![]() His current research interests are in quantum Shannon theory, quantum optical communication, quantum computational complexity theory, and quantum error correction. He is a recipient of the Career Development Award from the US National Science Foundation and is Associate Editor for Quantum Information Theory at IEEE Transactions on Information Theory and New Journal of Physics. Wilde is an Associate Professor in the Department of Physics and Astronomy and the Center for Computation and Technology at Louisiana State University. In this talk, I will review these quantum generalizations of the classical Renyi relative entropy, discuss their relevant information-theoretic properties, and the applications mentioned above. Finally, a generalization now known as the Petz–Renyi relative entropy plays a critical role for statements of achievability in quantum communication. Another generalization is known as the geometric Renyi relative entropy and finds its use in establishing strong converse theorems for feedback assisted protocols, which apply to quantum key distribution and distributed quantum computing scenarios. A set of boxes is used to indicate how many bits of entropy a section of the. It has also found use in establishing strong converse theorems (fundamental communication capacity limitations) for a variety of quantum communication tasks. Surprise Function': s(u), log P(u) De nition 2. ((The comic illustrates the relative strength of passwords assuming basic. One generalization is known as the sandwiched Renyi relative entropy and finds its use in characterizing asymptotic behavior in quantum hypothesis testing. The past decade of research in quantum information theory has witnessed extraordinary progress in understanding communication over quantum channels, due in large part to quantum generalizations of the classical Renyi relative entropy. Quantum Renyi relative entropies and their use Mark Wilde – Professor, Louisiana State University These guarantees largely improve previously known results under much milder assumptions and cannot be significantly improved under general assumptions.← List all talks. In particular, the obvious analog to (3.19) does not hold for relative entropy. ![]() Rnyi entropy a generalization of Shannon entropy it is one of a family of functionals for quantifying the diversity, uncertainty or randomness of a system. The properties of relative entropy rate are more difficult to demonstrate. We describe a variant of the recently proposed Relative Entropy Policy Search algorithm and show that its regret after $T$ episodes is $2\sqrt$ in the full information setting. Quantum relative entropy a measure of distinguishability between two quantum states. We assume that the learner is given access to a finite action space $\A$ and the state space $\X$ has a layered structure with $L$ layers, so that state transitions are only possible between consecutive layers. The natural performance measure in this learning problem is the regret defined as the difference between the total loss of the best stationary policy and the total loss suffered by the learner. Relative Entropy and Mutual Information in Gaussian Statistical Field Theory. Relative entropy is a method which quantifies the extent of the configurational phase-space overlap between two molecular ensembles 20. We study the problem of online learning in finite episodic Markov decision processes where the loss function is allowed to change between episodes. Bibtex Metadata Paper Reviews Supplemental ![]()
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