https://doi.org/10.1140/epjqt/s40507-025-00335-4
Brief Report
The discriminative ability on anomaly detection using quantum kernels for shipping inspection
1
Graduate school of Media and Governance, Keio University, 252-0882, Fujisawa, Japan
2
Graduate school of Science and Technology, Keio University, 223-8522, Yokohama, Japan
3
Keio University Sustainable Quantum AI Center, Keio University, 108-8345, Minato, Japan
4
Digital Innovation Division, TOPPAN Holdings, 110-8560, Tokyo, Japan
Received:
21
August
2024
Accepted:
25
February
2025
Published online:
21
March
2025
We aim to use quantum machine learning to detect various anomalies in image inspection by using small size data. Assuming the possibility that the expressive power of the quantum kernel space is superior to that of the classical kernel space, we are studying a quantum machine learning model. Through trials of image inspection processes not only for factory products but also for products including agricultural products, the importance of trials on real data is recognized. In this study, training was carried out on SVMs embedded with various quantum kernels on a small number of agricultural product image data sets collected in the markets. The quantum kernels prepared in this study consisted of a smaller number of rotating gates and control gates. The F1 scores for each quantum kernel showed a significant effect of using CNOT gates. After confirming the results with a quantum simulator, the usefulness of the quantum kernels was confirmed on a quantum computer. Learning with SVMs embedded with specific quantum kernels showed significantly higher values of the AUC compared to classical kernels. The reason for the lack of learning in quantum kernels is considered to be due to kernel concentration or exponential concentration similar to the Baren plateau. The reason why the F1 score does not increase as the number of features increases is suggested to be due to exponential concentration, while at the same time it is possible that only certain features have discriminative ability. Furthermore, it is suggested that controlled Toffoli gate may be a promising quantum kernel component.
Key words: Small size data / Quantum kernel / SVM / Anomaly detection / Controlled gate
© The Author(s) 2025
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