This paper presents a practical field of quantum technology and its contribution to smart grids, and their future in smart sustainable cities (SSCs). Smart grids are among the important basis of an SSC, so its factors such as forecasting, optimization, turbine design have a significant role. Based on quantum technology's amazing features, it has conspicuous applications in smart grids including weather prediction, or demand response forecasting. This paper represents SSCs, smart grids, and quantum technology and its use in the ladders. Then, defines the areas of smart grids in which quantum technology can make a noticeable development. Respectively, it compares quantum technology with other regular supercomputing technologies. Ultimately, quantum technology is a kind of technology that has a noticeable contribution potentiality to other technologies to make the world to be processed in a faster, and much more accurate way, and smart grids, SSCs are, also, in this future.
This paper presents a practical field of quantum technology and its contribution to smart grids, and their future in smart sustainable cities (SSCs). Smart grids are among the important basis of an SSC, so its factors such as forecasting, optimization, turbine design have a significant role. Based on quantum technology's amazing features, it has conspicuous applications in smart grids including weather prediction, or demand response forecasting. This paper represents SSCs, smart grids, and quantum technology and its use in the ladders. Then, defines the areas of smart grids in which quantum technology can make a noticeable development. Respectively, it compares quantum technology with other regular supercomputing technologies. Ultimately, quantum technology is a kind of technology that has a noticeable contribution potentiality to other technologies to make the world to be processed in a faster, and much more accurate way, and smart grids, SSCs are, also, in this future.
In this paper, a practical field of quantum technology known as “quantum technology, and weather prediction of smart grids”, and their future have been analyzed. Smart grids are among the significant fundamentals of a smart city and its supplying sources. So, factors such as weather prediction, or demand response are among important factors and have a crucial role. Also, experts should use highly accurate models to predict these factors such as weather prediction Therefore, when they are combined with Artificial Intelligence (AI), and Neural Networks (NN), they will be more efficient. However, in the smart grids of future Smart Sustainable Cities (SSCs) that the gathered big data has enormous amount, the classical AI, and ANN models will inefficient. Accordingly, using a higher technology known as “Quantum technology”, and its practical features, and potentials, has noticeable applications in smart grids, also, weather prediction. This paper presents weather prediction of smart grids, their regular methods, and quantum technology with its usage in the ladders. Respectively, it compares Quantum Neural Networks (QNN) method with other regular techniques that are ANN-based in the area of weather prediction. Finally, it is concluded that quantum technology and Quantum Neural Networks (QNN) have the potential to be combined with other techniques which leads to accurate models with ultrafast processing, and execution speed. Accordingly, they can be used in every field, and smart grids or weather predictions are not an exception.
Abstract Computational complexities in modern power systems are reportedly increasing daily, and it is anticipated that traditional computers might be inadequate to provide the computation prerequisite in future complex power grids. In that given context, quantum computing (QC) can be considered a next‐generation alternative solution to deal with upcoming computational challenges in smart grids. The QC is a relatively new yet promising technology that leverages the unique phenomena of quantum mechanics in processing information and computations. This emerging paradigm shows a significant potential to overcome the barrier of computational limitations with better and faster solutions in optimization, simulations, and machine learning problems. In recent years, substantial progress in developing advanced quantum hardware, software, and algorithms have made QC more feasible to apply in various research areas, including smart grids. It is evident that considerable research has already been carried out, and such efforts are remarkably continuing. As QC is a highly evolving field of study, a brief review of the existing literature will be vital to realize the state‐of‐art on QC for smart grid applications. Therefore, this article summarizes the research outcomes of the most recent papers, highlights their suggestions for utilizing QC techniques for various smart grid applications, and further identifies the potential smart grid applications. Several real‐world QC case studies in various research fields besides power and energy systems are demonstrated. Moreover, a brief overview of available quantum hardware specifications, software tools, and algorithms is described with a comparative analysis.
Quantum computers make it possible to solve hard planning problems, accurately simulate how grids change over time, and improve the efficiency of grid activities. Quantum algorithms have changed the way grids are managed, how demand is predicted, and how energy is distributed in real time. In addition, AI technologies like machine learning, deep learning, and others improve quantum computing by looking at the huge amounts of data that smart power grids create. Using quantum mechanics in AI models and algorithms makes it possible for advanced control systems to handle grid processes automatically, adapt to changing conditions, and quickly fix problems. This research looks into how these technologies could be used, what problems they might cause, and what areas of research should be done in the future to help build safe, resilient, and efficient energy infrastructure. When quantum computing and artificial intelligence come together, they will change the energy business right away. This will create a power grid environment that is smarter, cleaner, and more resilient.
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.
Abstract: The integration of Quantum Artificial Intelligence (Quantum AI) into smart cities represents a transformative leap in traffic management and urban planning. Traditional AI-driven urban systems, while effective, struggle with real-time adaptability, large-scale data processing, and complex optimization challenges. Quantum AI, leveraging quantum superposition, entanglement, and parallel computation, enables cities to optimize traffic flow, infrastructure planning, energy management, and public safety with unparalleled speed and accuracy. This chapter explores how Quantum AI enhances predictive traffic modeling, real-time traffic light synchronization, and autonomous vehicle coordination, reducing congestion and improving mobility efficiency. Additionally, Quantum AI is reshaping urban planning by enabling smart infrastructure development, sustainable resource management, and proactive disaster response systems. Despite challenges such as high computational costs, data privacy concerns, and ethical implications, Quantum AI holds the potential to revolutionize city management, paving the way for resilient, sustainable, and highly adaptive urban environments. The future of smart cities will be defined by AI-powered quantum ecosystems, ensuring seamless urban mobility, enhanced sustainability, and intelligent decision-making for next-generation urban development. Keywords: Quantum AI, smart cities, traffic optimization, urban planning, quantum computing, real-time traffic management, predictive analytics, sustainable urban development, artificial intelligence, autonomous transportation, disaster response, smart infrastructure, quantum-enhanced mobility.
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.