Computing Paradigm

Computing paradigms are undergoing a significant shift, driven by the need for more efficient and powerful computation across diverse applications. Current research focuses on integrating quantum computing with classical methods, particularly in machine learning, exploring architectures like quantum-hybrid support vector machines and generative adversarial networks to improve performance and address privacy concerns through techniques like federated learning and fully homomorphic encryption. This research is crucial for advancing fields like artificial intelligence, cybersecurity, and scientific modeling, offering the potential for faster, more efficient, and privacy-preserving solutions.

Papers