72,806 research outputs found
Magnon squeezing in an antiferromagnet: reducing the spin noise below the standard quantum limit
At absolute zero temperature, thermal noise vanishes when a physical system
is in its ground state, but quantum noise remains as a fundamental limit to the
accuracy of experimental measurements. Such a limitation, however, can be
mitigated by the formation of squeezed states. Quantum mechanically, a squeezed
state is a time-varying superposition of states for which the noise of a
particular observable is reduced below that of the ground state at certain
times. Quantum squeezing has been achieved for a variety of systems, including
the electromagnetic field, atomic vibrations in solids and molecules, and
atomic spins, but not so far for magnetic systems. Here we report on an
experimental demonstration of spin wave (i.e., magnon) squeezing. Our method
uses femtosecond optical pulses to generate correlations involving pairs of
magnons in an antiferromagnetic insulator, MnF2. These correlations lead to
quantum squeezing in which the fluctuations of the magnetization of a
crystallographic unit cell vary periodically in time and are reduced below that
of the ground state quantum noise. The mechanism responsible for this squeezing
is stimulated second order Raman scattering by magnon pairs. Such squeezed
states have important ramifications in the emerging fields of spintronics and
quantum computing involving magnetic spin states or the spin-orbit coupling
mechanism
Divergence of Dipole Sums and the Nature of Non-Lorentzian Exponentially Narrow Resonances in One-Dimensional Periodic Arrays of Nanospheres
Origin and properties of non-Lorentzian spectral lines in linear chains of
nanospheres are discussed. The lines are shown to be super-exponentially narrow
with the characteristic width proportional to exp[-C(h/a)^3] where C is a
numerical constant, h the spacing between the nanospheres in the chain and a
the sphere radius. The fine structure of these spectral lines is also
investigated.Comment: 9 pages, 4 figure
High-quality positrons from a multi-proton bunch driven hollow plasma wakefield accelerator
By means of hollow plasma, multiple proton bunches work well in driving
nonlinear plasma wakefields and accelerate electrons to energy frontier with
preserved beam quality. However, the acceleration of positrons is different
because the accelerating structure is strongly charge dependent. There is a
discrepancy between keeping a small normalized emittance and a small energy
spread. This results from the conflict that the plasma electrons used to
provide focusing to the multiple proton bunches dilute the positron bunch. By
loading an extra electron bunch to repel the plasma electrons and meanwhile
reducing the plasma density slightly to shift the accelerating phase with a
conducive slope to the positron bunch, the positron bunch can be accelerate to
400 GeV (40% of the driver energy) with an energy spread as low as 1% and well
preserved normalized emittance. The successful generation of high quality and
high energy positrons paves the way to the future energy frontier lepton
colliders.Comment: 14 pages, 5 figure
Magnetoresistance in the s-d Model with Arbitrary Impurity Spin
The magnetoresistance, the number of the localized electrons, and the s-wave
scattering phase shift at the Fermi level for the s-d model with arbitrary
impurity spin are obtained in the ground state. To obtain above results some
known exact results of the Bethe ansatz method are used. As the impurity spin S
= 1/2, our results coincide with those obtained by Ishii \textit{et al%}. The
compairsion between the theoretical and experimental magneticresistence for
impurity S = 1/2 is re-examined.Comment: 6 pages, 2 figure
From Discrete Hopping to Continuum Modeling on Vicinal Surfaces with Applications to Si(001) Electromigration
Coarse-grained modeling of dynamics on vicinal surfaces concentrates on the
diffusion of adatoms on terraces with boundary conditions at sharp steps, as
first studied by Burton, Cabrera and Frank (BCF). Recent electromigration
experiments on vicinal Si surfaces suggest the need for more general boundary
conditions in a BCF approach. We study a discrete 1D hopping model that takes
into account asymmetry in the hopping rates in the region around a step and the
finite probability of incorporation into the solid at the step site. By
expanding the continuous concentration field in a Taylor series evaluated at
discrete sites near the step, we relate the kinetic coefficients and
permeability rate in general sharp step models to the physically suggestive
parameters of the hopping models. In particular we find that both the kinetic
coefficients and permeability rate can be negative when diffusion is faster
