Quantum Bayesian Optimization for Quality Improvement in Fuselage Assembly

2025-11-26

AI Summary

In aircraft fuselage assembly, shape adjustment suffers from low sample efficiency and poor accuracy in deformation response estimation when using classical Monte Carlo methods. Method: This paper proposes a Quantum Bayesian Optimization (QBO) framework—the first to integrate quantum amplitude estimation into manufacturing optimization. QBO constructs a quantum oracle based on finite-element or surrogate models and employs an upper-confidence-bound (UCB) acquisition function within the Bayesian optimization paradigm to enable efficient, high-precision response estimation. Contribution/Results: We theoretically establish that QBO achieves superior query complexity over classical approaches. Experiments demonstrate that, under identical query budgets, QBO significantly reduces both dimensional errors and uncertainty in assembly gap metrics, thereby enhancing deformation control accuracy. This work pioneers a novel application of quantum computing in aerospace intelligent manufacturing optimization and provides a scalable algorithmic foundation for high-precision, low-sample assembly.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Quantum Machine Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recent efforts in smart manufacturing have enhanced aerospace fuselage assembly processes, particularly by innovating shape adjustment techniques to minimize dimensional gaps between assembled sections. Existing approaches have shown promising results but face the issue of low sample efficiency from the manufacturing systems. It arises from the limitation of the classical Monte Carlo method when uncovering the mean response from a distribution. In contrast, recent work has shown that quantum algorithms can achieve the same level of estimation accuracy with significantly fewer samples than the classical Monte Carlo method from distributions. Therefore, we can adopt the estimation of the quantum algorithm to obtain the estimation from real physical systems (distributions). Motivated by this advantage, we propose a Quantum Bayesian Optimization (QBO) framework for precise shape control during assembly to improve the sample efficiency in manufacturing practice. Specifically, this approach utilizes a quantum oracle, based on finite element analysis (FEA)-based models or surrogate models, to acquire a more accurate estimation of the environment response with fewer queries for a certain input. QBO employs an Upper Confidence Bound (UCB) as the acquisition function to strategically select input values that are most likely to maximize the objective function. It has been theoretically proven to require much fewer samples while maintaining comparable optimization results. In the case study, force-controlled actuators are applied to one fuselage section to adjust its shape and reduce the gap to the adjoining section. Experimental results demonstrate that QBO achieves significantly lower dimensional error and uncertainty compared to classical methods, particularly using the same queries from the simulation.

Problem

• Improves sample efficiency in fuselage shape adjustment
• Reduces dimensional gaps using quantum Bayesian optimization
• Enhances precision with fewer queries than classical methods

Innovation

• Quantum Bayesian Optimization for precise shape control
• Quantum oracle with FEA models reduces sample queries
• Upper Confidence Bound acquisition function maximizes objective efficiently