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IEEE International Conference on Cyber Security and Resilience (CSR)

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

Engin Zeydan, Josep Mangues‐Bafalluy, Yekta Türk, Abdullah Aydeger, Madhusanka Liyanage

Published Aug 4, 2025• 0 citations
Publisher

Abstract

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.

DOI
10.1109/csr64739.2025.11130166

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As edge cloud environments become increasingly complex, efficient service management requires advanced orchestration strategies that can handle diverse and dynamic network conditions. In this paper, we present a novel quantum-based orchestration framework that leverages quantum search algorithm (Grover in particular) to optimize the management of services within the edge cloud continuum. We present a multi-objective optimization model that considers critical factors such as latency, bandwidth, reliability, and error minimization, and formulate the problem mathematically to capture the complexity of modern network environments. Through both analytical comparisons and simulation results, we show that the Grover algorithm can offer computational advantages over classical search methods, especially in scenarios on cases when the parameter space is very large. Our results include a detailed analysis of theoretical computational complexity, an estimate of execution times, and the identification of crossover points where quantum methods outperform traditional approaches. Finally, we provide insight into the challenges and future directions of integrating quantum computing into network management frameworks, setting the stage for further progress in this area.
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Cloud computing has transformed modern digital infrastructure by enabling on-demand access to scalable computational resources. However, the increasing complexity of dynamic workloads and the need for efficient resource allocation present persistent challenges in maintaining performance, cost efficiency, and energy sustainability. This paper introduces a Quantum-Driven Optimization (QDO) framework, a hybrid quantum-classical approach designed to enhance cloud resource allocation by integrating quantum computing techniques with classical optimization methods. The proposed framework leverages Quantum Annealing (QA) and the Variational Quantum Eigensolver (VQE) to optimize resource distribution, minimizing energy consumption and operational costs while maximizing throughput and utilization efficiency. Experimental evaluations demonstrate that the QDO framework achieves a 27% improvement in resource utilization, a 34% reduction in operational costs, and a 21% enhancement in task completion time compared to traditional heuristic-based approaches. Additionally, the hybrid model reduces Service Level Agreement (SLA) violations by 18%, ensuring robust Quality of Service (QoS) for cloud users. The framework employs classical algorithms for preprocessing and decision-making while delegating complex optimization tasks to quantum solvers, ensuring scalability across diverse cloud environments. This study highlights the transformative potential of hybrid quantum-classical computing in addressing cloud resource allocation challenges. The results indicate significant improvements in energy efficiency, cost-effectiveness, and system responsiveness, making the QDO framework a viable solution for next-generation cloud infrastructures. Future research directions include extending the framework to multi-cloud architectures and investigating advanced quantum algorithms for further optimization gains.
This paper investigates the potential of accelerating dynamic programming (DP) algorithms by incorporating search strategies derived from quantum mechanics, specifically quantum tunneling and superposition. The core premise is that the inherent exploration capabilities of quantum phenomena can be simulated through quantum-inspired search algorithms, such as quantum annealing or evolutionary algorithms mimicking tunneling behavior, to efficiently navigate the solution space of DP problems. We propose a framework where a classical DP algorithm is augmented with a quantum-inspired search component. The aim is to leverage the strengths of both approaches – the precision and systematic nature of DP combined with the potentially faster exploration offered by quantum-inspired techniques. The effectiveness of this approach is evaluated through theoretical analysis and conceptual design, focusing on scenarios where traditional DP methods struggle with large state spaces or complex decision boundaries. This research contributes to a new paradigm in algorithm design, bridging the gap between classical and quantum computing by utilizing quantum-inspired methods to optimize classical algorithms. The core idea is to treat the DP problem as a tunneling event within a high-dimensional space, significantly reducing the time required to find optimal solutions.
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