Work

AdaReQ: An Adaptive Reuploading Quantum Model for Resource-Efficient Cybersecurity Threat Detection

Year: 2026

Type: article

Abstract: Quantum machine learning (QML) presents promising opportunities for advancing cybersecurity, yet its implementation on Noisy Intermediate-Scale Quantum (NISQ) hardware remains challenging. This study first evaluates three feature maps (IQP, Angle, and Fourier embeddings) using Quantum Neural Networks (QNNs) to identify the most effective encoding strategy for each cybersecurity threat dataset. The selected feature map is then applied in two additional QML models: a data re-uploading approach and a hybrid quantum–classical model based on observable construction. We introduce AdaReQ, a novel architecture that dynamically alternates between two embeddings to adapt to dataset-specific characteristics. Evaluated on the EMBER, BODMAS, and HIKARI-2021 datasets, AdaReQ achieves 86.2% accuracy on EMBER (vs. 92.5% state-of-the-art QML), surpasses the state-of-the-art QML benchmark on BODMAS with 96.1% accuracy, and outperforms recent classical baselines on HIKARI-2021 with 98.4% accuracy, all while using only 4 qubits and 24 trainable parameters, compared to alternative models requiring more than twice the parameters and four times the qubits.

Source: Applied Artificial Intelligence

DOI: 10.1080/08839514.2026.2721305

Authors: Amina Zahar, Mohamed El Aidi, Zouhair Elamrani Abou Elassad, Othmane El Meslouhi, Fadoua Khennou

Institutions: Université de Moncton, Cadi Ayyad University

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,195

Funder: Mitacs