72,806 research outputs found

    Magnon squeezing in an antiferromagnet: reducing the spin noise below the standard quantum limit

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    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

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    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

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    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

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    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

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    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

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    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

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    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

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    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

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    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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