Variational methods and their applications to frustrated quantum spin models
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Quantum spin models are useful in many areas of physics, such as strongly correlated materials and quantum phase transitions, or, generally, quantum many-body systems. Most of the models of interest are not analytically solvable. Therefore they are often investigated using computational methods. However, spin models with frustrated interactions are not easily simulated numerically with existing methods, and more effective algorithms are needed. In this thesis, I will cover two areas of quantum spin research: 1. studies of several quantum spin models and 2. development of more efficient computational methods. The discussion of the computational methods and new algorithms is integrated with the physical properties of the models and new results obtained. I study the frustrated S=1/2 J1-J2 model Heisenberg model, the J-Q model, the Ising model with a transverse magnetic field, and a two-orbital spin model describing the magnetic properties of iron pnictides. I will discuss several computational algorithms, including a cluster variational method using mean-field boundary conditions, variational quantum Monte Carlo simulation with clusters-based wave functions, as well as a method I call "optilization" -- an algorithm constructed in order to accelerate the process of optimization with a large number of parameters. I apply it to matrix product states.
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