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 angle-based data-embedding strategies can substantially improve anomaly-detection accuracy and the representability of the underlying data across different datasets. Starting with toy examples using low-dimensional data, we visually demonstrate how different embedding techniques affect the model’s representability. We then extend our analysis to complex, higher-dimensional datasets, highlighting the significant impact of embedding methods on QAE performance.