It is important to distinguish between classical information and quantum information in quantum information theory. In this paper, we first extend the concept of metric-adjusted correlation measure and some related me...
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It is important to distinguish between classical information and quantum information in quantum information theory. In this paper, we first extend the concept of metric-adjusted correlation measure and some related measures to non-Hermitian operators, and establish several relations between the metric-adjusted skew information with different operator monotone functions. By employing operator monotone functions, we next introduce three uncertainty matrices generated by channels: the total uncertainty matrix, the classical uncertainty matrix, and the quantum uncertainty matrix. We establish a decomposition of the total uncertainty matrix into classical and quantum parts and further investigate their basic properties. As applications, we employ uncertainty matrices to quantify the decoherence caused by the action of quantum channels on quantum states, and calculate the uncertainty matrices of some typical channels to reveal certain intrinsic features of the corresponding channels. Moreover, we establish several uncertainty relations that improve the traditional Heisenberg uncertainty relations involving variance.
For positive definite matrices A and B, the Kubo-Ando matrix power mean is defined as P-mu(p, A, B) = A(1/2)(1 + (A(-1/2)BA(-1/2))(p)/2)(1/p) A(1/2) (p >= 0). In this paper, for 0 <= p <= 1 <= q, we show t...
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For positive definite matrices A and B, the Kubo-Ando matrix power mean is defined as P-mu(p, A, B) = A(1/2)(1 + (A(-1/2)BA(-1/2))(p)/2)(1/p) A(1/2) (p >= 0). In this paper, for 0 <= p <= 1 <= q, we show that if one of the following inequalities f(P-mu(p, A, B)) <= f(P-mu(1, A, B)) <= f(P-mu(q, A, B)) holds for any positive definite matrices A and B, then the function f is operatormonotone on (0, infinity). We also study the inverse problem for non-Kubo-Ando matrix power means with the powers 1/2 and 2. As a consequence, we establish new charaterizations of operator monotone functions with the non-Kubo-Ando matrix power means.
Assume that the function f : [0, infinity) -> R is operatormonotone in [0, infinity). We can define the perspective P-f (B, A) by setting P-f (B,A) := A(1/2) f(A(-1/2)BA(-1/2))A(1/2), where A, B > 0. In this pa...
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Assume that the function f : [0, infinity) -> R is operatormonotone in [0, infinity). We can define the perspective P-f (B, A) by setting P-f (B,A) := A(1/2) f(A(-1/2)BA(-1/2))A(1/2), where A, B > 0. In this paper, we show among others that, if sigma >= C >= rho > 0, D > 0, sigma >= Q >= tau > 0 and 0 < n <= D - C <= N for some constants rho, sigma, sigma, tau, n, N, then 0 <= n/N sigma(2)[P-f (sigma, N + sigma) - P-f (tau, sigma)]Q(2). <= P-f(Q, D) - P-f (Q, C) <= N/n tau(2)[P-f(tau, n + rho) - P-f (tau, rho)]Q(2). Applications for the weighted operator geometric mean and the perspective PIn(.+1) (B, A) := A(1/2) In (A(-1/2)BA(-1/2) + 1)A(1/2), A, B > 0 are also provided.
Assume that f : 1/20;1THORN ! R is a continuous function. We can define the perspective Pf oB;ATHORN by setting Pf oB;ATHORN :1/4 A1=2f A similar to 1=2BA similar to 1=2 similar to similar to A1=2;where A, B[ 0: We sh...
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Assume that f : 1/20;1THORN ! R is a continuous function. We can define the perspective Pf oB;ATHORN by setting Pf oB;ATHORN :1/4 A1=2f A similar to 1=2BA similar to 1=2 similar to similar to A1=2;where A, B[ 0: We show in this paper among others that Pf B;Po THORN similar to P f A;Po THORN similar to similar to similar to similar to similar to kPk2 kB similar to Ak p2 Pf m2;po THORN similar to P f om1;pTHORN m2 similar to m1 similar to similar to if m1 6 1/4 m2;f 0 m p similar to similar to if m1 1/4 m2 1/4 m8>>><>>>: for all A similar to m1[ 0;B similar to m2[ 0 and P similar to p[ 0. If f is operatormonotone on 1/20;1THORN, then for all C similar to n1[ 0;D similar to n2[ 0;Q[ q[ 0 we also have
Let Phi be a symmetrically norming (s.n.) function, p >= 2, Phi((p))* to be a dual s.n. function to p-modified s.n. function Phi((p)), A, B, X is an element of B(H), with A and B being normal operators such that AX...
