Prof. Jan C. Aurich received his Ph.D. degree from Leibniz University Hannover in 1995. From 1990 to 1995, he was a research assistant at the Institute for Production Engineering and Machine Tools, Leibniz University of Hanover, and led the "CAD/CAPP" team from 1993. From 1995 to 2002, he played a management role in both production and development departments at Daimler AG. In 2002, he became the chair professor of the Institute for Manufacturing Technology and Production Systems at TU Kaiserslautern. In 2013-2014, he was a Fulbright Visiting Professor at UC Davis, USA. Prof. Aurich is a member of the German National Academy of Science and Engineering (acatech) and the German Academic Association for Production Technology (WGP), and a fellow of the International Academy for Production Engineering (CIRP). His research focuses on digital production, micro-manufacturing, product–service systems, and sustainable manufacturing. Digital twins (DTs) in manufacturing are part of an integrated system which comprises the physical entity, the virtual entity, their connections, data, and services. Within this framework, the service dimension is of particular importance, as it enables functionalities such as monitoring and control, prediction, optimization, and adaptive decision-making in a dynamic manufacturing environment. To realize these, substantial demands are placed on the underlying computational methods, particularly for tasks characterized by high combinatorial complexity, the need for accurate physics-based process representations, and underlying dynamic conditions. Classical computational approaches are approaching their limits when it comes to meeting the real-time requirements of DT applications and realizing their full potential.

Quantum computing represents a computational paradigm that can address these limitations. Gate-based quantum computing constitutes a universal approach capable of executing arbitrary quantum algorithms, encompassing optimization, simulation, and machine learning applications. Its applicability in manufacturing remains constrained by the limited maturity of available hardware, particularly due to noise, limited coherence times, restricted circuit depth, and the substantial overhead associated with fault-tolerant computation. Quantum annealing, by contrast, is architecturally restricted to combinatorial optimization but operates on dedicated hardware with a higher technological readiness, which makes it the tool of choice for near-term industrial applications.

Both approaches, gate-based quantum computing and quantum annealing have been explored in manufacturing contexts by the authors. This contribution presents use cases and maps them onto the DT-manufacturing application taxonomy, examining which DT functionalities can be supported by which quantum computing approach and at which level of the manufacturing system. The use cases are assessed with respect to their suitability, resulting in a structured overview of the integration potential of quantum-assisted methods within digital twins of manufacturing systems.