Successive data injection in conditional quantum GAN applied to time series anomaly detection

Abstract

Abstract 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 (QGAN) 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, a new high‐dimensional encoding approach, named Successive Data Injection (SuDaI) is proposed. In this approach, SuDaI explores a larger portion of the quantum state, compared to the conventional angle encoding method used predominantly in the literature. This is achieved through repeated data injections into the quantum state. SuDaI encoding allows the authors 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.

Keywords

• learning (artificial intelligence)
• quantum computing