Hybrid Quantum-Classical Algorithms for Optimizing Resource Allocation in Cloud-Based Big Data Environments

Abstract

The unprecedented growth of data in the digital age has necessitated the development of efficient and scalable resource allocation strategies for cloud-based big data environments. Traditional classical computing approaches often struggle to cope with the computational complexity of large-scale optimization problems involving resource allocation. Quantum computing, with its unique computational paradigm, offers promising avenues for tackling such challenges. This research explores the potential of hybrid quantum-classical algorithms for optimizing resource allocation in cloud-based big data environments. By leveraging the strengths of both quantum and classical computing, these algorithms aim to achieve superior performance and scalability compared to classical approaches alone. The article presents a comprehensive analysis of various hybrid quantum-classical algorithms, their theoretical foundations, and their practical applications in resource allocation problems. Additionally, it discusses the challenges and future research directions in this emerging field, paving the way for more efficient and effective resource allocation strategies in the era of big data.