The Shastry-Sutherland model and its generalizations have been shown to capture emergent complex magnetic properties from geometric frustration in several quasi-two-dimensional quantum magnets. Using an $sd$ exchange model, we show here that metallic
Shastry-Sutherland magnets can exhibit topological Hall effect driven by magnetic skyrmions under realistic conditions. The magnetic properties are modelled with competing symmetric Heisenberg and asymmetric Dzyaloshinskii-Moriya exchange interactions, while a coupling between the spins of the itinerant electrons and the localized moments describes the magnetotransport behavior. Our results, employing complementary Monte Carlo simulations and a novel machine learning analysis to investigate the magnetic phases, provide evidence for field-driven skyrmion crystal formation for extended range of Hamiltonian parameters. By constructing an effective tight-binding model of conduction electrons coupled to the skyrmion lattice, we clearly demonstrate the appearance of topological Hall effect. We further elaborate on effects of finite temperatures on both magnetic and magnetotransport properties.
Implementing variational quantum algorithms with noisy intermediate-scale quantum machines of up to a hundred qubits is nowadays considered as one of the most promising routes towards achieving a quantum practical advantage. In multiqubit circuits, r
unning advanced quantum algorithms is hampered by the noise inherent to quantum gates which distances us from the idea of universal quantum computing. Based on a one-dimensional quantum spin chain with competing symmetric and asymmetric pairwise exchange interactions, herein we discuss the capabilities of quantum algorithms with special attention paid to a hardware-efficient variational eigensolver. A delicate interplay between magnetic interactions allows one to stabilize a chiral state that destroys the homogeneity of magnetic ordering, thus making this solution highly entangled. Quantifying entanglement in terms of quantum concurrence, we argue that, while being capable of correctly reproducing a uniform magnetic configuration, the hardware-efficient ansatz meets difficulties in providing a detailed description to a noncollinear magnetic structure. The latter naturally limits the application range of variational quantum computing to solve quantum simulation tasks.
In this paper, we provide a comprehensive theoretical analysis of the electronic structure of InAs(111) surfaces with a special attention paid to the energy region close to the fundamental bandgap. Starting from the bulk electronic structure of InAs
as calculated using PBE functional with included Hubbard correction and spin-orbit coupling, we deliver proper values for the bandgap, split-off energy, as well as effective electron, light- and heavy-hole masses in full consistency with available experimental results. On the basis of optimized atomic surfaces we recover scanning tunneling microscopy images, which being supplied with accessible experimental data make it possible to speculate on the formation of electron accumulation layer for both As- and In-terminated InAs(111) surfaces. Moreover, these results are accompanied by band structure simulations of conduction band states.
The numerical emulation of quantum systems often requires an exponential number of degrees of freedom which translates to a computational bottleneck. Methods of machine learning have been used in adjacent fields for effective feature extraction and d
imensionality reduction of high-dimensional datasets. Recent studies have revealed that neural networks are further suitable for the determination of macroscopic phases of matter and associated phase transitions as well as efficient quantum state representation. In this work, we address quantum phase transitions in quantum spin chains, namely the transverse field Ising chain and the anisotropic XY chain, and show that even neural networks with no hidden layers can be effectively trained to distinguish between magnetically ordered and disordered phases. Our neural network acts to predict the corresponding crossovers finite-size systems undergo. Our results extend to a wide class of interacting quantum many-body systems and illustrate the wide applicability of neural networks to many-body quantum physics.
Hybrid quantum-classical algorithms have been proposed as a potentially viable application of quantum computers. A particular example - the variational quantum eigensolver, or VQE - is designed to determine a global minimum in an energy landscape spe
cified by a quantum Hamiltonian, which makes it appealing for the needs of quantum chemistry. Experimental realizations have been reported in recent years and theoretical estimates of its efficiency are a subject of intense effort. Here we consider the performance of the VQE technique for a Hubbard-like model describing a one-dimensional chain of fermions with competing nearest- and next-nearest-neighbor interactions. We find that recovering the VQE solution allows one to obtain the correlation function of the ground state consistent with the exact result. We also study the barren plateau phenomenon for the Hamiltonian in question and find that the severity of this effect depends on the encoding of fermions to qubits. Our results are consistent with the current knowledge about the barren plateaus in quantum optimization.
