Simulation of quantum random walks using interference of classical field

We suggest a theoretical scheme for the simulation of quantum random walks on a line using beam splitters, phase shifters and photodetectors. Our model enables us to simulate a quantum random walk with use of the wave nature of classical light fields. Furthermore, the proposed set-up allows the analysis of the effects of decoherence. The transition from a pure mean photon-number distribution to a classical one is studied varying the decoherence parameters.Comment: extensively revised version; title changed; to appear on Phys. Rev.

Degradation of pesticides in biobeds

Non-Peer ReviewedPesticides are used to protect crops from unwanted pests thereby increasing food quality and quantity. However, one side-effect of using pesticides is their ability to pollute surface and groundwaters through diffuse (non-localized) or localized (point) sources. Biobeds were introduced in Sweden in 1993 as a means to protect the environment from point source pollution by pesticides arising from farm activities such as filling of sprayers and sprayer rinsate. A biobed is a hole in the ground into which a mixture of straw, compost and topsoil (2:1:1 by volume) is added and cover with a grass layer. The biobed mix creates a favourable environment for containment and microbial degradation of applied pesticides. The objective of this study is to investigate the relationship between active ingredient breakdown and carbon dioxide emission in both topsoil and biobed mix after pesticide application. Results indicate a five-fold reduction in the half-life of 2,4-D in the biobed mix compared to topsoil. Rapid degradation of some sulphonylurea herbicides occurred in the biobed mix despite their known persistence in soil. There was a correlation between active ingredient breakdown and carbon dioxide emission

Entanglement in bipartite generalized coherent states

Entanglement in a class of bipartite generalized coherent states is discussed. It is shown that a positive parameter can be associated with the bipartite generalized coherent states so that the states with equal value for the parameter are of equal entanglement. It is shown that the maximum possible entanglement of 1 bit is attained if the positive parameter equals $\sqrt{2}$. The result that the entanglement is one bit when the relative phase between the composing states is $\pi$ in bipartite coherent states is shown to be true for the class of bipartite generalized coherent states considered.Comment: 10 pages, 4 figures; typos corrected and figures redrawn for better clarit

Quantum quincunx in cavity quantum electrodynamics

Published versio

Electric current circuits in astrophysics

Cosmic magnetic structures have in common that they are anchored in a dynamo, that an external driver converts kinetic energy into internal magnetic energy, that this magnetic energy is transported as Poynting fl ux across the magnetically dominated structure, and that the magnetic energy is released in the form of particle acceleration, heating, bulk motion, MHD waves, and radiation. The investigation of the electric current system is particularly illuminating as to the course of events and the physics involved. We demonstrate this for the radio pulsar wind, the solar flare, and terrestrial magnetic storms

Quantum walks: a comprehensive review

Quantum walks, the quantum mechanical counterpart of classical random walks, is an advanced tool for building quantum algorithms that has been recently shown to constitute a universal model of quantum computation. Quantum walks is now a solid field of research of quantum computation full of exciting open problems for physicists, computer scientists, mathematicians and engineers. In this paper we review theoretical advances on the foundations of both discrete- and continuous-time quantum walks, together with the role that randomness plays in quantum walks, the connections between the mathematical models of coined discrete quantum walks and continuous quantum walks, the quantumness of quantum walks, a summary of papers published on discrete quantum walks and entanglement as well as a succinct review of experimental proposals and realizations of discrete-time quantum walks. Furthermore, we have reviewed several algorithms based on both discrete- and continuous-time quantum walks as well as a most important result: the computational universality of both continuous- and discrete- time quantum walks.Comment: Paper accepted for publication in Quantum Information Processing Journa

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem