Generative Artificial Intelligence for Next-Generation Security Paradigms

15Quantum Computing and Generative AI-Securing the Future of Information

Deeya Shalya¹*, Rimon Ranjit Das² and Gurpreet Kaur¹

¹Amity Institute of Information Technology, Amity University, Noida, India

²L’Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie (IMPMC), Sorbonne Université, Jussieu, Paris, France

Abstract

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 ...