Quantum-coherent phase oscillations in synchronization

Recently, several studies have investigated synchronization in quantum-mechanical limit-cycle oscillators. However, the quantum nature of these systems remained partially hidden, since the dynamics of the oscillator's phase was overdamped and therefore incoherent. We show that there exist regimes of underdamped and even quantum-coherent phase motion, opening up new possibilities to study quantum synchronization dynamics. To this end, we investigate the Van der Pol oscillator (a paradigm for a self-oscillating system) synchronized to an external drive. We derive an effective quantum model which fully describes the regime of underdamped phase motion and additionally allows us to identify the quality of quantum coherence. Finally, we identify quantum limit cycles of the phase itself.Comment: 6 pages + Supplemental Materia

Dynamically Generated Synthetic Electric Fields for Photons

Static synthetic magnetic fields give rise to phenomena including the Lorentz force and the quantum Hall effect even for neutral particles, and they have by now been implemented in a variety of physical systems. Moving towards fully dynamical synthetic gauge fields allows, in addition, for backaction of the particles' motion onto the field. If this results in a time-dependent vector potential, conventional electromagnetism predicts the generation of an electric field. Here, we show how synthetic electric fields for photons arise self-consistently due to the nonlinear dynamics in a driven system. Our analysis is based on optomechanical arrays, where dynamical gauge fields arise naturally from phonon-assisted photon tunneling. We study open, one-dimensional arrays, where synthetic magnetic fields are absent. However, we show that synthetic electric fields can be generated dynamically, which, importantly, suppress photon transport in the array. The generation of these fields depends on the direction of photon propagation, leading to a novel mechanism for a photon diode, inducing nonlinear nonreciprocal transport via dynamical synthetic gauge fields.Comment: 12 pages, 5 figures; Fig. 2 and Fig. 3 modified in v2; paragraph "The basic physics behind our results" added in v2; revised introduction including new references in v3; Fig. 1 modified in v3; extended supplementary material in v

Finite-temperature evaluation of the Fermi density operator

A rational expansion of the Fermi density operator is proposed. This approach allows to calculate efficiently physical properties of fermionic systems at finite temperatures without solving an eigenvalue problem. Using N evaluations of the Green's function, the Fermi density operator can be approximated, subject to a given precision, in the energy interval from -A to infinity with A proportional to N. The presented method may become especially useful for electronic structure calculations involving the calculation of charge densities.Comment: 6 pages, 4 Postscript figures, submitted to J. Comp. Phy

Learning Spiking Neural Controllers for In-Silico Navigation Experiments

Artificial neural networks have been employed in many areas of cognitive systems research, ranging from low-levelcontrol tasks to high-level cognition. However, there is only little work on the use of spiking neural networks in these fields.In this project, we developed a virtual environment to explore solving navigation tasks using spiking neural networks. We firstused an existing experimental setup and compared the results to validate the developed environment. An evolutionary approachis used to set the parameters of a spiking neural network controlling a robot to navigate without collisions. In a second set ofexperiments, we trained the network via reinforcement learning which was implemented as a reward-based STDP protocol. Ourresults validate the correctness of the developed virtual environment and demonstrate the usefulness of using such a platform.The virtual environment guarantees the reproducibility of our experiments and can be easily adapted for future research

Exploiting Image-trained CNN Architectures for Unconstrained Video Classification

We conduct an in-depth exploration of different strategies for doing event detection in videos using convolutional neural networks (CNNs) trained for image classification. We study different ways of performing spatial and temporal pooling, feature normalization, choice of CNN layers as well as choice of classifiers. Making judicious choices along these dimensions led to a very significant increase in performance over more naive approaches that have been used till now. We evaluate our approach on the challenging TRECVID MED'14 dataset with two popular CNN architectures pretrained on ImageNet. On this MED'14 dataset, our methods, based entirely on image-trained CNN features, can outperform several state-of-the-art non-CNN models. Our proposed late fusion of CNN- and motion-based features can further increase the mean average precision (mAP) on MED'14 from 34.95% to 38.74%. The fusion approach achieves the state-of-the-art classification performance on the challenging UCF-101 dataset

Asymptotic performance of port-based teleportation

Quantum teleportation is one of the fundamental building blocks of quantum Shannon theory. While ordinary teleportation is simple and efficient, port-based teleportation (PBT) enables applications such as universal programmable quantum processors, instantaneous non-local quantum computation and attacks on position-based quantum cryptography. In this work, we determine the fundamental limit on the performance of PBT: for arbitrary fixed input dimension and a large number $N$ of ports, the error of the optimal protocol is proportional to the inverse square of $N$. We prove this by deriving an achievability bound, obtained by relating the corresponding optimization problem to the lowest Dirichlet eigenvalue of the Laplacian on the ordered simplex. We also give an improved converse bound of matching order in the number of ports. In addition, we determine the leading-order asymptotics of PBT variants defined in terms of maximally entangled resource states. The proofs of these results rely on connecting recently-derived representation-theoretic formulas to random matrix theory. Along the way, we refine a convergence result for the fluctuations of the Schur-Weyl distribution by Johansson, which might be of independent interest.Comment: 68 pages, 4 figures; comments welcome! v2: minor fixes, added plots comparing asymptotic expansions to exact formulas, code available at https://github.com/amsqi/port-base

Kurswertreaktionen auf die Ankündigung von Going Private : Transaktionen am deutschen Kapitalmarkt

Der Beitrag ist wie folgt aufgebaut. In Abschnitt 2 wird zunächst der Begriff des Going Private definiert und anhand kurzer Ausführungen zu den handelnden Akteuren Einblicke in die Funktionsweise des deutschen Going Private-Marktes vermittelt. In Abschnitt 3 wird die zu überprüfende Hypothese abgeleitet, dass eine Going Private-Ankündigung mit einer positiven Kurswertreaktion verbunden ist. Abschnitt 4 legt den Prozess der Stichprobenauswahl offen und liefert einige deskriptive Informationen zur Marktkapitalisierung deutscher Going Private- Unternehmen. Anschließend wird in Abschnitt 5 die verwendete Untersuchungsmethode der Ereignisstudie beschrieben. Im nachfolgenden Abschnitt 6 werden die Untersuchungsergebnisse präsentiert. Dabei wird nicht nur die Hypothese überprüft, dass Going Private- Ankündigungen eine positive Kurswertreaktion hervorrufen. Vielmehr werden auch Aussagen über die Geschwindigkeit der Kursanpassung getroffen. In Abschnitt 7 wird ferner untersucht, inwiefern sich die kumulierten abnormalen Renditen bezüglich spezifischer Charakteristika der Going Private-Transaktionen unterscheiden. Der abschließende Abschnitt 8 fasst die Ergebnisse der Untersuchung zusammen. --