Photon Subtraction by Many-Body Decoherence

We experimentally and theoretically investigate the scattering of a photonic quantum field from another stored in a strongly interacting atomic Rydberg ensemble. Considering the many-body limit of this problem, we derive an exact solution to the scattering-induced spatial decoherence of multiple stored photons, allowing for a rigorous understanding of the underlying dissipative quantum dynamics. Combined with our experiments, this analysis reveals a correlated coherence-protection process in which the scattering from one excitation can shield all others from spatial decoherence. We discuss how this effect can be used to manipulate light at the quantum level, providing a robust mechanism for single-photon subtraction, and experimentally demonstrate this capability

Hierarchical Gaussian process mixtures for regression

As a result of their good performance in practice and their desirable analytical properties, Gaussian process regression models are becoming increasingly of interest in statistics, engineering and other fields. However, two major problems arise when the model is applied to a large data-set with repeated measurements. One stems from the systematic heterogeneity among the different replications, and the other is the requirement to invert a covariance matrix which is involved in the implementation of the model. The dimension of this matrix equals the sample size of the training data-set. In this paper, a Gaussian process mixture model for regression is proposed for dealing with the above two problems, and a hybrid Markov chain Monte Carlo (MCMC) algorithm is used for its implementation. Application to a real data-set is reported

Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELC

Surrogate Models and Mixtures of Experts in Aerodynamic Performance Prediction for Mission Analysis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140436/1/6.2014-2301.pd

Enhancement of Rydberg-mediated single-photon nonlinearities by electrically tuned Förster resonances

We demonstrate experimentally that Stark-tuned Förster resonances can be used to substantially increase the interaction between individual photons mediated by Rydberg interaction inside an optical medium. This technique is employed to boost the gain of a Rydberg-mediated single-photon transistor and to enhance the non-destructive detection of single Rydberg atoms. Furthermore, our all-optical detection scheme enables high-resolution spectroscopy of two-state Förster resonances, revealing the fine structure splitting of high-n Rydberg states and the non-degeneracy of Rydberg Zeeman substates in finite fields. We show that the ∣50S1/2,48S1/2⟩↔∣49P1/2,48P1/2⟩ pair state resonance in 87Rb enables simultaneously a transistor gain G>100 and all-optical detection fidelity of single Rydberg atoms F>0.8. We demonstrate for the first time the coherent operation of the Rydberg transistor with G>2 by reading out the gate photon after scattering source photons. Comparison of the observed readout efficiency to a theoretical model for the projection of the stored spin wave yields excellent agreement and thus successfully identifies the main decoherence mechanism of the Rydberg transistor

Plasma–liquid interactions: a review and roadmap

Plasma–liquid interactions represent a growing interdisciplinary area of research involving plasma science, fluid dynamics, heat and mass transfer, photolysis, multiphase chemistry and aerosol science. This review provides an assessment of the state-of-the-art of this multidisciplinary area and identifies the key research challenges. The developments in diagnostics, modeling and further extensions of cross section and reaction rate databases that are necessary to address these challenges are discussed. The review focusses on non-equilibrium plasmas