The convergence of digital twin technology and quantum computing is opening new horizons for the modeling, control, and optimization of smart grid systems. This paper explores the current research landscape at the intersection of these fields, with a focus on how quantum algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA), can enhance the performance of digital twins in smart energy systems. We conduct a thematic literature review and identify key research trends, technical challenges, and gaps in real-world adoption. A conceptual framework is proposed to integrate quantum modules into classical digital twin architectures. Through a concrete use case, we illustrate the potential benefits of this hybrid approach for smart grid operation. The study concludes by highlighting future research directions toward building scalable, secure, and interoperable quantum-enhanced digital twins for cyber-physical energy systems.
The convergence of digital twin technology and quantum computing is opening new horizons for the modeling, control, and optimization of smart grid systems. This paper explores the current research landscape at the intersection of these fields, with a focus on how quantum algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA), can enhance the performance of digital twins in smart energy systems. We conduct a thematic literature review and identify key research trends, technical challenges, and gaps in real-world adoption. A conceptual framework is proposed to integrate quantum modules into classical digital twin architectures. Through a concrete use case, we illustrate the potential benefits of this hybrid approach for smart grid operation. The study concludes by highlighting future research directions toward building scalable, secure, and interoperable quantum-enhanced digital twins for cyber-physical energy systems.
Rapid urbanization and climate change demands require transformative strategies for energy management in smart cities. Traditional computational techniques encounter substantial difficulties in optimizing intricate, dynamic, and large-scale energy resource allocation issues characteristic of contemporary smart grids. This conceptual review examines the emerging paradigm of quantum-enhanced resource allocation strategies for sustainable energy management in smart cities. This paper expands on the foundational framework of quantum computing applications for smart grid digital twins proposed by Lemo et al., synthesizing current research, developing an integrated conceptual model, and identifying critical research trajectories. We systematically analyze how quantum algorithms—specifically Quantum Annealing (QA), the Quantum Approximate Optimization Algorithm (QAOA), and the Variational Quantum Eigensolver (VQE)—can tackle significant optimization challenges in energy distribution, demand-response management, and renewable energy integration. Our analysis highlights the intersection of quantum computing and digital twin technology as a crucial facilitator for real-time, adaptive energy management systems. We propose a five-layer Quantum-Digital Twin (Q-DT) integration framework and examine its implications for sustainable urban development. The paper concludes by identifying critical research deficiencies, such as the necessity for standardized problem mapping, empirical benchmarking studies, and ethical governance frameworks, while delineating a prospective research agenda for achieving quantum advantage in smart city energy ecosystems. Keywords: Quantum Computing, Resource Allocation, Intelligent Urban Environments, Sustainable Energy Management, Digital Twins, Quantum Optimization, Smart Grid, Integration of Quantum and Digital Twins Aims Research Journal Reference Format: Ojeniyi, J. A., Fasola, O. O., Onyeabor, G. A., Mohammed, A. A, Aliyu, A. A., Ahmed, H. M. & Ndanusah, U. H. (2026): A Conceptual Review of Quantum-Enhanced Resource Allocation Strategies for Sustainable Energy Management in Smart Cities. Advances in Multidisciplinary Research Journal. Vol. 12 No. 1, Pp 55-61. www.isteams.net/aimsjournal. dx.doi.org/10.22624/AIMS/V12N1P4
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.
Abstract Digital twins (DTs) offer high-fidelity digital mappings of physical systems, enabling real-time monitoring, control, and operation across various domains. In future smart grids, the establishment of DTs faces escalating computational challenges due to the involvement of millions of distributed energy resources (DERs), the integration of urban energy systems such as gas and heat networks, and numerous environmental uncertainties, compounded by the need for multiple types of millisecond or even microsecond transient simulation. Leveraging quantum entanglement-enabled parallelism, quantum computing has the promise to significantly speedup the solution of the complex algebraic and differential equations, which is central to DT simulation. Building on this premise, we introduce the concept of quantum digital twins (QuanDT) as an operational framework for integrating quantum computing into DT applications. A DC-DC buck converter is used as a simple but representative case to exemplify the operation of QuanDT in a closed-loop setting and provide a concrete starting point for extending the paradigm toward the QuanDT ecosystem framework in smart grids. Quantum-in-the-loop (QIL) experiments are conducted, in which the QuanDT is implemented via ideal quantum simulation on classical hardware, to illustrate the functionality and effectiveness when quantum computing resources become available and advantageous. We also investigate the application regimes and enabling conditions under which QuanDT may meaningfully benefit from quantum computing, delineating both its potential advantages and current limitations.
