Optimal Quantum Data Compression with Side Information at the Sender and Receiver

SPEAKER: Dr. Jon Yard, CaltechAbstract: Information theory - as introduced by Shannon almost 60 years ago - studies the asymptotic limits for the processing of statistically modeled information in such problems as data compression and coding for noisy channels. In recent years, a rich generalization of Shannon's theory has emerged which incorporates distinctly quantum mechanical resources such as qubits (quantum bits), entangled quantum states, and quantum channels. In this talk, I will present the optimal solution to a general quantum data compression problem where the sender and receiver each have quantum side information. The corresponding optimal protocol - quantum state redistribution - provides the first known operational interpretation to quantum conditional mutual information. The optimal rates satisfy certain elegant and intuitive properties, while admitting a general "thermodynamical" organizing principle that I will recall. I will conclude by sketching an existence proof for the optimal protocol, while showing how state redistribution generalizes and organizes a number of recent results in quantum information theory such as the celebrated fully quantum reverse Shannon and fully quantum Slepian-Wolf protocols.Bio Jon Yard received his Ph.D. from the Information theory group of Tom Cover at Stanford University in 2005. Since then, he has been a postdoc with Patrick Hayden at McGill University and also with the Institute for Quantum Information at Caltech. This summer, he will begin a postdoc with the Quantum Institute at Los Alamos National Laboratories.Host: Prof. Todd Brun, tbrun@usc.edu

Location: Frank R. Seaver Science Center (SSC) - 319

Audiences: Everyone Is Invited

Contact: Mayumi Thrasher