Collaborative Quantum Computing for Structural Damage Identification

As one of the most revolutionary frontiers in modern technology, quantum computing has recently demonstrated immense potential in the field of structural mechanics. Currently, universal gate-based quantum computers and specialized quantum annealers represent the two mainstream technological paradigms within the domain. However, existing research has predominantly focused on using a single quantum platform to accelerate specific optimization steps. Consequently, the complementary advantages of these two architectures have not been fully exploited to achieve a collaborative solution for holistic mechanical problems. To address it, this paper proposes a collaborative quantum algorithm for structural damage identification. In this framework, damage parameter inversion is executed on a gate-based quantum computer by mapping stiffness reduction factors onto qubits, accelerating the global state search via quantum superposition and entanglement. Concurrently, structural static analysis is reformulated as a quadratic unconstrained binary optimization problem and solved on a quantum annealer to parallelize response computations. To evaluate its performance, the proposed algorithm is tested on three distinct structural models. Numerical results demonstrate that the algorithm not only successfully identifies structural damage but also exhibits significant efficiency improvements.