137 research outputs found
Random matrix techniques in quantum information theory
The purpose of this review article is to present some of the latest
developments using random techniques, and in particular, random matrix
techniques in quantum information theory. Our review is a blend of a rather
exhaustive review, combined with more detailed examples -- coming from research
projects in which the authors were involved. We focus on two main topics,
random quantum states and random quantum channels. We present results related
to entropic quantities, entanglement of typical states, entanglement
thresholds, the output set of quantum channels, and violations of the minimum
output entropy of random channels
Spectra of sparse non-Hermitian random matrices: an analytical solution
We present the exact analytical expression for the spectrum of a sparse
non-Hermitian random matrix ensemble, generalizing two classical results in
random-matrix theory: this analytical expression forms a non-Hermitian version
of the Kesten-Mckay law as well as a sparse realization of Girko's elliptic
law. Our exact result opens new perspectives in the study of several physical
problems modelled on sparse random graphs. In this context, we show
analytically that the convergence rate of a transport process on a very sparse
graph depends upon the degree of symmetry of the edges in a non-monotonous way.Comment: 5 pages, 5 figures, 12 pages supplemental materia
Eigenvalues and Singular Values of Products of Rectangular Gaussian Random Matrices
We derive exact analytic expressions for the distributions of eigenvalues and
singular values for the product of an arbitrary number of independent
rectangular Gaussian random matrices in the limit of large matrix dimensions.
We show that they both have power-law behavior at zero and determine the
corresponding powers. We also propose a heuristic form of finite size
corrections to these expressions which very well approximates the distributions
for matrices of finite dimensions.Comment: 13 pages, 3 figure
Eigenvalue distributions for some correlated complex sample covariance matrices
The distributions of the smallest and largest eigenvalues for the matrix
product , where is an complex Gaussian matrix
with correlations both along rows and down columns, are expressed as determinants. In the case of correlation along rows, these expressions are
computationally more efficient than those involving sums over partitions and
Schur polynomials reported recently for the same distributions.Comment: 11 page
Two remarks on generalized entropy power inequalities
This note contributes to the understanding of generalized entropy power
inequalities. Our main goal is to construct a counter-example regarding
monotonicity and entropy comparison of weighted sums of independent identically
distributed log-concave random variables. We also present a complex analogue of
a recent dependent entropy power inequality of Hao and Jog, and give a very
simple proof.Comment: arXiv:1811.00345 is split into 2 papers, with this being on
Spectra of Empirical Auto-Covariance Matrices
We compute spectra of sample auto-covariance matrices of second order
stationary stochastic processes. We look at a limit in which both the matrix
dimension and the sample size used to define empirical averages
diverge, with their ratio kept fixed. We find a remarkable scaling
relation which expresses the spectral density of sample
auto-covariance matrices for processes with dynamical correlations as a
continuous superposition of appropriately rescaled copies of the spectral
density for a sequence of uncorrelated random
variables. The rescaling factors are given by the Fourier transform
of the auto-covariance function of the stochastic process. We also obtain a
closed-form approximation for the scaling function
. This depends on the shape parameter , but
is otherwise universal: it is independent of the details of the underlying
random variables, provided only they have finite variance. Our results are
corroborated by numerical simulations using auto-regressive processes.Comment: 4 pages, 2 figure
Statistical mechanical analysis of the linear vector channel in digital communication
A statistical mechanical framework to analyze linear vector channel models in
digital wireless communication is proposed for a large system. The framework is
a generalization of that proposed for code-division multiple-access systems in
Europhys. Lett. 76 (2006) 1193 and enables the analysis of the system in which
the elements of the channel transfer matrix are statistically correlated with
each other. The significance of the proposed scheme is demonstrated by
assessing the performance of an existing model of multi-input multi-output
communication systems.Comment: 15 pages, 2 figure
Sparse random matrices and vibrational spectra of amorphous solids
A random matrix approach is used to analyze the vibrational properties of
amorphous solids. We investigated a dynamical matrix M=AA^T with non-negative
eigenvalues. The matrix A is an arbitrary real NxN sparse random matrix with n
independent non-zero elements in each row. The average values =0 and
dispersion =V^2 for all non-zero elements. The density of vibrational
states g(w) of the matrix M for N,n >> 1 is given by the Wigner quarter circle
law with radius independent of N. We argue that for n^2 << N this model can be
used to describe the interaction of atoms in amorphous solids. The level
statistics of matrix M is well described by the Wigner surmise and corresponds
to repulsion of eigenfrequencies. The participation ratio for the major part of
vibrational modes in three dimensional system is about 0.2 - 0.3 and
independent of N. Together with term repulsion it indicates clearly to the
delocalization of vibrational excitations. We show that these vibrations spread
in space by means of diffusion. In this respect they are similar to diffusons
introduced by Allen, Feldman, et al., Phil. Mag. B 79, 1715 (1999) in amorphous
silicon. Our results are in a qualitative and sometimes in a quantitative
agreement with molecular dynamic simulations of real and model glasses.Comment: 24 pages, 7 figure
Random matrix ensembles of time correlation matrices to analyze visual lifelogs
Visual lifelogging is the process of automatically recording images and other sensor data for the purpose of aiding memory recall. Such lifelogs are usually created using wearable cameras. Given the vast amount of images that are maintained in a visual lifelog, it is a significant challenge for users to deconstruct a sizeable collection of images into meaningful events. In this paper, random matrix theory (RMT) is applied to a cross-correlation matrix C, constructed using SenseCam lifelog data streams to identify such events. The analysis reveals a number of eigenvalues that deviate from the spectrum suggested by RMT. The components of the deviating eigenvectors are found to correspond to âdistinct significant eventsâ in the visual lifelogs. Finally, the cross-correlation matrix C is cleaned by separating the noisy part from the non-noisy part. Overall, the RMT technique is shown to be useful to detect major events in SenseCam images
Perceptron capacity revisited: classification ability for correlated patterns
In this paper, we address the problem of how many randomly labeled patterns
can be correctly classified by a single-layer perceptron when the patterns are
correlated with each other. In order to solve this problem, two analytical
schemes are developed based on the replica method and Thouless-Anderson-Palmer
(TAP) approach by utilizing an integral formula concerning random rectangular
matrices. The validity and relevance of the developed methodologies are shown
for one known result and two example problems. A message-passing algorithm to
perform the TAP scheme is also presented
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