Potential of Quantum Computing Applications for Smart Grid Digital Twins and Future Directions
- URL: http://arxiv.org/abs/2508.18654v1
- Date: Tue, 26 Aug 2025 03:54:24 GMT
- Title: Potential of Quantum Computing Applications for Smart Grid Digital Twins and Future Directions
- Authors: Arianne Ornella Lemo, Ahmad Mohammad Saber, Deepa Kundur, Adam W. Skorek,
- Abstract summary: The convergence of digital twin technology and quantum computing is opening new horizons for the modeling, control, and optimization of smart grid systems.<n>This paper reviews the current research landscape at the intersection of these fields, with a focus on how quantum algorithms can enhance the performance of digital twins in smart energy systems.
- Score: 1.784933900656067
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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 reviews the current research landscape at the intersection of these fields, with a focus on how quantum algorithms 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. Further, a conceptual framework is proposed to integrate quantum modules into classical digital twin architectures. The potential benefits of this hybrid approach for smart grid operation and future research directions are also discussed.
Related papers
- Artificial intelligence for representing and characterizing quantum systems [49.29080693498154]
Efficient characterization of large-scale quantum systems is a central challenge in quantum science.<n>Recent advances in artificial intelligence (AI) have emerged as a powerful tool to address this challenge.<n>This review discusses how each of these AI paradigms contributes to two core tasks in quantum systems characterization.
arXiv Detail & Related papers (2025-09-05T08:41:24Z) - Quantum-Accelerated Wireless Communications: Concepts, Connections, and Implications [59.0413662882849]
Quantum computing is poised to redefine the algorithmic foundations of communication systems.<n>This article outlines the fundamentals of quantum computing in a style familiar to the communications society.<n>We highlight a mathematical harmony between quantum and wireless systems, which makes the topic more enticing to wireless researchers.
arXiv Detail & Related papers (2025-06-25T22:25:47Z) - A Survey of Quantum Generative Adversarial Networks: Architectures, Use Cases, and Real-World Implementations [0.0]
Quantum Generative Adversarial Networks (QGANs) have emerged as a promising direction in quantum machine learning.<n>This survey provides a comprehensive overview of QGAN models, highlighting key advances from theoretical proposals to experimental realizations.
arXiv Detail & Related papers (2025-06-22T11:45:27Z) - Quantum computing and artificial intelligence: status and perspectives [6.883057868222979]
It describes how quantum computing could support the development of innovative AI solutions.<n>It also examines use cases of classical AI that can empower research and development in quantum technologies.
arXiv Detail & Related papers (2025-05-29T08:15:23Z) - A Survey of Quantum Transformers: Architectures, Challenges and Outlooks [82.4736481748099]
Quantum Transformers integrate the representational power of classical Transformers with the computational advantages of quantum computing.<n>Since 2022, research in this area has rapidly expanded, giving rise to diverse technical paradigms and early applications.<n>This paper presents the first comprehensive, systematic, and in-depth survey of quantum Transformer models.
arXiv Detail & Related papers (2025-04-04T05:40:18Z) - On the impact of key design aspects in simulated Hybrid Quantum Neural Networks for Earth Observation [46.271239108950816]
This research investigates how different quantum libraries behave when training hybrid quantum models.
We also examine the benefits of hybrid quantum attention-based models in Earth Observation applications.
arXiv Detail & Related papers (2024-10-11T10:04:29Z) - From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks [56.51893966016221]
Quantum Graph Neural Networks (QGNNs) represent a novel fusion of quantum computing and Graph Neural Networks (GNNs)
This paper critically reviews the state-of-the-art in QGNNs, exploring various architectures.
We discuss their applications across diverse fields such as high-energy physics, molecular chemistry, finance and earth sciences, highlighting the potential for quantum advantage.
arXiv Detail & Related papers (2024-08-12T22:53:14Z) - Quantum Neural Networks for Solving Power System Transient Simulation Problem [0.0]
We introduce two novel Quantum Neural Networks (QNNs), proposing them as effective alternatives to conventional simulation techniques.
Our application of these QNNs successfully simulates two small power systems, demonstrating their potential to achieve good accuracy.
This research marks a pioneering effort in applying quantum computing to power system simulations but also expands the potential of quantum technologies in addressing intricate engineering challenges.
arXiv Detail & Related papers (2024-05-19T02:18:04Z) - Towards Quantum-Native Communication Systems: State-of-the-Art, Trends, and Challenges [27.282184604334603]
The survey examines technologies such as quantumdomain (QD) multi-input multi-output, QD non-orthogonal multiple access, quantum secure direct communication, QD resource allocation, QD routing, and QD artificial intelligence.<n>The current status of quantum sensing, quantum radar, and quantum timing is briefly reviewed in support of future applications.
arXiv Detail & Related papers (2023-11-09T09:45:52Z) - Quantum machine learning for image classification [39.58317527488534]
This research introduces two quantum machine learning models that leverage the principles of quantum mechanics for effective computations.
Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era.
A second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process.
arXiv Detail & Related papers (2023-04-18T18:23:20Z) - Warm-Starting and Quantum Computing: A Systematic Mapping Study [35.19840943615427]
We collect and analyze scientific literature on warm-starting techniques in the quantum computing domain.
We aim to help quantum software engineers to categorize warm-starting techniques and apply them in practice.
arXiv Detail & Related papers (2023-03-10T18:50:00Z) - DQC$^2$O: Distributed Quantum Computing for Collaborative Optimization
in Future Networks [54.03701670739067]
We propose an adaptive distributed quantum computing approach to manage quantum computers and quantum channels for solving optimization tasks in future networks.
Based on the proposed approach, we discuss the potential applications for collaborative optimization in future networks, such as smart grid management, IoT cooperation, and UAV trajectory planning.
arXiv Detail & Related papers (2022-09-16T02:44:52Z) - Quantum Geometric Machine Learning for Quantum Circuits and Control [78.50747042819503]
We review and extend the application of deep learning to quantum geometric control problems.
We demonstrate enhancements in time-optimal control in the context of quantum circuit synthesis problems.
Our results are of interest to researchers in quantum control and quantum information theory seeking to combine machine learning and geometric techniques for time-optimal control problems.
arXiv Detail & Related papers (2020-06-19T19:12:14Z) - Methods for Accelerating Geospatial Data Processing Using Quantum
Computers [0.0]
This paper describes an approach to satellite image classification using a universal quantum enhancement to convolutional neural networks.
We find a performance improvement over previous quantum efforts in this domain and identify potential refinements that could lead to an eventual quantum advantage.
We benchmark these networks using the SAT-4 satellite imagery data set in order to demonstrate the utility of machine learning techniques in the space industry.
arXiv Detail & Related papers (2020-04-07T02:14:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.