| Title |
Quantum autoencoders for anomaly detection |
| Translation of Title |
Kvantiniai autoenkoderiai anomalijų aptikimui. |
| Authors |
Yntykbay, Danial |
| Full Text |
|
| Pages |
68 |
| Keywords [eng] |
Quantum Autoencoder, QAE, Quantum Machine Learning, (QML), Quantum Anomaly Detection, Quantum Artifical Intelligence, QAI, Quantum kNN, Anomaly Detection |
| Abstract [eng] |
This thesis investigates quantum autoencoders for anomaly detection under strong dimension‐ ality constraints. We show that simple QAE architectures suffer from overfitting and poor class sep‐ arability when aggressive compression is applied, leading to elevated false‐positive rates (FPR). To address these limitations, we introduce a BrainBox layer that enhances encoder expressivity while preserving a compact circuit structure. When combined with dropout and ℓ2 regularization, the proposed approach yields more stable latent representations, improves reconstruction fidelity, and reduces FPR while maintaining strong detection performance. Experiments using classical and quantum kNN classifiers demonstrate consistent improvements in F1‐score and PR‐AUC, highlighting the importance of architectural design and regularization in practical quantum anomaly detection. |
| Dissertation Institution |
Vilniaus universitetas. |
| Type |
Master thesis |
| Language |
English |
| Publication date |
2026 |
