Quantum circuit autoencoders for anomalous image detection
Yedong Jiang · 2024
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.