Feature extraction Archives - IEEE Transactions on Quantum Engineering

QATNet: A Lightweight Quantum-Classical Tabular Network for Low-Latency Intrusion Detection

March 24, 2026Abubakar Danasabe, Muhammad Afaq, Aiman El-Maleh, Ajmal Khan, Sami El Ferik, Zeeshan KaleemEarly Access, Open AccessAccuracy, Adaptation models, Computational modeling, Encoding, Feature extraction, Intrusion detection, Pipelines, Principal component analysis, Quantum circuit, Real-time systems

Abstract: Modern communication environments ranging from smart mobility infrastructures to digitally integrated healthcare systems demand adaptable and trustworthy network security. In this situation, Software-Defined Networks (SDNs) play a vital role by providing programmable control and global visibility, enabling rapid policy updates and fine-grained traffic management. As a result, the need for efficient and interpretable intrusion […]

Measurement-Informed Safe Reinforcement Learning for Quantum Battery Charging via Harmonic-Syndrome Diagnostics and BMS Constraints

March 3, 2026Sangkeum Lee, Beomdo Park, Junseong Park, Hyeonseok Jang, Hoon Jeong, Taewook HeoQuantum ComputingBatteries, Battery charge measurement, Couplings, Feature extraction, Hardware, Harmonic analysis, Pipelines, Protocols, Reinforcement learning, Safety

Abstract: Quantum batteries promise ultrafast energy storage but are highly sensitive to noise, drift, and hardware constraints, making safe high-performance charging a central challenge for noisy intermediate-scale quantum devices. We propose a measurement-informed safe control framework that couples harmonic-spectrum-based syndrome diagnostics—H2/H1, H3/H1, and frequency drift—with a battery management system (BMS)-constrained curriculum reinforcement learning (RL) policy. […]

Quantum Annealing for Robust Principal Component Analysis

February 6, 2026Ian Tomeo, Panagiotis Markopoulos, Andreas SavakisQuantum ComputingAnnealing, Computers, Feature extraction, Hardware, Matrix decomposition, Optimization, Principal component analysis, Quantum annealing, Qubit, Robustness

Abstract: Principal component analysis is commonly used for dimensionality reduction, feature extraction, denoising, and visualization. The most commonly used principal component analysis method is based upon optimization of the L2-norm; however, the L2-norm is known to exaggerate the contribution of errors and outliers. When optimizing over the L1-norm, the components generated are known to exhibit […]

Application of Quantum Recurrent Neural Network in Low-Resource Language Text Classification

March 9, 2024Wenbin Yu, Lei Yin, Chengjun Zhang, Yadang Chen, Alex X. LiuQuantum InformationComputational modeling, Feature extraction, Integrated circuit modeling, Qubit, Task analysis, Text categorization, Vectors

Text sentiment analysis is an important task in natural language processing and has always been a hot research topic. However, in low-resource regions such as South Asia, where languages like Bengali are widely used, the research interest is relatively low compared to high-resource regions due to limited computational resources, flexible word order, and high inflectional […]

Prediction of Solar Irradiance One Hour Ahead Based on Quantum Long Short-Term Memory Network

June 14, 2023Yunjun YuQuantum ComputingAtmospheric modeling, Computational modeling, Data models, Feature extraction, Forecasting, Mathematical models, Predictive models

The short-term forecasting of photovoltaic (PV) power generation ensures the scheduling and dispatching of electrical power, helps design a PV-integrated energy management system, and enhances the security of grid operation. However, due to the randomness of solar energy, the output of the PV system will fluctuate, which will affect the safe operation of the grid. […]