58,466 research outputs found
Integrated frequency comb source of heralded single photons
We report an integrated photon pair source based on a CMOS-compatible microring resonator that generates multiple, simultaneous, and independent photon pairs at different wavelengths in a frequency comb compatible with fiber communication wavelength division multiplexing channels (200 GHz channel separation) and with a linewidth that is compatible with quantum memories (110 MHz). It operates in a self-locked pump configuration, avoiding the need for active stabilization, making it extremely robust even at very low power levels
Compressively characterizing high-dimensional entangled states with complementary, random filtering
The resources needed to conventionally characterize a quantum system are
overwhelmingly large for high- dimensional systems. This obstacle may be
overcome by abandoning traditional cornerstones of quantum measurement, such as
general quantum states, strong projective measurement, and assumption-free
characterization. Following this reasoning, we demonstrate an efficient
technique for characterizing high-dimensional, spatial entanglement with one
set of measurements. We recover sharp distributions with local, random
filtering of the same ensemble in momentum followed by position---something the
uncertainty principle forbids for projective measurements. Exploiting the
expectation that entangled signals are highly correlated, we use fewer than
5,000 measurements to characterize a 65, 536-dimensional state. Finally, we use
entropic inequalities to witness entanglement without a density matrix. Our
method represents the sea change unfolding in quantum measurement where methods
influenced by the information theory and signal-processing communities replace
unscalable, brute-force techniques---a progression previously followed by
classical sensing.Comment: 13 pages, 7 figure
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
Fast and Accurate Simulation Technique for Large Irregular Arrays
A fast full-wave simulation technique is presented for the analysis of large
irregular planar arrays of identical 3-D metallic antennas. The solution method
relies on the Macro Basis Functions (MBF) approach and an interpolatory
technique to compute the interactions between MBFs. The Harmonic-polynomial
(HARP) model is established for the near-field interactions in a modified
system of coordinates. For extremely large arrays made of complex antennas, two
approaches assuming a limited radius of influence for mutual coupling are
considered: one is based on a sparse-matrix LU decomposition and the other one
on a tessellation of the array in the form of overlapping sub-arrays. The
computation of all embedded element patterns is sped up with the help of the
non-uniform FFT algorithm. Extensive validations are shown for arrays of
log-periodic antennas envisaged for the low-frequency SKA (Square Kilometer
Array) radio-telescope. The analysis of SKA stations with such a large number
of elements has not been treated yet in the literature. Validations include
comparison with results obtained with commercial software and with experiments.
The proposed method is particularly well suited to array synthesis, in which
several orders of magnitude can be saved in terms of computation time.Comment: The paper was submitted to IEEE Transaction on Antennas and
Propagation on 01 - Feb.- 2017. The paper is 12 pages with 18 figure
Tensor Computation: A New Framework for High-Dimensional Problems in EDA
Many critical EDA problems suffer from the curse of dimensionality, i.e. the
very fast-scaling computational burden produced by large number of parameters
and/or unknown variables. This phenomenon may be caused by multiple spatial or
temporal factors (e.g. 3-D field solvers discretizations and multi-rate circuit
simulation), nonlinearity of devices and circuits, large number of design or
optimization parameters (e.g. full-chip routing/placement and circuit sizing),
or extensive process variations (e.g. variability/reliability analysis and
design for manufacturability). The computational challenges generated by such
high dimensional problems are generally hard to handle efficiently with
traditional EDA core algorithms that are based on matrix and vector
computation. This paper presents "tensor computation" as an alternative general
framework for the development of efficient EDA algorithms and tools. A tensor
is a high-dimensional generalization of a matrix and a vector, and is a natural
choice for both storing and solving efficiently high-dimensional EDA problems.
This paper gives a basic tutorial on tensors, demonstrates some recent examples
of EDA applications (e.g., nonlinear circuit modeling and high-dimensional
uncertainty quantification), and suggests further open EDA problems where the
use of tensor computation could be of advantage.Comment: 14 figures. Accepted by IEEE Trans. CAD of Integrated Circuits and
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