2 citations · 2 across the 6 of their papers we have counts for
7 papers · 1 filter
Quantum Noise Mitigation with Adaptive Zero-Noise Extrapolation: A Contextual Multi-Armed Bandits Approach
Ratun Rahman, Dinh C. Nguyen
Variational quantum circuits (VQCs) are central to near-term quantum computing, yet their practical deployment is severely hindered by noise. While existing error mitigation method…
Tackling Heterogeneity in Quantum Federated Learning: An Integrated Sporadic-Personalized Approach
Ratun Rahman, Shaba Shaon, Dinh C. Nguyen
Quantum federated learning (QFL) emerges as a powerful technique that combines quantum computing with federated learning to efficiently process complex data across distributed quan…
Escaping Barren Plateaus in Variational Quantum Algorithms Using Negative Learning Rate in Quantum Internet of Things
Ratun Rahman, Dinh C. Nguyen
Variational Quantum Algorithms (VQAs) are becoming the primary computational primitive for next-generation quantum computers, particularly those embedded as resource-constrained ac…
Towards Heterogeneous Quantum Federated Learning: Challenges and Solutions
Ratun Rahman, Dinh C. Nguyen, Christo Kurisummoottil Thomas +1
Quantum federated learning (QFL) combines quantum computing and federated learning to enable decentralized model training while maintaining data privacy. QFL can improve computatio…
SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization
Ratun Rahman, Atit Pokharel, Md Raihan Uddin +1
Quantum federated learning (QFL) is an emerging field that has the potential to revolutionize computation by taking advantage of quantum physics concepts in a distributed machine l…
Differentially Private Federated Quantum Learning via Quantum Noise
Atit Pokharel, Ratun Rahman, Shaba Shaon +2
Quantum federated learning (QFL) enables collaborative training of quantum machine learning (QML) models across distributed quantum devices without raw data exchange. However, QFL…