The convergence of quantum computing and decentralized energy systems presents a transformative opportunity to address the scalability, security, and real-time intelligence challenges of modern smart grids. This paper provides a comprehensive review of how quantum technologies-including quantum optimization, quantum machine learning (QML), and quantum cryptography-can be integrated into big data-driven smart grid architectures. First, we classify the diverse data sources, analytics bottlenecks, and cyber-physical risks intrinsic to decentralized grids, highlighting the limitations of classical methods under high-dimensional, real-time constraints. Next, we examine the state-of-the-art in quantum optimization techniques, such as quantum approximate optimization algorithm (QAOA), quantum annealing (QA), and hybrid variational methods, for unit commitment (UC), market clearing, mobility scheduling, and state estimation. We then survey quantum-enhanced cybersecurity approaches, including quantum key distribution (QKD), post-quantum cryptography (PQC), and blockchain-integrated control systems, mapping them to grid communication and access control layers. Furthermore, we present a taxonomy of quantum-enabled peer-to-peer (P2P) energy trading platforms, emphasizing cryptographic trust, privacy preservation, and decentralized consensus mechanisms. Cross-domain analysis reveals how quantum modules can augment classical infrastructure to enable secure, scalable, and privacy-preserving grid intelligence. By unifying computation, security, and transaction layers, this review establishes a foundational framework for future research and deployment of quantum-resilient smart grid systems.
The convergence of quantum computing and decentralized energy systems presents a transformative opportunity to address the scalability, security, and real-time intelligence challenges of modern smart grids. This paper provides a comprehensive review of how quantum technologies-including quantum optimization, quantum machine learning (QML), and quantum cryptography-can be integrated into big data-driven smart grid architectures. First, we classify the diverse data sources, analytics bottlenecks, and cyber-physical risks intrinsic to decentralized grids, highlighting the limitations of classical methods under high-dimensional, real-time constraints. Next, we examine the state-of-the-art in quantum optimization techniques, such as quantum approximate optimization algorithm (QAOA), quantum annealing (QA), and hybrid variational methods, for unit commitment (UC), market clearing, mobility scheduling, and state estimation. We then survey quantum-enhanced cybersecurity approaches, including quantum key distribution (QKD), post-quantum cryptography (PQC), and blockchain-integrated control systems, mapping them to grid communication and access control layers. Furthermore, we present a taxonomy of quantum-enabled peer-to-peer (P2P) energy trading platforms, emphasizing cryptographic trust, privacy preservation, and decentralized consensus mechanisms. Cross-domain analysis reveals how quantum modules can augment classical infrastructure to enable secure, scalable, and privacy-preserving grid intelligence. By unifying computation, security, and transaction layers, this review establishes a foundational framework for future research and deployment of quantum-resilient smart grid systems.
Quantum computing (QC) has established itself as a disruptive technology that has the potential to enhance computational capabilities across next-generation energy systems. Its integration into smart grids can enable intelligent decision-making, secure control mechanisms, and advanced optimization strategies. However, existing research remains methodologically fragmented and lacks a unified discussion for practical adoption. This highlights the need for a systematic assessment of the current research landscape to establish structured pathways for effective integration. To address this gap, we have presented a scientometric review that examines the intersection of QC and smart grids based on 133 articles published between 2020 and 2025. The study investigates publication trends, keyword co-occurrence networks, and centrality–density interactions to trace the intellectual evolution of Quantum-enabled Smart Grids (QmSGs). The findings identify post-quantum cryptography and quantum key distribution as dominant motor themes within the field. In parallel, emerging research frontiers include hybrid quantum–classical optimization, federated quantum learning, and AI-driven quantum energy management. The thematic analysis reveals several critical challenges, including scalability constraints, interoperability gaps, and the lack of standardized benchmarking frameworks. Based on these insights, we have proposed a forward-looking research pathway that highlights actionable opportunities for future development. In particular, stronger integration efforts are needed in areas such as explainable quantum learning, adaptive resource orchestration, and sustainable grid governance. These insights can help to enhance the practical relevance and understanding of the field.