Airfoil shape optimization via coherent Ising machine

TL;DR

This paper introduces a quantum computing-based framework using a coherent Ising machine for efficient airfoil shape optimization, achieving significant speedups and enabling Pareto front extraction in aerodynamic design.

Contribution

It develops a hardware-compatible quadratic binary formulation for airfoil optimization and demonstrates quantum acceleration on CIM hardware with high parallel capacity.

Findings

Achieved three orders of magnitude speedup over classical methods.

Successfully located global optima for NACA airfoils.

Extracted entire Pareto front in a single hardware run.

Abstract

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 high-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 compose trade-off scenarios into a single optimization. Validated on the NACA 4-digit airfoil series using CIM hardware with 615 spins, the framework successfully locates the global optimum with a computational speedup of three orders of magnitude compared to the classical simulated annealing. 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.