<p>Quantum computing, leveraging the properties of quantum physics such as quantum superposition and entanglement, possesses the potential for exponential acceleration compared to classical computing. It can significantly enhance solution efficiency in topology optimization and effectively avoid the entrapment in local optima. This paper proposes a hybrid classical-quantum computing framework to solve the stress-constrained topology optimization problem for truss structures. Initially, structural analyses are performed on a classical computer to determine the stresses of truss members. Then, the optimization problem is formulated through incremental updates of member cross-sectional areas to make it compatible with a quantum annealer. The update strategy consists of a directional-control function and a magnitude-control function. By embedding stress constraints directly into the directional-control function, the original optimization problem is reformulated as a quadratic unconstrained binary optimization model suitable for quantum annealing. To realize a balance between solution accuracy and iteration efficiency, a dynamic strategy for adjusting the magnitude of area increments is proposed. Thus, the quantum annealer can effectively achieve the optimal solutions. When only the access time of the quantum processing unit is considered, the results from 2D and 3D examples of truss topology optimization validate the effectiveness of the proposed framework, and demonstrate the great potential of quantum computing in structural optimization.</p>