QuanDT: Quantum Digital Twin with Applications to Smart Grid

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Authors: Wenxuan Ma, Mengxiang Liu, Yu Shang, Kuan-cheng Chen, Yitian Zhou, Rong Guo, Yifan Liu, Ruilong Deng, Peng Cheng, J K Chen
Cited by: 1

Abstract

Abstract Digital twins (DTs) offer high-fidelity digital mappings of physical systems, enabling real-time monitoring, control, and operation across various domains. In future smart grids, the establishment of DTs faces escalating computational challenges due to the involvement of millions of distributed energy resources (DERs), the integration of urban energy systems such as gas and heat networks, and numerous environmental uncertainties, compounded by the need for multiple types of millisecond or even microsecond transient simulation. Leveraging quantum entanglement-enabled parallelism, quantum computing has the promise to significantly speedup the solution of the complex algebraic and differential equations, which is central to DT simulation. Building on this premise, we introduce the concept of quantum digital twins (QuanDT) as an operational framework for integrating quantum computing into DT applications. A DC-DC buck converter is used as a simple but representative case to exemplify the operation of QuanDT in a closed-loop setting and provide a concrete starting point for extending the paradigm toward the QuanDT ecosystem framework in smart grids. Quantum-in-the-loop (QIL) experiments are conducted, in which the QuanDT is implemented via ideal quantum simulation on classical hardware, to illustrate the functionality and effectiveness when quantum computing resources become available and advantageous. We also investigate the application regimes and enabling conditions under which QuanDT may meaningfully benefit from quantum computing, delineating both its potential advantages and current limitations.

Fields of Study & Subjects

Model Reduction and Neural Networks Advancements in Semiconductor Devices and Circuit Design Cloud Computing and Resource Management

Publication details

Journal / source
Research Square
Publisher
Springer Science and Business Media LLC
Publication date
22 January 2026
Document type
Preprint