5,318 research outputs found
Sampled-data design for robust control of a single qubit
This paper presents a sampled-data approach for the robust control of a
single qubit (quantum bit). The required robustness is defined using a sliding
mode domain and the control law is designed offline and then utilized online
with a single qubit having bounded uncertainties. Two classes of uncertainties
are considered involving the system Hamiltonian and the coupling strength of
the system-environment interaction. Four cases are analyzed in detail including
without decoherence, with amplitude damping decoherence, phase damping
decoherence and depolarizing decoherence. Sampling periods are specifically
designed for these cases to guarantee the required robustness. Two sufficient
conditions are presented for guiding the design of unitary control for the
cases without decoherence and with amplitude damping decoherence. The proposed
approach has potential applications in quantum error-correction and in
constructing robust quantum gates.Comment: 33 pages, 5 figures, minor correction
QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
Over the past decade, machine learning techniques have revolutionized how
research is done, from designing new materials and predicting their properties
to assisting drug discovery to advancing cybersecurity. Recently, we added to
this list by showing how a machine learning algorithm (a so-called learner)
combined with an optimization routine can assist experimental efforts in the
realm of tuning semiconductor quantum dot (QD) devices. Among other
applications, semiconductor QDs are a candidate system for building quantum
computers. The present-day tuning techniques for bringing the QD devices into a
desirable configuration suitable for quantum computing that rely on heuristics
do not scale with the increasing size of the quantum dot arrays required for
even near-term quantum computing demonstrations. Establishing a reliable
protocol for tuning that does not rely on the gross-scale heuristics developed
by experimentalists is thus of great importance. To implement the machine
learning-based approach, we constructed a dataset of simulated QD device
characteristics, such as the conductance and the charge sensor response versus
the applied electrostatic gate voltages. Here, we describe the methodology for
generating the dataset, as well as its validation in training convolutional
neural networks. We show that the learner's accuracy in recognizing the state
of a device is ~96.5 % in both current- and charge-sensor-based training. We
also introduce a tool that enables other researchers to use this approach for
further research: QFlow lite - a Python-based mini-software suite that uses the
dataset to train neural networks to recognize the state of a device and
differentiate between states in experimental data. This work gives the
definitive reference for the new dataset that will help enable researchers to
use it in their experiments or to develop new machine learning approaches and
concepts.Comment: 18 pages, 6 figures, 3 table
The HiSCORE concept for gamma-ray and cosmic-ray astrophysics beyond 10\,TeV
Air-shower measurements in the primary energy range beyond 10 TeV can be used
to address important questions of astroparticle and particle physics. The most
prominent among these questions are the search for the origin of charged
Galactic cosmic rays and the so-far little understood transition from Galactic
to extra-galactic cosmic rays. A very promising avenue towards answering these
fundamental questions is the construction of an air-shower detector with
sufficient sensitivity for gamma-rays to identify the accelerators and large
exposure to achieve accurate spectroscopy of local cosmic rays. With the new
ground-based large-area (up to 100 square-km) wide-angle (Omega ~ 0.6-0.85 sr)
air-shower detector concept HiSCORE (Hundred*i Square-km Cosmic ORigin
Explorer), we aim at exploring the cosmic ray and gamma-ray sky
(accelerator-sky) in the energy range from few 10s of TeV to 1 EeV using the
non-imaging air-Cherenkov detection technique. The full detector simulation is
presented here. The resulting sensitivity of a HiSCORE-type detector to
gamma-rays will extend the energy range so far accessed by other experiments
beyond energies of 50 - 100 TeV, thereby opening up the ultra high energy
gamma-ray (UHE gamma-rays, E > 10 TeV) observation window.Comment: 31 pages, 15 figures, accepted by Astroparticle Physics, DOI
information: 10.1016/j.astropartphys.2014.03.00
SciTech News Volume 71, No. 3 (2017)
Columns and Reports
From the Editor.........................3
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Science-Technology Division....5
Chemistry Division....................8
Conference Report, Marion E, Sparks Professional Development Award Recipient..9
Engineering Division................10
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of the Engineering Division .....18
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The Expanded Very Large Array
In almost 30 years of operation, the Very Large Array (VLA) has proved to be
a remarkably flexible and productive radio telescope. However, the basic
capabilities of the VLA have changed little since it was designed. A major
expansion utilizing modern technology is currently underway to improve the
capabilities of the VLA by at least an order of magnitude in both sensitivity
and in frequency coverage. The primary elements of the Expanded Very Large
Array (EVLA) project include new or upgraded receivers for continuous frequency
coverage from 1 to 50 GHz, new local oscillator, intermediate frequency, and
wide bandwidth data transmission systems to carry signals with 16 GHz total
bandwidth from each antenna, and a new digital correlator with the capability
to process this bandwidth with an unprecedented number of frequency channels
for an imaging array. Also included are a new monitor and control system and
new software that will provide telescope ease of use. Scheduled for completion
in 2012, the EVLA will provide the world research community with a flexible,
powerful, general-purpose telescope to address current and future astronomical
issues.Comment: Added journal reference: published in Proceedings of the IEEE,
Special Issue on Advances in Radio Astronomy, August 2009, vol. 97, No. 8,
1448-1462 Six figures, one tabl
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
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Large-area epitaxial growth of curvature-stabilized ABC trilayer graphene.
The properties of van der Waals (vdW) materials often vary dramatically with the atomic stacking order between layers, but this order can be difficult to control. Trilayer graphene (TLG) stacks in either a semimetallic ABA or a semiconducting ABC configuration with a gate-tunable band gap, but the latter has only been produced by exfoliation. Here we present a chemical vapor deposition approach to TLG growth that yields greatly enhanced fraction and size of ABC domains. The key insight is that substrate curvature can stabilize ABC domains. Controllable ABC yields ~59% were achieved by tailoring substrate curvature levels. ABC fractions remained high after transfer to device substrates, as confirmed by transport measurements revealing the expected tunable ABC band gap. Substrate topography engineering provides a path to large-scale synthesis of epitaxial ABC-TLG and other vdW materials
The Atacama Cosmology Telescope: The polarization-sensitive ACTPol instrument
The Atacama Cosmology Telescope (ACT) is designed to make high angular
resolution measurements of anisotropies in the Cosmic Microwave Background
(CMB) at millimeter wavelengths. We describe ACTPol, an upgraded receiver for
ACT, which uses feedhorn-coupled, polarization-sensitive detector arrays, a 3
degree field of view, 100 mK cryogenics with continuous cooling, and meta
material anti-reflection coatings. ACTPol comprises three arrays with separate
cryogenic optics: two arrays at a central frequency of 148 GHz and one array
operating simultaneously at both 97 GHz and 148 GHz. The combined instrument
sensitivity, angular resolution, and sky coverage are optimized for measuring
angular power spectra, clusters via the thermal Sunyaev-Zel'dovich and kinetic
Sunyaev-Zel'dovich signals, and CMB lensing due to large scale structure. The
receiver was commissioned with its first 148 GHz array in 2013, observed with
both 148 GHz arrays in 2014, and has recently completed its first full season
of operations with the full suite of three arrays. This paper provides an
overview of the design and initial performance of the receiver and related
systems
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