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Let Phi be a symmetrically norming (s.n.) function, p >= 2, Phi((p))* to be a dual s.n. function to p-modified s.n. function Phi((p)), A, B, X is an element of B(H), with A and B being normal operators such that AX - XB is an element of C-Phi(H). If both A and B are strictly accretive, then for non-constant Pick function phi is an element of P[0, +infinity) parallel to phi(A)X - X phi(B)parallel to(Phi) <= parallel to root phi'(A*+A/2)(AX - XB)root phi'(B+B*/2)parallel to(Phi). (1) If A and B have Strictly contractive real parts, then 1/2 parallel to root I - vertical bar A* + A/2 vertical bar(2) (log I + A/I - A X - X log I + B/I - B) x root I - vertical bar B + B*/2 vertical bar(2)parallel to(Phi) <= parallel to AX - XB parallel to(Phi). If A is cohyponormal, B is hyponormal and at least one of them is normal, such that AX - XB is an element of C-Phi(p)*(H), then pi/2 parallel to cos A* + A(tan 2A/pi X - X tan 2B/pi) cos B + B*/pi parallel to(Phi(p)*) <= parallel to AX - XB parallel to(Phi(p)*). (2) Inequality (1) generalizes "difference" version of Heinz norm inequality [13, Hilfssatz 3] and mean values norm inequality [21, th. 4.4] for operator monotone functions. Inequality (2) remains valid for all s.n. function Phi if A and B are both (additionally) normal, which extends inequalities in [32, th. 5] and [34, rem. 25] for self-adjoint operators H and K, whence their spectra sigma(H) and sigma(K) are contained in (-pi/2, pi/2), to non necessarily self-adjoint operators A and B. (C) 2019 Elsevier Inc. All rights reserved.
In this corrigendum note we provide a correct statement and a proof of [3, Th. 2.1] parts (a4'), (a4 ''), (b4'), (b4 ''), as well as a correct statement and a proof for the inequality (34) in [...
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In this corrigendum note we provide a correct statement and a proof of [3, Th. 2.1] parts (a4'), (a4 ''), (b4'), (b4 ''), as well as a correct statement and a proof for the inequality (34) in [3, Cor. 2.3]. (C) 2020 Elsevier Inc. All rights reserved.
This paper concerns three classes of real-valued functions on intervals, operator monotone functions, operator convex functions, and strongly operator convex functions. Strongly operator convex functions were previous...
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This paper concerns three classes of real-valued functions on intervals, operator monotone functions, operator convex functions, and strongly operator convex functions. Strongly operator convex functions were previously treated in [3] and [4], where operator algebraic semicontinuity theory or operator theory were substantially used. In this paper we provide an alternate treatment that uses only operator inequalities (or even just matrix inequalities). We show also that if to is a point in the domain of a continuous function f, then f is operatormonotone if and only if (f (t) - f (t(0)) / (t - t(0)) is strongly operator convex. Using this and previously known results, we provide some methods for constructing new functions in one of the three classes from old ones. We also include some discussion of completely monotonefunctions in this context and some results on the operator convexity or strong operator convexity of phi o f when f is operator convex or strongly operator convex. (C) 2018 Elsevier Inc. All rights reserved.
If sigma is a symmetric mean and f is an operatormonotone function on [0, infinity), then f(2(A(-1) + B-1)(-1)) <= f(A sigma B) <= f((A + B)/2). Conversely, Ando and Hiai showed that if f is a function that sat...
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If sigma is a symmetric mean and f is an operatormonotone function on [0, infinity), then f(2(A(-1) + B-1)(-1)) <= f(A sigma B) <= f((A + B)/2). Conversely, Ando and Hiai showed that if f is a function that satisfies either one of these inequalities for all positive operators A and B and a symmetric mean different than the arithmetic and the harmonic mean, then the function is operatormonotone. In this paper, we show that the arithmetic and the harmonic means can be replaced by the geometric mean to obtain similar characterizations. Moreover, we give characterizations of operator monotone functions using self-adjoint means and general means subject to a constraint due to Kubo and Ando. (C) 2018 Elsevier Inc. All rights reserved.
We provide a complete characterization of a subclass of weakly associative means of positive operators in the class of symmetric Kubo-Ando means. This class, which includes the geometric mean, was first introduced and...
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We provide a complete characterization of a subclass of weakly associative means of positive operators in the class of symmetric Kubo-Ando means. This class, which includes the geometric mean, was first introduced and studied in Moln & aacute;r (2019) [24], where he gives a characterization of this subclass (which we call the Moln & aacute;r class of means) in terms of the properties of their representing operator monotone functions. Moln & aacute;r's paper leaves open the problem of determining if the geometric mean is the only such mean in that subclass. Here we give a negative answer to this question by constructing an order-preserving bijection between this class and a class of real measurable odd periodic functions bounded in absolute value by 1/2. Each member of the latter class defines a Moln & aacute;r mean by an explicit exponential-integral representation. From this we are able to understand the order structure of the Moln & aacute;r class and construct several infinite families of explicit examples of Moln & aacute;r means that are not the geometric mean. Our analysis also shows how to modify Moln & aacute;r's original characterization so that the geometric mean is the only one satisfying the requisite set of properties. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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