  Aerodynamics plays an important role in aviation industry and aircraft
design. Detecting and minimizing the phenomenon of flow separation from
scattered pressure data on airfoil is critical for ensuring stable and
efficient aviation. However, since it is challenging to understand the
mechanics of flow field separation, the aerodynamic parameters are emphasized
for the identification and control of flow separation. It has been investigated
extensively using traditional algorithms and machine learning methods such as
the support vector machine (SVM) models. Recently, a growing interest in
quantum computing and its applications among wide research communities sheds
light upon the use of quantum techniques to solve aerodynamic problems. In this
paper, we apply qSVM, a quantum SVM algorithm based on the quantum annealing
model, to identify whether there is flow separation, with their performance in
comparison to the widely-used classical SVM. We show that our approach
outperforms the classical SVM with an 11.1% increase of the accuracy, from
0.818 to 0.909, for this binary classification task. We further develop
multi-class qSVMs based on one-against-all algorithm. We apply it to classify
multiple types of the attack angles to the wings, where the advantage over the
classical multi-class counterpart is maintained with an accuracy increased from
0.67 to 0.79, by 17.9%. Our work demonstrates a useful quantum technique for
classifying flow separation scenarios, and may promote rich investigations for
quantum computing applications in fluid dynamics.
