Optimized power system planning and operation are core to delivering a low-cost and high-reliability transition path to net-zero carbon emissions. The major technological changes associated with net zero, including the rapid adoption of renewables, electrification of transport and heating, and system-wide digitalization, each increase the scope for optimization to create value, but at the cost of greater computational complexity. Although power system optimization problems are now posing challenges for even the largest exa-scale supercomputers, a new avenue for progress has been opened by recent breakthroughs in quantum computing. Quantum computing offers a fundamentally new computational infrastructure with different capabilities and trade-offs and is reaching a level of maturity where, for the first time, a practical advantage over classical computing is available for specific applications. In this review, we identify significant and wide-ranging opportunities for quantum computing to offer value for power system optimization. In addition to reviewing the latest work on quantum computing for simulation-based and combinatorial power system optimization applications, we also review state-of-the-art theoretical work on quantum convex optimization and machine learning and map this to power system optimization applications where quantum computing is underexplored. Based on our review, we analyze challenges for industry implementation and scale-up and propose directions for future research.
Optimized power system planning and operation are core to delivering a low-cost and high-reliability transition path to net-zero carbon emissions. The major technological changes associated with net zero, including the rapid adoption of renewables, electrification of transport and heating, and system-wide digitalization, each increase the scope for optimization to create value, but at the cost of greater computational complexity. Although power system optimization problems are now posing challenges for even the largest exa-scale supercomputers, a new avenue for progress has been opened by recent breakthroughs in quantum computing. Quantum computing offers a fundamentally new computational infrastructure with different capabilities and trade-offs and is reaching a level of maturity where, for the first time, a practical advantage over classical computing is available for specific applications. In this review, we identify significant and wide-ranging opportunities for quantum computing to offer value for power system optimization. In addition to reviewing the latest work on quantum computing for simulation-based and combinatorial power system optimization applications, we also review state-of-the-art theoretical work on quantum convex optimization and machine learning and map this to power system optimization applications where quantum computing is underexplored. Based on our review, we analyze challenges for industry implementation and scale-up and propose directions for future research.
The aim of this paper is to report on the state of the art of the literature on the most recent challenges in the energy domain that can be addressed through the use of quantum computing technology. More in detail, to the best of the authors’ knowledge, the scope of the literature review considered in this paper is specifically limited to forecasting, grid management (namely, scheduling, dispatching, stability, and reliability), battery production, solar cell production, green hydrogen and ammonia production, and carbon capture. These challenges have been identified as the most relevant business needs currently expressed by energy companies on their path towards a net-zero economy. A critical discussion of the most relevant methodological approaches and experimental setups is provided, together with an overview of future research directions. Overall, the key finding of the paper, based on the proposed literature review, is twofold: namely, (1) quantum computing has the potential to trigger significant transformation in the energy domain by drastically reducing CO2 emissions, especially those relative to battery production, solar cell production, green hydrogen and ammonia production, as well as point-source and direct-air carbon capture technology; and (2) quantum computing offers enhanced optimization capability relative to relevant challenges that concern forecasting solar and wind resources, as well as managing power demand, facility allocation, and ensuring reliability and stability in power grids.
