Quantum Computing and Generative AI‐Securing the Future of Information

Quantum computing changes the way computational problems can be solved by taking advantage of special qubit features such as superposition and entanglement. Owing to these features, a quantum computer can efficiently search through a large amount of solution space and therefore tackle problems that are otherwise too hard for classical computers. In the field of quantum machine learning (QML), Quantum Generative Adversarial Networks (QGANs) are coming to the forefront as a very productive area. QGANs are based on the experimentally successful classical Generative Adversarial Networks (GANs) and, therefore, utilize an adversarial approach, where a generator competes with a discriminator. The data-synthesizing unit called the generator tries to generate data samples that are statistically similar to the actual data, while the discriminator tries to tell the real data apart from any artificially created data samples. Such layers of adversarial-tuned feedback culminate in the improvement of the generator in making realistic simulated data. Even though a considerable amount of research is already published that substantiates the numerous prospects of quantum computing but still the practical implementation of it seems hypothetical. Even so, QGANs are likely to revolutionize many fields by making it possible to synthesize new and fascinating complex realistic data. This review work aims to serve as a valuable resource for researchers and practitioners interested in understanding the exciting field of QGANs and their potential contributions to the broader landscape of quantum machine learning.