The study of zinc oxide, within the homogeneous electron gas approximation, results in overhybridization of zinc $3d$ shell with oxygen $2p$ shell, a problem shown for most transition metal chalcogenides. This problem can be partially overcome by usi
ng LDA+$U$ (or, GGA+$U$) methodology. However, in contrast to the zinc $3d$ orbital, Hubbard type correction is typically excluded for the oxygen $2p$ orbital. In this work, we provide results of electronic structure calculations of an oxygen vacancy in ZnO supercell from ab initio perspective, with two Hubbard type corrections, $U_{mathrm{Zn}-3d}$ and $U_{mathrm{O}-2p}$. The results of our numerical simulations clearly reveal that the account of $U_{mathrm{O}-2p}$ has a significant impact on the properties of bulk ZnO, in particular the relaxed lattice constants, effective mass of charge carriers as well as the bandgap. For a set of validated values of $U_{mathrm{Zn}-3d}$ and $U_{mathrm{O}-2p}$ we demonstrate the appearance of a localized state associated with the oxygen vacancy positioned in the bandgap of the ZnO supercell. Our numerical findings suggest that the defect state is characterized by the highest overlap with the conduction band states as obtained in the calculations with no Hubbard-type correction included. We argue that the electronic density of the defect state is primarily determined by Zn atoms closest to the vacancy.
Originating from image recognition, methods of machine learning allow for effective feature extraction and dimensionality reduction in multidimensional datasets, thereby providing an extraordinary tool to deal with classical and quantum models in man
y-body physics. In this study, we employ a specific unsupervised machine learning technique -- self-organizing maps -- to create a low-dimensional representation of microscopic states, relevant for macroscopic phase identification and detecting phase transitions. We explore the properties of spin Hamiltonians of two archetype model system: a two-dimensional Heisenberg ferromagnet and a three-dimensional crystal, Fe in the body centered cubic structure. The method of self-organizing maps, that is known to conserve connectivity of the initial dataset, is compared to the cumulant method theory and is shown to be as accurate while being computationally more efficient in determining a phase transition temperature. We argue that the method proposed here can be applied to explore a broad class of second-order phase transition systems, not only magnetic systems but also, for example, order-disorder transitions in alloys.
Recently, it was shown both theoretically and experimentally that certain three-dimensional (3D) materials have Dirac points in the Brillouin zone, thus being 3D analogs of graphene. Moreover, it was suggested that under specific conditions a pair of
localized impurities placed inside a three-dimensional Dirac semimetal may lead to the formation of an unusual antibonding state. In the meantime, the effect of vibrational degrees of freedom which are present in any realistic system has avoided attention. In this work, we address the influence of phonons on characteristic features of (anti)bonding state, and discuss how these results can be tested experimentally via local probing, namely, inelastic electron tunneling spectroscopy curve obtained in STM measurements.
An external off-resonant pumping is proposed as a tool to control the Dzyaloshinskii-Moriya interaction (DMI) in ferromagnetic layers with strong spin-orbit coupling. Combining theoretical analysis with numerical simulations for an $s$-$d$-like model
we demonstrate that linearly polarized off-resonant light may help stabilizing novel noncollinear magnetic phases by inducing a strong anisotropy of the DMI. We also investigate how with the application of electromagnetic pumping one can control the stability, shape and size of individual skyrmions to make them suitable for potential applications.
The emerging field of spinoptronics has a potential to supersede the functionality of modern electronics, while a proper description of strong light-matter coupling pose the most intriguing questions from both fundamental scientific and technological
perspectives. In this paper we address a highly relevant issue for such a development. We theoretically explore spin dynamics on the surface of a 3D topological insulator (TI) irradiated with an off-resonant high-frequency electromagnetic wave. The strong coupling between electrons and the electromagnetic wave drastically modifies the spin properties of TI. The effects of irradiation are shown to result in anisotropy of electron energy spectrum near the Dirac point and suppression of spin current and are investigated in detail in this work.
