Images are a very useful form of information from medical diagnosis to scientific discovery. There are some areas where images are not enough but with the help of Generative Adversarial Networks (GANs) this problem is solved at some limit. Due to the success of classical generative adversarial networks, the quantum researcher created quantum version of GAN and also called as quantum generative adversarial network (QGANs) which works on quantum computers which use quantum mechanics techniques such as quantum entanglement, superposition, teleportation and many more. The successful implementation in real life of quantum computers are not easy because of limited quantum resources and access to quantum processors but instead of that there are different QGAN models for different purpose, such as drug discovery, finance, image generation and many more which shown good result as compare to their classical counterparts. This study is reviewed based on authors that proposed different QGAN models along with the techniques proposed on those papers for generating images, which datasets was considered, which parameters are considered, what their limitations and how to future research directions to overcome those problems.
Images are a very useful form of information from medical diagnosis to scientific discovery. There are some areas where images are not enough but with the help of Generative Adversarial Networks (GANs) this problem is solved at some limit. Due to the success of classical generative adversarial networks, the quantum researcher created quantum version of GAN and also called as quantum generative adversarial network (QGANs) which works on quantum computers which use quantum mechanics techniques such as quantum entanglement, superposition, teleportation and many more. The successful implementation in real life of quantum computers are not easy because of limited quantum resources and access to quantum processors but instead of that there are different QGAN models for different purpose, such as drug discovery, finance, image generation and many more which shown good result as compare to their classical counterparts. This study is reviewed based on authors that proposed different QGAN models along with the techniques proposed on those papers for generating images, which datasets was considered, which parameters are considered, what their limitations and how to future research directions to overcome those problems.
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.
Generative adversarial network (GAN) is one of the most promising methods for unsupervised learning in recent years. GAN works via adversarial training concept and has shown excellent performance in the fields image synthesis, image super-resolution, video generation, image translation, etc. Compared with classical algorithms, quantum algorithms have their unique advantages in dealing with complex tasks, quantum machine learning (QML) is one of the most promising quantum algorithms with the rapid development of quantum technology. Specifically, Quantum generative adversarial network (QGAN) has shown the potential exponential quantum speedups in terms of performance. Meanwhile, QGAN also exhibits some problems, such as barren plateaus, unstable gradient, model collapse, absent complete scientific evaluation system, etc. How to improve the theory of QGAN and apply it that have attracted some researcher. In this paper, we comprehensively and deeply review recently proposed GAN and QAGN models and their applications, and we discuss the existing problems and future research trends of QGAN.
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.
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.
Generative Adversarial Networks (GANs) have demonstrated remarkable success in generating realistic data across various domains. However, traditional GANs often struggle with mode collapse, difficulty in capturing complex data distributions, and challenges in generating high-dimensional data effectively. This paper proposes Quantum-Inspired Generative Adversarial Networks (QGANs), a novel approach that incorporates principles of quantum mechanics – specifically superposition and interference – into the GAN architecture. QGANs utilize quantum circuits to generate latent vectors in a superposition state, enabling richer sampling and enhanced diversity in the generated data. Interference patterns are then strategically applied to refine the generated output, leading to improved quality and complexity. We argue that this direct application of quantum concepts offers a promising avenue for overcoming the limitations of classical GANs and achieving superior generative performance. This work presents a foundational framework for QGANs, outlining the core mechanisms and potential benefits, and lays the groundwork for future research in this area.
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.