Integrating Quantum Network Optimization into Queueing and Scheduling Models for Cloud Computing Environments

Integrating quantum network optimization with traditional cloud computing presents a groundbreaking approach to enhancing computational efficiency and resource management. This study investigates the quantifiable impact of quantum-classical hybrid systems in cloud environments, focusing on resource utilization enhancement and task scheduling optimization. Using a MATLAB-based simulation environment interfaced with Variational Quantum Eigensolver (VQE) simulations, this research implements a three-layer architecture across 50 nodes, combining quantum optimization algorithms with classical scheduling techniques through a GPS queue model. The hybrid quantum-classical framework achieves remarkable improvements, including a 180% increase in task throughput, 70% reduction in response time, 41.5% improvement in resource utilization, and 27.1% enhancement in energy efficiency while maintaining 85% efficiency under peak load conditions and demonstrating 99.5% success rate for short-term tasks and 97.8% for long-running operations. Despite hardware limitations of existing quantum processors and scalability constraints, the system establishes a foundation for future developments, including advanced error correction mechanisms, expanded system scalability beyond 50 nodes, and enhanced resource utilization. These findings establish quantum-enhanced cloud computing as a viable solution for next-generation cloud infrastructure, particularly in managing complex workloads and optimizing resource allocation.