collaborators

32 papers

quant-ph2026

QuantumChain: Blockchain-Backed Quantum Federated Learning for Financial Fraud Detection

Epameinondas Douros, Konstantinos Dalampekis, Nouhaila Innan +2

Financial fraud detection is challenged by decentralized data, severe class imbalance, and privacy constraints. This paper presents QuantumChain, a secure Quantum Federated Learnin…

quant-ph2026

MDQEC-QAS: Meta-Decoding for Quantum Error Correction with Hardware-Aware VQC Search and Confidence-Gated Recovery

Prashant Kumar Choudhary, Nouhaila Innan, Muhammad Shafique +1

We propose a unified meta-decoding framework for quantum error correction that learns syndrome-to-recovery mappings across multiple stabilizer codes and noise settings, without req…

quant-ph2026

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…

cs.SE2026

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…

quant-ph2026

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…

cs.LG2026

FAQNAS: FLOPs-aware Hybrid Quantum Neural Architecture Search using Genetic Algorithm

Muhammad Kashif, Shaf Khalid, Alberto Marchisio +2

Hybrid Quantum Neural Networks (HQNNs), which combine parameterized quantum circuits with classical neural layers, are emerging as promising models in the noisy intermediate-scale…