Quantum support vector machines for aerodynamic classification
arXiv:2208.07138 · doi:10.34133/icomputing.0057
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.
arXiv comment12 pages, 4 figures, 3 supplementary figures
References in corpus (24)
- Supervised learning with quantum enhanced feature spaces
- Quantum Annealing in the Transverse Ising Model
- Quantum support vector machine for big data classification
- Quantum machine learning in feature Hilbert spaces
- An introduction to quantum machine learning
- Power of data in quantum machine learning
- Support vector machines on the D-Wave quantum annealer
- Quantum algorithm and circuit design solving the Poisson equation
- Quantum annealing versus classical machine learning applied to a simplified computational biology problem
- Prospects for Quantum Enhancement with Diabatic Quantum Annealing
- Quantum spectral methods for differential equations
- Shortcuts to Adiabaticity in Digitized Adiabatic Quantum Computing
- Benchmarking Advantage and D-Wave 2000Q quantum annealers with exact cover problems
- Quantum algorithm for non-homogeneous linear partial differential equations
- Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot database
- Quantum Algorithm for the Vlasov Equation
- Quantum Fast Poisson Solver: the algorithm and modular circuit design
- Learning to swim in potential flow
- Ensemble-filtered vortex modeling of strongly disturbed aerodynamic flows
- Data-driven Modeling of Rotating Detonation Waves
- Leading edge vortex formation and wake trajectory: Synthesizing measurements, analysis, and machine learning
- Implementation of quantum stochastic walks for function approximation, two-dimensional data classification, and sequence classification
- Balanced k-Means Clustering on an Adiabatic Quantum Computer
- Quantum Computation for Pricing the Collateralized Debt Obligations