Deconvolution Estimation in Measurement Error Models: The R Package decon

Data from many scientific areas often come with measurement error. Density or distribution function estimation from contaminated data and nonparametric regression with errors in variables are two important topics in measurement error models. In this paper, we present a new software package decon for R, which contains a collection of functions that use the deconvolution kernel methods to deal with the measurement error problems. The functions allow the errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the fast Fourier transform algorithm for density estimation with error-free data to the deconvolution kernel estimation. We discuss the practical selection of the smoothing parameter in deconvolution methods and illustrate the use of the package through both simulated and real examples.

Integrating Neural Networks with a Quantum Simulator for State Reconstruction

We demonstrate quantum many-body state reconstruction from experimental data generated by a programmable quantum simulator, by means of a neural network model incorporating known experimental errors. Specifically, we extract restricted Boltzmann machine (RBM) wavefunctions from data produced by a Rydberg quantum simulator with eight and nine atoms in a single measurement basis, and apply a novel regularization technique to mitigate the effects of measurement errors in the training data. Reconstructions of modest complexity are able to capture one- and two-body observables not accessible to experimentalists, as well as more sophisticated observables such as the R\'enyi mutual information. Our results open the door to integration of machine learning architectures with intermediate-scale quantum hardware.Comment: 15 pages, 13 figure

NOON states from cavity-enhanced down-conversion: High quality and super-resolution

Indistinguishable photons play a key role in quantum optical information technologies. We characterize the output of an ultra-bright photon-pair source using multi-particle tomography [R. B. A. Adamson et al., Phys. Rev. Lett. 98, 043601 (2007)] and separately identify coherent errors, decoherence, and distinguishability. We demonstrate generation of high-quality indistinguishable pairs and polarization NOON states with 99% fidelity to an ideal NOON state. Using a NOON state we perform a super-resolving angular measurement with 90% visibility.Comment: 4 Pages, 5 figure

Techniques for improving the accuracy of cyrogenic temperature measurement in ground test programs

The performance of a sensor is often evaluated by determining to what degree of accuracy a measurement can be made using this sensor. The absolute accuracy of a sensor is an important parameter considered when choosing the type of sensor to use in research experiments. Tests were performed to improve the accuracy of cryogenic temperature measurements by calibration of the temperature sensors when installed in their experimental operating environment. The calibration information was then used to correct for temperature sensor measurement errors by adjusting the data acquisition system software. This paper describes a method to improve the accuracy of cryogenic temperature measurements using corrections in the data acquisition system software such that the uncertainty of an individual temperature sensor is improved from plus or minus 0.90 deg R to plus or minus 0.20 deg R over a specified range

Deconvolution Estimation in Measurement Error Models: The R Package decon

Data from many scientific areas often come with measurement error. Density or distribution function estimation from contaminated data and nonparametric regression with errors in variables are two important topics in measurement error models. In this paper, we present a new software package decon for R, which contains a collection of functions that use the deconvolution kernel methods to deal with the measurement error problems. The functions allow the errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the fast Fourier transform algorithm for density estimation with error-free data to the deconvolution kernel estimation. We discuss the practical selection of the smoothing parameter in deconvolution methods and illustrate the use of the package through both simulated and real examples