Revealing nonclassicality via s-ordered phase-space distribution

Nonclassical states play a crucial role in both theoretical and experimental investigations of quantum optics, and there is a wide interest in characterization and quantification of nonclassicality. By exploiting the freedom of the parameter s in the s-ordered phase-space distribution introduced by Cahill and Glauber [Phys. Rev. 177, 1882(1969)], we develop a method to reveal and quantify optical nonclassicality via the divided difference of the s-ordered phase-space distribution. Our approach y...     

Calculation of Feynman loop integration and phase-space integration via auxiliary mass flow

We extend the auxiliary-mass-flow(AMF) method originally developed for Feynman loop integration to calculate integrals which also involve phase-space integration.The flow of the auxiliary mass from the boundary(∞) to the physical point(0+) is obtained by numerically solving differential equations with respective to the auxiliary mass.For problems with two or more kinematical invariants,the AMF method can be combined with the traditional differential-equation method,providing systematic boundary conditions and a highly nontrivial self-consistency check.The method is described in detail using a pedagogical example of e+e-→γ*→tt+X at NNLO.We show that the AMF method can systematically and efficiently calculate integrals to high precision.     

Transformations of measures via their generalized densities

In this note we describe algorithms for obtaining formulae for transformations of measures on infinite dimensional topological vector spaces or manifolds, generated by transformations of the domains of the measures and by transformations of the range.     

Entropy computing via integration over fractal measures

We discuss the properties of invariant measures corresponding to iterated function systems (IFSs) with place-dependent probabilities and compute their Renyi entropies, generalized dimensions, and multifractal spectra. It is shown that with certain dynamical systems, one can associate the corresponding IFSs in such a way that their generalized entropies are equal. This provides a new method of computing entropy for some classical and quantum dynamical systems. Numerical techniques are based on integration over the fractal measures. (c) 2000 American Institute of Physics.     

Unified theory of model reduction via Gleason measures

Previous work on the stochastic realization and approximation problem has cast this problem in the framework of theRV-coefficient, a measure of correlation recently introduced in the multivariate statistical literature. This allowed the introduction of a common measure for the goodness of fit for the different realization algorithms. This paper explores the deeper geometrical and logical foundation for this common measure in a unified theory for the data-driven and the exact covariance approaches.     

Evaluation of failure probability via surrogate models

Evaluation of failure probability of a given system requires sampling of the system response and can be computationally expensive. Therefore it is desirable to construct an accurate surrogate model for the system response and subsequently to sample the surrogate model. In this paper we discuss the properties of this approach. We demonstrate that the straightforward sampling of a surrogate model can lead to erroneous results, no matter how accurate the surrogate model is. We then propose a hybrid approach by sampling both the surrogate model in a “large” portion of the probability space and the original system in a “small” portion. The resulting algorithm is significantly more efficient than the traditional sampling method, and is more accurate and robust than the straightforward surrogate model approach. Rigorous convergence proof is established for the hybrid approach, and practical implementation is discussed. Numerical examples are provided to verify the theoretical findings and demonstrate the efficiency gain of the approach.     

Thermal heat reservoirs via Gauss' principle of least constraint; Dissipation,chaos, and phase-space dimensionality loss in one-dimensional chains

We use Gauss' principle of least constraint to impose different kinetic temperatures on the two halves of a periodic one-dimensional chain. The thermodynamic result is heat flow, as predicted by the Second Law of Thermodynamics. The statistical-mechanical result can be either a phase-space limit cycle or a strange attractor, depending on the chain length and the size of the temperature difference. We document the sensitivity of the Lyapunov spectrum and the underlying phase-space topology by varying the chain length and the size of the kinetic-temperature difference.     

利用非共振Jaynes-Cummings模型构建通用原子克隆机

利用耦合多原子同时与单模腔场的非共振相互作用,提出了一个1→2的通用量子克隆机方案.该方案装置简单,仅需三个单模腔场和一个经典场,且在制备过程中腔场可始终处于真空态.也分析了该方案在实验上的可行性.     

Detecting and quantifying temporal correlations in stochastic resonance via information theory measures

We show that Information Theory quantifiers are suitable tools for detecting and for quantifying noise-induced temporal correlations in stochastic resonance phenomena. We use the Bandt & Pompe (BP) method [Phys. Rev. Lett. 88, 174102 (2002)] to define a probability distribution, P, that fully characterizes temporal correlations. The BP method is based on a comparison of neighboring values, and here is applied to the temporal sequence of residence-time intervals generated by the paradigmatic model of a Brownian particle in a sinusoidally modulated bistable potential. The probability distribution P generated via the BP method has associated a normalized Shannon entropy, H[P], and a statistical complexity measure, C[P], which is defined as proposed by Rosso et al. [Phys. Rev. Lett. 99, 154102 (2007)]. The statistical complexity quantifies not only randomness but also the presence of correlational structures, the two extreme circumstances of maximum knowledge (“perfect order") and maximum ignorance (“complete randomness") being regarded an “trivial", and in consequence, having complexity C = 0. We show that both, H and C, display resonant features as a function of the noise intensity, i.e., for an optimal level of noise the entropy displays a minimum and the complexity, a maximum. This resonant behavior indicates noise-enhanced temporal correlations in the sequence of residence-time intervals. The methodology proposed here has great potential for the precise detection of subtle signatures of noise-induced temporal correlations in real-world complex signals.     

Implementing a Universal Quantum Cloning Machine via Adiabatic Evolution in Ion-Trap System

A scheme for the realization of a universal quantum cloning machine is proposed in this paper. The present protocol does not need the vibrational mode to act as the memory and it is robust against small changes of experimental parameters due to adiabatic passages. Furthermore, the scheme may be realized based on current technology.     

Unsupervised Recognition of Informative Features via Tensor Network Machine Learning and Quantum Entanglement Variations

Given an image of a white shoe drawn on a blackboard, how are the white pixels deemed(say by human minds)to be informative for recognizing the shoe without any labeling information on the pixels? Here we investigate such a “white shoe” recognition problem from the perspective of tensor network(TN) machine learning and quantum entanglement. Utilizing a generative TN that captures the probability distribution of the features as quantum amplitudes, we propose an unsupervised recognition scheme of i...     

Phase synchronization between solar radiation and wind speed data from some locations across Nigeria via nonlinear recurrence measures

In this paper, we investigate phase synchronization between the solar radiation and wind speed data from different stations across Nigeria, located within latitude 3° and 14°N. The linear correlation and recurrence techniques are used to investigate the linear and nonlinear relationship, respectively. The results of the nonlinear relationship, using Cross recurrence plot (CRP), show that the underlying dynamics of the two sets of data are characterized by nonlinearity, nonstationarity and deterministic chaos. Furthermore, Correlation between probabilities of recurrence (CPR) is used to quantify the degree of synchronization between the two meteorological parameters during dry and wet seasons. High [low] CPR values, which indicate strong [weak] synchronization are obtained for dry [wet] season in both the northern and southern regions. However, for each season the CPR values for northern region are higher than the corresponding values for the southern region. This may be due to strong [weak] coupling between the two meteorological parameters which is attributed to strong [weak] effect of west African monsoon during dry [wet] season.