تقرير
Latent Style-based Quantum GAN for high-quality Image Generation
| العنوان: | Latent Style-based Quantum GAN for high-quality Image Generation |
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| المؤلفون: | Chang, Su Yeon, Thanasilp, Supanut, Saux, Bertrand Le, Vallecorsa, Sofia, Grossi, Michele |
| سنة النشر: | 2024 |
| المجموعة: | Quantum Physics |
| مصطلحات موضوعية: | Quantum Physics |
| الوصف: | 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. Comment: 28 pages, 22 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.02668 |
| رقم الأكسشن: | edsarx.2406.02668 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |