In electric vehicles (EV) ecosystem, Charging-as-a-Service (CaaS) with subscription models has emerged as a promising approach to optimize EV charging infrastructure while ensuring cost-effectiveness and accessibility. However, traditional optimization methods struggle to handle the complex, dynamic nature of large-scale EV charging networks. This paper proposes a novel Charging -as-a-Service (CaaS) business model leveraging the Variational Quantum Eigensolver (VQE) algorithm to optimize energy management in Vehicle-to-Grid (V2G) systems. By integrating quantum computing with V2G infrastructures, the proposed model addresses the increasing computational complexity of large-scale energy optimization in smart grids with high EV penetration. The VQE algorithm is utilized to minimize the system Hamiltonian representing energy cost and grid stability parameters, offering superior performance over classical optimization approaches in terms of scalability and convergence. The paper explores the integration of quantum computing to enhance CaaS subscription models by leveraging quantum optimization algorithms for real-time charging scheduling, demand forecasting, and grid resilience. The CaaS model enables stakeholders including EV fleet operators, energy providers, and grid managers to access quantum computing resources on demand, reducing upfront investment and enabling adaptive, high-performance energy decision-making. Simulation results demonstrate the feasibility and benefits of the quantum-enhanced model, including improved grid resilience, reduced peak load, and enhanced economic returns for participants. By harnessing the power of quantum computing, this study aims to revolutionize EV charging by enabling faster decision-making and improved resource allocation.