Quantum Computing and Digital Twins

Jeerisuda Khumsikiew, Rojjares Netthong, Bancha Yingngam

Harpreet Kaur Channi

S. Chidambaranathan

Kapil Saini, Ajmer Singh, Anjali Ahuja +2 more

David Melvin Braga, Bharat S. Rawal

Digital computers have transformed human contact, employment, and time management, yet they have limits in drug development. Classical computers can only hold one piece of data at a time, making it impossible to replicate complicated chemical and biological processes. In contrast, quantum computing constructs physical theories with particles such as electrons or photons. Despite these limits, digital computers continue to play an important role when paired with AI, machine learning, and quantum computing. Digital Twin Systems (DTS) are more extensive and integrated uses of digital twin technology that include entire systems or networks rather than just one physical object. This paper compares various DTS and highlights challenges with those DTS.

Wenxuan Ma, Mengxiang Liu, Shang Yu +7 more

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.