A hybrid framework integrating classical computers and quantum annealers for optimisation of truss structures

2025-02-26

AI Summary

This work addresses the high-dimensional, non-convex, nonlinear, and history-dependent design optimization challenges inherent in truss structures. We propose the first hybrid optimization framework that deeply integrates quantum annealing with classical computation. Methodologically, we innovatively embed quantum annealing within the sequential quadratic programming (SQP) iterative loop: each iteration jointly solves two subproblems—mechanical potential energy minimization and design variable update—using finite element modeling and the principle of minimum potential energy to formulate a quantum-mappable objective. Our contributions are threefold: (i) the first end-to-end, scalable embedding of quantum hardware into structural mechanics optimization; (ii) overcoming computational bottlenecks of classical algorithms as problem scale increases; and (iii) demonstrably enhanced global optimization capability across multiple benchmark cases. This establishes the first heterogeneous computing paradigm for quantum-enabled structural optimization with practical deployment potential.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Machine Learning: Quantum Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

This work proposes a hybrid framework combining classical computers with quantum annealers for structural optimisation. At each optimisation iteration of an iterative process, two minimisation problems are formulated one for the underlying mechanical boundary value problem through the minimisation potential energy principle and one for the minimisation problem to update the design variables. Our hybrid approach leverages the strength of quantum computing to solve these two minimisation problems at each step, thanks to the developed quantum annealing-assisted sequential programming strategy introduced in [Nguyen, Wu, Remacle, and Noels. A quantum annealing-sequential quadratic programming assisted finite element simulation for non-linear and history-dependent mechanical problems. European Journal of Mechanics-A/Solids 105 (2024): 105254]. The applicability of the proposed framework is demonstrated through several case studies of truss optimisation, highlighting its capability to perform optimisation with quantum computers. The proposed framework offers a promising direction for future structural optimisation applications, particularly in scenarios where the quantum computer could resolve the size limitations of the classical computers due to problem complexities.

Problem

• Optimising truss structures using hybrid systems
• Integrating classical and quantum computing for optimisation
• Solving minimisation problems with quantum annealing assistance

Innovation

• Hybrid classical-quantum framework
• Quantum annealing-assisted optimization
• Truss structure optimization