12 citations · 26 across the 19 of their papers we have counts for
5 papers · 2 filters
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…
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…
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…
Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise
Ratun Rahman, Atit Pokharel, Dinh C. Nguyen
Quantum Federated Learning (QFL) is an emerging paradigm that combines quantum computing and federated learning (FL) to enable decentralized model training while maintaining data p…