QEA-FAD: Quantum Enhanced Autoencoders for Anomaly Detection in Finance and Healthcare

Quantum Auto-Encoders (QAEs) represent a transformative approach to anomaly detection, leveraging quantum computational power to address challenges in domains such as healthcare and finance. In this study, we focus on detecting anomalies in breast cancer diagnosis and fraudulent financial transactions. QAEs are trained exclusively on non-anomalous data, enabling anomaly detection through their inability to faithfully reconstruct anomalous points. Reconstruction quality is evaluated using fidelity tests for simulations and SWAP tests for quantum hardware, ensuring reliable and robust performance across diverse environments. Our work introduces QEAFAD: Quantum Enhanced Autoencoders for Anomaly Detection, incorporating enhanced and patch-based architectures that demonstrate significant advancements in precision. By integrating quantum entanglement into the encoding and decoding processes, via shared Bell pairs and direct qubit entanglement. Our framework improves correlations and delivers statistically significant performance gains. Benchmarks on the MNIST dataset validate the scalability of our approach, while JAX-accelerated optimization reduces training time on financial datasets. Experiments on IBM and Amazon quantum hardware confirm the practical feasibility of QAEs. These advancements highlight the efficiency of QAEs, achieving superior accuracy with reduced computational resources, making them a compelling solution for real-world anomaly detection challenges in critical applications.