226 research outputs found
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
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