Quantum anomaly detection in real scarce data
Anomaly detection on small and unbalanced datasets remains very challenging in machine learning, although this scenario is common in several domains, including healthcare, cybersecurity, finance, and energy. Data augmentation and generative AI may mitigate training-data scarcity, but they often fall short because anomalies are, by definition, unpredictable, rare, and highly diverse events compared to high-probability normal data. Overfitting to pseudo-anomalies, model collapse, high-dimensional data, uninterpretable black-box models, and validation challenges are typical issues limiting their practical applicability. In this context, quantum machine learning may provide a promising and mo...
| Authors | Emanuele Casciaro, Fabio Mascherpa, Alfonso Amendola et al. |
|---|---|
| Primary category | quant-ph |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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