Quantum-assisted Stacking Sequence Retrieval and Laminated Composite Design

TL;DR

This paper presents a quantum computing approach to optimize stacking sequences for laminated composites in aerospace, incorporating manufacturing constraints and demonstrating scalability and competitiveness against classical algorithms.

Contribution

It introduces an enhanced quantum algorithm framework for stacking sequence retrieval, including manufacturing constraints and a scalable simulation method, advancing aerospace composite design optimization.

Findings

F-VQE algorithm improves solution probability shaping.

Incorporating manufacturing constraints aligns the model with real-world demands.

Delineates scalability of the approach up to 200 plies using DMRG.

Abstract

We, the QAIMS lab lab at the Aerospace Faculty of TU Delft, participated as finalists in the Airbus/BMW Quantum Computing Challenge 2024. Stacking sequence retrieval, a complex combinatorial task within a bi-level optimization framework, is crucial for designing laminated composites that meet aerospace requirements for weight, strength, and stiffness. This document presents the scientifically relevant sections of our submission, which builds on our prior research on applying quantum computation to this challenging design problem. For the competition, we expanded our previous work in several significant ways. First, we incorporated a full set of manufacturing constraints into our algorithmic framework, including those previously established theoretically but not yet demonstrated, thereby aligning our approach more closely with real-world manufacturing demands. We implemented the F-VQE algorithm, which enhances the probability shaping of optimal solutions, improving on simpler variational quantum algorithms. Our approach also demonstrates flexibility by accommodating diverse objectives as well as finer ply-angle increments alongside the previously demonstrated conventional ply angles. Scalability was tested using the DMRG algorithm, which, despite limitations in entanglement representation, enabled simulations with up to 200 plies. Results were directly compared to conventional stacking sequence retrieval algorithms with DMRG showing high competitiveness. Given DMRG's limited entanglement capabilities, it serves as a conservative baseline, suggesting potential for even greater performance on fully realized quantum systems. This document serves to make our competition results publicly available as we prepare a formal publication on these findings and their implications for aerospace materials design optimization.