Estimating Patterns of Classical and Quantum Skyrmion States

In this review we discuss the latest results concerning development of the machine learning algorithms for characterization of the magnetic skyrmions that are topologically-protected magnetic textures originated from the Dzyaloshinskii-Moriya interaction that competes Heisenberg isotropic exchange in ferromagnets. We show that for classical spin systems there is a whole pool of machine approaches allowing their accurate phase classification and quantitative description on the basis of few magnetization snapshots. In turn, investigation of the quantum skyrmions is a less explored issue, since there are fundamental limitations on the simulation of such wave functions with classical supercomputers. One needs to find the ways to imitate quantum skyrmions on near-term quantum computers. In this respect, we discuss implementation of the method for estimating structural complexity of classical objects for characterization of the quantum skyrmion state on the basis of limited number of bitstrings obtained from the projective measurements

An effective spin model on the honeycomb lattice for the description of magnetic properties in two-dimensional Fe3_3GeTe2_2

Fe$_3$GeTe$_2$ attracts significant attention due to technological perspectives of realizing room temperature ferromagnetism in two-dimensional materials. Here we show that due to structural peculiarities of the Fe$_3$GeTe$_2$ monolayer, short distance between the neighboring iron atoms induces a strong exchange coupling. This strong coupling allows us to consider them as an effective cluster with a magnetic moment $\sim$5 $\mu_B$, giving rise to a simplified spin model on a bipartite honeycomb lattice with the reduced number of long-range interactions. The simplified model perfectly reproduces the results of the conventional spin model, but allows for a more tractable description of the magnetic properties of Fe$_3$GeTe$_2$, which is important, e.g., for large-scale simulations. Also, we discuss the role of biaxial strain in the stabilization of ferromagnetic ordering in Fe$_3$GeTe$_2$.Comment: 7 pages, 7 figure

Field evolution of the spin-liquid candidate YbMgGaO4

We report magnetization, heat capacity, thermal expansion, and magnetostriction measurements down to mK temperatures on the triangular antiferromagnet YbMgGaO$_4$. Our data exclude the formation of the distinct $\frac13$-plateau phase observed in other triangular antiferromagnets, but reveal plateau-like features in second derivatives of the free energy, magnetic susceptibility and specific heat, at $\mu_0H$ = 1.0 - 2.5 T for $H\parallel{}c$ and 2 - 5 T for $H\perp{}c$. Using Monte-Carlo simulations of a realistic spin Hamiltonian, we ascribe these features to non-monotonic changes in the magnetization and the $\frac12$-plateau that is smeared out by the random distribution of exchange couplings in YbMgGaO$_4$

Benchmarking a boson sampler with Hamming nets

Analyzing the properties of complex quantum systems is crucial for further development of quantum devices, yet this task is typically challenging and demanding with respect to required amount of measurements. A special attention to this problem appears within the context of characterizing outcomes of noisy intermediate-scale quantum devices, which produce quantum states with specific properties so that it is expected to be hard to simulate such states using classical resources. In this work, we address the problem of characterization of a boson sampling device, which uses interference of input photons to produce samples of non-trivial probability distributions that at certain condition are hard to obtain classically. For realistic experimental conditions the problem is to probe multi-photon interference with a limited number of the measurement outcomes without collisions and repetitions. By constructing networks on the measurements outcomes, we demonstrate a possibility to discriminate between regimes of indistinguishable and distinguishable bosons by quantifying the structures of the corresponding networks. Based on this we propose a machine-learning-based protocol to benchmark a boson sampler with unknown scattering matrix. Notably, the protocol works in the most challenging regimes of having a very limited number of bitstrings without collisions and repetitions. As we expect, our framework can be directly applied for characterizing boson sampling devices that are currently available in experiments.Comment: 14 page