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Register a new userSome problems of low-dimensional physics
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Authors
Yuri Kornyushin
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Fermi and kinetic energy are usually calculated in periodic boundary conditions model, which is not self-consistent for low-dimensional problems, where particles are confined. Thus for confined particles the potential box model was used self-consistently to calculate Fermi and kinetic energies in 3-, 2-, and 1-dimensional cases. This approach is much more logical and self-consistent. Then the conditions for neglecting dimensions, that is conditions under which the movement of particles in the box could be considered as 2- and 1- dimensional, were derived.
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We study numerically the cluster structure of random ensembles of two NP-hard optimization problems originating in computational complexity, the vertex-cover problem and the number partitioning problem. We use branch-and-bound type algorithms to obtain exact solutions of these problems for moderate system sizes. Using two methods, direct neighborhood-based clustering and hierarchical clustering, we investigate the structure of the solution space. The main result is that the correspondence between solution structure and the phase diagrams of the problems is not unique. Namely, for vertex cover we observe a drastic change of the solution space from large single clusters to multiple nested levels of clusters. In contrast, for the number-partitioning problem, the phase space looks always very simple, similar to a random distribution of the lowest-energy configurations. This holds in the ``easy/solvable phase as well as in the ``hard/unsolvable phase.
We report macroscopic magnetic measurements carried out in order to detect and characterize field-induced quantum entanglement in low dimensional spin systems. We analyze the pyroborate MgMnB_2O_5 and the and the warwickite MgTiOBO_3, systems with spin 5/2 and 1/2 respectively. By using the magnetic susceptibility as an entanglement witness we are able to quantify entanglement as a function of temperature and magnetic field. In addition, we experimentally distinguish for the first time a random singlet phase from a Griffiths phase. This analysis opens the possibility of a more detailed characterization of low dimensional materials.
Sampling equilibrium ensembles of dense polymer mixtures is a paradigmatically hard problem in computational physics, even in lattice-based models. Here, we develop a formalism based on interacting binary tensors that allows for tackling this problem using quantum annealing machines. Our approach is general in that properties such as self-avoidance, branching, and looping can all be specified in terms of quadratic interactions of the tensors. Microstates realizations of different lattice polymer ensembles are then seamlessly generated by solving suitable discrete energy-minimization problems. This approach enables us to capitalize on the strengths of quantum annealing machines, as we demonstrate by sampling polymer mixtures from low to high densities, using the D-Wave quantum computer. Our systematic approach offers a promising avenue to harness the rapid development of quantum computers for sampling discrete models of filamentous soft-matter systems.
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We report results of MD simulations of amorphous ice in the pressure range 0 - 22.5 kbar. The high-density amorphous ice (HDA) prepared by compression of Ih ice at T = 80 K is annealed to T = 170 K at intermediate pressures in order to generate relaxed states. We confirm the existence of recently observed phenomena, the very high-density amorphous ice and a continuum of HDA forms. We suggest that both phenomena have their origin in the evolution of the network topology of the annealed HDA phase with decreasing volume, resulting at low temperatures in the metastability of a range of densities.
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