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

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

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

The reservoir network: A new network topology for district heating and cooling

Thermal district networks are effective solutions to substitute fossil fuels with renewable energy sources for heating and cooling. Moreover, thermal networking of buildings allows energy efficiency to be further increased. The waste heat from cooling can be reused for heating in thermal district systems. Because of bidirectional energy flows between prosumers, thermal networks require new hydraulic concepts. In this work, we present a novel network topology for simultaneous heating and cooling: the reservoir network. The reservoir network is robust in operation due to hydraulic decoupling of transfer stations, integrates heat sources and heat sinks at various temperature levels and is flexible in terms of network expansion. We used Modelica simulations to compare the new single-pipe reservoir network to a basecase double-pipe network, taking yearly demand profiles of different building types for heating and cooling. The electric energy consumed by the heat pumps and circulations pumps differs between the reservoir and base case networks by less than 1%. However, if the reservoir network is operated with constant instead of variable mass flow rate, the total electrical consumption can increase by 48% compared to the base case. As with any other network topology, the design and control of such networks is crucial to achieving energy efficient operation. Investment costs for piping and trenching depend on the district layout and dimensioning of the network. If a ring layout is applied in a district, the reservoir network with its single-pipe configuration is more economical than other topologies. For a linear layout, the piping costs are slightly higher for the reservoir network than for the base case because of larger pipe diameters

Propagation of relativistic charged particles in ultracold atomic gases with Bose-Einstein condensates

We study theoretically some effects produced by a propagation of the charged particles in dilute gases of alkali-metal atoms in the state with Bose-Einstein condensates. The energy change of the high-speed (relativistic) particle that corresponds to the Cherenkov effect in the condensate is investigated. We show that in the studied cases the particle can both loose and receive the energy from a gas. We find the necessary conditions for the particle acceleration in the multi-component condensate. It is shown that the Cherenkov effect in Bose-Einstein condensates can be used also for defining the spectral characteristics of atoms.Comment: 6 pages, 3 figure

Supervised learning magnetic skyrmion phases

We propose and apply simple machine learning approaches for recognition and classification of complex non-collinear magnetic structures in two-dimensional materials. The first approach is based on the implementation of the single-hidden-layer neural network that only relies on the z projections of the spins. In this setup one needs a limited set of magnetic configurations to distinguish ferromag- netic, skyrmion and spin spiral phases, as well as their different combinations in transitional areas of the phase diagram. The network trained on the configurations for square-lattice Heisenberg model with Dzyaloshinskii-Moriya interaction can classify the magnetic structures obtained from Monte Carlo calculations for triangular lattice and vice versa. The second approach we apply, a minimum distance method performs a fast and cheap classification in cases when a particular configuration is to be assigned to only one magnetic phase. The methods we propose are also easy to use for analysis of the numerous experimental data collected with spin-polarized scanning tunneling microscopy and Lorentz transmission electron microscopy experiments.Comment: 9 pages, 14 figures. Accepted for publication in Physical Review