Innovative solutions and developments are being inspected to tackle rising electrical power demand to be supplied by clean forms of energy. The integration of renewable energy generations, varying nature loads, importance of active role of distribution system and consumer participation in grid operation has changed the landscape of classical power grids. Implementation of smarter applications to plan, monitor, operate the grid safely are deemed paramount for efficient, secure and reliable functioning of grid. These smarter applications for modern power systems demand capabilities such as real-time monitoring, dynamic security analysis, stochastic power flow calculations, large-scale data analytics, and high-dimensional combinatorial and constrained optimization. Although sophisticated computations to process gigantic volume of data to produce useful information in a time critical manner is the paradigm of future grid operations, these enhanced functionalities impose significantly higher computational demands compared to traditional approaches used in conventional power systems. Advancements in quantum technologies holds promising solution for dealing with demanding computational complexity of power system related applications. In this article, we lay out clear motivations for seeking quantum solutions for solving computational burden challenges associated with power system applications. Next, we present the fundamental principles of quantum computing, introduce key quantum algorithms, and offer a comparison of the computational load between classical and quantum approaches for few important mathematical problems, indicating their relevance to various power system applications. Additionally, we provide an overview of quantum solutions for various power system related applications available in current literature and suggest future topics for research. We further highlight challenges with existing quantum solutions for exploiting full quantum capabilities. To this end, this article serves as a bridge for power engineers to the quantum domain by outlining fundamental principles of quantum computation, facilitating a smoother transition to the future of power system computations and also provides quantum experts with insights into new application areas for quantum computing within power 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.
The computational demands of modern power systems are increasing rapidly, posing significant challenges to conventional computing approaches. Traditional methods may not be sufficient to handle the scale and complexity of future power grid operations. In this context, quantum computing (QC) emerges as a potential next-generation solution to address these limitations. Empowered with the quantum mechanics principles such as qubits, superposition and entanglement, QC can be used to efficiently handle the complex computation problems of large-scale grids. Building on this potential, our paper reviews the existing research done in regard to QC applications in smart grids and classifies them on the basis of three core QC's advantages namely - optimization, big data forecasting and security. In the end, we discuss the current challenges associated with employing QC in smart grids.
Innovative solutions and developments are being inspected to tackle rising electrical power demand to be supplied by clean forms of energy. The integration of renewable energy generations, varying nature loads, importance of active role of distribution system and consumer participation in grid operation has changed the landscape of classical power grids. Implementation of smarter applications to plan, monitor, operate the grid safely are deemed paramount for efficient, secure and reliable functioning of grid. Although sophisticated computations to process gigantic volume of data to produce useful information in a time critical manner is the paradigm of future grid operations, it brings along the burden of computational complexity. Advancements in quantum technologies holds promising solution for dealing with demanding computational complexity of power system related applications. In this article, we lay out clear motivations for seeking quantum solutions for solving computational burden challenges associated with power system applications. Next we present an overview of quantum solutions for various power system related applications available in current literature and suggest future topics for research. We further highlight challenges with existing quantum solutions for exploiting full quantum capabilities. Additionally, this paper serves as a bridge for power engineers to the quantum world by outlining essential quantum computation fundamentals for enabling smoother transition to future of power system computations.
Quantum computing has emerged as a transformative computational paradigm capable of addressing limitations increasingly encountered by classical computing systems as Moore’s law approaches physical and economic constraints. By leveraging quantum mechanical principles such as superposition, entanglement, and interference, quantum systems offer the potential for significant computational advantages in solving complex problems that are currently infeasible for conventional architectures. This review critically synthesizes recent advances in quantum computing by examining foundational principles, major algorithmic breakthroughs, evolving hardware platforms, and interdisciplinary real-world applications. A structured narrative literature review was conducted using major scientific databases, including PubMed, IEEE Xplore, Nature, ScienceDirect, and arXiv, with emphasis on peer-reviewed studies, landmark foundational research, and major industry developments published between 2018 and 2026. Key findings indicate substantial progress in superconducting, trapped ion, photonic, and topological quantum systems, alongside innovations in algorithms such as Shor’s, Grover’s, variational quantum methods, and quantum machine learning. Despite these advancements, major challenges including decoherence, noise, scalability limitations, and fault-tolerant error correction remain substantial barriers to commercial maturity. Future implications suggest quantum computing may profoundly influence artificial intelligence, cybersecurity, optimization, molecular simulation, and scientific discovery, particularly through hybrid classical-quantum systems and cloud-based democratization. Although practical deployment remains transitional, quantum computing is positioned to become a foundational technology with broad scientific, industrial, and societal implications.