In this article we propose a quantum version of Shannon's conditional entropy. Given two density matrices ρ and σ on a finite dimensional Hilbert space and with S(ρ) = —Tr ρ ln ρ being the usual von Neumann entropy, this quantity S(ρ | σ) is concave in ρ and satisfies 0 ≤ S(ρ | σ) ≤ S(ρ), a quantum analogue of Shannon's famous inequality. Thus we view S(ρ | σ) as the entropy of ρ conditioned by σ. The second inequality is an equality if σ is a multiple of the identity. In contrast to the classical case, however, S(ρ | ρ) = 0 if and only if the non-vanishing eigenvalues of ρ are all non-degenerate. Also in general and again in contrast to the corresponding classical situation S(ρ, σ) = S(σ) + S(ρ | σ) is not symmetric in ρ and σ even if they commute. We also show that there is no quantum version of conditional entropy in terms of two density matrices, which shares more properties with the classical case and which in particular reduces to the classical case when the two density matrices commute. As an alternative we propose to use spectral resolutions of the unit matrix instead of density matrices. We briefly compare this with the algebraic approach of Connes and Størmer and Connes, Narnhofer and Thirring.
In this article we propose a quantum version of Shannon's conditional entropy. Given two density matrices ρ and σ on a finite dimensional Hilbert space and with S(ρ) = —Tr ρ ln ρ being the usual von Neumann entropy, this quantity S(ρ | σ) is concave in ρ and satisfies 0 ≤ S(ρ | σ) ≤ S(ρ), a quantum analogue of Shannon's famous inequality. Thus we view S(ρ | σ) as the entropy of ρ conditioned by σ. The second inequality is an equality if σ is a multiple of the identity. In contrast to the classical case, however, S(ρ | ρ) = 0 if and only if the non-vanishing eigenvalues of ρ are all non-degenerate. Also in general and again in contrast to the corresponding classical situation S(ρ, σ) = S(σ) + S(ρ | σ) is not symmetric in ρ and σ even if they commute. We also show that there is no quantum version of conditional entropy in terms of two density matrices, which shares more properties with the classical case and which in particular reduces to the classical case when the two density matrices commute. As an alternative we propose to use spectral resolutions of the unit matrix instead of density matrices. We briefly compare this with the algebraic approach of Connes and Størmer and Connes, Narnhofer and Thirring.
In this article we propose a quantum version of Shannon's conditional entropy. Given two density matrices rho and sigma on a finite dimensional Hilbert space and with S(rho)=−trrholnrho being the usual von Neumann entropy, this quantity S(rho∣sigma) is concave in rho and satisfies 0leS(rho∣sigma)leS(rho), a quantum analogue of Shannon's famous inequality. Thus we view S(rho∣sigma) as the entropy of rho conditioned by sigma.
Von Neumann defined the informational entropy of a density matrix ρ by the expression S(ρ)=−tr(ρ ln ρ). [J. Von Neumann, Die Mathematischen Grundlagen der Quantummechanik (Springer-Verlag, Berlin, 1932)]. Here, starting from the definitions of Shannon entropy and of Renyi entropy of random variables, and using some (reasonable) rules of inference, two expressions are obtained for the quantum entropy of a given square (deterministic) matrix, irrespective of any probabilistic framework and/or any randomization technique. These definitions apply directly to linear operators on Hilbert spaces even when they are not of positive trace class, and they contain Von Neumann entropy as a special case. These new measures of uncertainty could provide new approaches to some problems related to structure complexity.
