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Quantum Tsallis-Jensen-Shannon divergence and its bounds

Fatemeh Hassanzad, Hossein Mehri‐Dehnavi, Hamzeh Agahi

Published Jan 30, 2024• 0 citations
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Abstract

In this paper, we first introduce some properties of quantum Tsallis-Jensen-Shannon divergence for two density matrices. Then one of the beautiful and very simple applications of Hermit-Hadamard’s inequality [S. Dragomir, et. al. Math. Methods Appl. Sci., 1–15 (2020)] for log-convexity in the concepts of quantum entropies and quantum Tsallis-Jensen-Shannon is given.

DOI
10.22541/au.170666828.88481188/v1

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In this paper, we first introduce some properties of quantum Tsallis-Jensen-Shannon divergence for two density matrices. Then one of the beautiful and very simple applications of Hermit-Hadamard’s inequality [S. Dragomir, et. al. Math. Methods Appl. Sci., 1–15 (2020)] for log-convexity in the concepts of quantum entropies and quantum Tsallis-Jensen-Shannon is given.
One of the beautiful and very simple inequalities for a convex function is the Hermit-Hadamard inequality [S. Mehmood, et. al. Math. Methods Appl. Sci., 44 (2021) 3746], [S. Dragomir, et. al., Math. Methods Appl. Sci., in press]. The concept of log-convexity is a stronger property of convexity. Recently, the refined Hermit-Hadamard’s inequalities for log-convex functions were introduced by researchers [C. P. Niculescu, Nonlinear Anal. Theor., 75 (2012) 662]. In this paper, by the Hermit-Hadamard inequality, we introduce two parametric Tsallis quantum relative entropy, two parametric Tsallis-Lin quantum relative entropy and two parametric quantum Jensen-Shannon divergence in quantum information theory. Then some properties of quantum Tsallis-Jensen-Shannon divergence for two density matrices are investigated by this inequality. \newline \textbf{Keywords:} \textit{ Hermit-Hadamard’s inequality; log-convexity; Density matrices; Quantum relative entropy; Tsallis quantum relative entropy; quantum Jensen-Shannon divergence divergence.
One of the beautiful and very simple inequalities for a convex function is the Hermite–Hadamard inequality. The concept of ‐convexity is a strong variant of convexity. In this paper, by the Hermite–Hadamard inequality, we introduce two‐parametric Tsallis quantum relative entropy, two‐parametric Tsallis–Lin quantum relative entropy, and two‐parametric quantum Jensen–Shannon divergence in quantum information theory. Then some properties of quantum Tsallis–Jensen–Shannon divergence for two density matrices are investigated by this inequality.
Some inequalities for quantum fff-divergence of matrices are obtained. It is shown that for normalised convex functions it is nonnegative. Some upper bounds for quantum fff-divergence in terms of variational and χ2\chi ^{2}χ2-distance are provided. Applications for some classes of divergence measures such as Umegaki and Tsallis relative entropies are also given.
In a recent paper, the generalization of the Jensen-Shannon divergence in the context of quantum theory has been studied [Majtey et al., Phys. Rev. A 72, 052310 (2005)]. This distance between quantum states has shown to verify several of the properties required for a good distinguishability measure. Here we investigate the metric character of this distance. More precisely we show, formally for pure states and by means of a numerical procedure for mixed states, that its square root verifies the triangle inequality.
Many problems of statistical and quantum mechanics can be established in terms of a distance; in the first case the distance is usually defined between probability distributions; in the second one, between quantum states. The present work is devoted to review the main properties of a distance known as the Jensen‐Shannon divergence (JSD) in its classical and quantum version. We present two examples of application of this distance: in the first one we use it as a quantifiers of the stochastic resonance phenomenon in ion channels; in the second one we use the JSD to propose a geometrical view of entanglement for two qubits states.