Quantum Wasserstein GAN: a novel approach for generating anomalous timeseries data

Anomaly detection in time series is constrained by the scarcity and unpredictability of real anomalous events. Classical supervised and generative approaches struggle to capture the statistical complexity of rare, high-impact anomalies while maintaining realistic normal dynamics. This work introduces a Quantum Wasserstein Generative Adversarial Network (QWGAN) for synthetic anomaly generation that separates the modeling of normal and anomalous behaviors using quantum parameterized circuits. Leveraging quantum superposition and entanglement, the QWGAN achieves a richer generative capacity with an order-of-magnitude reduction in parameters compared to classical counterparts. The model is trained and evaluated on cyber security datasets, demonstrating lower Earth Mover’s Distance and volatility error while preserving temporal structure and distributional fidelity. A windowed synthesis mechanism enables controlled injection of variable-length point and contextual anomalies. Our results demonstrate that QWGAN provides a scalable, parameter-efficient approach for generating high-fidelity synthetic anomalous time series data to enable effective benchmarking and augmentation of anomaly detection models under limited anomaly data.