Airfoil shape optimization via coherent Ising machine

Airfoil shape optimization presents a challenge where classical solvers frequently struggle with computational efficiency and local minima. In the promising paradigm of quantum computing, the coherent Ising machine (CIM), a specialized physical solver, offers acceleration capabilities. However, its native discrete binary architecture restricts the application in aerodynamic design. To bridge this gap, we propose a comprehensive framework that translates airfoil shape optimization into hardware-compliant quadratic unconstrained binary optimization formulations. We integrate higher-order response surface models via the Rosenberg order reduction, enabling the CIM to capture strong nonlinearities in the aerodynamic performance response. Furthermore, we introduce a block-diagonal scalarization strategy that composes trade-off scenarios into a single optimization. The framework is validated on the NACA 4-digit airfoil series using up to 778 spins on the CIM hardware. It successfully locates the approximate global optimum and achieves a computational speedup of three orders of magnitude over classical simulated annealing. Moreover, the parallel embedding capacity allows for the extraction of an entire optimal Pareto front in a single hardware execution. This work demonstrates a viable, quantum-enhanced paradigm for engineering optimization.

Extreme Mechanics Letters, 85, 102484