Using interpolation classes and the generalized Petz's trace inequality we give a new matrix inequality which might play an important role in the quantum information theory: For any pair of positive definite matrices A,B∈Mn(C) we have Tr((A−B)(exp(A)−exp(B))≤Tr(|A(exp(A)−1)−B(exp(B)−1)|).
Consider a function F(X,Y) of pairs of positive matrices with values in the positive matrices such that whenever X and Y commute F(X,Y)=XpYq. Our first main result gives conditions on F such that Tr[X log(F(Z,Y))]≤Tr[X(p log X+q log Y)] for all X,Y,Z such that TrZ=TrX. (Note that Z is absent from the right side of the inequality.) We give several examples of functions F to which the theorem applies. Our theorem allows us to give simple proofs of the well-known logarithmic inequalities of Hiai and Petz and several new generalizations of them which involve three variables X,Y,Z instead of just X,Y alone. The investigation of these logarithmic inequalities is closely connected with three quantum relative entropy functionals: The standard Umegaki quantum relative entropy D(X∥Y)=Tr[X(log X− log Y]), and two others, the Donald relative entropy DD(X∥Y), and the Belavkin–Stasewski relative entropy DBS(X∥Y). They are known to satisfy DD(X∥Y)≤D(X∥Y)≤DBS(X∥Y). We prove that the Donald relative entropy provides the sharp upper bound, independent of Z on Tr[X log(F(Z,Y))] in a number of cases in which F(Z,Y) is homogeneous of degree 1 in Z and −1 in Y. We also investigate the Legendre transforms in X of DD(X∥Y) and DBS(X∥Y), and show how our results for these lead to new refinements of the Golden–Thompson inequality.
It is known that for a completely positive and trace preserving (cptp) map N, Trexp{logσ+N†[logN(ρ)−logN(σ)]}⩽Trρ when ρ, σ, N(ρ), and N(σ) are strictly positive. We state and prove a relevant version of this inequality for the hitherto unaddressed case of these matrices being nonnegative. Our treatment also provides an alternate proof for the strictly positive case.
Relating to finding possible upper bounds for the probability of error for discriminating between two quantum states, it is well-known that \begin{align*} \mathrm{tr}(A+B) - \mathrm{tr}|A-B|\leq 2\, \mathrm{tr}\big(f(A)g(B)\big) \end{align*} holds for every positive-valued matrix monotone function f, where g(x)=x/f(x), and all positive definite matrices A and B. In this paper, we introduce a new class of functions that satisfy the above inequality. As a consequence, we derive a novel estimation of the quantum Chernoff bound. Additionally, we characterize matrix decreasing functions and establish matrix Powers-Störmer type inequalities for perspective functions.
Aim: Supposebe Si be 2×2 is density matrix,π={πi}ni=1 is any a probability distribution,and A(s)≡∑i=1 n πiS11+si is invertible,for any 0≤s≤1,H(x)=-xlogx,and then Tr[A(s)s{∑j=1 n πjS11+sj(logS11+sj)2}-A(s)-1+s{∑j=1 n πjH(S11+sj)}2]≥0 which is a generalization of an inequality proved by K.Yanagi and others.Method: These problems are settled by applying Caushy-schwarz inequality,Jensen's inequality and some property of trace.Results: These inequalities related matrix logarithm give partial answer of the open problem posed by Yanagi.Conclusion: Some further studies on this base will been done because the results of those trace inequalities is just one case.
The R{é}nyi entropy is one of the important information measures that generalizes Shannon's entropy. The quantum R{é}nyi entropy has a fundamental role in quantum information theory, therefore, bounding this quantity is of vital importance. Another important quantity is R{é}nyi relative entropy on which R{é}nyi generalization of the conditional entropy, and mutual information are defined based. Thus, finding lower bound for R{é}nyi relative entropy is our goal in this paper. We use matrix inequalities to prove new bounds on the entropy of type β, R{é}nyi entropy.