A Search for New Galactic Magnetars in Archival Chandra and XMM-Newton Observations

We present constraints on the number of Galactic magnetars, which we have established by searching for sources with periodic variability in 506 archival Chandra observations and 441 archival XMM-Newton observations of the Galactic plane (|b|<5 degree). Our search revealed four sources with periodic variability on time scales of 200-5000 s, all of which are probably accreting white dwarfs. We identify 7 of 12 known Galactic magnetars, but find no new examples with periods between 5 and 20 s. We convert this non-detection into limits on the total number of Galactic magnetars by computing the fraction of the young Galactic stellar population that was included in our survey. We find that easily-detectable magnetars, modeled after persistent anomalous X-ray pulsars, could have been identified in 5% of the Galactic spiral arms by mass. If we assume there are 3 previously-known examples within our random survey, then there are 59 (+92,-32) in the Galaxy. Transient magnetars in quiescence could have been identified throughout 0.4% of the spiral arms, and the lack of new examples implies that <540 exist in the Galaxy (90% confidence). Similar constraints are found by considering the detectability of transient magnetars in outburst by current and past X-ray missions. For assumed lifetimes of 1e4 yr, we find that the birth rate of magnetars could range between 0.003 and 0.06 per year. Therefore, the birth rate of magnetars is at least 10% of that for normal radio pulsars. The magnetar birth rate could exceed that of radio pulsars, unless the lifetimes of transient magnetars are >1e5 yr. Obtaining better constraints will require wide-field X-ray or radio searches for transient X-ray pulsars similar to XTE J1810--197, AX J1845.0--0250, CXOU J164710.2--455216, and 1E 1547.0-5408.Comment: 16 pages, 10 figures, one with a bit of color. submitted to Ap

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

Generating random density matrices

We study various methods to generate ensembles of random density matrices of a fixed size N, obtained by partial trace of pure states on composite systems. Structured ensembles of random pure states, invariant with respect to local unitary transformations are introduced. To analyze statistical properties of quantum entanglement in bi-partite systems we analyze the distribution of Schmidt coefficients of random pure states. Such a distribution is derived in the case of a superposition of k random maximally entangled states. For another ensemble, obtained by performing selective measurements in a maximally entangled basis on a multi--partite system, we show that this distribution is given by the Fuss-Catalan law and find the average entanglement entropy. A more general class of structured ensembles proposed, containing also the case of Bures, forms an extension of the standard ensemble of structureless random pure states, described asymptotically, as N \to \infty, by the Marchenko-Pastur distribution.Comment: 13 pages in latex with 8 figures include

Random graph states, maximal flow and Fuss-Catalan distributions

For any graph consisting of $k$ vertices and $m$ edges we construct an ensemble of random pure quantum states which describe a system composed of $2m$ subsystems. Each edge of the graph represents a bi-partite, maximally entangled state. Each vertex represents a random unitary matrix generated according to the Haar measure, which describes the coupling between subsystems. Dividing all subsystems into two parts, one may study entanglement with respect to this partition. A general technique to derive an expression for the average entanglement entropy of random pure states associated to a given graph is presented. Our technique relies on Weingarten calculus and flow problems. We analyze statistical properties of spectra of such random density matrices and show for which cases they are described by the free Poissonian (Marchenko-Pastur) distribution. We derive a discrete family of generalized, Fuss-Catalan distributions and explicitly construct graphs which lead to ensembles of random states characterized by these novel distributions of eigenvalues.Comment: 37 pages, 24 figure

Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming

Data-driven modeling of nonlinear dynamical systems often require an expert user to take critical decisions a priori to the identification procedure. Recently an automated strategy for data driven modeling of \textit{single-input single-output} (SISO) nonlinear dynamical systems based on \textit{Genetic Programming} (GP) and \textit{Tree Adjoining Grammars} (TAG) has been introduced. The current paper extends these latest findings by proposing a \textit{multi-input multi-output} (MIMO) TAG modeling framework for polynomial NARMAX models. Moreover we introduce a TAG identification toolbox in Matlab that provides implementation of the proposed methodology to solve multi-input multi-output identification problems under NARMAX noise assumption. The capabilities of the toolbox and the modelling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models

Laws of large numbers for eigenvectors and eigenvalues associated to random subspaces in a tensor product

Given two positive integers $n$ and $k$ and a parameter $t\in (0,1)$, we choose at random a vector subspace $V_{n}\subset \mathbb{C}^{k}\otimes\mathbb{C}^{n}$ of dimension $N\sim tnk$. We show that the set of $k$-tuples of singular values of all unit vectors in $V_n$ fills asymptotically (as $n$ tends to infinity) a deterministic convex set $K_{k,t}$ that we describe using a new norm in $\R^k$. Our proof relies on free probability, random matrix theory, complex analysis and matrix analysis techniques. The main result result comes together with a law of large numbers for the singular value decomposition of the eigenvectors corresponding to large eigenvalues of a random truncation of a matrix with high eigenvalue degeneracy.Comment: v3 changes: minor typographic improvements; accepted versio

Convex optimization of programmable quantum computers

A fundamental model of quantum computation is the programmable quantum gate array. This is a quantum processor that is fed by a program state that induces a corresponding quantum operation on input states. While being programmable, any finite-dimensional design of this model is known to be non-universal, meaning that the processor cannot perfectly simulate an arbitrary quantum channel over the input. Characterizing how close the simulation is and finding the optimal program state have been open questions for the past 20 years. Here, we answer these questions by showing that the search for the optimal program state is a convex optimization problem that can be solved via semi-definite programming and gradient-based methods commonly employed for machine learning. We apply this general result to different types of processors, from a shallow design based on quantum teleportation, to deeper schemes relying on port-based teleportation and parametric quantum circuits