Spin squeezing and precision probing with light and samples of atoms in the gaussian approximation

We consider an ensemble of trapped atoms interacting with a continuous wave laser field. For sufficiently polarized atoms and for a polarized light field, we may approximate the non-classical components of the collective spin angular momentum operator for the atoms and the Stokes vectors of the field by effective position and momentum variables for which we assume a gaussian state. Within this approximation, we present a theory for the squeezing of the atomic spin by polarization rotation measurements on the probe light. We derive analytical expressions for the squeezing with and without inclusion of the noise effects introduced by atomic decay and by photon absorption. The theory is readily adapted to the case of inhomogeneous light-atom coupling [A. Kuzmich and T.A.B. Kennedy, Phys. Rev. Lett. Vol. 92, 030407 (2004)]. As a special case, we show how to formulate the theory for an optically thick sample by slicing the gas into pieces each having only small photon absorption probability. Our analysis of a realistic probing and measurement scheme shows that it is the maximally squeezed component of the atomic gas that determines the accuracy of the measurement.Comment: 12 pages, 5 figure

Magnetometry with entangled atomic samples

We present a theory for the estimation of a scalar or a vector magnetic field by its influence on an ensemble of trapped spin polarized atoms. The atoms interact off-resonantly with a continuous laser field, and the measurement of the polarization rotation of the probe light, induced by the dispersive atom-light coupling, leads to spin-squeezing of the atomic sample which enables an estimate of the magnetic field which is more precise than that expected from standard counting statistics. For polarized light and polarized atoms, a description of the non-classical components of the collective spin angular momentum for the atoms and the collective Stokes vectors of the light-field in terms of effective gaussian position and momentum variables is practically exact. The gaussian formalism describes the dynamics of the system very effectively and accounts explicitly for the back-action on the atoms due to measurement and for the estimate of the magnetic field. Multi-component magnetic fields are estimated by the measurement of suitably chosen atomic observables and precision and efficiency is gained by dividing the atomic gas in two or more samples which are entangled by the dispersive atom-light interaction.Comment: 8 pages, 11 figure

Errors in quantum optimal control and strategy for the search of easily implementable control pulses

We introduce a new approach to assess the error of control problems we aim to optimize. The method offers a strategy to define new control pulses that are not necessarily optimal but still able to yield an error not larger than some fixed a priori threshold, and therefore provide control pulses that might be more amenable for an experimental implementation. The formalism is applied to an exactly solvable model and to the Landau-Zener model, whose optimal control problem is solvable only numerically. The presented method is of importance for applications where a high degree of controllability of the dynamics of quantum systems is required.Comment: 13 pages, 3 figure

Sensitivity optimization in quantum parameter estimation

We present a general framework for sensitivity optimization in quantum parameter estimation schemes based on continuous (indirect) observation of a dynamical system. As an illustrative example, we analyze the canonical scenario of monitoring the position of a free mass or harmonic oscillator to detect weak classical forces. We show that our framework allows the consideration of sensitivity scheduling as well as estimation strategies for non-stationary signals, leading us to propose corresponding generalizations of the Standard Quantum Limit for force detection.Comment: 15 pages, RevTe

Nonlinear Semi-Analytic Methods for Trajectory Estimation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76298/1/AIAA-29106-621.pd

Interactions between downslope flows and a developing cold-air pool

A numerical model has been used to characterize the development of a region of enhanced cooling in an alpine valley with a width of order (Formula presented.) km, under decoupled stable conditions. The region of enhanced cooling develops largely as a region of relatively dry air which partitions the valley atmosphere dynamics into two volumes, with airflow partially trapped within the valley by a developing elevated inversion. Complex interactions between the region of enhanced cooling and the downslope flows are quantified. The cooling within the region of enhanced cooling and the elevated inversion is almost equally partitioned between radiative and dynamic effects. By the end of the simulation, the different valley atmospheric regions approach a state of thermal equilibrium with one another, though this cannot be said of the valley atmosphere and its external environment.Peer reviewe

Information, disturbance and Hamiltonian quantum feedback control

We consider separating the problem of designing Hamiltonian quantum feedback control algorithms into a measurement (estimation) strategy and a feedback (control) strategy, and consider optimizing desirable properties of each under the minimal constraint that the available strength of both is limited. This motivates concepts of information extraction and disturbance which are distinct from those usually considered in quantum information theory. Using these concepts we identify an information trade-off in quantum feedback control.Comment: 13 pages, multicol Revtex, 2 eps figure

Building robust prediction models for defective sensor data using Artificial Neural Networks

Predicting the health of components in complex dynamic systems such as an automobile poses numerous challenges. The primary aim of such predictive systems is to use the high-dimensional data acquired from different sensors and predict the state-of-health of a particular component, e.g., brake pad. The classical approach involves selecting a smaller set of relevant sensor signals using feature selection and using them to train a machine learning algorithm. However, this fails to address two prominent problems: (1) sensors are susceptible to failure when exposed to extreme conditions over a long periods of time; (2) sensors are electrical devices that can be affected by noise or electrical interference. Using the failed and noisy sensor signals as inputs largely reduce the prediction accuracy. To tackle this problem, it is advantageous to use the information from all sensor signals, so that the failure of one sensor can be compensated by another. In this work, we propose an Artificial Neural Network (ANN) based framework to exploit the information from a large number of signals. Secondly, our framework introduces a data augmentation approach to perform accurate predictions in spite of noisy signals. The plausibility of our framework is validated on real life industrial application from Robert Bosch GmbH.Comment: 16 pages, 7 figures. Currently under review. This research has obtained funding from the Electronic Components and Systems for European Leadership (ECSEL) Joint Undertaking, the framework programme for research and innovation Horizon 2020 (2014-2020) under grant agreement number 662189-MANTIS-2014-

Efficient low-order approximation of first-passage time distributions

We consider the problem of computing first-passage time distributions for reaction processes modelled by master equations. We show that this generally intractable class of problems is equivalent to a sequential Bayesian inference problem for an auxiliary observation process. The solution can be approximated efficiently by solving a closed set of coupled ordinary differential equations (for the low-order moments of the process) whose size scales with the number of species. We apply it to an epidemic model and a trimerisation process, and show good agreement with stochastic simulations.Comment: 5 pages, 3 figure