As the name implies, Monte Carlo (MC) methods employ random numbers to solve
problems. The range of problems that may be treated by MC is substantial; these include …

Quantum Monte Carlo simulations, while being efficient for bosons, suffer from the “negative
sign problem” when applied to fermions—causing an exponential increase of the computing …

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 …

Simulations of complex systems have seen rapid progress over the last decade not only due
to the continuous acceleration of computer resources but also due to improvements of …

The classical Metropolis sampling method is a cornerstone of many statistical modeling
applications that range from physics, chemistry, and biology to economics. This method is …

The recently proposed full configuration interaction quantum Monte Carlo method allows
access to essentially exact ground-state energies of systems of interacting fermions …

We present a quantum Monte Carlo method capable of sampling the full density matrix of a
many-particle system at finite temperature. This allows arbitrary reduced density matrix …

This book is devoted to the description of Bosonic and Fermionic systems: metallic clusters;
quantum dots, wires, rings and molecules; trapped Fermi and Bose atoms; liquid drops of …

Fixed node diffusion quantum Monte Carlo (FN-DMC) is an increasingly used computational
approach for investigating the electronic structure of molecules, solids, and surfaces with …

Quantum Monte Carlo simulations of quantum many-body systems are plagued by the
Fermion sign problem. The computational complexity of simulating Fermions scales …