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Information-Spectrum Approaches to Quantum Information Theory

Hiroshi Nagaoka

Published Jan 1, 2002• 0 citations

Abstract

One of the principal themes of information theory is to establish a link between two different kinds of quantities: one is an operational quantity which is defined through the asymptotic optimality on some concrete concept such as code length, compression rate, error probability, etc., while the other is an abstract information quantity such as the entropy, divergence, mutual information, etc. The same theme is now being pursued extensively in quantum information theory, and lots of mathematical results have been reported so far. In the so-called information-spectrum methods in the classical information theory [1], the process of establishing a link between an operatinal quantity and an entropy-like information quantity is intentionally divided into two parts, making the third kind of quantities — information spectrum — intervene. In the methods, the asymptotic optimality of an operational quantity is characterized by a limiting expression on information spectra (asymptotic behavior of logarithmic likelihoods) in the most general setting, while the information-spectrum quantity is rewritten to an entropylike quantity in a specific situation mostly as a direct consequence of a limiting theorem in the probability theory such as the law of large numbers, the Shannon-McMillan-Breiman theorem, etc. Such a framework brings not only generality but also transparency of mathematical arguments. Extending the information-spectrum methods to the quantum case is an attractive subject, which is expected to provide both the asymptotic optimality of operational quantities and the limiting law governing quantum stochastic situations with transparent and comprehensive understanding. In this talk I would like to review recent attempts to develop the quantum informationspectrum methods made by Hayashi, Ogawa and myself, mainly based on [2, 3, 4], and discuss their significance in the quantum information theory.

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One of the principal themes of information theory is to establish a link between two different kinds of quantities: one is an operational quantity which is defined through the asymptotic optimality on some concrete concept such as code length, compression rate, error probability, etc., while the other is an abstract information quantity such as the entropy, divergence, mutual information, etc. The same theme is now being pursued extensively in quantum information theory, and lots of mathematical results have been reported so far. In the so-called information-spectrum methods in the classical information theory [1], the process of establishing a link between an operatinal quantity and an entropy-like information quantity is intentionally divided into two parts, making the third kind of quantities — information spectrum — intervene. In the methods, the asymptotic optimality of an operational quantity is characterized by a limiting expression on information spectra (asymptotic behavior of logarithmic likelihoods) in the most general setting, while the information-spectrum quantity is rewritten to an entropylike quantity in a specific situation mostly as a direct consequence of a limiting theorem in the probability theory such as the law of large numbers, the Shannon-McMillan-Breiman theorem, etc. Such a framework brings not only generality but also transparency of mathematical arguments. Extending the information-spectrum methods to the quantum case is an attractive subject, which is expected to provide both the asymptotic optimality of operational quantities and the limiting law governing quantum stochastic situations with transparent and comprehensive understanding. In this talk I would like to review recent attempts to develop the quantum informationspectrum methods made by Hayashi, Ogawa and myself, mainly based on [2, 3, 4], and discuss their significance in the quantum information theory.

Many of the traditional results in information theory, such as the channel coding theorem or the source coding theorem, are restricted to scenarios where the underlying resources are independent and identically distributed (i.i.d.) over a large number of uses. To overcome this limitation, two different techniques, the information spectrum method and the smooth entropy framework, have been developed independently. They are based on new entropy measures, called spectral entropy rates andsmoothentropies, respectively, that generalize Shannon entropy (in the classical case) and von Neumann entropy (in the more general quantum case). Here, we show that the two techniques are closely related. More precisely, the spectral entropy rate can be seen as the asymptotic limit of the smooth entropy. Our results apply to the quantum setting and thus include the classical setting as a special case.

The information-spectrum analysis made by Han for classical hypothesis testing for simple hypotheses is extended to a unifying framework including both classical and quantum hypothesis testing. The results are also applied to fixed-length source coding when loosening the normalizing condition for probability distributions and for quantum states. We establish general formulas for several quantities relating to the asymptotic optimality of tests/codes in terms of classical and quantum information spectra

Many of the traditional results in information theory, such as the channel coding theorem or the source coding theorem, are restricted to scenarios where the underlying resources are in- dependent and identically distributed (i.i.d.) over a large number of uses. To overcome this limitation, two different techniques, the informationspectrummethodandthesmoothentropyframework, have been developed independently. They are based on new en- tropymeasures, calledspectralentropy ratesandsmooth entropies, respectively,that generalizeShannon entropy (in the classicalcase) and von Neumann entropy (in the more general quantum case). Here, we show that the two techniques are closely related. More precisely, the spectral entropy rate can be seen as the asymptotic limit of the smooth entropy. Our results apply to the quantum set- ting and thus include the classical setting as a special case.

Abstract Quantum information science is a new and rapidly developing discipline that studies the general laws governing the transmission, storage, and processing of information in systems that obey the laws of quantum mechanics. It makes extensive use of matrix theory and operator theory on Hilbert spaces to investigate the capabilities of such systems and to develop principles for their rational design. An important consequence of the development of quantum information science is a substantial clarification of the logical structure and foundations of quantum mechanics and of its relation to reality. These lectures are devoted to the foundations of the mathematical theory of quantum systems: states, observables, communication channels, their entropic and information characteristics, and quantum information-processing protocols. The lectures provide a detailed introduction to the statistical structure of quantum theory and the necessary background from classical information theory; hence, knowledge of the basic mathematical subjects—analysis, linear algebra, and probability theory—is sufficient for understanding the material. In the first part of the course, we use the “standard” statistical model of a quantum system, but consider it in comparison with the classical statistical description. A consistent development of this approach leads to a generalized statistical model of quantum mechanics, in which observables (in general, “unsharp”) are described by probability operator-valued measures, while evolutions (in general, irreversible) are described by completely positive maps. Introductory material on these concepts, which underlie mathematical models of quantum information systems in the presence of noise and errors and have numerous applications in quantum information science, is presented in the second part of the course.

These notes (in French) provide a concise introduction to information theory, spanning its classical foundations: Shannon entropy, joint and conditional entropies, mutual information, statistical correlations, and Markov chains; as well as Shannon's source coding and channel capacity theorems, which govern the optimal compression and transmission of information through noisy channels. They also cover the quantum extensions of these concepts, including the density operator formalism, distances between quantum states, von Neumann entropy, coherent information, conditional entropy, entanglement theory, quantum discord, and quantum resource theory.

We introduce two variants of the information spectrum relative entropy defined by Tomamichel and Hayashi, which have the particular advantage of satisfying the data-processing inequality, i.e., monotonicity under quantum operations. This property allows us to obtain one-shot bounds for various information-processing tasks in terms of these quantities. Moreover, these relative entropies have a second-order asymptotic expansion, which in turn yields tight second-order asymptotics for optimal rates of these tasks in the independent and identically distributed setting. The tasks studied in this paper are fixed-length quantum source coding, noisy dense coding, entanglement concentration, pure-state entanglement dilution, and transmission of information through a classical-quantum channel. In the latter case, we retrieve the second-order asymptotics obtained by Tomamichel and Tan. Our results also yield the known second-order asymptotics of fixed-length classical source coding derived by Hayashi. The second-order asymptotics of entanglement concentration and dilution provide a refinement of the inefficiency of these protocols-a quantity which, in the case of entanglement dilution, was studied by Harrow and Lo. We prove how the discrepancy between the optimal rates of these two processes in the second-order implies the irreversibility of entanglement concentration established by Kumagai and Hayashi. In addition, the spectral divergence rates of the information spectrum approach (ISA) can be retrieved from our relative entropies in the asymptotic limit. This enables us to directly obtain the more general results of the ISA from our one-shot bounds.