This series consists of talks in the area of Condensed Matter.
We discuss the thermodynamic properties of the model
exchange quantum spin ice material Yb_2Ti_2O_7. Using exchange parameters
recently determined from high-field neutron scattering measurements, we
calculate the thermodynamic properties of this model system. We find very good
agreement with the heat capacity, entropy and magnetization measurements on the
materials. We show that, in the weak quantum regime, quantum fluctuations lead
to the selection, within the spin-ice manifold, of a conventional ordered
This talk will be about non-equilibrium many-body physics in integer quantum Hall edge states far from equilibrium. Recent experiments have generated a highly non-thermal electron distribution by bringing together at a point contact two quantum Hall edge states originating from sources at different potentials. The relaxation of this distribution to a stationary form is observed as a function of distance downstream from the contact [Phys. Rev. Lett. 105, 056803 (2010)]. I will discuss the broader context for the experiments and a physical picture of the equilibration process.
Electrons in conjugated organic polymers and molecules are strongly correlated since most of these systems are quasi one-dimensional. Experimental evidences include existence of two photons below one photon state, observation of negative spin densities in polyene radicals and qualitatively different behavior of optical gaps in polyenes and closely related symmetric cynanine dyes in the thermodynamic limiy. In this talk, I will introduce the model Hamiltonians for the electron states in conjugated systems.
Large quantum fluctuations in certain quantum spin systems destroy long range magnetic order such as antiferromagnetism. Resulting paramagnetic states are called a quantum spin liquids. These states support emergent gauge fields [1]. Under certain conditions, emergent gauge fields condense in the ground state, leading to a chiral spin liquid state [2]. A condensed `magnetic field' for example, correspond to presence of spontaneous circulating spin current or spin `chirality'[3].
The ground state phase of spin-1/2 J1-J2 antiferromagnetic Heisenberg model on square lattice in the maximally frustrated regime (J2 ~ 0.5J1) has been debated for decades. Here we study this model by using a recently proposed novel numerical method - the cluster update algorithm for tensor product states (TPSs). The ground state energies at finite sizes and in the thermodynamic limit (with finite size scaling) are in good agreement with the state of art exact diagonalization study, and
We propose a form of parallel computing on classical computers that is based on matrix product states. The virtual parallelization is accomplished by evolving all possible results for multiple inputs, with bits represented by matrices. The action by classical probabilistic 1-bit and deterministic 2-bit gates such as NAND are implemented in terms of matrix operations and, as opposed to quantum computing, it is possible to copy bits. We present a way to explore this method of computation to solve search problems and count the number of solutions.
As helium-4 is cooled below 2.17 K in undergoes a phase transition to a state of matter known as a superfluid which supports flow without viscosity. This type of dissipationless transport can be observed by forcing helium to travel through a narrow constriction that the normal liquid could not penetrate. Recent advances in nanofabrication techniques allow for the construction of smooth pores with nanometer radii, that approach the truly one dimensional limit.
One of the biggest challenges in physics is to develop accurate and efficient methods that can solve many currently intractable problems in correlated quantum or statistical systems. Tensor-network model/state is drawing more and more attention since it captures the feature of the area law and is absent from the sign problem. The evaluation of the expectation value of the observables can be reduced to the contraction of a tensor-network, which can be done by means of renormalization group method, and this is exactly what tensor renormalization group (TRG) method has done.