In this dissertation, I present my original research in the development of algorithms for computing ground-state properties of strongly-correlated electronic systems from first principles. I present three main algorithms. First, I present a 'semistochastic' projection algorithm, dubbed Semistochastic Quantum Monte Carlo, which combines the best qualities of deterministic and stochastic methods for projecting out a ground state wavefunction in a basis of Slater determinants. This new algorithm can treat systems as large as a fully-stochastic algorithm can, while dramatically reducing the statistical uncertainty and bias by treating the most important part of the problem deterministically. Second, I present an efficient algorithm for sampling...
Quantum Monte Carlo (QMC) methods are among the most accurate for computing ground state properties ...
We review a suite of stochastic vector computational approaches for studying the electronic structur...
International audienceWe present a new method for the optimization of large configuration interactio...
Development of exponentially scaling methods has seen great progress in tackling larger systems than...
In this thesis we investigate the recently developed Full Configuration Interaction Quantum Monte Ca...
This thesis details four research projects related to zero temperature quantum Monte Carlo. Chapters...
2noOver the past several decades, computational approaches to studying strongly-interacting systems ...
We extend the recently proposed heat-bath configuration interaction (HCI) method [Holmes, Tubman, Um...
142 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.Finally, we explore three alg...
This thesis, whose topic is quantum chemistry algorithms, is made in the context of the change in pa...
Emergent many-body phenomena are at the core of the exciting properties of strongly-correlated mater...
Featuring detailed explanations of the major algorithms used in quantum Monte Carlo simulations, thi...
We tutorially review the determinantal Quantum Monte Carlo method for fermionic systems, using the H...
Accurate first-principles calculations can provide valuable predictions for material-specific proper...
Path integral Monte Carlo (PIMC) is a quantum-level simulation method based on a stochastic sampling...
Quantum Monte Carlo (QMC) methods are among the most accurate for computing ground state properties ...
We review a suite of stochastic vector computational approaches for studying the electronic structur...
International audienceWe present a new method for the optimization of large configuration interactio...
Development of exponentially scaling methods has seen great progress in tackling larger systems than...
In this thesis we investigate the recently developed Full Configuration Interaction Quantum Monte Ca...
This thesis details four research projects related to zero temperature quantum Monte Carlo. Chapters...
2noOver the past several decades, computational approaches to studying strongly-interacting systems ...
We extend the recently proposed heat-bath configuration interaction (HCI) method [Holmes, Tubman, Um...
142 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.Finally, we explore three alg...
This thesis, whose topic is quantum chemistry algorithms, is made in the context of the change in pa...
Emergent many-body phenomena are at the core of the exciting properties of strongly-correlated mater...
Featuring detailed explanations of the major algorithms used in quantum Monte Carlo simulations, thi...
We tutorially review the determinantal Quantum Monte Carlo method for fermionic systems, using the H...
Accurate first-principles calculations can provide valuable predictions for material-specific proper...
Path integral Monte Carlo (PIMC) is a quantum-level simulation method based on a stochastic sampling...
Quantum Monte Carlo (QMC) methods are among the most accurate for computing ground state properties ...
We review a suite of stochastic vector computational approaches for studying the electronic structur...
International audienceWe present a new method for the optimization of large configuration interactio...