Feshbach Resonance Induced Fano Interference in Photoassociation

We consider photoassociation from a state of two free atoms when the continuum state is close to a magnetic field induced Feshbach resonance and analyze Fano interference in photoassociation. We show that the minimum in photoassociation profiles characterized by the Fano asymmetry parameter $q$ is independent of laser intensity, while the maximum explicitly depends on laser intensity. We further discuss the possibility of nonlinear Fano effect in photoassociation near a Feshbach resonance.Comment: 6 pages 4 figures, substantial change with new results in version

Entanglement of two distant Bose-Einstein condensates by detection of Bragg-scattered photons

We show that it is possible to generate entanglement between two distant Bose-Einstein condensates by detection of Hanbury Brown-Twiss type correlations in photons Bragg-scattered by the condensates. Upon coincident detection of two photons by two detectors, the projected joint state of two condensates is shown to be non-Gaussian. We verify the existence of entanglement by showing that the partially transposed state is negative. Further we use the inequality in terms of higher order moments to confirm entanglement. Our proposed scheme can be generalized for multiple condensates and also for spinor condensates with Bragg scattering of polarized light with the latter capable of producing hyper entanglement.Comment: 9 pages, 5 figure

Genetic programming approaches to learning fair classifiers

Society has come to rely on algorithms like classifiers for important decision making, giving rise to the need for ethical guarantees such as fairness. Fairness is typically defined by asking that some statistic of a classifier be approximately equal over protected groups within a population. In this paper, current approaches to fairness are discussed and used to motivate algorithmic proposals that incorporate fairness into genetic programming for classification. We propose two ideas. The first is to incorporate a fairness objective into multi-objective optimization. The second is to adapt lexicase selection to define cases dynamically over intersections of protected groups. We describe why lexicase selection is well suited to pressure models to perform well across the potentially infinitely many subgroups over which fairness is desired. We use a recent genetic programming approach to construct models on four datasets for which fairness constraints are necessary, and empirically compare performance to prior methods utilizing game-theoretic solutions. Methods are assessed based on their ability to generate trade-offs of subgroup fairness and accuracy that are Pareto optimal. The result show that genetic programming methods in general, and random search in particular, are well suited to this task.Comment: 9 pages, 7 figures. GECCO 202

Suppression of power-broadening in strong-coupling photoassociation in the presence of a Feshbach resonance

Photoassociation (PA) spectrum in the presence of a magnetic Feshbach resonance is analyzed. Nonperturbative solution of the problem yields analytical expressions for PA linewidth and shift which are applicable for arbitrary PA laser intensity and magnetic field tuning of Feshbach Resonance. We show that by tuning magnetic field close to Fano minimum, it is possible to suppress power broadening at increased laser intensities. This occurs due to quantum interference of PA transitions from unperturbed and perturbed continuum. Line narrowing at high laser intensities is accompanied by large spectral shifts. We briefly discuss important consequences of line narrowing in cold collisions.Comment: 12 pages, 5 figure

Entangling Two Bose-Einstein Condensates by Stimulated Bragg Scattering

We propose an experiment for entangling two spatially separated Bose-Einstein condensates by Bragg scattering of light. When Bragg scattering in two condensates is stimulated by a common probe, the resulting quasiparticles in the two condensates get entangled due to quantum communication between the condensates via probe beam. The entanglement is shown to be significant and occurs in both number and quadrature phase variables. We present two methods of detecting the generated entanglement.Comment: 4 pages, Revte

Entangling two Bose Einstein condensates in a double cavity system

We propose a scheme to transfer the quantum state of light fields to the collective density excitations of a Bose Einstein condensate (BEC) in a cavity. This scheme allows to entangle two BECs in a double cavity setup by transferring the quantum entanglement of two light fields produced from a nondegenerate parametric amplifier (NOPA) to the collective density excitations of the two BECs. An EPR state of the collective density excitations can be created by a judicious choice of the system parameters.Comment: 3 figure

Fault Tolerance and Scaling in e-Science Cloud Applications: Observations from the Continuing Development of MODISAzure

It can be natural to believe that many of the traditional issues of scale have been eliminated or at least greatly reduced via cloud computing. That is, if one can create a seemingly wellfunctioning cloud application that operates correctly on small or moderate-sized problems, then the very nature of cloud programming abstractions means that the same application will run as well on potentially significantly larger problems. In this paper, we present our experiences taking MODISAzure, our satellite data processing system built on the Windows Azure cloud computing platform, from the proof-of-concept stage to a point of being able to run on significantly larger problem sizes (e.g., from national-scale data sizes to global-scale data sizes). To our knowledge, this is the longest-running eScience application on the nascent Windows Azure platform. We found that while many infrastructure-level issues were thankfully masked from us by the cloud infrastructure, it was valuable to design additional redundancy and fault-tolerance capabilities such as transparent idempotent task retry and logging to support debugging of user code encountering unanticipated data issues. Further, we found that using a commercial cloud means anticipating inconsistent performance and black-box behavior of virtualized compute instances, as well as leveraging changing platform capabilities over time. We believe that the experiences presented in this paper can help future eScience cloud application developers on Windows Azure and other commercial cloud providers