Leveraging quantum computing's intrinsic properties to enhance machine learning has shown promise, with quantum generative adversarial networks (QGANs) demonstrating benefits in data generation. However, the application of QGANs to complex unsupervised image-to-image (I2I) translation remains unexplored. Moreover, classical neural networks often suffer from large parameter spaces, posing challenges for GAN-based I2I methods. Inspired by the fact that unsupervised I2I translation is essentially an approximate reversible problem, we propose a lightweight invertible hybrid quantum-classical unsupervised I2I translation model - iHQGAN, by harnessing the invertibility of quantum computing. Specifically, iHQGAN employs two mutually approximately reversible quantum generators with shared parameters, effectively reducing the parameter scale. To ensure content consistency between generated and source images, each quantum generator is paired with an assisted classical neural network (ACNN), enforcing a unidirectional cycle consistency constraint between them. Simulation experiments were conducted on 19 sub-datasets across three tasks. Qualitative and quantitative assessments indicate that iHQGAN effectively performs unsupervised I2I translation with excellent generalization and can outperform classical methods that use low-complexity CNN-based generators. Additionally, iHQGAN, as with classical reversible methods, reduces the parameter scale of classical irreversible methods via a reversible mechanism. This study presents the first versatile quantum solution for unsupervised I2I translation, extending QGAN research to more complex image generation scenarios and offering a quantum approach to decrease the parameters of GAN-based unsupervised I2I translation methods.
Leveraging quantum computing's intrinsic properties to enhance machine learning has shown promise, with quantum generative adversarial networks (QGANs) demonstrating benefits in data generation. However, the application of QGANs to complex unsupervised image-to-image (I2I) translation remains unexplored. Moreover, classical neural networks often suffer from large parameter spaces, posing challenges for GAN-based I2I methods. Inspired by the fact that unsupervised I2I translation is essentially an approximate reversible problem, we propose a lightweight invertible hybrid quantum-classical unsupervised I2I translation model - iHQGAN, by harnessing the invertibility of quantum computing. Specifically, iHQGAN employs two mutually approximately reversible quantum generators with shared parameters, effectively reducing the parameter scale. To ensure content consistency between generated and source images, each quantum generator is paired with an assisted classical neural network (ACNN), enforcing a unidirectional cycle consistency constraint between them. Simulation experiments were conducted on 19 sub-datasets across three tasks. Qualitative and quantitative assessments indicate that iHQGAN effectively performs unsupervised I2I translation with excellent generalization and can outperform classical methods that use low-complexity CNN-based generators. Additionally, iHQGAN, as with classical reversible methods, reduces the parameter scale of classical irreversible methods via a reversible mechanism. This study presents the first versatile quantum solution for unsupervised I2I translation, extending QGAN research to more complex image generation scenarios and offering a quantum approach to decrease the parameters of GAN-based unsupervised I2I translation methods.
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.
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 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) 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.
Quantum machine learning consists in taking advantage of quantum computations to generate classical data. A potential application of quantum machine learning is to harness the power of quantum computers for generating classical data, a process essential to a multitude of applications such as enriching training datasets, anomaly detection, and risk management in finance. Given the success of Generative Adversarial Networks in classical image generation, the development of its quantum versions has been actively conducted. However, existing implementations on quantum computers often face significant challenges, such as scalability and training convergence issues. To address these issues, we propose LatentQGAN, a novel quantum model that uses a hybrid quantum-classical GAN coupled with an auto-encoder. Although it was initially designed for image generation, the LatentQGAN approach holds potential for broader application across various practical data generation tasks. Experimental outcomes on both classical simulators and noisy intermediate scale quantum computers have demonstrated significant performance enhancements over existing quantum methods, alongside a significant reduction in quantum resources overhead.