collaborators

54 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

QSTAR: Quantum Selective Transfer with Adaptive Routing

Saim Rehman, Nouhaila Innan, Muhammad Shafique

Quantum transfer learning (QTL) is often evaluated by replacing a classical classifier with a fixed variational quantum head, but this hides a key question: when is the quantum bra…

quant-ph2026

PN-QNN: Harnessing Physical Noise as a Native Regularizer in Photonic Hybrid Quantum Neural Networks

Farah Elnakhal, Alberto Marchisio, Nouhaila Innan +2

Physical noise in near-term quantum hardware is usually treated as a nuisance to suppress. We ask whether it can instead act as a hardware-native regularizer for photonic hybrid qu…

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…

quant-ph2026

Quantum Reservoir Computing for Short-Term Power Load Forecasting in Resource-Constrained Energy Systems

Mansi Od, Param Pathak, Nouhaila Innan +1

Short-term load forecasting is essential for reliable energy management, but practical deployment on edge devices requires models that remain accurate under limited memory, finite…