Exploring the Quantum Frontiers of Generative Adversarial Networks in Chemical Catalysis

Resumen

Quantum computing and generative Artificial Intelligence (AI) are two burgeoning fields that hold the promise of fundamentally revolutionizing the computational and data science landscapes. While quantum computing is renowned for enabling faster and more efficient calculations through the principles of superposition and entanglement, generative AI models, particularly Generative Adversarial Networks (GANs), are at the forefront of creating novel data and information, paving the way for unprecedented advancements in various domains.

The synthesis of these two fields can potentially spearhead a paradigm shift in the realm of generative AI, fostering the development of quantum-inspired generative models that meld the computational prowess of quantum computing with the data generation capabilities of AI. This thesis delineates the current state-of-the-art in both fields and explores their burgeoning intersection, with a keen focus on the infusion of quantum principles in enhancing generative AI models.

A critical aspect of this exploration is an in-depth study into the application of GANs in the field of chemical catalysis, a domain where the generation of new data can significantly accelerate the discovery and optimization of catalysts. In particular, this work investigates the potential of using GANs to generate novel chemical catalysis processes, emphasizing the creation and optimization of Copper (Cu) binary catalysts. By leveraging the capabilities of quantum computing, these models can potentially facilitate the discovery of more efficient and effective catalytic processes, opening new opportunities for research and development in the field.

The overall aim of this thesis is to unravel the potential advantages and challenges of integrating quantum computing in generative AI, and to pave a road map for future directions in this exciting interdisciplinary domain. Through a meticulous analysis of specific generative AI models, this work endeavors to decipher how quantum principles can augment their capabilities, thereby delineating a promising trajectory for the future of quantum-enhanced generative AI.