  As with many tasks in engineering, structural design frequently involves
navigating complex and computationally expensive problems. A prime example is
the weight optimization of laminated composite materials, which to this day
remains a formidable task, due to an exponentially large configuration space
and non-linear constraints. The rapidly developing field of quantum computation
may offer novel approaches for addressing these intricate problems. However,
before applying any quantum algorithm to a given problem, it must be translated
into a form that is compatible with the underlying operations on a quantum
computer. Our work specifically targets stacking sequence retrieval with
lamination parameters. To adapt this problem for quantum computational methods,
we map the possible stacking sequences onto a quantum state space. We further
derive a linear operator, the Hamiltonian, within this state space that
encapsulates the loss function inherent to the stacking sequence retrieval
problem. Additionally, we demonstrate the incorporation of manufacturing
constraints on stacking sequences as penalty terms in the Hamiltonian. This
quantum representation is suitable for a variety of classical and quantum
algorithms for finding the ground state of a quantum Hamiltonian. For a
practical demonstration, we performed state-vector simulations of two
variational quantum algorithms and additionally chose a classical tensor
network algorithm, the DMRG algorithm, to numerically validate our approach.
Although this work primarily concentrates on quantum computation, the
application of tensor network algorithms presents a novel quantum-inspired
approach for stacking sequence retrieval.
