Multi-Objective Quantum Task Scheduling using the Preschool Education Optimization Algorithm

In the evolving landscape of quantum computing, efficient task scheduling remains a significant challenge due to the inherent complexity and resource constraints of quantum systems. This research introduces a novel metaheuristic approach Preschool Education Optimization Algorithm (PEOA) for multi-objective scheduling in quantum computing environments. The proposed model aims to optimize three critical performance parameters: makespan, energy consumption, and throughput. Implemented using the SimPy simulation framework, the algorithm is rigorously evaluated on the CEA-Curie workload, a benchmark known for its high-performance computing characteristics. PEOA mimics the adaptive and collaborative learning behavior observed in preschool environments, enabling intelligent exploration and exploitation in the solution space. The comparative analysis with established algorithms such as BACOA, EOA, and ROA demonstrates that PEOA outperforms them in minimizing makespan and energy usage while significantly enhancing throughput. These findings highlight the potential of PEOA as a robust and scalable solution for task scheduling in quantum computing systems, offering a new direction for future quantum-aware optimization strategies.