As the demand for high-performance and dynam-ically adaptable network services continues to grow, the convergence of quantum computing and network orchestration represents a promising development. This paper explores the transformative potential of quantum computing in the orches-tration of network services and introduces new paradigms that transcend classical limitations. Using a case study, we propose a workflow for an exemplary link failure process for generative AI (GenAI) and quantum computing-based service orchestration in an edge cloud (EC) network. Later, we address the challenges of integrating services based on quantum computing (using a quan-tum search algorithm to select the best connections) with GenAI-based topology control and management in an orchestration framework. Our work also introduces multi-solution quantum search for orchestrating services across different edge clouds, emphasizing efficiency and accuracy. Algorithmic considerations, including the impact of the oracle and hybrid approaches, are also thoroughly explored. We conclude the paper with potentials and comparisons of possible GenAl techniques and quantum search algorithms in terms of their advantages and disadvantages.
As the demand for high-performance and dynam-ically adaptable network services continues to grow, the convergence of quantum computing and network orchestration represents a promising development. This paper explores the transformative potential of quantum computing in the orches-tration of network services and introduces new paradigms that transcend classical limitations. Using a case study, we propose a workflow for an exemplary link failure process for generative AI (GenAI) and quantum computing-based service orchestration in an edge cloud (EC) network. Later, we address the challenges of integrating services based on quantum computing (using a quan-tum search algorithm to select the best connections) with GenAI-based topology control and management in an orchestration framework. Our work also introduces multi-solution quantum search for orchestrating services across different edge clouds, emphasizing efficiency and accuracy. Algorithmic considerations, including the impact of the oracle and hybrid approaches, are also thoroughly explored. We conclude the paper with potentials and comparisons of possible GenAl techniques and quantum search algorithms in terms of their advantages and disadvantages.
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.
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.
The rapid proliferation of grid-edge distributed energy resources has significantly increased the operational complexity of modern power systems. Consequently, conventional computational techniques face growing scalability and computational-efficiency challenges in addressing large-scale optimization and control, uncertainty management, nonlinear dynamics, and combinatorial decision-making in smart grid operations. Quantum computing has therefore emerged as a promising computational paradigm that can complement classical methods in addressing selected computationally intensive problems. In this context, this paper presents a comprehensive structured review of quantum computing applications in smart grid operations. Following a transparent keyword-based literature search, the paper classifies and assesses existing studies across monitoring and estimation, system planning, operation and control, security, reliability and resilience, stability assessment, data-driven intelligence, and digital twin technologies. The paper also describes fundamental quantum-computing concepts and key algorithms, highlighting their relevance for power system applications. Furthermore, it reviews the current state of quantum hardware, software frameworks, simulators, cloud services, and emerging hardware-agnostic ecosystems that support cross-platform application development and deployment. The reviewed studies are examined by implementation environment, benchmarking practices, application scale, and evidence of computational advantage. Finally, the principal challenges associated with practical implementation are discussed, and future research directions are outlined. This paper provides a consolidated and evidence-calibrated perspective on the current state and future potential of quantum computing for next-generation smart grid operations.
The integration of quantum computing into multi-cloud environments represents a transformative paradigm in distributed computing, offering substantial improvements in computational efficiency, security resilience, and adaptive workload orchestration. However, the heterogeneous nature of cloud infrastructures, combined with the nascent and resource-sensitive characteristics of quantum systems, introduces significant architectural, operational, and security challenges. This paper proposes an optimized quantum computing integration approach designed specifically for multi-cloud ecosystems, emphasizing scalable architecture design, intelligent workload distribution, and enhanced cryptographic security mechanisms.The study synthesizes foundational quantum computing principles with contemporary multi-cloud orchestration strategies to develop a hybrid architectural framework. The proposed model leverages quantum-enhanced optimization techniques, including quantum approximate optimization principles, to dynamically allocate computational tasks across distributed cloud nodes. Additionally, it integrates quantum-safe security protocols to mitigate emerging cyber threats in cloud environments. Prior research highlights the relevance of such integration, particularly in solving complex optimization problems and enhancing system-level security (Brown & White, 2022; Cao et al., 2020).A structured literature synthesis reveals that while cloud computing and quantum computing have individually matured, their convergence remains underexplored in practical deployment contexts. This paper addresses this gap by introducing an architecture that supports adaptive workload balancing, quantum-classical hybrid execution layers, and secure inter-cloud communication channels. The findings indicate that quantum-enhanced orchestration can significantly reduce computational latency and improve resource utilization efficiency in multi-cloud systems.
Quantum computing represents a paradigm shift in computational power, offering unprecedented potential for optimizing Key Performance Indicators (KPIs) in telecommunications. This paper explores the application of quantum computing to telecom KPI optimization, contrasting its capabilities with those of generative artificial intelligence (AI). Telecom networks face increasing complexity due to rising data demands, 5 G deployment, and user expectations, necessitating advanced optimization techniques beyond traditional methods. While generative AI has shown promise in modeling network behaviour and predicting KPIs, it struggles with scalability and computational efficiency for large-scale, real-time systems. Quantum computing, leveraging principles like superposition and entanglement, excels in solving combinatorial optimization problems inherent in telecom networks, such as resource allocation and latency reduction. Through a detailed comparison, this paper highlights quantum computing’s superior speed, precision, and ability to handle multi-dimensional KPI datasets. A conceptual quantum framework for telecom optimization is proposed, supported by theoretical examples. The study concludes that while generative AI offers flexibility, quantum computing provides transformative advantages for future telecom systems, paving the way for nextgeneration network efficiency. Challenges such as hardware limitations and algorithm development are also discussed, alongside future research directions.
The growing integration of cloud computing and artificial intelligence (AI) has remarkably transformed modern computing, enabling organizations and researchers to process vast amounts of data and solve increasing complex problems. However, as data volumes and computational requirements continue to grow, traditional computing systems are increasingly challenged in meeting these demands. In this context, quantum computing has evolved as a revolutionary technology with the potential to perform certain computations far more efficiently than classical computers. When integrated with cloud platforms, quantum computing becomes accessible to a broader community through remote, on-demand services, giving rise to the concept of Quantum Cloud Computing. At the same time, Quantum Machine Learning (QML) is opening new possibilities by combining the power of quantum computing with advanced machine learning techniques. Together, these technologies are shaping a new paradigm known as Quantum Cloud Intelligence, where cloud infrastructure, quantum resources, and AI work in synergy to enhance computational capabilities and support intelligent decision-making. This chapter examines the foundations and evolution of Quantum Cloud Intelligence, highlighting its key technologies, architectures, and emerging applications. It explores how quantum-enabled cloud platforms can contribute to fields such as healthcare, finance, cybersecurity, scientific research, smart cities, and industrial automation. The chapter also discusses the opportunities offered by quantum-enhanced analytics, optimization, and predictive modelling, while addressing important challenges related to scalability, security, privacy, hardware constraints, and skill development. Looking ahead, the chapter considers future trends including hybrid quantum-classical systems, Quantum Computing as a Service (QCaaS), and the development of scalable quantum AI ecosystems. It argues that the integration of quantum computing, cloud technologies, and AI has the potential to redefine the future of intelligent computing and drive innovation across multiple disciplines. As these technologies continue to mature, Quantum Cloud Intelligence is expected to play a pivotal role in addressing complex global challenges and shaping the next generation of digital transformation.