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.
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 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 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.
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 growing complexity of healthcare systems requires advanced computational models for real-time monitoring, secure data exchange, and intelligent decision-making. Digital Twins (DTs) provide virtual representations of physical healthcare entities, enabling continuous patient monitoring and personalized care. However, classical DT frameworks face limitations in scalability, computational efficiency, and security. Recent studies have introduced Quantum Digital Twins (QDTs) to enhance performance through quantum computing, addressing challenges such as quantum-resistant security and efficient task offloading in healthcare environments. Despite these advances, most existing QDT models remain constrained by fundamental challenges related to quantum hardware limitations, hybrid classical-quantum system integration, cloud-based quantum access, scalability, and clinical trust. This paper provides a comprehensive review of QDTs for healthcare, with a particular focus on identifying and analyzing the key challenges that currently hinder their real-world adoption. Furthermore, it outlines critical research directions and enabling strategies aimed at advancing the development of secure, reliable, and clinically viable quantum digital twin systems for next-generation healthcare applications.
The proliferation of Internet of Things (IoT) devices has created a critical demand for highly energy-efficient and timing-accurate Very Large-Scale Integration (VLSI) circuits. Traditional simulation and modeling techniques often fall short in capturing real-time performance metrics, particularly under dynamic operating conditions and process variations. Digital Twin (DT) technology has emerged as a transformative approach, enabling real-time mirroring and prediction of physical systems. However, classical DT frameworks are computationally limited when simulating complex VLSI behaviors involving multiple non-linear interactions and stringent power constraints. To address these challenges, this research proposes a novel quantum computing-enhanced digital twin framework designed for low-power VLSI circuits in IoT applications. The proposed system leverages the computational parallelism and probabilistic modeling capabilities of quantum algorithms to accelerate the simulation and optimization of power and timing metrics. A hybrid quantum-classical architecture is employed, where a quantum variational circuit models the probabilistic switching behavior of logic gates, while classical components handle data acquisition and control operations. Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) are integrated to minimize energy dissipation and optimize timing paths, respectively. Real-time telemetry from IoT hardware is used to continuously update the twin, ensuring fidelity with the physical circuit. Experimental evaluations on industry-standard benchmark circuits (e.g., ISCAS-85, ITC-99) demonstrate significant improvements in dynamic power estimation accuracy and critical path timing prediction when compared to classical simulation methods. Energy consumption was reduced by up to 17%, and timing deviations were minimized by 22% under varying operating conditions. Moreover, the quantum-DT framework proved robust in the presence of noise and variations, exhibiting scalability for large-scale circuit designs. This study highlights the potential of integrating quantum computing with digital twin technology to meet the stringent design goals of modern VLSI systems, especially in resource-constrained IoT environments. The proposed framework not only enhances modeling precision but also enables real-time adaptive optimization, paving the way for next-generation intelligent circuit design and autonomous system calibration.
With the power grid growing more complex every day with the inclusion of new sensors and regulatory approvals that enable end-users and small local developers to participate in the grid, it is becoming challenging for conventional smart grid simulation, emulation, and testing technologies to keep up. In this work, we propose that quantum-encoded real-time simulations can be helpful under the new paradigm and operational circumstances to solve optimization problems for power grids. By leveraging the principles of quantum mechanics, the proposed quantum-in-loop (QIL) framework will enable better and faster optimization solutions based on real-world, real-time data streams, facilitating the real-time planning and operations of electrical grids that rely on millions of distributed sensors and controllers. Furthermore, QIL will allow researchers and engineers to assist utilities in designing, developing, and de-risking algorithms to optimize power grid operation and resilience by considering inputs from millions of grid-connected devices. QIL framework is being developed to have a self-limiting triage mechanism, which will help engineers and practitioners identify fundamental physical limits on quantum processors, revealing what quantum algorithms can and cannot do in utility-specific use cases and must continue to count on classical high-performance computing infrastructure.