Optimizing Network Services with Quantum Dynamic Programming and Grover’s Search

As network service requirements and computational complexity continue to increase, efficient resource management and optimization techniques are essential to ensure performance and scalability. In the context of edge cloud networks, there are computational challenges associated with optimal path selection and resource allocation. This paper explores the integration of quantum computing with dynamic programming (DP) to solve path optimization problems in network service orchestration. More specifically, we investigate two approaches to DP: the classical DP method, which provides exact solutions by systematically evaluating all possible states, and a quantum-enhanced version, which uses the Grover search algorithm to accelerate for performance comparisons in terms of accuracy and execution times. Simulation results provide insights into the comparative performance of classical and quantum DP approaches, with an emphasis on search time improvements. Beyond computational performance, we discuss practical considerations including the constraints of current quantum hardware, algorithmic efficiency improvements, scalability challenges, and integration with classical systems. Additionally, we evaluate energy efficiency, costbenefit trade-offs, resilience, and regulatory concerns associated with quantum-enabled service orchestration.