5 papers
A System Aware Resource Allocation for Distributed Workflows in Quantum Computing Environments
Abhishek Sawaika, Udaya Parampalli, Rajkumar Buyya
Rapid advancements in cloud based platforms providing access to quantum computing capabilities have opened up several challenges for efficient usage of these highly delicate and co…
Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics
Abhishek Sawaika, Durga Pritam Suggisetti, Udaya Parampalli +1
Learning with large-scale datasets and information-critical applications, such as in High Energy Physics (HEP), demands highly complex, large-scale models that are both robust and…
MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments
Abhishek Sawaika, Samuel Yen-Chi Chen, Udaya Parampalli +1
Reinforcement learning (RL) is one of the most practical ways to learn from real-life use-cases. Motivated from the cognitive methods used by humans makes it a widely acceptable st…
Quantum Federated Learning: Architectural Elements and Future Directions
Siva Sai, Abhishek Sawaika, Prabhjot Singh +1
Federated learning (FL) focuses on collaborative model training without the need to move the private data silos to a central server. Despite its several benefits, the classical FL…
A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection
Abhishek Sawaika, Swetang Krishna, Tushar Tomar +5
Rapid growth of digital transactions has led to a surge in fraudulent activities, challenging traditional detection methods in the financial sector. To tackle this problem, we intr…