59 citations · 224 across the 62 of their papers we have counts for
15 papers · 2 filters
Quantum Bayesian Networks for Machine Learning in Oil-Spill Detection
Owais Ishtiaq Siddiqui, Nouhaila Innan, Alberto Marchisio +2
Quantum Machine Learning (QML) has shown promise in diverse applications such as environmental monitoring, healthcare diagnostics, and financial modeling. However, its practical im…
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…
LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networks
Nouhaila Innan, Alberto Marchisio, Mohamed Bennai +1
Predicting loan eligibility with high accuracy remains a significant challenge in the finance sector. Accurate predictions enable financial institutions to make informed decisions,…
FL-QDSNNs: Federated Learning with Quantum Dynamic Spiking Neural Networks
Nouhaila Innan, Alberto Marchisio, Muhammad Shafique
We present Federated Learning-Quantum Dynamic Spiking Neural Networks (FL-QDSNNs), a privacy-preserving framework that maintains high predictive accuracy on non-IID client data. It…
MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption
Siddhant Dutta, Nouhaila Innan, Sadok Ben Yahia +2
The integration of fully homomorphic encryption (FHE) in federated learning (FL) has led to significant advances in data privacy. However, during the aggregation phase, it often re…
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…
Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML
Siddhant Dutta, Pavana P Karanth, Pedro Maciel Xavier +5
The widespread deployment of products powered by machine learning models is raising concerns around data privacy and information security worldwide. To address this issue, Federate…
AQ-PINNs: Attention-Enhanced Quantum Physics-Informed Neural Networks for Carbon-Efficient Climate Modeling
Siddhant Dutta, Nouhaila Innan, Sadok Ben Yahia +1
The growing computational demands of artificial intelligence (AI) in addressing climate change raise significant concerns about inefficiencies and environmental impact, as highligh…
QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction
Siddhant Dutta, Nouhaila Innan, Alberto Marchisio +2
Financial market prediction and optimal trading strategy development remain challenging due to market complexity and volatility. Our research in quantum finance and reinforcement l…
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…
PO-QA: A Framework for Portfolio Optimization using Quantum Algorithms
Kamila Zaman, Alberto Marchisio, Muhammad Kashif +1
Portfolio Optimization (PO) is a financial problem aiming to maximize the net gains while minimizing the risks in a given investment portfolio. The novelty of Quantum algorithms li…
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…
QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection
Nouhaila Innan, Alberto Marchisio, Mohamed Bennai +1
This study introduces the Quantum Federated Neural Network for Financial Fraud Detection (QFNN-FFD), a cutting-edge framework merging Quantum Machine Learning (QML) and quantum com…
FedQNN: Federated Learning using Quantum Neural Networks
Nouhaila Innan, Muhammad Al-Zafar Khan, Alberto Marchisio +2
In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a framework for training Quantum Machine Learning (QML) models via distributed networks. Conv…
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…