Leveraging Quantum computing for synthetic image generation and recognition with Generative Adversarial Networks and Convolutional Neural Networks

The generation and classification of synthetic images is a challenging and important task in the digital age. Generative Adversarial Networks are powerful tools for creating high-quality synthetic images, but they face limitations in terms of complexity, scalability, and efficiency. Quantum computing offers a promising alternative to enhance the performance of Generative Adversarial Networks and overcome these limitations. This paper proposes a Quantum Generative Adversarial Network model for generating synthetic images using the MNIST dataset. We compare the Quantum Generative Adversarial Network model with a classical Deep Convolutional Generative Adversarial Network model, and the proposed Quantum Generative Adversarial Network model has a significantly shorter simulation time and lower generator and discriminator losses, indicating a better quality and realism of the generated images. We also propose a Hybrid Quantum-Classical Convolutional Neural Network model for detecting synthetic images generated by the Quantum Generative Adversarial Network model. We compare the proposed Hybrid Quantum-Classical Convolutional Neural Network model with a classical Convolutional Neural Network model, and the Hybrid Quantum-Classical Convolutional Neural Network model has better accuracy and improved computation time, indicating a more efficient and effective classification of real and generated synthetic images. This paper demonstrates the potential of quantum computing for advancing the field of synthetic image generation and classification. It opens up new avenues for future research and development in this domain.