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arXiv:2406.02668

Latent Style-based Quantum GAN for high-quality Image Generation

Su Yeon Chang, Supanut Thanasilp, Bertrand Le Saux, Sofia Vallecorsa, Michele Grossi

Published Jun 4, 2024• 7 citations
arXivHTMLPDF

Abstract

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.

DOI
10.48550/arxiv.2406.02668

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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 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.
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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.
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 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 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.