Quantum machine learning (QML) has attracted increasing attention due to its excellent performance on clas-sification and identification tasks. In quantum machine learning methods, quantum generative adversarial networks (QGANs) are an extension of classical generative adversarial networks in the field of quantum computing. However, current quan-tum generation techniques are limited by quantum computing resources, these techniques are only validated on small-scale datasets, and the images generated by QGANs are of lower quality. To improve the image quality generated by QGANs, we introduce a new method to construct latent spaces based on quantum principal component analysis, to ensure high-quality data output of QGANs. In addition, we employ a parameterized quantum circuit instead of a sub module of quantum principal component analysis (Quantum Phase Estimation) to reduce the circuit depths and the number of gates. Experimental results indicate that our new method, which utilizes the new latent space as the input for QGANs, outperforms the original QGAN in terms of performance metrics Frechet Inception Distance and Cosine similarity. Specifically, our method achieves a 20 % lower Frechet Inception Distance score and 9 % higher cosine similarity compared to the original QGAN.
Quantum machine learning (QML) has attracted increasing attention due to its excellent performance on clas-sification and identification tasks. In quantum machine learning methods, quantum generative adversarial networks (QGANs) are an extension of classical generative adversarial networks in the field of quantum computing. However, current quan-tum generation techniques are limited by quantum computing resources, these techniques are only validated on small-scale datasets, and the images generated by QGANs are of lower quality. To improve the image quality generated by QGANs, we introduce a new method to construct latent spaces based on quantum principal component analysis, to ensure high-quality data output of QGANs. In addition, we employ a parameterized quantum circuit instead of a sub module of quantum principal component analysis (Quantum Phase Estimation) to reduce the circuit depths and the number of gates. Experimental results indicate that our new method, which utilizes the new latent space as the input for QGANs, outperforms the original QGAN in terms of performance metrics Frechet Inception Distance and Cosine similarity. Specifically, our method achieves a 20 % lower Frechet Inception Distance score and 9 % higher cosine similarity compared to the original QGAN.
Abstract Quantum generative adversarial networks (QGANs) have demonstrated strong capabilities in tasks like synthetic data generation and detecting anomalies. Recent developments have increasingly integrated traditional machine learning techniques to boost the performance of QGANs. Motivated by this progress, we propose an innovative QGAN architecture that incorporates a classical learning component and employs a dual-generator design. Our approach improves upon the traditional hybrid quantum–classical GAN structure and introduces a redesigned loss function tailored for the new model. Experiments on multiple datasets indicate that our method surpasses previous techniques in image generation quality, achieving a 1.38% average reduction in Fréchet inception distance scores compared to the current state-of-the-art, and improvements of 6.52%, 0.36%, and 0.38% in structural similarity index, cosine similarity, and peak signal-to-noise ratio metrics, respectively. Additionally, our architecture supports the generation of larger images (up to 78 × 78 ), as verified on the CelebA dataset. Simulations conducted in noisy conditions further confirm the robustness and effectiveness of both the proposed architecture and loss function.
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 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.
Abstract Integration of quantum computing in generative machine learning models has the potential to offer benefits such as training speed-up and superior feature extraction. However, the existing quantum generative adversarial networks (QGANs) fail to generate high-quality images due to their patch-based, pixel-wise learning approaches. These methods capture only local details, ignoring the global structure and semantic information of images. In this work, we address these challenges by proposing a quantum image generative learning (QIGL) approach for high-quality medical image generation. Our proposed quantum generator leverages variational quantum circuit approach addressing scalability issues by extracting principal components from the images instead of dividing them into patches. Additionally, we integrate the Wasserstein distance within the QIGL framework to generate a diverse set of medical samples. Through a systematic set of simulations on x-ray images from knee osteoarthritis and medical MNIST datasets, our model demonstrates superior performance, achieving the lowest Fréchet inception distance scores compared to its classical counterpart and advanced QGAN models reported in the literature.
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
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.