Continuous configuration time-dependent self-consistent field method for polyatomic quantum dynamical problems

A new continuous configuration time-dependent self-consistent field method has been developed to study polyatomic dynamical problems by using the discrete variable representation for the reaction system, and applied to a reaction system coupled to a bath. The method is very efficient because the equations involved are as simple as those in the traditional single configuration approach, and can account for the correlations between the reaction system and bath modes rather well. (C) American Institute of Physics.

Dynamical Systems in the Sensorimotor Loop: On the Interrelation Between Internal and External Mechanisms of Evolved Robot Behavior

Huelse, M., Wischmann, S., Manoonpong, P., Twickel, A.v., Pasemann, F.: Dynamical Systems in the Sensorimotor Loop: On the Interrelation Between Internal and External Mechanisms of Evolved Robot Behavior. In: M. Lungarella, F. Iida, J. Bongard, R. Pfeifer (Eds.) 50 Years of Artificial Intelligence, LNCS 4850, Springer, 186 - 195, 2007.

The acquisition of phonology in a dynamical model of human information processing: a preliminary account

The problem of the acquisition of first language phonology is dealt with within the general information-processing perspective. In this sense, language acquisition is viewed as a process of biologically founded pattern formation due to information exchanges between an adult and a child. Moreover, the process is cognitive in that the child, as a goal-seeking and error correcting individual, undertakes an intricate task of compressing a huge variety of linguistic stimuli in order to build an effective information code. It is further assumed that the basic mechanism which leads to the establishment of fully articulate linguistic ability is that of simulation. The mechanism works through a compression of a set of initial variables (i.e. initial conditions) into a minimum length algorithm and a subsequent construction of an integrated system of language-specific attractors. It is only then that the language user is capable of participating in an information transaction in a fully developed manner.

Dynamical Instabilities and Deterministic Chaos in Ballistic Electron Motion in Semiconductor Superlattices

We consider the motion of ballistic electrons within a superlattice miniband under the influence of an alternating electric field. We show that the interaction of electrons with the self-consistent electromagnetic field generated by the electron current may lead to the transition from regular to chaotic dynamics. We estimate the conditions for the experimental observation of this deterministic chaos and discuss the similarities of the superlattice system with the other condensed matter and quantum optical systems.

Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Simultaneous Learning of Non-Linear Manifold and Dynamical Models for High-Dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Dynamical and steady-state requirements for effective entanglement transfer

Entanglement transfer processes from a continuous-variable (CV) to a qubit system have primary importance in quantum information processing due to some practical implications in the realization of a quantum network. A CV system can propagate entanglement while a qubit system is easy to manipulate. We study conditions to entangle two atomic qubits with a two-mode squeezed field driving two cavities containing the atoms.

Dynamical entanglement transfer for quantum-information networks

A key element in the architecture of a quantum-information processing network is a reliable physical interface between fields and qubits. We study a process of entanglement transfer engineering, where two remote qubits respectively interact with an entangled two-mode continuous-variable (CV) field. We quantify the entanglement induced in the qubit state at the expenses of the loss of entanglement in the CV system. We discuss the range of mixed entangled states which can be obtained with this setup. Furthermore, we suggest a protocol to determine the residual entangling power of the light fields inferring, thus, the entanglement left in the field modes which, after the interaction, are no longer in a Gaussian state. Two different setups are proposed: a cavity-QED system and an interface between superconducting qubits and field modes. We address in detail the practical difficulties inherent in these two proposals, showing that the latter is promising in many aspects.

Dynamical simulation of inelastic quantum transport

A method for correlated quantum electron-ion dynamics is combined with a method for electronic open boundaries to simulate in real time the heating, and eventual equilibration at an elevated vibrational energy, of a quantum ion under current flow in an atomic wire, together with the response of the current to the ionic heating. The method can also be used to extract inelastic current voltage corrections under steady-state conditions. However, in its present form the open-boundary method contains an approximation that limits the resolution of current-voltage features. The results of the simulations are tested against analytical results from scattering theory. Directions for the improvement of the method are summarized at the end.

Dynamical Equations For The Period Vectors In A Periodic System Under Constant External Stress

The purpose of this paper is to derive the dynamical equations for the period vectors of a periodic system under constant external stress. The explicit starting point is Newton’s second law applied to halves of the system. Later statistics over indistinguishable translated states and forces associated with transport of momentum are applied to the resulting dynamical equations. In the final expressions, the period vectors are driven by the imbalance between internal and external stresses. The internal stress is shown to have both full interaction and kinetic-energy terms.

The 90-degree problem of scattering theory revisited: dynamical turbulence effects versus nonlinear effects

The 90° problem of cosmic-ray transport theory is revisited in this paper. By using standard forms of the wave spectrum in the solar wind, the pitch-angle Fokker–Planck coefficient and the parallel mean free path are computed for different resonance functions. A critical comparison is made of the strength of 90° scattering due to plasmawave effects, dynamical turbulence effects and nonlinear effects. It is demonstrated that, only for low-energy cosmic particles, dynamical effects are usually dominant. The novel results presented here are essential for an effective comparison of heliospheric observations for the parallel mean free path with the theoretical model results.

Time operators and shift representation of dynamical systems

We introduce and characterise time operators for unilateral shifts and exact endomorphisms. The associated shift representation of evolution is related to the spectral representation by a generalized Fourier transform. We illustrate the results for a simple exact system, namely the Renyi map.