Research Data Accompanying the Publication: "Quantum Computing and Tensor Networks for Laminate Design: A Novel Approach to Stacking Sequence Retrieval"

DOI:10.4121/ae276609-55b0-4af1-88c0-1102b1b58990.v2
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/ae276609-55b0-4af1-88c0-1102b1b58990

Datacite citation style

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 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/ae276609-55b0-4af1-88c0-1102b1b58990.v2
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Version 2 - 2024-09-20 (latest)
Version 1 - 2024-02-12

This data repository contains generated data files from the experiments in the paper:

A. Wulff et al.: Comput. Methods Appl. Mech. Eng. 432 (2024) 117380,

doi: 10.1016/j.cma.2024.117380


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 performed numerical state-vector 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 quantum-inspired approach for stacking sequence retrieval.


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

History

  • 2024-02-12 first online
  • 2024-09-20 published, posted

Publisher

4TU.ResearchData

Format

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

Organizations

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

DATA

Files (5)