QUANTUM COMPUTING MEETS DISTRIBUTED SYSTEMS IN AI: A TECHNICAL OVERVIEW

Abstract

The convergence of quantum computing and distributed artificial intelligence systems represents a transformative advancement in computational science. This article explores the architectural frameworks, implementation strategies, and practical applications of quantum-distributed systems in AI. The article examines quantum cloud platforms and their sophisticated middleware layers, analyzing the quantum-classical interface components and distributed memory management systems. The article investigates quantum-enhanced optimization techniques for machine learning, particularly focusing on the Quantum Approximate Optimization Algorithm (QAOA) and quantum-inspired classical algorithms. Furthermore, the article explores distributed quantum networks, examining quantum state distribution protocols and entanglement-based methods. The article addresses critical technical challenges in coherence time limitations and scale-out architecture while highlighting breakthrough applications in financial technology, drug discovery, and climate modeling. This article demonstrates that the integration of quantum computing with distributed AI systems offers unprecedented capabilities in solving complex computational problems across various domains.