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20242026
most citedFedQNN: Federated Learning using Quantum Neural Networks

59 citations · 224 across the 62 of their papers we have counts for

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47 papers · 1 filter

quant-ph2026

QIAP: A Quantum-Inspired Authentication Protocol with BB84-Based Channel Monitoring

Owais Ishtiaq Siddiqui, Nouhaila Innan, Hira Zaheer +1

Authentication in quantum-aware communication settings requires mechanisms that verify user legitimacy while also assessing whether the communication channel exhibits excessive dis…

quant-ph2026

Q-PhotoMarket: A Design Space Exploration Framework for Photonic Hybrid Quantum Neural Networks in Financial Market Prediction

Alberto Marchisio, Hanzalah Mohamed Siraj, Muhammad Kashif +2

Photonic quantum computing has recently emerged as a promising platform for hybrid quantum machine learning due to its native realization of linear-optical circuits and the computa…

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

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…

quant-ph2026

QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation

Prerana Ramkumar, Nouhaila Innan, Muhammad Shafique

Scene Graph Generation (SGG) requires relational reasoning over objects and their interactions, but performance is often limited by severe long-tail predicate imbalance. Classical…

quant-ph2026

Game, Set, Quantum: Parameterized Quantum Circuit for Correlated Equilibrium in Bayesian Games

Param Pathak, Vidhi Oad, Nouhaila Innan +2

Strategic decision-making among many agents under incomplete information is central to economics, security, and multi-agent artificial intelligence (AI). Computing equilibria in su…

quant-ph2026

Meta-Quantum Ensemble Framework for Robust Network Intrusion Detection

Ritvik Bhatnagar, Nouhaila Innan, Angel Arul Jothi J. +1

Intrusion Detection Systems (IDSs) must maintain high detection sensitivity while operating under strict false-positive constraints, a challenge intensified by class imbalance and…

quant-ph2026

Towards Fair Benchmarking of Quantum Transfer Learning for Visual Classification

Nouhaila Innan, Saim Rehman, Muhammad Shafique

Quantum Transfer Learning (QTL) offers a promising approach for visual quantum machine learning under near-term constraints, where limited qubit counts, shallow circuit depths, and…

quant-ph2026

Hybrid Quantum-Classical Neural Architecture Search

Alberto Marchisio, Muhammad Kashif, Nouhaila Innan +1

Hybrid quantum-classical neural networks (HQNNs) are emerging as a practical approach for quantum machine learning in the noisy intermediate-scale quantum (NISQ) era, as they combi…

quant-ph2026

HQTN-SER: Speech Emotion Recognition with Hybrid Quantum Tensor Networks

Mahad Mohtashim, Nouhaila Innan, Muhammad Shafique

Speech emotion recognition (SER) remains fragile in real-world conditions because emotional cues are subtle, speaker-dependent, and easily confounded by recording variability, whil…

quant-ph2026

EFaaS: A Quantum-Classical Serverless Entangled Scheduler for Hybrid Variational Algorithms

Abolfazl Younesi, Nouhaila Innan, Alberto Marchisio +1

As quantum computing enters the Utility Era, realizing near-term advantage relies heavily on Hybrid Variational Quantum Algorithms (VQAs). These algorithms require a tightly couple…

quant-ph2026

Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices

Farah Elnakhal, Alberto Marchisio, Nouhaila Innan +2

Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challenging under hardware and optimi…

quant-ph2026

A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction

Nouhaila Innan, M. Murali Karthick, Simeon Kandan Sonar +2

Dynamic link prediction is important for modeling evolving interactions in social, communication, financial, and transportation networks. Classical temporal graph models capture ch…

quant-ph2026

QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting

Alberto Marchisio, Aayan Ebrahim, Nouhaila Innan +2

Accurate and efficient time-series forecasting remains a challenging problem for both classical and quantum neural architectures, particularly in multivariate environmental setting…

quant-ph2026

GAT-QNN: Genetic Algorithm-Based Training of Hybrid Quantum Neural Networks

Tasnim Ahmed, Alberto Marchisio, Muhammad Kashif +2

Hybrid Quantum Neural Networks (HQNNs) combine classical learning with parameterized quantum circuits, but their practical performance is often limited by (i) the noise of Noisy In…

quant-ph2026

SPATE: Spiking-Phase Adaptive Temporal Encoding for Quantum Machine Learning

Nouhaila Innan, Rachmad Vidya Wicaksana Putra, Muhammad Shafique

Most quantum machine learning (QML) pipelines still rely on static encodings such as angle and amplitude maps, and this limits their ability to handle temporal information. To addr…

quant-ph2026

QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks

Kooshan Maleki, Alberto Marchisio, Muhammad Shafique

Designing quantum neural networks (QNNs) that are both accurate and deployable on NISQ hardware is challenging. Handcrafted ansatze must balance expressivity, trainability, and res…

quant-ph2026

Scaling Laws for Hybrid Quantum Neural Networks: Depth, Width, and Quantum-Centric Diagnostics

Danil Vyskubov, Kirill Vyskubov, Nouhaila Innan +1

Hybrid quantum neural networks are increasingly explored for classification, yet it remains unclear how their performance and quantum behavior scale with circuit depth and qubit co…

quant-ph2025

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…

quant-ph2025

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-ph2025★ 3 cited

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…