Cambridge University Press, UK, 2016. — 502 p. — ISBN-10: 1107006422.Featuring detailed explanations of the major algorithms used in quantum Monte Carlo simulations, this is the first textbook of its kind to provide a pedagogical overview of the field and its applications. The book provides a comprehensive introduction to the Monte Carlo method, its use, and its foundations, and examines algorithms for the simulation of quantum many-body lattice problems at finite and zero temperature. These algorithms include continuous-time loop and cluster algorithms for quantum spins, determinant methods for simulating fermions, power methods for computing ground and excited states, and the variational Monte Carlo method. Also discussed are continuous-time algorithms for quantum impurity models and their use within dynamical mean-field theory, along with algorithms for analytically continuing imaginary-time quantum Monte Carlo data. The parallelization of Monte Carlo simulations is also addressed. This is an essential resource for graduate students, teachers, and researchers interested in quantum Monte Carlo techniques. Contents Monte Carlo basics Introduction Monte Carlo basics Data analysis Monte Carlo for classical many-body problems Quantum Monte Carlo primer Finite temperature Finite-temperature quantum spin algorithms Determinant method Continuous-time impurity solvers Zero temperature Variational Monte Carlo Power methods Fermion ground state methods Other topics Analytic continuation Parallelization Appendixes Alias method Rejection method Extended-ensemble methods Loop/cluster algorithms: SU(N) model Long-range interactions Thouless’s theorem Hubbard-Stratonovich transformations Multi-electron propagator Zero temperature determinant method Anderson impurity model: chain representation Anderson impurity model: action formulation Continuous-time auxiliary-field algorithm Continuous-time determinant algorithm Correlated sampling The Bryan algorithm
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