Quantum Computing and Tensor Networks for Laminate Design: A Novel Approach to Stacking Sequence Retrieval

• URL: http://arxiv.org/abs/2402.06455v2
• Date: Tue, 9 Jul 2024 09:48:01 GMT
• Title: Quantum Computing and Tensor Networks for Laminate Design: A Novel Approach to Stacking Sequence Retrieval
• Authors: Arne Wulff, Boyang Chen, Matthew Steinberg, Yinglu Tang, Matthias Möller, Sebastian Feld,
• Abstract summary: A prime example is the weight optimization of laminated composite materials, which to this day remains a formidable problem. The rapidly developing field of quantum computation may offer novel approaches for addressing these intricate problems.
• Score: 1.6421520075844793
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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