Wulff, Arne; Chen, Boyang; Steinberg, Matthew; Tang, Yinglu; Möller, Matthias et. al. (2024): Research Data Accompanying the Publication: "Quantum Computing and Tensor Networks for Laminate Design: A Novel Approach to Stacking Sequence Retrieval". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/ae276609-55b0-4af1-88c0-1102b1b58990.v1

Dataset

x choose version: version 2 - 2024-09-20 (latest)

• Aerospace Engineering
• Materials Engineering
• Quantum Physics
• Engineering
• Physical Sciences
• Information and Computing Sciences

composite laminates, quantum computing, stacking sequence retrieval, tensor networks

2024

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Arne Wulff , Boyang Chen , Matthew Steinberg , Yinglu Tang , Matthias Möller , Sebastian Feld

This data repository contains the generated data files from the experiments in the paper: https://arxiv.org/abs/2402.06455 .

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, which is typically the second phase in a common bi-level 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 chose a classical tensor network algorithm, the DMRG algorithm, to numerically validate our approach. For this purpose, we derived a matrix product operator representation of the loss function Hamiltonian and the penalty terms. Numerical trials with this algorithm successfully yielded approximate solutions, while exhibiting a tradeoff between accuracy and runtime. Although this work primarily concentrates on quantum computation, the application of tensor network algorithms presents a novel quantum-inspired approach for stacking sequence retrieval.

For further information on the data in this repository, view the 'README.md' file.

• 2024-02-12 first online, published, posted

4TU.ResearchData

ZIP archive (zip), Jupyter notebook (ipynb), HTML page (html), Markdown documentation (md)

• https://arxiv.org/abs/2402.06455
• https://github.com/ArneWulff/stacking-sequence-retrieval-with-dmrg

TU Delft, Faculty of Aerospace Engineering, Aerospace Structures and Materials;
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Quantum & Computer Engineering;
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Applied Mathematics