11,418 research outputs found
Measuring the degree of unitarity for any quantum process
Quantum processes can be divided into two categories: unitary and non-unitary
ones. For a given quantum process, we can define a \textit{degree of the
unitarity (DU)} of this process to be the fidelity between it and its closest
unitary one. The DU, as an intrinsic property of a given quantum process, is
able to quantify the distance between the process and the group of unitary
ones, and is closely related to the noise of this quantum process. We derive
analytical results of DU for qubit unital channels, and obtain the lower and
upper bounds in general. The lower bound is tight for most of quantum
processes, and is particularly tight when the corresponding DU is sufficiently
large. The upper bound is found to be an indicator for the tightness of the
lower bound. Moreover, we study the distribution of DU in random quantum
processes with different environments. In particular, The relationship between
the DU of any quantum process and the non-markovian behavior of it is also
addressed.Comment: 7 pages, 2 figure
Assessing Protein Conformational Sampling Methods Based on Bivariate Lag-Distributions of Backbone Angles
Despite considerable progress in the past decades, protein structure prediction remains one of the major unsolved problems in computational biology. Angular-sampling-based methods have been extensively studied recently due to their ability to capture the continuous conformational space of protein structures. The literature has focused on using a variety of parametric models of the sequential dependencies between angle pairs along the protein chains. In this article, we present a thorough review of angular-sampling-based methods by assessing three main questions: What is the best distribution type to model the protein angles? What is a reasonable number of components in a mixture model that should be considered to accurately parameterize the joint distribution of the angles? and What is the order of the local sequence–structure dependency that should be considered by a prediction method? We assess the model fits for different methods using bivariate lag-distributions of the dihedral/planar angles. Moreover, the main information across the lags can be extracted using a technique called Lag singular value decomposition (LagSVD), which considers the joint distribution of the dihedral/planar angles over different lags using a nonparametric approach and monitors the behavior of the lag-distribution of the angles using singular value decomposition. As a result, we developed graphical tools and numerical measurements to compare and evaluate the performance of different model fits. Furthermore, we developed a web-tool (http://www.stat.tamu.edu/∼madoliat/LagSVD) that can be used to produce informative animations
Magnetic Excitations in the High Tc Iron Pnictides
We calculate the expected finite frequency neutron scattering intensity based
on the two-sublattice collinear antiferromagnet found by recent neutron
scattering experiments as well as by theoretical analysis on the iron
oxypnictide LaOFeAs. We consider two types of superexchange couplings between
Fe atoms: nearest-neighbor coupling J1 and next-nearest-neighbor coupling J2.
We show how to distinguish experimentally between ferromagnetic and
antiferromagnetic J1. Whereas magnetic excitations in the cuprates display a
so-called resonance peak at (pi,pi) (corresponding to a saddlepoint in the
magnetic spectrum) which is at a wavevector that is at least close to nesting
Fermi-surface-like structures, no such corresponding excitations exist in the
iron pnictides. Rather, we find saddlepoints near (pi,pi/2) and (0,pi/2)(and
symmetry related points). Unlike in the cuprates, none of these vectors are
close to nesting the Fermi surfaces.Comment: 4 pages, 5 figure
Comment on 'Note on the dog-and-rabbit chase problem in introductory kinematics'
We comment on the recent paper by Yuan Qing-Xin and Du Yin-Xiao (Eur. J.
Phys. 29 (2008) N43-N45).Comment: 2 pages, no figure
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