Quantum computation and quantum information
Quantum computation and quantum information
Smooth entropies and the quantum information spectrum
IEEE Transactions on Information Theory
The operational meaning of min- and max-entropy
IEEE Transactions on Information Theory
A fully quantum asymptotic equipartition property
IEEE Transactions on Information Theory
The quantum capacity of channels with arbitrarily correlated noise
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
General formulas for capacity of classical-quantum channels
IEEE Transactions on Information Theory
An Information-Spectrum Approach to Classical and Quantum Hypothesis Testing for Simple Hypotheses
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
Assisted entanglement distillation
Quantum Information & Computation
An intuitive proof of the data processing inequality
Quantum Information & Computation
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In classical and quantum informationl theory operational quantities such as the amount of randomness that can be extracted from a given source or the amount of space needed to store given data are normally characterized by one of two entropy measures, called smooth min-entropy and smooth max-entropy respecively. While both entropies are equal to the von Neumann entropy In certain special cases (e.g., asymptotically, for many independent repetitions of the given data), their values can differ arbitrarily in the general case. In this paper, a recently discovered duality relation between (nonsmooth) min- and max-entropies is extended to the smooth case. More precisely, it is shown that the smooth min-entropy of a system A conditioned on a system B equals the negative of the smooth max-entropy of A conditioned on a purifying system C. This result immediately implies that certain operational quantities (such as the amount of compression and the amount of randomness that can be extracted from given data) are related. We explain how such relations have applications in cryptographic security proofs.