A Quantum-Assisted Framework for Energy-Efficient Cloud Task Scheduling Using the Quantum Approximate Optimization Algorithm and Variational Quantum Eigensolver

Task scheduling with the cloud computing domain constitutes a significant challenge, especially when the objective is to improve energy efficiency without detracting from performance metrics. Traditional optimization methodologies, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), frequently encounter difficulties related to scalability and convergence velocity in expansive, dynamic cloud ecosystems. This manuscript introduces a quantum-assisted hybrid paradigm that amalgamates the Quantum Approximate Optimization Algorithm (QAOA) with the Variational Quantum Eigensolver (VQE) to facilitate energyefficient task scheduling within cloud computing platforms. The QAOA is utilized to investigate near-optimal mappings of tasks to resources by addressing combinatorial scheduling dilemmas, whereas the VQE refines the energy consumption model via variational circuit optimization techniques. Compared to classical metaheuristic approaches, the proposed hybrid framework exhibits enhanced efficiency in minimizing energy consumption by 23.6 %, decreasing the makespan by 17.4 %, and increasing resource utilization by 17.7 %. Empirical findings derived from a quantum simulator also indicate a 36.6 % acceleration in convergence relative to GA and a 10.3% improvement in solution accuracy when juxtaposed with PSO. These findings substantiate the premise that quantum-assisted hybrid optimization presents a viable path towards the realization of sustainable and highperformance cloud computing infrastructures.