Quantum Monte Carlo

By Matthias Troyer, Ying. 2004

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. ST John
2. Guifré Vidal
3. Gábor Csányi
4. Michael Zwolak
5. Andrew J. Daley
6. Michael J. Hartmann
7. J. Dubail
8. Boris N. Khoromskij

Citations in the corpus

• Neural Message Passing for Quantum Chemistry (2017, neutral): “Therefore scientists have developed a hierarchy of approximations to quantum mechanics with varying tradeoffs of speed and accuracy, such as Density Functional Theory (DFT) with a variety of functionals (Becke, 1993; Hohenberg & Kohn, 1964), the GW approximation (Hedin, 1965), and Quantum Monte-Carl”
• Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials. (2016, methodology): “For example, this could be the quantum-mechanical (QM) total energy, obtained from (approximately) solving the QM Hamiltonian (for instance, using Density Functional Theory, Quantum Monte Carlo, or quantum chemistry methods such as Coupled Cluster Theory).”
• Mixed-state dynamics in one-dimensional quantum lattice systems: a time-dependent superoperator renormalization algorithm. (2004, methodology): “And yet, ingenious methods such as quantum Monte Carlo techniques [3] can be used to approximately evaluate, e.”
• Solving the quantum many-body problem with artificial neural networks (2016, unknown): “[1–3].”