Elements of information theory
Elements of information theory
Quantum computation and quantum information
Quantum computation and quantum information
Smooth entropies and the quantum information spectrum
IEEE Transactions on Information Theory
Simple and tight bounds for information reconciliation and privacy amplification
ASIACRYPT'05 Proceedings of the 11th international conference on Theory and Application of Cryptology and Information Security
Universally composable privacy amplification against quantum adversaries
TCC'05 Proceedings of the Second international conference on Theory of Cryptography
Coding theorem and strong converse for quantum channels
IEEE Transactions on Information Theory
Strong converse and Stein's lemma in quantum hypothesis testing
IEEE Transactions on Information Theory
General formulas for capacity of classical-quantum channels
IEEE Transactions on Information Theory
General formulas for fixed-length quantum entanglement concentration
IEEE Transactions on Information Theory
Asymptotic Entanglement Manipulation of Bipartite Pure States
IEEE Transactions on Information Theory
The quantum capacity of channels with arbitrarily correlated noise
IEEE Transactions on Information Theory
A conceptually simple proof of the quantum reverse Shannon theorem
TQC'10 Proceedings of the 5th conference on Theory of quantum computation, communication, and cryptography
An intuitive proof of the data processing inequality
Quantum Information & Computation
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Two new relative entropy quantities, called the min-and max-relative entropies, are introduced and their properties are investigated. The well-known min-and max-entropies, introduced by Renner, are obtained from these. We define a new entanglement monotone, which we refer to as the max-relative entropy of entanglement, and which is an upper bound to the relative entropy of entanglement. We also generalize the min-and max-relative entropies to obtain smooth min-and max-relative entropies. These act as parent quantities for the smooth Rényi entropies (ETH Zurich, Ph.D. dissertation, 2005), and allow us to define the analogues of the mutual information, in the Smooth Rényi Entropy framework. Further, the spectral divergence rates of the Information Spectrum approach are shown to be obtained from the smooth min- and max-relative entropies in the asymptotic limit.