With the growing scale and maturity of Quantum Computing (QC), research on the application of QC algorithms for structural problems is gaining momentum. In this talk, the author presents recent works of his team on the applications of various QC and quantum-inspired algorithms on structural optimization problems. In particular, a discrete truss sizing optimization problem is transformed to a Quadratic Unconstrained Binary Optimization (QUBO) format. Qubit variables are connected to the design choices through one-hot encoding. A symbolic approach is then taken to embed the qubit variables in the stiffness matrix of the truss system, which is assembled using the Finite Element Method (FEM). In solving this symbolic FEM equation, the solution variables, and hence the objective function thereof, become naturally fractional functions of the qubit variables. An iterative approximation scheme is then used to transform the fractional objective function into a non-fractional, QUBO format, lending itself applicable for QC algorithms such as Quantum Annealing [1]. In addition to truss optimization, a composite stacking sequence retrieval problem, a key step in the optimization of composite laminates, is transformed into a quantum optimization problem. The laminate is represented as a quantum system, where each ply is represented by a few qubits, the number of which depends on the choices of fibre angles per ply. The stacking sequences of the laminate are mapped into quantum state vectors. The objective function and manufacturing constraints are represented as the Hamiltonian of the quantum system. The optimal stacking sequence solution is then embedded in the minimum eigenstate of the Hamiltonian, which can be solved by several classical and QC algorithms such as Variational Quantum Algorithms and DMRG. Solutions are obtained for laminates ranging from 6 plies to 200 plies, demonstrating the effectiveness of the new formulation [2]. REFERENCES 1. Wils K., Chen B.*, A symbolic approach to discrete structural optimization using quantum annealing, Mathematics, Vol. 11, 2023. 2. Wulff A., Chen B.*, Steinberg M., Tang Y., Möller M., Feld S., Quantum Computing and Tensor Networks for Laminate Design: A Novel Approach to Stacking Sequence Retrieval, Computer Methods in Applied Mechanics and Engineering, Vol. 432, pp. 117380, 2024.