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Classical GAN architectures have shown interesting results for solvinganomaly detection problems in general and for time series anomalies inparticular, such as those arising in communication networks. In recent years,several quantum GAN architectures have been proposed in the literature. Whendetecting anomalies in time series using QGANs, huge challenges arise due tothe limited number of qubits compared to the size of the data. To address thesechallenges, we propose a new high-dimensional encoding approach, namedSuccessive Data Injection (SuDaI). In this approach, we explore a largerportion of the quantum state than that in the conventional angle encoding, themethod used predominantly in the literature, through repeated data injectionsinto the quantum state. SuDaI encoding allows us to adapt the QGAN for anomalydetection with network data of a much higher dimensionality than with theexisting known QGANs implementations. In addition, SuDaI encoding applies toother types of high-dimensional time series and can be used in contexts beyondanomaly detection and QGANs, opening up therefore multiple fields ofapplication.

Successive Data Injection in Conditional Quantum GAN Applied to Time Series Anomaly Detection