Depenbrock, Stefan (2013): Tensor networks for the simulation of strongly correlated systems. Dissertation, LMU München: Faculty of Physics 

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Abstract
This thesis treats the classical simulation of stronglyinteracting manybody quantummechanical systems in more than one dimension using matrix product states and the more general tensor product states. Contrary to classical systems, quantum manybody systems possess an exponentially larger number of degrees of freedom, thereby significantly complicating their numerical treatment on a classical computer. For this thesis two different representations of quantum manybody states were employed. The first, the socalled matrix product states (MPS) form the basis for the extremely successful density matrix renormalization group (DMRG) algorithm. While originally conceived for onedimensional systems, MPS are in principle capable of describing arbitrary quantum manybody states. Using concepts from quantum information theory it is possible to show that MPS provide a representation of onedimensional quantum systems that scales polynomially in the number of particles, therefore allowing an efficient simulation of onedimensional systems on a classical computer. One of the key results of this thesis is that MPS representations are indeed efficient enough to describe even large systems in two dimensions, thereby enabling the simulation of such systems using DMRG. As a demonstration of the power of the DMRG algorithm, it is applied to the Heisenberg antiferromagnet with spin $S = 1/2$ on the kagome lattice. This model's ground state has long been under debate, with proposals ranging from static spin configurations to socalled quantum spin liquids, states where quantum fluctuations destroy conventional order and give rise to exotic quantum orders. Using a fully $SU(2)$symmetric implementation allowed us to handle the exponential growth of entanglement and to perform a largescale study of this system, finding the ground state for cylinders of up to 700 sites. Despite employing a onedimensional algorithm for a twodimensional system, we were able to compute the spin gap (i.e. the energy gap to the first spinful excitation) and study the ground state properties, such as the decay of correlation functions, the static spin structure factors, and the structure and distribution of the nearestneighbor spinspin correlations. Additionally, by applying a new tool from quantum information theory, the topological entanglement entropy, we could also with high confidence demonstrate the ground state of this model to be the elusive gapped $Z_2$ quantum spin liquid with topological order. To complement this study, we also considered the extension of MPS to higher dimensions, known as tensor product states (TPS). We implemented an optimization algorithm exploiting symmetries for this class of states and applied it to the bilinearbiquadraticbicubic Heisenberg model with spin $S=3/2$ on the $z=3$ Bethe lattice. By carefully analyzing the simulation data we were able to determine the presence of both conventional and symmetryprotected topological order in this model, thereby demonstrating the analytically predicted existence of the Haldane phase in higher dimensions within an extended region of the phase diagram. Key properties of this symmetryprotected topological order include a doubling of the levels in the entanglement spectrum and the presence of edge spins, both of which were confirmed in our simulations. This finding simultaneously validated the applicability of the novel TPS algorithms to the search for exotic order.
Item Type:  Thesis (Dissertation, LMU Munich) 

Keywords:  Tensor Networks, DMRG, Kagome 
Subjects:  500 Natural sciences and mathematics 500 Natural sciences and mathematics > 530 Physics 
Faculties:  Faculty of Physics 
Language:  English 
Date of oral examination:  22. July 2013 
1. Referee:  Schollwöck, Ulrich 
MD5 Checksum of the PDFfile:  96134ef71d7166e201af8bb52574436f 
Signature of the printed copy:  0001/UMC 21382 
ID Code:  15963 
Deposited On:  06. Aug 2013 05:38 
Last Modified:  20. Jul 2016 10:33 