相关论文: Using hyper-optimized tensor networks and first-pr…
Machine learning potentials have emerged as a powerful tool to extend the time and length scales of first principles-quality simulations. Still, most machine learning potentials cannot distinguish different electronic spin orientations and…
Using the Ginzburg-Landau theory in very general terms, we develop a simple scaling procedure which allows to establish the temperature dependence of the upper critical field and the value of the superconducting critical temperature Tc of…
The tensor network states (TNS) methods combined with Monte Carlo (MC) techniques have been proved a powerful algorithm for simulating quantum many-body systems. However, because the ground state energy is a highly non-linear function of…
Two-dimensional high-spin bipartite honeycomb networks, where anisotropy, competing exchange interactions, and spin fluctuations interplay, provide an alternative platform to test theoretical models that distinguish between classical and…
Various types of mixed spin two-dimensional Heisenberg networks are investigated by means of Monte Carlo simulations. This study aims at interpreting quantitatively the thermodynamical properties of two-dimensional molecule-based magnets…
The existence of spontaneous magnetization of Ising spins on directed Barabasi-Albert networks is investigated with seven neighbors, by using Monte Carlo simulations. In large systems we see the magnetization for different temperatures T to…
We consider the extended Hubbard model and introduce a corresponding Heisenberg-like problem written in terms of spin operators. The derived formalism is reminiscent of Anderson's idea of the effective exchange interaction and takes into…
Magnetic phenomena of the superantiferromagnetic Ising model in both uniform longitudinal ($H$) and transverse ($\Omega $) magnetic fields are studied by employing a mean-field variational approach based on Peierls-Bogoliubov inequality for…
While classical spin systems in random networks have been intensively studied, much less is known about quantum magnets in random graphs. Here, we investigate interacting quantum spins on small-world networks, building on mean-field theory…
Magnetic properties for the one-dimensional multicomponent quantum spin system with the excitation gap are studied based on the integrable spin model introduced by Bariev {\it et al}. By exactly computing the magnetization, we show how the…
Monte Carlo simulations are performed for the S = 1/2 XY and ferro- and antiferromagnetic Heisenberg model in two dimensions using the loop algorithm. Thermodynamic properties of all these models are investigated in wide temperature range.…
Understanding the equilibrium properties and out of equilibrium dynamics of quantum field theories are key aspects of fundamental problems in theoretical particle physics and cosmology. However, their classical simulation is highly…
We have proposed an efficient algorithm to calculate physical quantities in the translational invariant three-dimensional tensor networks, which is particularly relevant to the study of the three-dimensional classical statistical models and…
Low-dimensional quantum magnets, particularly those with strong spin frustration, are characterized by their notable spin fluctuations. Nuclear magnetic resonance (NMR) serves as a sensitive probe of low-energy fluctuations that offers…
We introduce a growing one-dimensional quenched spin model that bases on asymmetrical one-side Ising interactions in the presence of external field. Numerical simulations and analytical calculations based on Markov chain theory show that…
The magnetic properties of the diluted magnetic semiconductors (DMS) (Ga, Mn)As and (Ga, Mn)N are investigated by means of an effective Heisenberg model, whose exchange parameters are obtained from first-principle calculations. The…
The entangling power of a unitary operator quantifies its ability to generate entanglement from product states and provides a natural probe of quantum many-body dynamics. Entanglement extremization at points of enhanced symmetry has…
Magnetic nanoparticle hyperthermia is an attractive emerging cancer treatment, but the acting microscopic energy deposition mechanisms are not well understood and optimization suffers. We describe several approximate forms for the…
Tensor network theory and quantum simulation are respectively the key classical and quantum computing methods in understanding quantum many-body physics. Here, we introduce the framework of hybrid tensor networks with building blocks…
Experimental data obtained with the perovskite compounds Pr$_{0.55}$Sr$_{0.45}$MnO$_{3}$ show that the magnetization decreases with increasing temperature $T$ and undergoes a very sharp phase transition to the paramagnetic phase. The sharp…