A high-temperature expansion method for calculating paramagnetic exchange interactions

The method for calculating the isotropic exchange interactions in the paramagnetic phase is proposed. It is based on the mapping of the high-temperature expansion of the spin-spin correlation function calculated for the Heisenberg model onto Hubbard Hamiltonian one. The resulting expression for the exchange interaction has a compact and transparent formulation. The quality of the calculated exchange interactions is estimated by comparing the eigenvalue spectra of the Heisenberg model and low-energy magnetic part of the Hubbard model. By the example of quantum rings with different hopping setups we analyze the contributions from the different part of the Hubbard model spectrum to the resulting exchange interaction.Comment: 8 pages, 8 figure

Neural network agent playing spin Hamiltonian games on a quantum computer

Quantum computing is expected to provide new promising approaches for solving the most challenging problems in material science, communication, search, machine learning and other domains. However, due to the decoherence and gate imperfection errors modern quantum computer systems are characterized by a very complex, dynamical, uncertain and fluctuating computational environment. We develop an autonomous agent effectively interacting with such an environment to solve magnetism problems. By using the reinforcement learning the agent is trained to find the best-possible approximation of a spin Hamiltonian ground state from self-play on quantum devices. We show that the agent can learn the entanglement to imitate the ground state of the quantum spin dimer. The experiments were conducted on quantum computers provided by IBM. To compensate the decoherence we use local spin correction procedure derived from a general sum rule for spin-spin correlation functions of a quantum system with even number of antiferromagnetically-coupled spins in the ground state. Our study paves a way to create a new family of the neural network eigensolvers for quantum computers.Comment: Local spin correction procedure was used to compensate real device errors; comparison with variational approach was adde

Monte Carlo study of magnetic nanoparticles adsorbed on halloysite Al2Si2O5(OH)4Al_2Si_2O_5(OH)_4 nanotubes

We study properties of magnetic nanoparticles adsorbed on the halloysite surface. For that a distinct magnetic Hamiltonian with random distribution of spins on a cylindrical surface was solved by using a nonequilibrium Monte Carlo method. The parameters for our simulations: anisotropy constant, nanoparticle size distribution, saturated magnetization and geometrical parameters of the halloysite template were taken from recent experiments. We calculate the hysteresis loops and temperature dependence of the zero field cooling (ZFC) susceptibility, which maximum determines the blocking temperature. It is shown that the dipole-dipole interaction between nanoparticles moderately increases the blocking temperature and weakly increases the coercive force. The obtained hysteresis loops (e.g., the value of the coercive force) for Ni nanoparticles are in reasonable agreement with the experimental data. We also discuss the sensitivity of the hysteresis loops and ZFC susceptibilities to the change of anisotropy and dipole-dipole interaction, as well as the 3d-shell occupation of the metallic nanoparticles; in particular we predict larger coercive force for Fe, than for Ni nanoparticles.Comment: 10 pages, 12 figure

Bimeron nanoconfined design

We report on the stabilization of the topological bimeron excitations in confined geometries. The Monte Carlo simulations for a ferromagnet with a strong Dzyaloshinskii-Moriya interaction revealed the formation of a mixed skyrmion-bimeron phase. The vacancy grid created in the spin lattice drastically changes the picture of the topological excitations and allows one to choose between the formation of a pure bimeron and skyrmion lattice. We found that the rhombic plaquette provides a natural environment for stabilization of the bimeron excitations. Such a rhombic geometry can protect the topological state even in the absence of the magnetic field.Comment: 5 pages, 7 figure

Profile approach for recognition of three-dimensional magnetic structures

We propose an approach for low-dimensional visualisation and classification of complex topological magnetic structures formed in magnetic materials. Within the approach one converts a three-dimensional magnetic configuration to a vector containing the only components of the spins that are parallel to the z axis. The next crucial step is to sort the vector elements in ascending or descending order. Having visualized profiles of the sorted spin vectors one can distinguish configurations belonging to different phases even with the same total magnetization. For instance, spin spiral and paramagnetic states with zero total magnetic moment can be easily identified. Being combined with a simplest neural network our profile approach provides a very accurate phase classification for three-dimensional magnets characterized by complex multispiral states even in the critical areas close to phases transitions. By the example of the skyrmionic configurations we show that profile approach can be used to separate the states belonging to the same phase