The use of quantum generative adversarial networks (qGANs) is based on an efficient approach that simulates high-energy particle showers using a reduced model complexity. In this study, we analyze complete and hybrid qGAN architectures for the creation of particle shower images and compare their training behavior, convergence and accuracy. The results show a stable training of the entire qGAN, which can generate the general parameters of the shower well. The hybrid design also exhibits competitive performance with the medium discriminator yielding the highest accuracy of all the tested configurations. However, with a lower or higher discriminator the decrease of performance is due to the imbalance between the capacity of the generator and discriminator. The results show that quantum models can be represented with a much smaller number of trainable parameters, and still capture the representational capabilities of classical models, something that can be studied in greater detail with larger models and more sophisticated physics-based metrics.
The use of quantum generative adversarial networks (qGANs) is based on an efficient approach that simulates high-energy particle showers using a reduced model complexity. In this study, we analyze complete and hybrid qGAN architectures for the creation of particle shower images and compare their training behavior, convergence and accuracy. The results show a stable training of the entire qGAN, which can generate the general parameters of the shower well. The hybrid design also exhibits competitive performance with the medium discriminator yielding the highest accuracy of all the tested configurations. However, with a lower or higher discriminator the decrease of performance is due to the imbalance between the capacity of the generator and discriminator. The results show that quantum models can be represented with a much smaller number of trainable parameters, and still capture the representational capabilities of classical models, something that can be studied in greater detail with larger models and more sophisticated physics-based metrics.
Abstract The prospect of quantum computing with a potential exponential speed-up compared to classical computing identifies it as a promising method in the search for alternative future High Energy Physics (HEP) simulation approaches. HEP simulations, such as employed at the Large Hadron Collider at CERN, are extraordinarily complex and require an immense amount of computing resources in hardware and time. For some HEP simulations, classical machine learning models have already been successfully developed and tested, resulting in several orders of magnitude speed-up. In this research, we proceed to the next step and explore whether quantum computing can provide sufficient accuracy, and further improvements, suggesting it as an exciting direction of future investigations. With a small prototype model, we demonstrate a full quantum Generative Adversarial Network (GAN) model for generating downsized eight-pixel calorimeter shower images. The advantage over previous quantum models is that the model generates real individual images containing pixel energy values instead of simple probability distributions averaged over a test sample. To complete the picture, the results of the full quantum GAN model are compared to hybrid quantum-classical models using a classical discriminator neural network.
Quantum generative modeling is a very active area of research in looking for practical advantage in data analysis. Quantum generative adversarial networks (QGANs) are leading candidates for quantum generative modeling and have been applied to diverse areas, from high-energy physics to image generation. The latent style-based QGAN, relying on a classical variational autoencoder to encode the input data into a latent space and then using a style-based QGAN for data generation has been proven to be efficient for image generation or drug design, hinting at the use of far less trainable parameters than their classical counterpart to achieve comparable performance, however this advantage has never been systematically studied. We present in this work the first comprehensive experimental analysis of this advantage of QGANS applied to SAT4 image generation, obtaining an exponential advantage in capacity scaling for a quantum generator in the hybrid latent style-based QGAN architecture. Careful tuning of the autoencoder is crucial to obtain stable, reliable results. Once this tuning is performed and defining training optimality as when the training is stable and the FID score is low and stable as well, the optimal capacity (or number of trainable parameters) of the classical discriminator scales exponentially with respect to the capacity of the quantum generator, and the same is true for the capacity of the classical generator. This hints toward a type of quantum advantage for quantum generative modeling.
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 Generative Adversarial Networks (QGANs) combine quantum circuit based generative models with classical discriminators to exploit quantum advantages in generative modeling. This work investigates a hybrid QGAN architecture with a quantum-enhanced generator and a classical discriminator, applied to the Modified National Institute of Standards and Technology (MNIST) dataset. We implement and benchmark three different quantum circuit ansätze - namely a basic entangler circuit, a simple two-design circuit and a strongly entangling circuit, with each QGAN model being constructed using six layers of the respective ansätze. To manage the 28x28 image complexity, the generator employs a patch wise scheme, dividing each image into small patches generated by separate sub-circuits. The performance of each ansatz is evaluated in terms of training stability and output image quality. Our results demonstrate that the choice of quantum circuit ansatz significantly impacts the QGAN’s ability to learn the distribution of handwritten digits. In particular, the strongly entangling ansatz achieves the best performance at the cost of stability and image fidelity among the three yielding recognizable digit images. While no quantum advantage was demonstrated, this study provides comparative analysis and insights into designing effective quantum generators for hybrid QGANs on image data, serving as a benchmark for future developments in quantum-enhanced generative models.
We investigate the capabilities of quantum generative adversarial networks (QGANs) in image generations tasks. Our analysis centers on fully quantum implementations of both the generator and discriminator. Through extensive numerical testing of current main architectures, we find that QGANs struggle to generalize across datasets, converging on merely the average representation of the training data. When the output of the generator is a pure-state, we analytically derive a lower bound for the discriminator quality given by the fidelity between the pure-state output of the generator and the target data distribution, thereby providing a theoretical explanation for the limitations observed in current models. Our findings reveal fundamental challenges in the generalization capabilities of existing quantum generative models. While our analysis focuses on QGANs, the results carry broader implications for the performance of related quantum generative models.
Generative Adversarial Networks (GANs) have demonstrated immense potential in synthesizing diverse and high-fidelity images. However, critical questions remain unanswered regarding how quantum principles might best enhance their representational and computational capacity. In this paper, we investigate hybrid quantum-classical GAN architectures supplemented by transfer learning to systematically examine whether incorporating Variational Quantum Circuits (VQCs) into the generator, the discriminator, or both improves performance over a fully classical baseline. Our findings indicate that fully hybrid models, which incorporate VQCs in both the generator and the discriminator, produce images with higher quality and achieve more favorable quantitative metrics compared to their fully classical counterparts. In particular, placing the quantum block in the generator appears to accelerate the early emergence of visual structure, whereas placing it in the discriminator slows early visual convergence but improves the final quantitative quality metric. Incorporating quantum blocks into both networks yields the strongest overall performance. Moreover, the model sustains comparable performance even when the dataset size is reduced. Overall, the results underscore that carefully integrating quantum computing with classical adversarial training and pretrained feature extraction can enrich GAN-based image synthesis. These insights open avenues for future work on higher-resolution tasks, alternative quantum circuit designs, and experimentation with emerging quantum hardware.