7 papers · 1 filter
QuaRK: A Quantum Reservoir Kernel for Time Series Learning
Abdallah Aaraba, Soumaya Cherkaoui, Ola Ahmad +1
Quantum reservoir computing offers a promising route for time series learning by modelling sequential data via rich quantum dynamics while the only training required happens at the…
Quantum Gated Recurrent GAN with Gaussian Uncertainty for Network Anomaly Detection
Wajdi Hammami, Soumaya Cherkaoui, Jean-Frederic Laprade +2
Anomaly detection in time-series data is a critical challenge with significant implications for network security. Recent quantum machine learning approaches, such as quantum kernel…
Multivariate Time Series Forecasting with Gate-Based Quantum Reservoir Computing on NISQ Hardware
Wissal Hamhoum, Soumaya Cherkaoui, Jean-Frederic Laprade +2
Quantum reservoir computing (QRC) offers a hardware-friendly approach to temporal learning, yet most studies target univariate signals and overlook near-term hardware constraints.…
Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series
Wajdi Hammami, Soumaya Cherkaoui, Shengrui Wang
Quantum computing may offer new approaches for advancing machine learning, including in complex tasks such as anomaly detection in network traffic. In this paper, we introduce a qu…
QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting
Abdallah Aaraba, Soumaya Cherkaoui, Ola Ahmad +4
Forecasting in probabilistic time series is a complex endeavor that extends beyond predicting future values to also quantifying the uncertainty inherent in these predictions. Gauss…
Successive Data Injection in Conditional Quantum GAN Applied to Time Series Anomaly Detection
Benjamin Kalfon, Soumaya Cherkaoui, Jean-Frédéric Laprade +2
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 c…
Neural forecasting at scale
Philippe Chatigny, Shengrui Wang, Jean-Marc Patenaude +1
We study the problem of efficiently scaling ensemble-based deep neural networks for multi-step time series (TS) forecasting on a large set of time series. Current state-of-the-art…