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.
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.
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 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.
Quantum Computing (QC) and Artificial Intelligence (AI) have emerged as key technologies in the evolution of Industry 6.0, driving advancements in automation and advanced analytics, and process optimization. Their integration holds the potential to revolutionize sectors such as data science, healthcare, finance, and cybersecurity by enabling faster and more efficient computations through qubits, superposition, and quantum entanglement. However, the lack of structured knowledge regarding specific QC methodologies and applications in AI hinders its optimal implementation and development. Consequently, this study aims to identify the applications and variables associated with QC-AI integration. To this end, a systematic literature review was conducted following the PRISMA 2020 methodology, drawing on studies from Scopus and Web of Science databases. This enabled the analysis of trends, limitations, and opportunities in this technological convergence. This study aims to systematically examine the intersection of quantum computing and artificial intelligence by identifying the key technological features, integration requirements, and sectoral applications that define the current state of the field. The review contributes by mapping existing research, highlighting methodological approaches, and revealing gaps that may guide targeted advancements in hybrid quantum AI systems. The insights generated have the potential to accelerate innovation in high-impact domains such as healthcare, finance, energy, and cybersecurity. The findings indicate that the main advances in QC applied to AI focus on quantum optimization, Quantum Machine Learning (QML), and post-quantum cryptography. Notably, sectors such as energy, healthcare, and finance have shown significant progress in adopting these technologies. For example, in healthcare, QML has been applied to simulate molecular interactions to accelerate drug discovery, and in finance, it enhances predictive models for market behavior. The study concludes that although QC demonstrates substantial potential to enhance AI, its broader adoption remains constrained by reliance on NISQ hardware, the need for effective error correction, and the limited scalability of hybrid quantum classical algorithms. Addressing these challenges will be essential to establishing QML as a cornerstone of technological innovation and digital transformation. Additionally, this review introduces an integrative framework that categorizes key AI QC convergence dimensions and proposes a classification of application areas based on technical requirements and algorithmic capabilities. These contributions aim to guide future experimental validations and hybrid model development.
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 and artificial intelligence (AI) are two rapidly evolving technologies poised to redefine the future of computation and decision-making.The two fields unite under the name Quantum Artificial Intelligence (QAI) or Quantum Intelligence, which enables a transformative approach to problem-solving through the fusion of AI mental functions with quantum computing capabilities.This paper provides a comprehensive review of the current landscape, highlighting the opportunities and challenges in integrating quantum computing with AI systems.The research investigates current developments in quantum machine learning, quantum neural networks, and hybrid quantum-classical systems, which show promise for achieving rapid performance improvements and enhanced scalability and data processing capabilities.At the same time, it identifies critical barriers, including hardware limitations, algorithmic complexity, data encoding issues, and the lack of standardization in frameworks.The review further explores future directions, including the role of explainable AI, error correction, and interdisciplinary research in achieving practical quantum-AI synergy.By analyzing both the technological potential and the practical constraints, this paper aims to outline a roadmap for advancing quantum intelligence as a transformative force in next-generation computational paradigms.