QQLAOA: task scheduling with multi-objectives quantum mutation and Q-learning based arithmetic optimizer algorithm in cloud data centers

Cloud computing enables scalable and flexible resource access with reduced costs and higher efficiency. However, challenges such as resource management, energy optimization, and load balancing require innovative scheduling approaches. This research proposes a multi-objective hybrid algorithm integrating reinforcement learning, quantum mutation (QM), and metaheuristic optimization to enhance task scheduling in cloud environments. The QM operator improves search diversity, while Q-learning dynamically balances exploration and exploitation. The proposed method is validated using statistical tests and benchmark datasets (GoCJ and synthetic data). Results show a makespan reduction of up to 22.04% and energy savings of 90.64%, demonstrating superior efficiency compared to existing methods.