A Quantum-Classical GAN Approach for HighFidelity Brain MRI Resolution Enhancement

Generative Adversarial Networks (GANs) have been demonstrated to work efficiently in reconstructing and enhancing complex medical imaging, especially brain MRI. However, classic GANs face significant challenges in terms of inefficiency in handling high-dimensional datasets, mode collapse, and a rather high computational cost. This research proposes a novel Quantum-Classical Generative Adversarial Network (QC-GAN), which has been based on the implementation of quantum computing principles to overcome these problems. QC-GAN is realized by an 8-qubit quantum generator in combination with a classical discriminator. It demonstrates both improved fidelity in the generation of brain MRI images and efficiency in computation. The experimental results based on the OrganAMNIST dataset are used to compare the image quality and generation speed between QC-GAN and traditional GANs. This paper presents the potential of QC-GAN as a scalable, accurate, and efficient solution for medical imaging, especially in resource-constrained environments, opening a new path for enhanced diagnostic capabilities.