<p>The rapid growth of Internet scale computing has exposed critical limitations in existing edge cloud coordination mechanisms, particularly in terms of resilience, energy efficiency, and adaptability under heterogeneous and dynamic environments. Current optimization and learning based approaches often suffer from slow convergence, limited exploration capability, and poor robustness when managing distributed edge cloud resources. To address these challenges, this research proposes QRGEC: Quantum Reinforcement Learning with Golden Jackal Optimization for Resilient Edge Cloud Coordination in Internet Computing. The proposed hybrid framework integrates quantum focused policy exploration with adaptive metaheuristic tuning to enhance distributed Internet computing optimization. Policy representations are encoded using variational quantum circuits, enabling efficient exploration of high dimensional decision spaces. Furthermore, the Golden Jackal Optimization mechanism adaptively adjusts reinforcement parameters to improve convergence stability and accelerate learning, thereby enabling resilient and energy-efficient coordination across heterogeneous edge and cloud environments. A resilience aware scheduler seamlessly balances energy efficiency, latency, and recovery within dynamic edge cloud workloads. Extensive experimental evaluations in QRGEC demonstrate that the framework is capable of outperforming previously established deep reinforcement and quantum heuristic baselines with a latency reduction of 36.8%, an increase in energy efficiency of 24.7%, an improvement in resilience of 48.2%, and a sustained resource utilization of 94%. QRGEC also displays the ability to automatically recover from network congestion and failures, recover from network congestion, and maintain balances latency energy trade-offs. This emphasizes the efficiency of QRGEC in autonomous recovery from network failures, making latency energy balance adjustments, and conserving energy.</p>