current investigation, we propose a QGAN model based on Autoencoders (BAE-QCGAN) and evaluate its performance. By drawing upon the entanglement and expression capabilities of quantum circuits, our composite system outperforms classical GANs in terms of Frechet Inception Distance, requiring fewer training parameters and fewer iterations to converge. We also demonstrate that BAEQCGAN outperforms another quantum GAN, the patch-GAN, and is adapted for existing Noisy Intermediate Scale Quantum (NISQ) computers.
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.
One of the most promising applications in the era of Noisy Intermediate-Scale Quantum (NISQ) computing is quantum generative adversarial networks (QGANs), which offer significant quantum advantages over classical machine learning in various domains. However, QGANs suffer from mode collapse and lack explicit control over the features of generated outputs. To overcome these limitations, we propose InfoQGAN, a novel quantum-classical hybrid generative adversarial network that integrates the principles of InfoGAN with a QGAN architecture. Our approach employs a variational quantum circuit for data generation, a classical discriminator, and a Mutual Information Neural Estimator (MINE) to explicitly optimize the mutual information between latent codes and generated samples. Numerical simulations on synthetic 2D distributions and Iris dataset augmentation demonstrate that InfoQGAN effectively mitigates mode collapse while achieving robust feature disentanglement in the quantum generator. By leveraging these advantages, InfoQGAN not only enhances training stability but also improves data augmentation performance through controlled feature generation. These results highlight the potential of InfoQGAN as a foundational approach for advancing quantum generative modeling in the NISQ era.