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Towards quantum accelerated large-scale topology optimization
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DOI：10.1016/j.cma.2026.118819.png)
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Abstract
En 中文
We present an efficient topology optimization (TO) method that not only enhances computational efficiency on classical computing but also provides a practical pathway for leveraging quantum computing to achieve further acceleration. The method targets large-scale, multi-material TO of three-dimensional (3D) continuum structures, beyond prior quantum TO studies limited to small-scale and single-material problems. Building on our discrete-variable TO framework (DVTO-MT), which employs multi-cut optimization and trust regions to reduce iteration counts and thereby PDE solver calls, the proposed method introduces a modified Dantzig-Wolfe (MDW) decomposition to further reduce per-iteration optimization time. The MDW method exploits the block-angular structure of the problem to decompose the mixed-integer linear program (MILP) into reduced-size global and local sub-problems. Evaluations on large-scale 3D bridge design problems demonstrate orders-of-magnitude reductions in computational time, with robust performance even for designs exceeding 50 million variables where classical MILP solvers fail to converge. Furthermore, the computationally intensive local sub-problems are transformed into equivalent quadratic unconstrained binary optimization (QUBO) formulations for quantum acceleration. The resulting QUBOs require only sparse qubit connectivity, a crucial consideration for near-term quantum hardware, and linear construction cost, offering the potential for an additional order-of-magnitude speedup. All observed and estimated speedups become more significant with increasing problem size and when extending from single-material to multi-material designs, highlighting the potential of the proposed method, coupled with quantum computing, to address the scale and complexity of real-world TO challenges.
Keywords:
topology optimization
quantum computing
large-scale design
mixed-integer linear programming
quadratic unconstrained binary optimization
Journal
IF:
7.3
Papers:
1.3W
Citations:
5.6W
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