Selection rules for ultrafast laser excitation and detection of spin correlations dynamics in a cubic antiferromagnet

Exchange interactions determine the correlations between microscopic spins in magnetic materials. Probing the dynamics of these spin correlations on ultrashort length and time scales is, however rather challenging, since it requires simultaneously high spatial and high temporal resolution. Recent experimental demonstrations of laser-driven two-magnon modes - zone-edge excitations in antiferromagnets governed by exchange coupling - posed questions about the microscopic nature of the observed spin dynamics, the mechanism underlying its excitation, and their macroscopic manifestation enabling detection. Here, on the basis of a simple microscopic model, we derive the selection rules for cubic systems that describe the polarization of pump and probe pulses required to excite and detect dynamics of nearest-neighbor spin correlations, and can be employed to isolate such dynamics from other magnetic excitations and magneto-optical effects. We show that laser-driven spin correlations contribute to optical anisotropy of the antiferromagnet even in the absence of spin-orbit coupling. In addition, we highlight the role of subleading anisotropy in the spin system and demonstrate that the dynamics of the antiferromagnetic order parameter occurs only in next-to-leading order, determined by the smallness of the magnetic anisotropy as compared to the isotropic exchange interactions in the system. We expect that our results will stimulate and support further studies of magnetic correlations on the shortest length and time scale.Comment: 17 pages, 5 figure

All-thermal switching of amorphous Gd-Fe alloys: analysis of structural properties and magnetization dynamics

In recent years, there has been an intense interest in understanding the microscopic mechanism of thermally induced magnetization switching driven by a femtosecond laser pulse. Most of the effort has been dedicated to periodic crystalline structures while the amorphous counterparts have been less studied. By using a multiscale approach, i.e. first-principles density functional theory combined with atomistic spin dynamics, we report here on the very intricate structural and magnetic nature of amorphous Gd-Fe alloys for a wide range of Gd and Fe atomic concentrations at the nanoscale level. Both structural and dynamical properties of Gd-Fe alloys reported in this work are in good agreement with previous experiments. We calculated the dynamic behavior of homogeneous and inhomogeneous amorphous Gd-Fe alloys and their response under the influence of a femtosecond laser pulse. In the homogeneous sample, the Fe sublattice switches its magnetization before the Gd one. However, the temporal sequence of the switching of the two sublattices is reversed in the inhomogeneous sample. We propose a possible explanation based on a mechanism driven by a combination of the Dzyaloshiskii-Moriya interaction and exchange frustration, modeled by an antiferromagnetic second-neighbour exchange interaction between Gd atoms in the Gd-rich region. We also report on the influence of laser fluence and damping effects in the all-thermal switching.Comment: Accepted in Physical Review B as a regular article. It contains 14 pages and 14 figure

Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics

The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performance computing systems. An appealing and possibly more sustainable alternative is provided by novel neuromorphic paradigms, which directly implement ANNs in hardware. However, little is known about the actual benefits of running ANNs on neuromorphic hardware for use cases in scientific computing. Here we present a methodology for measuring the energy cost and compute time for inference tasks with ANNs on conventional hardware. In addition, we have designed an architecture for these tasks and estimate the same metrics based on a state-of-the-art analog in-memory computing (AIMC) platform, one of the key paradigms in neuromorphic computing. Both methodologies are compared for a use case in quantum many-body physics in two dimensional condensed matter systems and for anomaly detection at 40 MHz rates at the Large Hadron Collider in particle physics. We find that AIMC can achieve up to one order of magnitude shorter computation times than conventional hardware, at an energy cost that is up to three orders of magnitude smaller. This suggests great potential for faster and more sustainable scientific computing with neuromorphic hardware.Comment: 7 pages, 4 figures, submitted to APL Machine Learnin

Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics

The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performance computing systems. An appealing and possibly more sustainable alternative is provided by novel neuromorphic paradigms, which directly implement ANNs in hardware. However, little is known about the actual benefits of running ANNs on neuromorphic hardware for use cases in scientific computing. Here, we present a methodology for measuring the energy cost and compute time for inference tasks with ANNs on conventional hardware. In addition, we have designed an architecture for these tasks and estimate the same metrics based on a state-of-the-art analog in-memory computing (AIMC) platform, one of the key paradigms in neuromorphic computing. Both methodologies are compared for a use case in quantum many-body physics in two-dimensional condensed matter systems and for anomaly detection at 40 MHz rates at the Large Hadron Collider in particle physics. We find that AIMC can achieve up to one order of magnitude shorter computation times than conventional hardware at an energy cost that is up to three orders of magnitude smaller. This suggests great potential for faster and more sustainable scientific computing with neuromorphic hardware

Artificial neural network states for non-additive systems

Methods inspired from machine learning have recently attracted great interest in the computational study of quantum many-particle systems. So far, however, it has proven challenging to deal with microscopic models in which the total number of particles is not conserved. To address this issue, we propose a new variant of neural network states, which we term neural coherent states. Taking the Fröhlich impurity model as a case study, we show that neural coherent states can learn the ground state of non-additive systems very well. In particular, we observe substantial improvement over the standard coherent state estimates in the most challenging intermediate coupling regime. Our approach is generic and does not assume specific details of the system, suggesting wide applications

Optical control of competing exchange interactions and coherent spin-charge coupling in two-orbital Mott insulators

In order to have a better understanding of ultrafast electrical control of exchange interactions in multi-orbital systems, we study a two-orbital Hubbard model at half filling under the action of a time-periodic electric field. Using suitable projection operators and a generalized time-dependent canonical transformation, we derive an effective Hamiltonian which describes two different regimes. First, for a wide range of non-resonant frequencies, we find a change of the bilinear Heisenberg exchange $J_{\textrm{ex}}$ that is analogous to the single-orbital case. Moreover we demonstrate that also the additional biquadratic exchange interaction $B_{\textrm{ex}}$ can be enhanced, reduced and even change sign depending on the electric field. Second, for special driving frequencies, we demonstrate a novel spin-charge coupling phenomenon enabling coherent transfer between spin and charge degrees of freedom of doubly ionized states. These results are confirmed by an exact time-evolution of the full two-orbital Mott-Hubbard Hamiltonian