Quantum Autoencoders

Quantum autoencoders (QAEs) are quantum machine learning models aiming to compress and reconstruct data, primarily for anomaly detection and optimization problems. Research currently focuses on improving data embedding techniques to enhance QAE performance, exploring various ansatz designs and architectures like patch-based and 3D QAEs for different data types (e.g., time series, images, point clouds). These advancements demonstrate QAEs' potential to outperform classical counterparts in specific applications, particularly where resource efficiency and noise mitigation are crucial, impacting fields like industrial process monitoring and materials science.