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
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
Abstract Quantum machine learning has attracted considerable attention due to its potential in classification and recognition tasks. As a significant branch of this field, quantum generative adversarial networks (QGANs) have achieved notable progress in image generation. Although existing models have improved in terms of generation quality and efficiency, their performance remains suboptimal. To overcome these limitations, we propose HQSP-GAN, a hybrid quantum-classical generative adversarial model integrating a shared parameter mechanism. This model innovatively designs a quantum parameter sharing scheme to reduce the number of trainable parameters in the quantum module. Additionally, by incorporating attention mechanisms, the Wasserstein distance, and gradient penalty techniques, HQSP-GAN enhances training stability and generation performance. Extensive experiments on standard datasets such as MNIST and Fashion MNIST demonstrate that HQSP-GAN can generate high-quality grayscale images without additional dimensionality reduction or preprocessing. Moreover, it outperforms SOTO models including QINR-GAN and PQWGAN in metrics such as SSIM and PSNR, highlighting the efficiency and practicality of its architectural design.
Quantum Machine Learning (QML) has attracted significant attention for its potential to deliver exponential advantages over classical machine learning approaches, particularly in classification and recognition tasks. Quantum Generative Adversarial Networks (QGANs), a form of quantum machine learning, provide promising advantages in image processing and generation tasks when compared to classical technologies. However, the limitations of current quantum devices have led to suboptimal image quality and limited robustness in earlier methods. To overcome these challenges, we developed a hybrid quantum-classical approach, introducing CAQ, a quantum-classical Generative Adversarial Network (GAN) framework. Leveraging the latest WGAN-gradient penalty (GP) strategy, we trained and optimized the quantum generator, reduced the complexity of parameters, and implemented an adaptive noise input system that dynamically adjusts noise levels, thereby improving the model’s robustness. Additionally, we employed a remapping technique to transform the original image’s multimodal distribution into a unimodal one, thereby reducing the complexity of the learned distribution. Experiments on MNIST and Fashion-MNIST datasets show that CAQ generates grayscale images effectively, demonstrating its feasibility on near-term intermediate-scale quantum (NISQ) computers.
Quantum Generative Adversarial Networks (QGANs), as a rising paradigm in Quantum Machine Learning, have shown promising potential in image generation and processing. However, their output quality remains suboptimal, and existing research is largely limited to small-scale, proof-of-concept studies. In this work, we propose a hybrid quantum-classical GAN architecture, where the generator integrates parameterized quantum circuits (PQCs) and classical neural networks. This integration significantly enhances the visual quality of generated images. Our model leverages the complementary strengths of quantum and classical components and outperforms existing methods (Tsang et al. in IEEE Trans. Quantum Eng. 4:1–19, 2023 ; Gulrajani et al. in Proceedings of the 31st international conference on neural information processing systems. NIPS’17, Red Hook, pp. 5769–5779, 2017 ), particularly in terms of image fidelity. Experiments conducted on the MNIST family of datasets show that our hybrid approach achieves a 20.26% average reduction in Fréchet Inception Distance. Furthermore, it improves the Structural Similarity Index Measure, Cosine Similarity, and Peak Signal-to-Noise Ratio by 26.04%, 2.22%, and 7.62%, respectively. These results highlight the effectiveness of combining quantum computing with machine learning, and underscore the potential of hybrid quantum-classical models in advancing generative tasks.
Abstract Quantum machine learning (QML) has garnered significant attention for its potential to outperform classical machine learning.A notable area within QML is quantum generative adversarial networks (QGANs), which serve as the quantum equivalent to classical GANs, commonly used in image processing and generation.However, the content of images generated by existing QGANs is still uncontrollable.In this work, we introduce a conditional encoder for the quantum generator, enabling it to controllably produce multiple image types.While this approach can, in principle, be extended to any number of conditional generations, its practical applicability is limited by the finite qubit resources available.Our results show that it can generate images of satisfactory quality, with a Frechet Inception Distance (FID) score of 201.6, a Kernel Inception Distance (KID) score of 0.281, and a Learned Perceptual Image Patch Similarity (LPIPS) score of 0.461.Through quantum noise experiments, we show that when the noise intensity reaches 0.2, the relative changes in FID, KID, and LPIPS remain below 5%, indicating that the model is robust to moderate noise levels.Furthermore, in comparative experiments against without multicondition or limitted multicondition, our method outperforms the strongest baseline by 3%−11% , thereby demonstrating its stable superiority over current approaches.
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 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.