Modern smart grids demand robust computational solutions to manage real-time data processing, complex optimization, and fault resilience, especially with the integration of distributed and renewable energy resources (DERs). This study introduces a hybrid classical-quantum architecture tailored for these needs, leveraging classical computing for routine processing while employing quantum computing for high-complexity tasks. Experimental simulations demonstrate that the hybrid architecture reduces computation time by 40–41% and enhances energy efficiency by approximately 30% compared to traditional configurations. Additionally, its task allocation system improves scalability and resilience, making this architecture a viable solution for optimizing dynamic, large-scale smart grids. The findings offer a pathway toward a scalable and sustainable grid management system, bridging classical reliability with quantum advancements.
Modern smart grids demand robust computational solutions to manage real-time data processing, complex optimization, and fault resilience, especially with the integration of distributed and renewable energy resources (DERs). This study introduces a hybrid classical-quantum architecture tailored for these needs, leveraging classical computing for routine processing while employing quantum computing for high-complexity tasks. Experimental simulations demonstrate that the hybrid architecture reduces computation time by 40–41% and enhances energy efficiency by approximately 30% compared to traditional configurations. Additionally, its task allocation system improves scalability and resilience, making this architecture a viable solution for optimizing dynamic, large-scale smart grids. The findings offer a pathway toward a scalable and sustainable grid management system, bridging classical reliability with quantum advancements.
Quantum Green Computing represents a transformative paradigm that merges the power of quantum computational techniques with the principles of sustainability to drive innovation in renewable resource management.This paper investigates the interplay between quantum algorithms, energy-efficient system architectures, and advanced renewable energy technologies-including solar photovoltaics, wind energy systems, and bioenergy solutions.By leveraging the unique capabilities of quantum computing, such as superior optimization, high-dimensional modeling, and rapid problem-solving, the study explores novel strategies for addressing the inherent complexities of renewable energy integration.The research emphasizes three core dimensions: (1) the application of quantum optimization algorithms to enhance grid stability, load balancing, and real-time decision-making; (2) the development of energy-efficient quantum-inspired hardware architectures that align with green computing objectives; and (3) the formulation of predictive and adaptive models for renewable resource allocation using quantum-inspired approaches.Furthermore, the paper highlights case studies that demonstrate how quantum techniques can significantly improve grid management, predictive maintenance of renewable infrastructures, and long-term forecasting of resource availability under dynamic environmental conditions.By critically evaluating these emerging pathways, the study outlines a vision where quantum-enabled sustainability not only reduces carbon emissions but also maximizes energy utilization efficiency.This integration holds the potential to revolutionize the global renewable energy landscape, enabling resilient, lowcarbon ecosystems capable of supporting the growing demands of a sustainable future.
Advanced functionalities incorporated in the energy systems include the two-way communication feature, decentralized power generation, and the possibility to monitor operating conditions in real-time, which have already been achieved with the rapid deployment of smart grids. Nonetheless, current classical computing methods have reached severe constraints in facing the increase in complexity, massive volumes of data analysis, and intensive security demands of contemporary power grids. Quantum computation, based on the principles of superposition, entanglement, and quantum parallelism, has the transformative potential of surmounting these drawbacks. In this paper, how quantum computing can be applied to smart grid technologies in four significant areas including: (i) quantumenhanced optimization of dynamic energy distribution and unit commitment tasks, (ii) quantum machine learning to reliably forecast demand loads and renewable energy, (iii) quantum cryptography to secure communication and data verification, and (iv) quantum sensing to achieve high-fidelity fault detection and grid state monitoring is discussed. The findings point to the advantages of the use of quantum algorithms in solving complex computationally intractable optimization problems involved in grid management through near real-time solutions using quantum algorithms such as Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE). The issues of noise, decoherence, hardware scalability, and hybrid quantum-classical approaches toward near-term deployment are also examined. This paper demonstrates that quantum computing has the potential to substantially improve the reliability, security, and efficiency of next-generation smart grids.
The increasing complexity of modern energy systems—driven by distributed renewable generation, dynamic grid behavior, and multi-scale interactions—necessitates computational models capable of capturing nonlinearities, stochastic variability, and high-dimensional optimization landscapes. Embedding-based quantum simulations offer a promising path forward by integrating quantum solvers into classical energy modeling frameworks, enabling more accurate analysis of localized subsystems while maintaining tractable large-scale system evaluations. This study investigates quantum embedding techniques applied to power flow analysis, renewable integration, grid stability assessment, and energy storage optimization. By partitioning complex energy networks into quantum-treated active regions embedded within classical architectures, the approach enhances the fidelity of system-level simulations while reducing computational overhead. The research highlights the potential of hybrid quantum–classical workflows to accelerate decision-making in smart grid design, improve forecasting under uncertainty, and support the development of sustainable engineering solutions. The results outline a roadmap for deploying quantum-embedded simulations in future energy infrastructures.
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.
The Internet of Things holds immense promise for advancing sustainability by enabling intelligent management of resources in large-scale systems such as smart electrical grids. However, optimizing these complex systems often exceeds the capabilities of traditional classical computers. This research introduces the Quantum Nexus, a conceptual system architecture that integrates quantum computing with sustainable Internet of Things deployments. The proposed system leverages edge computing for initial data preprocessing and employs advanced post-quantum cryptographic techniques to ensure robust security against emerging computational threats. In simulations involving a smart grid network of one thousand nodes, a quantum-inspired algorithm called the Quantum Approximate Optimization Algorithm was compared against a classical Genetic Algorithm. The quantum approach reduced grid imbalance by eighteen point five percent and achieved solutions 3.2 times faster in complex scenarios. Although quantum computing involves substantial initial energy consumption, the overall operational energy savings demonstrate a net positive environmental impact in large-scale applications. The Quantum Nexus architecture thus offers a promising but challenging framework for sustainable Internet of Things systems, balancing optimization power with energy and security trade-offs.
In addressing the complexity and real-time demands of modern grid optimization in quantum technology, advancements in computing and other fields have provided new avenues for meeting these needs. This article proposes a structured hybrid quantum-classical framework that uses adiabatic quantum computing to plan the operation of large-scale power networks. A large number of experiments were conducted on benchmark power system cases with 14 to 300 nodes, including various topologies and random operating conditions. The aforementioned method first performs preprocessing and problem partitioning. Then, using spatial and temporal partitioning schemes, the most difficult subproblems are assigned to quantum subroutines. The results show that compared to the state-of-the-art traditional algorithms, the hybrid method has a constraint violation rate of less than 0.03 in all test cases, an average operational cost reduction of 6%, and energy savings of 18%. Runtime evaluation shows that the hybrid solver outperforms pure classical and purely quantum methods, and the computation time exhibits sublinear scaling as the grid size increases. The structure can be flexibly adjusted and can still function normally in emergencies, even if parameters and resources are modified or the topology changes. Research indicates that combining adiabatic quantum computing with hybrid optimization systems can enhance the system's scalability, solution quality, and computational efficiency. This method is currently being applied to the construction of new smart grids.