Successive Data Injection in Conditional Quantum GAN Applied to Time
  Series Anomaly Detection
Benjamin Kalfon Soumaya Cherkaoui Jean-Frédéric Laprade Ola Ahmad Shengrui Wang
Classical GAN architectures have shown interesting results for solving
anomaly detection problems in general and for time series anomalies in
particular, such as those arising in communication networks. In recent years,
several quantum GAN architectures have been proposed in the literature. When
detecting anomalies in time series using QGANs, huge challenges arise due to
the limited number of qubits compared to the size of the data. To address these
challenges, we propose a new high-dimensional encoding approach, named
Successive Data Injection (SuDaI). In this approach, we explore a larger
portion of the quantum state than that in the conventional angle encoding, the
method used predominantly in the literature, through repeated data injections
into the quantum state. SuDaI encoding allows us to adapt the QGAN for anomaly
detection with network data of a much higher dimensionality than with the
existing known QGANs implementations. In addition, SuDaI encoding applies to
other types of high-dimensional time series and can be used in contexts beyond
anomaly detection and QGANs, opening up therefore multiple fields of
application.