On the intersection of quantum computing and computational structural mechanics
With a focus on near-term quantum computing
G.B.L. Tosti Balducci (TU Delft - Group De Breuker)
R. De Breuker – Promotor (TU Delft - Aerospace Structures & Materials)
M. Möller – Promotor (TU Delft - Numerical Analysis)
B. Y. Chen – Copromotor (TU Delft - Group Chen)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Quantum computation encodes and operates on data in a radically different way than classical logic. This difference allows researchers in nearly all applied sciences to explore how quantum-accelerated computing could enhance the efficiency of their most computationally demanding tasks.
This thesis studies the introduction of quantum-accellerated computing in structural mechanics, a field that traditionally leverages computational techniques at both industrial and research scales. The scope is limited to practical and mostly near-term quantum computing. Therefore, the algorithms analyzed or proposed are evaluated for their runtime as end-to-end routines and for their ability to run on near-term quantum devices.
Given the vastness of the application domain, the research was developed in three separate threads. The first is a review of quantum algorithms applicable to partial differential equations (PDEs) in structural mechanics. The second is an application of a variational quantum algorithm for linear systems of equations to the discrete Poisson equation, while the last studies how quantum machine learning, specifically quantum kernel methods, discriminates damage-inducing loading states in a composite plate with a cutout.
Each of the three parts reveals the potentials and limitations of practical quantum-accelerated computing.
The PDE review questions the end-to-end advantage of fault-tolerant quantum algorithms and highlights the need to specialize near-term alternatives to problems in mechanics.
The work on the variational quantum linear solver emphasizes the matrix decomposition bottleneck and proposes a method to factorize the discrete Poisson equation matrix.
Finally, the work on kernel methods for damage identification shows how heuristically selected and trained quantum kernels reach scores comparable to classical best-practice kernels, but also hints at the fact that potential quantum advantage requires more systematic kernel optimization and retaining performance when scaling quantum systems beyond classical simulation.
help_outline