Smarsly, KayKaySmarslyPlönnigs, JörnJörnPlönnigsAl-Nasser, HebaHebaAl-Nasser2026-10-272026-10-272026-0913th International Conference on Structural Dynamics, EURODYN 2026https://hdl.handle.net/11420/65488Structural health monitoring (SHM) is vital for ensuring structural safety and reliability of civil infrastructure, and it requires analysis of large datasets and complex structural dynamics that strain classical computing. Quantum computing and quantum-inspired computing promise new ways to address computational problems that are intractable on classical computers. As datasets and model complexity grow, SHM faces computational bottlenecks in high-dimensional sensor data processing and complex optimization tasks that classical computing struggles to handle, and the potential of quantum and quantum-inspired approaches for SHM remains largely unexplored. To address this gap, the paper outlines core principles of quantum and quantum-inspired computing and conducts a multivocal literature review to evaluate the research landscape centered around quantum and quantum-inspired computing. Furthermore, potential application areas and use cases in SHM are identified. As a result, the review shows that adoption of quantum and quantum-inspired computing to conducting SHM tasks remains in an early stage, but promising opportunities are identified, including optimization, simulation, machine learning, and sensing. As quantum and quantum-inspired computing continue to mature, the findings are expected to provide a foundation for guiding future research endeavors and practical deployments of quantum and quantum-inspired computing to advance SHM tasks.enStructural health monitoring (SHM)quantum computing (QC)quantum-inspired computing (QiC)quantum algorithmsquantum machine learningresearch directionsTechnology::600: TechnologyConference Paper10.47964/1326.10753.072