Bounds on the heat kernel of the Schroedinger operator in a random electromagnetic field

We obtain lower and upper bounds on the heat kernel and Green functions of the Schroedinger operator in a random Gaussian magnetic field and a fixed scalar potential. We apply stochastic Feynman-Kac representation, diamagnetic upper bounds and the Jensen inequality for the lower bound. We show that if the covariance of the electromagnetic (vector) potential is increasing at large distances then the lower bound is decreasing exponentially fast for large distances and a large time.Comment: some technical improvements, new references, to appear in Journ.Phys.

Upper and lower bounds on the mean square radius and criteria for occurrence of quantum halo states

In the context of non-relativistic quantum mechanics, we obtain several upper and lower limits on the mean square radius applicable to systems composed by two-body bound by a central potential. A lower limit on the mean square radius is used to obtain a simple criteria for the occurrence of S-wave quantum halo sates.Comment: 12 pages, 2 figure

Exact upper and lower bounds on the difference between the arithmetic and geometric means

Let $X$ denote a nonnegative random variable with $\mathsf{E} X<\infty$. Upper and lower bounds on $\mathsf{E} X-\exp\mathsf{E}\ln X$ are obtained, which are exact, in terms of $V_X$ and $E_X$ for the upper bound and in terms of $V_X$ and $F_X$ for the lower bound, where $V_X:=\mathsf{Var}\sqrt X$, $E_X:=\mathsf{E}\big(\sqrt X-\sqrt{m_X}\,\big)^2$, $F_X:=\mathsf{E}\big(\sqrt{M_X}-\sqrt X\,\big)^2$, $m_X:=\inf S_X$, $M_X:=\sup S_X$, and $S_X$ is the support set of the distribution of $X$. Note that, if $X$ takes each of distinct real values $x_1,\dots,x_n$ with probability $1/n$, then $\mathsf{E} X$ and $\exp\mathsf{E}\ln X$ are, respectively, the arithmetic and geometric means of $x_1,\dots,x_n$.Comment: 8 pages; to appear in the Bulletin of the Australian Mathematical Society. Version 2: the condition that the random variable X is nonnegative was missing in the abstrac

Fractional Power Control for Decentralized Wireless Networks

We consider a new approach to power control in decentralized wireless networks, termed fractional power control (FPC). Transmission power is chosen as the current channel quality raised to an exponent -s, where s is a constant between 0 and 1. The choices s = 1 and s = 0 correspond to the familiar cases of channel inversion and constant power transmission, respectively. Choosing s in (0,1) allows all intermediate policies between these two extremes to be evaluated, and we see that usually neither extreme is ideal. We derive closed-form approximations for the outage probability relative to a target SINR in a decentralized (ad hoc or unlicensed) network as well as for the resulting transmission capacity, which is the number of users/m^2 that can achieve this SINR on average. Using these approximations, which are quite accurate over typical system parameter values, we prove that using an exponent of 1/2 minimizes the outage probability, meaning that the inverse square root of the channel strength is a sensible transmit power scaling for networks with a relatively low density of interferers. We also show numerically that this choice of s is robust to a wide range of variations in the network parameters. Intuitively, s=1/2 balances between helping disadvantaged users while making sure they do not flood the network with interference.Comment: 16 pages, in revision for IEEE Trans. on Wireless Communicatio

Properties of Classical and Quantum Jensen-Shannon Divergence

Jensen-Shannon divergence (JD) is a symmetrized and smoothed version of the most important divergence measure of information theory, Kullback divergence. As opposed to Kullback divergence it determines in a very direct way a metric; indeed, it is the square of a metric. We consider a family of divergence measures (JD_alpha for alpha>0), the Jensen divergences of order alpha, which generalize JD as JD_1=JD. Using a result of Schoenberg, we prove that JD_alpha is the square of a metric for alpha lies in the interval (0,2], and that the resulting metric space of probability distributions can be isometrically embedded in a real Hilbert space. Quantum Jensen-Shannon divergence (QJD) is a symmetrized and smoothed version of quantum relative entropy and can be extended to a family of quantum Jensen divergences of order alpha (QJD_alpha). We strengthen results by Lamberti et al. by proving that for qubits and pure states, QJD_alpha^1/2 is a metric space which can be isometrically embedded in a real Hilbert space when alpha lies in the interval (0,2]. In analogy with Burbea and Rao's generalization of JD, we also define general QJD by associating a Jensen-type quantity to any weighted family of states. Appropriate interpretations of quantities introduced are discussed and bounds are derived in terms of the total variation and trace distance.Comment: 13 pages, LaTeX, expanded contents, added references and corrected typo

Balanced distribution-energy inequalities and related entropy bounds

Let $A$ be a self-adjoint operator acting over a space $X$ endowed with a partition. We give lower bounds on the energy of a mixed state $\rho$ from its distribution in the partition and the spectral density of $A$. These bounds improve with the refinement of the partition, and generalize inequalities by Li-Yau and Lieb--Thirring for the Laplacian in $\R^n$. They imply an uncertainty principle, giving a lower bound on the sum of the spatial entropy of $\rho$, as seen from $X$, and some spectral entropy, with respect to its energy distribution. On $\R^n$, this yields lower bounds on the sum of the entropy of the densities of $\rho$ and its Fourier transform. A general log-Sobolev inequality is also shown. It holds on mixed states, without Markovian or positivity assumption on $A$.Comment: 21 page

Modulus of convexity for operator convex functions

Given an operator convex function $f(x)$, we obtain an operator-valued lower bound for $cf(x) + (1-c)f(y) - f(cx + (1-c)y)$, $c \in [0,1]$. The lower bound is expressed in terms of the matrix Bregman divergence. A similar inequality is shown to be false for functions that are convex but not operator convex.Comment: 5 pages, change of title. The new version shows that the main result of the original paper cannot be extended to convex functions that are not operator convex