DMRG Simulation of the SU(3) AFM Heisenberg Model

We analyze the antiferromagnetic $\text{SU}(3)$ Heisenberg chain by means of the Density Matrix Renormalization Group (DMRG). The results confirm that the model is critical and the computation of its central charge and the scaling dimensions of the first excited states show that the underlying low energy conformal field theory is the $\text{SU}(3)_1$ Wess-Zumino-Novikov-Witten model.Comment: corrections and improvements adde

Exact Results on Dynamical Decoupling by π\pi-Pulses in Quantum Information Processes

The aim of dynamical decoupling consists in the suppression of decoherence by appropriate coherent control of a quantum register. Effectively, the interaction with the environment is reduced. In particular, a sequence of $\pi$ pulses is considered. Here we present exact results on the suppression of the coupling of a quantum bit to its environment by optimized sequences of $\pi$ pulses. The effect of various cutoffs of the spectral density of the environment is investigated. As a result we show that the harder the cutoff is the better an optimized pulse sequence can deal with it. For cutoffs which are neither completely hard nor very soft we advocate iterated optimized sequences.Comment: 12 pages and 3 figure

Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system

Abstract A fully non-linear analysis of forcing influences on temperatures is performed in the climate system by means of neural network modelling. Two case studies are investigated, in order to establish the main factors that drove the temperature behaviour at both global and regional scales in the last 140 years. In particular, our neural network model shows the ability to catch non-linear relationships among these variables and to reconstruct temperature records with a high degree of accuracy. In this framework, we clearly show the need of including anthropogenic inputs for explaining the temperature behaviour at global scale and recognise the role of El Nino southern oscillation for catching the inter-annual variability of temperature data. Furthermore, we analyse the relative influence of global forcing and a regional circulation pattern in determining the winter temperatures in Central England, showing that the North Atlantic oscillation represents the driven element in this case study. Our modelling activity and results can be very useful for simple assessments of relationships in the complex climate system and for identifying the fundamental elements leading to a successful downscaling of atmosphere–ocean general circulation models

Efficient Coherent Control by Optimized Sequences of Pulses of Finite Duration

Reliable long-time storage of arbitrary quantum states is a key element for quantum information processing. In order to dynamically decouple a spin or quantum bit from a dephasing environment, we introduce an optimized sequence of $N$ control pulses of finite durations \tau\pp and finite amplitudes. The properties of this sequence of length $T$ stem from a mathematically rigorous derivation. Corrections occur only in order $T^{N+1}$ and \tau\pp^3 without mixed terms such as T^N\tau\pp or T^N\tau\pp^2. Based on existing experiments, a concrete setup for the verification of the properties of the advocated realistic sequence is proposed.Comment: 8 pages, 1 figur

A neural-network approach to radon short-range forecasting from concentration time series

The relevance of particulate radon progeny measurements for an estimation of the mixing height was recently established. Here, an attempt at a shortrange forecast of radon concentration is presented using a neural-network model applied at a 2-hour based time series. This forecasting activity leads to useful predictions of the mixing height during stability conditions

Scaling of excitations in dimerized and frustrated spin-1/2 chains

We study the finite-size behavior of the low-lying excitations of spin-1/2 Heisenberg chains with dimerization and next-to-nearest neighbors interaction, J_2. The numerical analysis, performed using density-matrix renormalization group, confirms previous exact diagonalization results, and shows that, for different values of the dimerization parameter \delta, the elementary triplet and singlet excitations present a clear scaling behavior in a wide range of \ell=L/\xi (where L is the length of the chain and \xi is the correlation length). At J_2=J_2c, where no logarithmic corrections are present, we compare the numerical results with finite-size predictions for the sine-Gordon model obtained using Luscher's theory. For small \delta we find a very good agreement for \ell > 4 or 7 depending on the excitation considered.Comment: 4 pages, 4 eps figures, RevTeX 4 class, same version as in PR

Hidden order in bosonic gases confined in one dimensional optical lattices

We analyze the effective Hamiltonian arising from a suitable power series expansion of the overlap integrals of Wannier functions for confined bosonic atoms in a 1d optical lattice. For certain constraints between the coupling constants, we construct an explicit relation between such an effective bosonic Hamiltonian and the integrable spin-$S$ anisotropic Heisenberg model. Therefore the former results to be integrable by construction. The field theory is governed by an anisotropic non linear $\sigma$-model with singlet and triplet massive excitations; such a result holds also in the generic non-integrable cases. The criticality of the bosonic system is investigated. The schematic phase diagram is drawn. Our study is shedding light on the hidden symmetry of the Haldane type for one dimensional bosons.Comment: 5 pages; 1 eps figure. Revised version, to be published in New. J. Phy

The effect of time-jitter in equispaced sampling wattmeters

This paper evaluates the effect of time-jitters in the equally spaced sampling wattmeters on the hypothesis of jitters uncorrelated with the input signals. The general case of two distinct time-jitters is considered, one common to the two channels and the other different for each one of them. The performance of the wattmeter has been evaluated by considering the asymptotic statistic parameters of the output. It has been shown that the different time-jitters introduce a bias and that both time-jitters contribute to the variance of the output. In any case, time-jitters introduce further bandwidth limitations which must be taken into account in the wattmeter accuracy evaluation. The theoretical results have been compared with simulated and experimental findings. Experimental results were obtained with a prototype in which both common and different time-jitters were separately added to the equally spaced sampling instants of the two input channels. In both cases, all the results were in good agreement with theoretical expectation

Performance function for time-jittered equispaced sampling wattmeters

This paper evaluates the effect of time-jitter in the equally spaced sampling wattmeters on the hypothesis of equal effects in the two channels and a jitter uncorrelated with the input signals. It is shown that time-jitter, which is a random fluctuation with respect to the nominal sampling time, introduces a frequency limitation which is evaluated together with that due to the sampling strategy and filtering algorithm. The theoretical results are compared with the simulated one

Energy-based predictions in Lorenz system by a unified formalism and neural network modelling

In the framework of a unified formalism for Kolmogorov-Lorenz systems, predictions of times of regime transitions in the classical Lorenz model can be successfully achieved by considering orbits characterised by energy or Casimir maxima. However, little uncertainties in the starting energy usually lead to high uncertainties in the return energy, so precluding the chance of accurate multi-step forecasts. In this paper, the problem of obtaining good forecasts of maximum return energy is faced by means of a neural network model. The results of its application show promising results