12 papers
Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection
Adam Innan, Mansour El Alami, Nouhaila Innan +2
Credit card fraud detection is fundamentally challenged by extreme class imbalance, where fraudulent transactions are rare yet operationally critical. This imbalance often biases s…
Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection
Mansour El Alami, Nouhaila Innan, Muhammad Shafique +1
As financial fraud becomes increasingly complex, effective detection methods are essential. Quantum Machine Learning (QML) introduces certain capabilities that may enhance both acc…
FiD-QAE: A Fidelity-Driven Quantum Autoencoder for Credit Card Fraud Detection
Mansour El Alami, Adam Innan, Nouhaila Innan +2
Credit card fraud detection is a critical task in financial security, as fraudulent transactions are rare, highly imbalanced, and often resemble legitimate ones. A wide range of cl…
Benchmarking VQE Configurations: Architectures, Initializations, and Optimizers for Silicon Ground State Energy
Zakaria Boutakka, Nouhaila Innan, Muhammed Shafique +2
Quantum computing presents a promising path toward precise quantum chemical simulations, particularly for systems that challenge classical methods. This work investigates the perfo…
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…
Next-Generation Quantum Neural Networks: Enhancing Efficiency, Security, and Privacy
Nouhaila Innan, Muhammad Kashif, Alberto Marchisio +2
This paper provides an integrated perspective on addressing key challenges in developing reliable and secure Quantum Neural Networks (QNNs) in the Noisy Intermediate-Scale Quantum…