Quantum Generator

Quantum generators are emerging as a novel approach to generating random numbers and data, offering potential advantages over classical methods. Current research focuses on developing and evaluating quantum generative models, including those based on quantum circuits and hybrid quantum-classical architectures like quantum generative adversarial networks (QGANs), often employing techniques like quantum convolutional neural networks for classification and evaluation metrics such as the quantum inception score (qIS). These efforts aim to improve the quality and efficiency of quantum random number generation and explore applications in areas such as image generation and simulations of complex physical systems. The ultimate goal is to establish the practical viability and advantages of quantum generators for various scientific and technological applications.