Abstract
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 nonlinear 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, which is typically the second phase in a common bilevel optimization procedure for minimizing the weight of composite structures. To adapt stacking sequence retrieval 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 numerical statevector simulations of two variational quantum algorithms and additionally chose a classical tensor network algorithm, the DMRG algorithm, to numerically validate our approach. For the DMRG algorithm, we derived a matrix product operator representation of the loss function Hamiltonian and the penalty terms. Although this work primarily concentrates on quantum computation, the application of tensor network algorithms presents a novel quantuminspired approach for stacking sequence retrieval.
Original language  English 

Article number  117380 
Number of pages  33 
Journal  Computer Methods in Applied Mechanics and Engineering 
Volume  432 
DOIs  
Publication status  Published  2024 
Keywords
 Composite laminates
 Quantum computing
 Stacking sequence retrieval
 Tensor networks
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Research Data Accompanying the Publication: "Quantum Computing and Tensor Networks for Laminate Design: A Novel Approach to Stacking Sequence Retrieval"
Wulff, A. (Creator), Chen, B. (Creator), Steinberg, M. A. (Creator), Tang, Y. (Creator), Möller, M. (Creator) & Feld, S. (Creator), TU Delft  4TU.ResearchData, 12 Feb 2024
DOI: 10.4121/AE27660955B04AF188C01102B1B58990
Dataset/Software: Dataset