High-Resolution Fashion Image Generation using Quantum-GAN
Ashish Solanki, Sandeep Singh Kang, Sanjay Singla, T. S. Gururaja · 2024
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.