Quantum circuit autoencoders for anomalous image detection

Yedong Jiang

doi:10.1117/12.3037933

7 August 2024 Quantum circuit autoencoders for anomalous image detection

Yedong Jiang

Author Affiliations +

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 132290M (2024) https://doi.org/10.1117/12.3037933
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

In the NISQ era, exploring quantum approaches capable of addressing real-world data analytics tasks has been a focal point of research. The detection of anomalous images is one of the classic data processing problems that can be considered to tackle using quantum computing. Inspired by the proposal of quantum circuit autoencoders (QCAE), we design a quantum method to tackle image anomaly detection tasks. In this work, we propose a method for encoding an image into a quantum circuit. Then, we employ the QCAE model to detect the anomalous images. Moreover, we create a scoring function to predict whether test data is an outlier. Finally, combining the trained QCAE with the scoring function, we obtain an anomaly detector. We use the handwritten digit image dataset to train detectors under various training setups and validate their performance on a test set. Our method achieves an f1-score of 0.8190 and a recall of 0.8600. The experimental results demonstrate the potential of quantum computing in handling data anomalies in the classical world, encouraging further exploration of QCAE applications in practical problems.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yedong Jiang "Quantum circuit autoencoders for anomalous image detection", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 132290M (7 August 2024); https://doi.org/10.1117/12.3037933

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