Quantum Generative Adversarial Network

Quantum Generative Adversarial Networks (QGANs) aim to leverage quantum computing's power to improve the generation of classical data, particularly images, by combining quantum and classical algorithms within a generative adversarial framework. Current research focuses on developing hybrid quantum-classical architectures, such as those incorporating variational quantum circuits or quantum-enhanced autoencoders, to address challenges like scalability and training convergence, often using noisy intermediate-scale quantum (NISQ) devices. These advancements hold promise for enhancing data generation tasks across various fields, including image synthesis, anomaly detection, and financial modeling, while also raising important considerations regarding data security and ownership.