15 citations · 24 across the 10 of their papers we have counts for
11 papers
QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing
Anton Firc, Nouhaila Innan, Vojtěch Staněk +6
A central goal of quantum machine learning is to identify applications in which small quantum circuits can improve task performance while remaining executable on real hardware. We…
VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?
Anton Firc, Martin Perešíni, Vojtěch Mrázek +7
Hybrid quantum-classical machine learning workflows repeatedly evaluate many small parametrized circuits during training and model exploration. In this regime, framework dispatch a…
Q-RAIL: A Reliability-Aware Framework for Quantum Federated Learning on Heterogeneous Noisy Hardware
Walid El Maouaki, Muhammad Shafique
Quantum federated learning (QFL) on NISQ hardware is highly sensitive to backend heterogeneity: some clients contribute informative updates, while others contribute noise-dominated…
QGCL: Quantum-Guided Clause Learning for Cryptanalytic SAT
Walid El Maouaki, Alberto Marchisio, Muhammad Shafique
Power side-channel attacks on AES exploit data-dependent physical leakage to recover secret keys, but turning noisy leakage observations into a verified AES-128 key remains a hard…
Q-LEAK: Quantum-Based LEAKage Verification for Side-Channel Countermeasures
Walid El Maouaki, Alberto Marchisio, Muhammad Shafique
Formal verification of power side-channel leakage and its countermeasures in cryptographic algorithms is challenging, as SAT-based methods fail to scale on XOR-heavy, time-unrolled…
RobQFL: Robust Quantum Federated Learning in Adversarial Environment
Walid El Maouaki, Nouhaila Innan, Alberto Marchisio +3
Quantum Federated Learning (QFL) merges privacy-preserving federation with quantum computing gains, yet its resilience to adversarial noise is unknown. We first show that QFL is as…
QFAL: Quantum Federated Adversarial Learning
Walid El Maouaki, Nouhaila Innan, Alberto Marchisio +3
Quantum federated learning (QFL) merges the privacy advantages of federated systems with the computational potential of quantum neural networks (QNNs), yet its vulnerability to adv…
Designing Robust Quantum Neural Networks via Optimized Circuit Metrics
Walid El Maouaki, Alberto Marchisio, Taoufik Said +2
In this study, we investigated the robustness of Quanvolutional Neural Networks (QuNNs) in comparison to their classical counterparts, Convolutional Neural Networks (CNNs), against…
Quantum Clustering for Cybersecurity
Walid El Maouaki, Nouhaila Innan, Alberto Marchisio +3
In this study, we develop a novel quantum machine learning (QML) framework to analyze cybersecurity vulnerabilities using data from the 2022 CISA Known Exploited Vulnerabilities ca…
RobQuNNs: A Methodology for Robust Quanvolutional Neural Networks against Adversarial Attacks
Walid El Maouaki, Alberto Marchisio, Taoufik Said +2
Recent advancements in quantum computing have led to the emergence of hybrid quantum neural networks, such as Quanvolutional Neural Networks (QuNNs), which integrate quantum and cl…
AdvQuNN: A Methodology for Analyzing the Adversarial Robustness of Quanvolutional Neural Networks
Walid El Maouaki, Alberto Marchisio, Taoufik Said +2
Recent advancements in quantum computing have led to the development of hybrid quantum neural networks (HQNNs) that employ a mixed set of quantum layers and classical layers, such…