Quantum Technologies and Edge Devices in Electrical Grids: Opportunities, Challenges, and Future Directions
Marjorie Hoegen, René Glebke, M. Sahnawaz Alam, Alessandro David, Juan Navarro Arenas, Nikolaus Wirtz, Mario Albanese, Daniele Carta, Felix Motzoi, Antonello Monti, Carsten Schuck, Andrea Benigni, Klaus Wehrle, Ferdinanda Ponci
eess.SYphysics.app-phquant-phSubmitted Mar 6, 2026• Announced Mar 9, 2026• 0 citations
In modern power systems, edge devices serve as local hubs that collect data, perform on-site computing, sense electrical parameters, execute control actions, and communicate with neighboring edge devices as part of the larger grid. However, as the number of monitored nodes and control loops grows, traditional edge devices face serious limits. They can become overloaded by complex signal processing and decision tasks, causing delays and higher energy use. Standard sensors hit a noise floor that prevents them from detecting miniature changes, making it harder to spot early signs of faults or instability. Meanwhile, conventional communication links struggle with bandwidth limits, security risks, and rising encryption demands, which together slow down and weaken the transfer of critical grid information. Quantum technologies have the potential to overcome these challenges. Quantum computers can deliver exponential speed-ups for optimization and machine-learning tasks that ordinary processors cannot handle. Quantum sensors can sense signals with atomic precision, giving edge devices a more precise view of grid dynamics. Quantum communication techniques, including quantum key distribution, offer methods to achieve information-theoretic security and ensure that information arrives quickly and without tampering. We explore how quantum technologies can be integrated into edge devices, highlighting both opportunities and challenges.
In modern power systems, edge devices serve as local hubs that collect data, perform on-site computing, sense electrical parameters, execute control actions, and communicate with neighboring edge devices as part of the larger grid. However, as the number of monitored nodes and control loops grows, traditional edge devices face serious limits. They can become overloaded by complex signal processing and decision tasks, causing delays and higher energy use. Standard sensors hit a noise floor that prevents them from detecting miniature changes, making it harder to spot early signs of faults or instability. Meanwhile, conventional communication links struggle with bandwidth limits, security risks, and rising encryption demands, which together slow down and weaken the transfer of critical grid information. Quantum technologies have the potential to overcome these challenges. Quantum computers can deliver exponential speed-ups for optimization and machine-learning tasks that ordinary processors cannot handle. Quantum sensors can sense signals with atomic precision, giving edge devices a more precise view of grid dynamics. Quantum communication techniques, including quantum key distribution, offer methods to achieve information-theoretic security and ensure that information arrives quickly and without tampering. We explore how quantum technologies can be integrated into edge devices, highlighting both opportunities and challenges.
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 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.
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.
Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing—an emerging paradigm to overcome the shortcomings of cloud-based computing—could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator—a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.
Smart grid depends on an advanced communication network to collect information from the power grid, and to disseminate control commands to the control devices. To safeguard the power grid, it is crucial to ensure information confidentiality in the communication networks. Quantum key distribution (QKD) protocols help in generating, and distributing secret keys between communication parties, and such secret keys are required in symmetric cryptography. The combination of QKD protocols, and symmetric cryptography are known to be unconditionally secure, which means information confidentiality can be guaranteed even against an eavesdropper, who has unlimited resources. This article provides a concise review of existing works on QKD protocols, and their applications in smart grid communications. Deploying QKD protocols in smart grid is challenging because distance between the control center, and control devices can be larger than the limits of existing protocols. Also, QKD protocols require an expensive quantum channel between each pair of sender, and receiver nodes, and there is large number of control devices with diverse capabilities in smart grid. We have classified existing works based on the challenges they have dealt with. Compared to the rich literature on QKD protocols in general, there are significantly fewer works in the specific context of smart grid. This can be an indication for opportunity to make a significant contribution. We have also identified a few research challenges that can be potential future works.
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.