59 citations · 224 across the 62 of their papers we have counts for
19 papers · 1 filter
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…
Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogates
Prashant Kumar Choudhary, Nouhaila Innan, Muhammad Shafique +1
Quantum circuit design is a key bottleneck for practical quantum machine learning on complex, real-world data. We present an automated framework that discovers and refines variatio…
IQNN-CS: Interpretable Quantum Neural Network for Credit Scoring
Abdul Samad Khan, Nouhaila Innan, Aeysha Khalique +1
Credit scoring is a high-stakes task in financial services, where model decisions directly impact individuals' access to credit and are subject to strict regulatory scrutiny. While…
Evaluating Quantum Amplitude Estimation for Pricing Multi-Asset Basket Options
Muhammad Kashif, Shaf Khalid, Nouhaila Innan +2
Accurate and efficient pricing of multi-asset basket options poses a significant challenge, especially when dealing with complex real-world data. In this work, we investigate the r…
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…
CircuitHunt: Automated Quantum Circuit Screening for Superior Credit-Card Fraud Detection
Nouhaila Innan, Akshat Singh, Muhammad Shafique
Designing effective quantum models for real-world tasks remains a key challenge within Quantum Machine Learning (QML), particularly in applications such as credit card fraud detect…
QUAV: Quantum-Assisted Path Planning and Optimization for UAV Navigation with Obstacle Avoidance
Nouhaila Innan, Muhammad Kashif, Alberto Marchisio +3
The growing demand for drone navigation in urban and restricted airspaces requires real-time path planning that is both safe and scalable. Classical methods often struggle with the…
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…
Quantum Portfolio Optimization with Expert Analysis Evaluation
Nouhaila Innan, Ayesha Saleem, Alberto Marchisio +1
Quantum algorithms have gained increasing attention for addressing complex combinatorial problems in finance, notably portfolio optimization. This study systematically benchmarks t…
PennyCoder: Efficient Domain-Specific LLMs for PennyLane-Based Quantum Code Generation
Abdul Basit, Minghao Shao, Muhammad Haider Asif +4
The growing demand for robust quantum programming frameworks has unveiled a critical limitation: current large language model (LLM) based quantum code assistants heavily rely on re…
KASPER: Kolmogorov Arnold Networks for Stock Prediction and Explainable Regimes
Vidhi Oad, Param Pathak, Nouhaila Innan +2
Forecasting in financial markets remains a significant challenge due to their nonlinear and regime-dependent dynamics. Traditional deep learning models, such as long short-term mem…
Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions
Alberto Marchisio, Muhammad Shafique
The growing need for intelligent, adaptive, and energy-efficient autonomous systems across fields such as robotics, mobile agents (e.g., UAVs), and self-driving vehicles is driving…
QAS-QTNs: Curriculum Reinforcement Learning-Driven Quantum Architecture Search for Quantum Tensor Networks
Siddhant Dutta, Nouhaila Innan, Sadok Ben Yahia +1
Quantum Architecture Search (QAS) is an emerging field aimed at automating the design of quantum circuits for optimal performance. This paper introduces a novel QAS framework emplo…
A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection
Abhishek Sawaika, Swetang Krishna, Tushar Tomar +5
Rapid growth of digital transactions has led to a surge in fraudulent activities, challenging traditional detection methods in the financial sector. To tackle this problem, we intr…
HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction
Prashant Kumar Choudhary, Nouhaila Innan, Muhammad Shafique +1
Financial time-series forecasting remains a challenging task due to complex temporal dependencies and market fluctuations. This study explores the potential of hybrid quantum-class…
QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution
Siddhant Dutta, Nouhaila Innan, Khadijeh Najafi +2
Recent advancements in Single-Image Super-Resolution (SISR) using deep learning have significantly improved image restoration quality. However, the high computational cost of proce…
A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG
Abdul Basit, Nouhaila Innan, Muhammad Haider Asif +4
Large Language Models (LLMs) offer powerful capabilities in code generation, natural language understanding, and domain-specific reasoning. Their application to quantum software de…
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…
MoENAS: Mixture-of-Expert based Neural Architecture Search for jointly Accurate, Fair, and Robust Edge Deep Neural Networks
Lotfi Abdelkrim Mecharbat, Alberto Marchisio, Muhammad Shafique +2
There has been a surge in optimizing edge Deep Neural Networks (DNNs) for accuracy and efficiency using traditional optimization techniques such as pruning, and more recently, empl…