The concept of entropy rst arose in the study of thermodynamics and statistical me- chanics. In information theory the so-called Shannon entropy plays a signi cant role, which measures how much information we gain, on average, when we learn the value of a random variable. An alternative interpretation is that the entropy of the random variable quanti es the amount of uncertainty about the random variable before we learn its value. The concept was introduced by Claude E. Shannon in his 1948 paper "A Mathematical Theory of Communication". In the rst part of the present work we consider this fundamental measure of information and show some of its properties, such as non-negativity. The relative entropy or the Kullback-Leibler divergence is a very useful measure of the distance between two prob- ability distributions. After introducing its de nition and other notions (joint entropy, conditional entropy or mutual information) we can prove additional assertions about the Shannon entropy and present statements about the relations between the above men- tioned quantities. In quantum information theory, there is a quantity, namely von Neumann entropy, which corresponds to Shannon entropy and originates from it. In the section Von Neu- mann entropy we will introduce this quantity which is a continuous functional on the states, or on their mathematical representatives, the density operators, and it ful ls in- equalities such as subadditivity and strong subaddititvity. Just as with the Shannon entropy, it is extremely useful to de ne a quantum version of the relative entropy, which is one of the most important numerical quantities appearing in quantum information theory. It is used as a measure of distinguishability between quantum states. Among others its properties are jointly convexity, monotonicity which are speci cally convenient. Transformations on structures which preserve operations, quantities or relations among elements appear in many areas of mathematics. These are the so-called preservers which show up also in physics and even in chemistry where they are usually called symmetries. Therefore, the study of preserver transformations is an important area of research. The third section of this work is based on a paper, in which Lajos Molnár described the structure every bijective map on the space of all density operators on a given Hilbert space, which preserves the relative entropy. This result is closely related to Wigner's famous theorem on the form of quantum mechanical symmetry transformations. The above theorem of Molnár can be extended into two directions. On the one hand, the result holds also in the case when the transformation under consideration acts on the space of all positive semide nite operators not only on density operators (i.e., we omit the condition of unit trace). On the other hand the statement remains valid even if we omit the strongly restrictive condition that the transformation is bijective. The second result is a joint work with Prof. Lajos Molnár which appeared in the journal Linear Algebra and Its Applications.
This article presents self-contained proofs of the strong subadditivity inequality for von Neumann’s quantum entropy, S(ρ), and some related inequalities for the quantum relative entropy, most notably its convexity and its monotonicity under stochastic maps. Moreover, the approach presented here, which is based on Klein’s inequality and Lieb’s theorem that the function A→Tr eK+log A is concave, allows one to obtain conditions for equality. In the case of strong subadditivity, which states that S(ρ123)+S(ρ2)⩽S(ρ12)+S(ρ23) where the subscripts denote subsystems of a composite system, equality holds if and only if log ρ123=log ρ12−log ρ2+log ρ23. Using the fact that the Holevo bound on the accessible information in a quantum ensemble can be obtained as a consequence of the monotonicity of relative entropy, we show that equality can be attained for that bound only when the states in the ensemble commute. The article concludes with an Appendix giving a short description of Epstein’s elegant proof of Lieb’s theorem.
Given the algebra of observables of a quantum system subject to selection rules, a state can be represented by different density matrices. As a result, different von Neumann entropies can be associated with the same state. Motivated by a minimality property of the von Neumann entropy of a density matrix with respect to its possible decompositions into pure states, we give a purely algebraic definition of entropy for states of an algebra of observables, thus solving the above ambiguity. The entropy so-defined satisfies all the desirable thermodynamic properties and reduces to the von Neumann entropy in the quantum mechanical case. Moreover, it can be shown to be equal to the von Neumann entropy of the unique representative density matrix belonging to the operator algebra of a multiplicity-free Hilbert-space representation.
The notion of entropy was introduced by Clausius in order to discuss the thermal behavior of physical systems. After the work of Clausius, Boltzmann, Gibbs and others, the beginning of the exact treatment of entropy was due to von Neumann and Shannon. Their starting point was very di erent, von Neumann was motivated by quantum mechanics and Shannon founded communication theory. The entropy of a state describing a physical system is a quantity expressing the uncertainty or randomness of the system. Shannon regarded this uncertainty attached to a physical system as the amount of information carried by the system, so that the entropy of a state can be read as the information carried by the state. His idea comes from the following consideration: If a physical system has a large uncertainty and one receives information on the system by some procedure, then the so obtained information is more valuable than that received from a system having less uncertainty. It is understood in probability theory that the notion of (Shannon or measure theoretic) entropy has successful applications in a variety of subjects because it determines the asymptotic behavior of certain probabilities in the course of independent trials. First we will discuss this phenomena for nite quantum systems. By a nite quantum system we mean an algebra of matrices which is stable under taking adjoint. (In other words, a nite quantum system is a nite dimensional C*algebra.) If A is such an algebra then there is a linear functional Tr which takes the value 1 at each minimal projection. It is "tracial " in the sense that Tr ab = Tr ba (a; b 2 A) Every functional! on A is determined by a density operator D! 2 A in the form!(a) = Tr D! a (a 2 A): The entropy S(!) of a functional! is de ned by means of its density operator as S(!) = Tr (D!): This notion was introduced by von Neumann in 1927 and we term it von Neumann's entropy or shortly entropy (cf. [1]). In fact, von Neumann de ned the entropy such