Quantum support vector machines for aerodynamic classification

TL;DR

This paper demonstrates that quantum support vector machines (qSVM) can outperform classical SVMs in classifying flow separation in aerodynamics, showing potential for quantum computing applications in fluid dynamics.

Contribution

The study introduces a quantum SVM approach for aerodynamic classification, achieving higher accuracy than classical methods in flow separation detection and multi-class wing attack angle classification.

Findings

qSVM outperforms classical SVM with 11.1% higher accuracy in flow separation detection.

Multi-class qSVM improves classification accuracy from 0.67 to 0.79.

Quantum techniques show promise for fluid dynamics applications.

Abstract

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.