1996
DOI: 10.1016/0024-3795(94)00211-8
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Monotone metrics on matrix spaces

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Cited by 480 publications
(584 citation statements)
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“…In general, the length of a vector (matrix) B ij may be position dependent; at a diagonal point A = diag(a 1 , a 2 , ..., a N ) the squared length of B is given by [1] that operator monotonicity implies that f maps R + → R + and is concave. The result of Morozova and Chentsov [32], completed and extended by Petz and Sudár [1,33], states that every monotone metric may be written in the form (2.16) up a proportionality constant. Conversely, this equation determines a monotone Riemannian metric, if the function f (t) = 1/c(t, 1) is: a) operator monotone, and b) self-transposed, f (1/t) = f (t)/t.…”
Section: Bures Metricmentioning
confidence: 99%
“…In general, the length of a vector (matrix) B ij may be position dependent; at a diagonal point A = diag(a 1 , a 2 , ..., a N ) the squared length of B is given by [1] that operator monotonicity implies that f maps R + → R + and is concave. The result of Morozova and Chentsov [32], completed and extended by Petz and Sudár [1,33], states that every monotone metric may be written in the form (2.16) up a proportionality constant. Conversely, this equation determines a monotone Riemannian metric, if the function f (t) = 1/c(t, 1) is: a) operator monotone, and b) self-transposed, f (1/t) = f (t)/t.…”
Section: Bures Metricmentioning
confidence: 99%
“…Following the work of Petz [44] (see also [3,4]) let us recall that a Riemannian metric on the manifold D of the density matrices ρ can be written in the form…”
Section: Monotone Riemannian Metrics: Generic Formulamentioning
confidence: 99%
“…In the geometrical approach to statistics proposed by Morozova andCencov [43] the monotone metrics can be introduced and studied from a unified point of view due to the work of Petz [44].…”
Section: Introductionmentioning
confidence: 99%
“…The requirement that the distance between density matrices expresses quantum statistical distinguishability implies that this distance must decrease under coarsegraining (stochastic maps). Unlike the classical case, it turns out that there are infinitely many monotone Riemannian metrics satisfying this requirement [24][25][26].…”
Section: On Information Geometry and Statistical Distinguishabilitymentioning
confidence: 99%