Check the flyer for this talk!
Abstract:
Quantum networks enable secure communication and distributed quantum computing by sharing entangled Einstein–Podolsky–Rosen (EPR) pairs between nodes. However, imperfections, noise, and decoherence degrade the fidelity of EPR pairs, requiring techniques like distillation and quantum error correction (QEC) to restore quality. Existing approaches apply these techniques statically, either compromising fidelity to maximize rate or reducing rate to improve fidelity, regardless of network demand, leading to inefficiencies. This talk introduces ReFINE, a dynamic approach that balances rate and fidelity based on EPR pair consumption demand in the network. ReFINE selectively distills EPR pairs and decides whether to use them immediately or preserve them in a Coding-Enhanced Memory (CEM), depending on the request arrival. This dynamic method ensures reliability without excessive resource consumption. By dynamically adjusting resources, ReFINE achieves a better fidelity-rate trade-off compared to prior work.
Bio:
Narges Alavisamani is a Ph.D. student in the School of Computer Science at Georgia Tech, advised by Professor Moinuddin Qureshi. Her research focuses on designing software and architecture for reliable and scalable quantum computers, spanning from real-time decoders to quantum networks. She earned her M.Sc. in Computer Science at Université Paris Diderot, joint with École Normale Supérieure and École Polytechnique, Paris, France. Before that, she completed her B.Sc. in Computer Engineering at the University of Tehran, Tehran, Iran. In addition to research, she actively contributes to the graduate student community as Vice President of the School of Computer Science Graduate Student Association.