The role of data embedding in quantum autoencoders for improved anomaly detection
This work addresses the problem of optimizing quantum autoencoders for anomaly detection, which is incremental as it focuses on improving existing methods through better data embedding.
The study investigated how different data embedding techniques affect quantum autoencoders' performance in anomaly detection, finding that enhanced embedding strategies substantially improve accuracy and data representability across various datasets.
The performance of Quantum Autoencoders (QAEs) in anomaly detection tasks is critically dependent on the choice of data embedding and ansatz design. This study explores the effects of three data embedding techniques, data re-uploading, parallel embedding, and alternate embedding, on the representability and effectiveness of QAEs in detecting anomalies. Our findings reveal that even with relatively simple variational circuits, enhanced data embedding strategies can substantially improve anomaly detection accuracy and the representability of underlying data across different datasets. Starting with toy examples featuring low-dimensional data, we visually demonstrate the effect of different embedding techniques on the representability of the model. We then extend our analysis to complex, higher-dimensional datasets, highlighting the significant impact of embedding methods on QAE performance.