Generative Adversarial Networks (GANs) is had proven the great results for image generation which opens the gateway for quantum researcher's. But the implementation on quantum computers is not straightforward as each pixel resembles single qubits and to process the 28x28 image it requires 784 qubits. Based on current resources at the number of qubits increase computational time increases. This study explores QGAN potentials to provide a method for generating images using features of quantum systems to improve the quantity and quality of the generated images. This research use the Fashion MNIST dataset because it this dataset has simple as well as complex structures and it consists of 28x28 gray-scale images with 9 fashion products. By considering those limitation of quantum resources for classical computers. Quantum-GAN framework use Principal Component Analysis (PCA) to reduce the number of features. In addition to that, framework is using shallow-depth quantum circuit and a reduced parameter set. This research also deals with vanishing gradient problem of GANs and to overcome this problem of this framework uses Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for fine-tuning training parameters of quantum circuits.
Generative Adversarial Networks (GANs) is had proven the great results for image generation which opens the gateway for quantum researcher's. But the implementation on quantum computers is not straightforward as each pixel resembles single qubits and to process the 28x28 image it requires 784 qubits. Based on current resources at the number of qubits increase computational time increases. This study explores QGAN potentials to provide a method for generating images using features of quantum systems to improve the quantity and quality of the generated images. This research use the Fashion MNIST dataset because it this dataset has simple as well as complex structures and it consists of 28x28 gray-scale images with 9 fashion products. By considering those limitation of quantum resources for classical computers. Quantum-GAN framework use Principal Component Analysis (PCA) to reduce the number of features. In addition to that, framework is using shallow-depth quantum circuit and a reduced parameter set. This research also deals with vanishing gradient problem of GANs and to overcome this problem of this framework uses Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for fine-tuning training parameters of quantum circuits.
Quantum machine learning (QML) has received increasing attention due to its potential to outperform classical machine learning methods in problems such as classification and identification tasks. A subclass of QML methods is quantum generative adversarial networks (QGANs) which have been studied as a quantum counterpart of classical GANs widely used in image manipulation and generation tasks. The existing work on QGANs is still limited to small-scale proof-of-concept examples based on images with significant down-scaling. Here we integrate classical and quantum techniques to propose a new hybrid quantum-classical GAN framework. We demonstrate its superior learning capabilities over existing quantum techniques by generating 28×28 pixels grey-scale images without dimensionality reduction or classical pre/post-processing on multiple classes of the standard MNIST and Fashion MNIST datasets, which achieves comparable results to classical frameworks with three orders of magnitude less trainable generator parameters. To gain further insight into the working of our hybrid approach, we systematically explore the impact of its parameter space by varying the number of qubits, the size of image patches, the number of layers in the generator, the shape of the patches and the choice of prior distribution. Our results show that increasing the quantum generator size generally improves the learning capability of the network. The developed framework provides a foundation for future design of QGANs with optimal parameter set tailored for complex image generation tasks
This study explores the frontiers of generative modelling by introducing quantum principles into the picture synthesis process via Quantum Generative Adversarial Networks (QGANs). QGANs provide a novel method to image production by utilising the intrinsic features of quantum systems to improve both the quantity and variety of generated images. Using the Fashion-MNIST dataset and Numeric dataset, which consists of 28×28 grayscale images depicting various fashion products, this study investigates the potential of QGANs in creating realistic and diversified fashion images across multiple classes. While this study, Obtained 0.50 Discriminator Loss along with 0.98 as Generator Loss on 90thEpoch for numeric dataset. This research demonstrates that QGANs can surpass classical models in terms of image generation, as evidenced by improved metrics and computational advantages. Additionally, by integrating physics principles such as the conservation of quantum information, this QGAN framework paves the way for disruptive advancements in computer vision, fashion design, and digital media production.
Abstract As quantum computing continues to advance alongside machine learning, Quantum Generative Adversarial Networks (QGANs) have gained attention as a compelling generative modeling approach in quantum machine learning. Despite their potential, most existing QGAN implementations rely on manually designed quantum circuits, which often suffer from high complexity and limited scalability. To overcome these limitations, we propose a Quantum Architecture Search (QAS) method built upon the SuperCircuit framework. This approach enables the automatic discovery of efficient quantum circuit structures tailored for generative tasks, thereby reducing gate count. However, applying SuperCircuit-based QAS directly to QGANs presents two primary challenges: an expansive search space and a lack of guarantees regarding circuit efficiency. To mitigate these issues, we incorporate Principal Component Analysis (PCA) and a PCA-guided feature distribution mechanism during preprocessing. This strategy both compresses the search space and ensures balanced allocation of principal components across sub-generators. Extensive experiments confirm the viability of the proposed framework. When employing the discovered quantum circuits, QAS-QGAN achieves image quality comparable to a state-of-the-art (SOTA) baseline (Silver et al 2023 2023 IEEE/CVF Int. Conf. on Computer Vision (ICCV) pp 7007–16) on both the MNIST and Fashion MNIST datasets. Notably, our model reduces the number of two-qubit gates by 52.08% on MNIST and 52.92% on Fashion MNIST, highlighting substantial improvements in quantum resource efficiency. Furthermore, the model successfully generates high-resolution facial images ( 109 × 89 ) on the CelebA dataset, demonstrating strong scalability and practical applicability.
Quantum generative modeling is among the promising candidates for achieving a practical advantage in data analysis. Nevertheless, one key challenge is to generate large-size images comparable to those generated by their classical counterparts. In this work, we take an initial step in this direction and introduce the Latent Style-based Quantum GAN (LaSt-QGAN), which employs a hybrid classical-quantum approach in training Generative Adversarial Networks (GANs) for arbitrary complex data generation. This novel approach relies on powerful classical auto-encoders to map a high-dimensional original image dataset into a latent representation. The hybrid classical-quantum GAN operates in this latent space to generate an arbitrary number of fake features, which are then passed back to the auto-encoder to reconstruct the original data. Our LaSt-QGAN can be successfully trained on realistic computer vision datasets beyond the standard MNIST, namely Fashion MNIST (fashion products) and SAT4 (Earth Observation images) with 10 qubits, resulting in a comparable performance (and even better in some metrics) with the classical GANs. Moreover, we analyze the barren plateau phenomena within this context of the continuous quantum generative model using a polynomial depth circuit and propose a method to mitigate the detrimental effect during the training of deep-depth networks. Through empirical experiments and theoretical analysis, we demonstrate the potential of LaSt-QGAN for the practical usage in the context of image generation and open the possibility of applying it to a larger dataset in the future.
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.
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.