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

TL;DR

This paper introduces SuDaI, a novel high-dimensional encoding method for quantum GANs, enabling effective anomaly detection in high-dimensional time series data despite limited qubits.

Contribution

The paper proposes SuDaI, a new successive data injection encoding technique that enhances quantum GANs' ability to handle high-dimensional time series data for anomaly detection.

Findings

SuDaI encoding improves quantum GAN performance on high-dimensional data.

Enables anomaly detection in network data with limited qubits.

Applicable to various high-dimensional time series beyond anomaly detection.

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 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.