Truss structures, widely used in engineering, consist of straight members transferring axial forces. Traditional analysis methods like FEM and the Force Method become computationally expensive for large-scale and nonlinear problems. Surrogate models using Artificial Neural Networks (ANNs), particularly Physics-Informed Neural Networks (PINNs), offer alternatives but require extensive training data and computational resources. Variational Quantum Algorithms (VQAs) address these challenges by leveraging quantum circuits for optimization with fewer parameters. Variational Quantum Circuits (VQCs) based on Quantum Neural Networks (QNNs) utilize quantum entanglement and superposition to approximate high-dimensional data efficiently, making them suitable for computationally intensive tasks like surrogate modeling in structural analysis. This study applies QNNs to truss analysis using 6-bar and 10-bar planar trusses, assessing their feasibility. Results indicate that residual-based loss functions enable QNNs to make reliable predictions, with increased layers improving accuracy and a higher Q-bit count contributing to performance, albeit marginally.

Abstract
1. 서론
2. 트러스 해석 모델과 손실함수
3. 양자 신경망
    3.1 개요
    3.2 양자 신경망의 구조
    3.3 트러스 해석 모델과 손실함수
4. 수치 해석 예제
    4.1 6-부재 평면 트러스
    4.2 10-부재 평면 트러스
5. 결론
감사의 글
References

• 하현주(한국기술교육대 건축공학과, 석사과정) | Ha Hyeon-Ju (Dept. of Architectural Eng, Koreatech Univ.)
• 손수덕(한국기술교육대 건축공학과, 대우교수, 공학 박사) | Shon Sudeok (Dept. of Architectural Eng, Koreatech Univ.) Corresponding author
• 이승재(한국기술교육대 건축공학과, 교수) | Lee Seung-Jae (Dept. of Architectural Eng, Koreatech Univ.)