Quantum Monte Carlo
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Quantum Monte Carlo is a large class of computer algorithms that simulate quantum systems with the idea of solving the quantum many-body problem. They use, in one way or another, the Monte Carlo method to handle the many-dimensional integrals that arise. Quantum Monte Carlo allows a direct representation of many-body effects in the wave function, at the cost of statistical uncertainty that can be reduced with more simulation time. For bosons without frustration, there exist numerically exact and polynomial-scaling algorithms. For fermions, there exist very good approximations and numerically exact exponentially scaling quantum Monte Carlo algorithms, but none that are both.
In principle, any physical system can be described by the many-body Schrödinger equation as long as the constituent particles are not moving "too" fast; that is, they are not moving near the speed of light. (The Relativistic Schrödinger equation can describe Particles moving near the speed of light.) This covers a wide range of electronic problems in condensed matter physics, so if we could solve the Schrödinger equation for a given system, we could predict its behavior, which has important applications in fields from computers to biology. This also includes the nuclei in Bose–Einstein condensate and superfluids such as liquid helium. The difficulty is that the Schrödinger equation involves a function of a number of coordinates that is exponentially large in the number of particles, and is therefore difficult, if not impossible, to solve even using parallel computing technology in a reasonable amount of time. Traditionally, theorists have approximated the many-body wave function as an antisymmetric function of one-body orbitals. This kind of formulation either limits the possible wave functions, as in the case of the Hartree-Fock (HF) approximation, or converges very slowly, as in configuration interaction. One of the reasons for the difficulty with an HF initial estimate (ground state seed, also known as Slater determinant) is that it is very difficult to model the electronic and nuclear cusps in the wavefunction. However, one does not generally model at this point of the approximation. As two particles approach each other, the wavefunction has exactly known derivatives.
Quantum Monte Carlo is a way around these problems because it allows us to model a many-body wavefunction of our choice directly. Specifically, we can use a Hartree-Fock approximation as our starting point but then multiplying it by any symmetric function, of which Jastrow functions are typical, designed to enforce the cusp conditions. Most methods aim at computing the ground state wavefunction of the system, with the exception of path integral Monte Carlo and finite-temperature auxiliary field Monte Carlo, which calculate the density matrix.
There are several quantum Monte Carlo methods, each of which uses Monte Carlo in different ways to solve the many-body problem:
Quantum Monte Carlo methods
- Zero-temperature (only ground state)
- Stochastic Green function (SGF) algorithm : An algorithm designed for bosons that can simulate any complicated lattice Hamiltonian that does not have a sign problem. Used in combination with a directed update scheme, this is a powerful tool.
- Variational Monte Carlo : A good place to start; it is commonly used in many sorts of quantum problems.
- Diffusion Monte Carlo : The most common high-accuracy method for electrons (that is, chemical problems), since it comes quite close to the exact ground-state energy fairly efficiently. Also used for simulating the quantum behavior of atoms, etc.
- Reptation Monte Carlo : Recent zero-temperature method related to path integral Monte Carlo, with applications similar to diffusion Monte Carlo but with some different tradeoffs.
- Gaussian quantum Monte Carlo
- Finite-temperature (thermodynamic)
- Path integral Monte Carlo : Finite-temperature technique mostly applied to bosons where temperature is very important, especially superfluid helium.
- Auxiliary field Monte Carlo : Usually applied to lattice problems, although there has been recent work on applying it to electrons in chemical systems.
- Stochastic Green Function (SGF) algorithm
- Monte Carlo method
- Quantum chemistry
- Density matrix renormalization group
- Time-evolving block decimation
- Metropolis algorithm
- Wavefunction optimization
- Monte Carlo molecular modeling
- Quantum chemistry computer programs
- V. G. Rousseau (May 2008). "Stochastic Green Function (SGF) algorithm". Phys. Rev. E 77 (5): 056705. arXiv:0711.3839. Bibcode:2008PhRvE..77e6705R. doi:10.1103/PhysRevE.77.056705.
- Hammond, B.J.; W.A. Lester & P.J. Reynolds (1994). Monte Carlo Methods in Ab Initio Quantum Chemistry. Singapore: World Scientific. ISBN 981-02-0321-7. OCLC 29594695.
- Nightingale, M.P.; Umrigar, Cyrus J., ed. (1999). Quantum Monte Carlo Methods in Physics and Chemistry. Springer. ISBN 978-0-7923-5552-6.
- W. M. C. Foulkes; L. Mitáš, R. J. Needs and G. Rajagopal (5 January 2001). "Quantum Monte Carlo simulations of solids". Rev. Mod. Phys. 73: 33–83. Bibcode:2001RvMP...73...33F. doi:10.1103/RevModPhys.73.33.
- Raimundo R. dos Santos (2003). "Introduction to Quantum Monte Carlo simulations for fermionic systems". Braz. J. Phys. 33: 36. arXiv:cond-mat/0303551. Bibcode:2003cond.mat..3551D. doi:10.1590/S0103-97332003000100003.
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