Quantum Machine Learning for Structural Health Monitoring
Vahid Yaghoubi (TU Delft - Group Yaghoubi Nasrabadi)
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Abstract
Nowadays, employing deep learning for Structural Health Monitoring is a common practice. However, one of the main challenges here is the lack of data. Several methods have been developed to address this issue. Quantum machine learning is known to be trained faster and with less data, therefore, it could be a suitable option to be used for this purpose. However, since at the current stage limited numbers of qubits can remain stable at the same time, hybrid quantum-classical deep learning approaches can be a replacement. In this study, the benefit of incorporating a quantum layer into a classical deep learner for detecting damage is investigated. For this purpose, a deep learning model with and without a quantum layer is used to predict damage in a wind turbine blade by using ultrasonic inspection data. The results indicate the benefit of employing hybrid quantum-classical ML in detecting damage.
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