near the step than on terraces. These ideas are used to provide an
understanding of recent electromigration experiment on Si(001) surfaces where
step bunching is induced by an electric field directed at various angles to the
steps.Comment: 10 pages, 4 figure
A study of randomness, correlations and collectivity in the nuclear shell model
A variable combination of realistic and random two-body interactions allows
the study of collective properties, such as the energy spectra and B(E2)
transition strengths in 44Ti, 48Cr and 24Mg. It is found that the average
energies of the yrast band states maintain the ordering for any degree of
randomness, but the B(E2) values lose their quadrupole collectivity when
randomness dominates the Hamiltonian. The high probability of the yrast band to
be ordered in the presence of pure random forces exhibits the strong
correlations between the different members of the band.Comment: 8 pages, 10 figures, 8 tables, submitted to Physical Review
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
Part 2 of this monograph builds on the introduction to tensor networks and
their operations presented in Part 1. It focuses on tensor network models for
super-compressed higher-order representation of data/parameters and related
cost functions, while providing an outline of their applications in machine
learning and data analytics. A particular emphasis is on the tensor train (TT)
and Hierarchical Tucker (HT) decompositions, and their physically meaningful
interpretations which reflect the scalability of the tensor network approach.
Through a graphical approach, we also elucidate how, by virtue of the
underlying low-rank tensor approximations and sophisticated contractions of
core tensors, tensor networks have the ability to perform distributed
computations on otherwise prohibitively large volumes of data/parameters,
thereby alleviating or even eliminating the curse of dimensionality. The
usefulness of this concept is illustrated over a number of applied areas,
including generalized regression and classification (support tensor machines,
canonical correlation analysis, higher order partial least squares),
generalized eigenvalue decomposition, Riemannian optimization, and in the
optimization of deep neural networks. Part 1 and Part 2 of this work can be
used either as stand-alone separate texts, or indeed as a conjoint
comprehensive review of the exciting field of low-rank tensor networks and
tensor decompositions.Comment: 232 page
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
Part 2 of this monograph builds on the introduction to tensor networks and
their operations presented in Part 1. It focuses on tensor network models for
super-compressed higher-order representation of data/parameters and related
cost functions, while providing an outline of their applications in machine
learning and data analytics. A particular emphasis is on the tensor train (TT)
and Hierarchical Tucker (HT) decompositions, and their physically meaningful
interpretations which reflect the scalability of the tensor network approach.
Through a graphical approach, we also elucidate how, by virtue of the
underlying low-rank tensor approximations and sophisticated contractions of
core tensors, tensor networks have the ability to perform distributed
computations on otherwise prohibitively large volumes of data/parameters,
thereby alleviating or even eliminating the curse of dimensionality. The
usefulness of this concept is illustrated over a number of applied areas,
including generalized regression and classification (support tensor machines,
canonical correlation analysis, higher order partial least squares),
generalized eigenvalue decomposition, Riemannian optimization, and in the
optimization of deep neural networks. Part 1 and Part 2 of this work can be
used either as stand-alone separate texts, or indeed as a conjoint
comprehensive review of the exciting field of low-rank tensor networks and
tensor decompositions.Comment: 232 page
Subwavelength internal imaging by means of the wire medium
Evanescent wave amplification is observed, for the first time to our
knowledge, inside a half-wavelength-thick wire medium slab used for
subwavelength imaging. The wire medium is analyzed using both a spatially
dispersive finite-difference time-domain (FDTD) method and a full-wave
commercial electromagnetic simulator CST Microwave Studio. In this work we
demonstrate that subwavelength details of a source placed at a distance of
one-tenth of a wavelength from a wire medium slab can be detected inside the
slab with a resolution of approximately one-tenth of a wavelength in spite of
the fact that they cannot be resolved at the front interface of the device, due
to the rapid decay of evanescent spatial harmonics in free space
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