Researchers at Shanghai Jiao Tong University have made a significant breakthrough in aircraft design using quantum computing and machine learning. By combining these advanced technologies, the team was able to achieve more accurate results in studying the flow of air over airplane wings and detecting flow separation.
Traditionally, engineers have studied the angles at which flow separation occurs to prevent aircraft stalls. However, the researchers at Shanghai Jiao Tong University, led by Xi-Jun Yuan and Zi-Qiao Chen, explored the potential of quantum computing and machine learning to tackle this problem with greater precision.
To compare the effectiveness of classical and quantum support vector machines, the team conducted two classification tasks. The first task involved binary classification to detect whether or not flow separation had occurred. The researchers utilized a small dataset obtained from pressure sensors on an airfoil in a wind tunnel. The dataset consisted of 27 cases without flow separation and 18 cases with flow separation.
By employing a quantum support vector machine instead of a classical one, the accuracy of flow separation classification increased from 81.8% to 90.9%. Similarly, when classifying the angle of attack after flow separation into four classes, the accuracy improved from 67.0% to 79.0%.
These findings suggest that utilizing quantum computing methods for fluid dynamics problems can yield faster and more accurate results, especially when dealing with large datasets. Not only does this have immense implications for aircraft design, but it also extends to other fields such as underwater navigation and target tracking.
The chosen classification algorithm, a quantum-annealing-based supervised machine learning algorithm called a support vector machine, proved highly effective. The team used the D-Wave Advantage 4.1 system, a physical quantum computing device known for its superior performance compared to classical counterparts.
Quantum annealing utilizes quantum fluctuations to search for the global minimum among a set of solutions. Unlike other optimization algorithms that often get stuck at local minimums, quantum annealing generates multiple good candidates for the global minimum, resulting in more accurate outcomes.
The Shanghai Jiao Tong University researchers’ work demonstrates the potential of quantum computing and machine learning in revolutionizing aircraft design and fluid dynamics. Their findings open up new avenues for exploration and innovation in various industries. The paper detailing their research has been published in the journal Intelligent Computing.
In conclusion, the integration of quantum computing and machine learning holds tremendous promise for solving complex problems in fluid dynamics, such as studying airflow over airplane wings. The breakthrough achieved by the researchers at Shanghai Jiao Tong University showcases the superior accuracy and speed of quantum support vector machines compared to classical methods. The implications extend not only to aircraft design but also to applications like underwater navigation and target tracking. This advancement paves the way for further advancements in the field, bringing us closer to more efficient and safer aircraft designs.
