Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Institute of Physics eBooks

Diffusion Monte Carlo

V. Apaja

Published Aug 1, 2025• 0 citations
Publisher

Abstract

A wave function written in analytical form is a simplistic version of the true wave function. Normally, a wave function evolves in time in an oscillatory fashion, each oscillation corresponds to an energy eigenvalue—quantities we can't compute in a many-body system. In imaginary time, however, the oscillations turn into decaying amplitudes, the higher the energy the faster it decays. This gives us the change to project out a low-energy state. The algorithm that does imaginary-time evolution is called diffusion Monte Carlo. It won't lead to a better analytical form of a wave function, but nevertheless lets us compute its properties, which, after all, is all we want.

DOI
10.1088/978-0-7503-6310-5ch4

Related papers

Real-Time Diffusion Monte Carlo Method

Ilkka Ruokosenmäki, Tapio T. Rantala

Direct sampling of multi-dimensional systems with quantum Monte Carlo methods allows exact account of many-body effects or particle correlations. The most straightforward approach to solve the Schrödinger equation, Diffusion Monte Carlo, has been used in several benchmark cases for other methods to pursue. Its robustness is based on direct sampling of a positive probability density for diffusion in imaginary time. It has been argued that the corresponding real time diffusion can not be realised, because the corresponding oscillating complex valued distribution can not be used to drive diffusion. Here, we demonstrate that this can be done by turning the distribution piecewise positive and normalizable, and also, by using four types of walkers. This study is a proof of concept demonstration using the well-known and transparent case: one-dimensional harmonic oscillator. Furthermore, we show that our novel method can be used to find not only the ground state but also excited states and even the time evolution of a given wave function. Considering fermionic systems, this method may turn out to be feasible for finding the wave function nodes for other approaches.

arXiv:1703.05558•Communications in Computational Physics•Oct 11, 2018•1 citation

Fourth-order diffusion Monte Carlo algorithms for solving quantum many-body problems

Harald Forbert, Siu A. Chin

By decomposing the important sampled imaginary time Schr\"odinger evolution operator to fourth order with positive coefficients, we derived a number of distinct fourth-order diffusion Monte Carlo algorithms. These sophisticated algorithms require higher derivatives of the drift velocity and local energy and are more complicated to program. However, they allowed very large time steps to be used, converged faster with lesser correlations, and virtually eliminated the step size error. We demonstrated the effectiveness of these quartic algorithms by solving for the ground-state energy and radial density distribution of bulk liquid helium.

Physical review. B, Condensed matter•Mar 21, 2001•36 citations

Path integral ground-state Monte Carlo

V. Apaja

Path integral Monte Carlo is paths in imaginary time, so it can be re-formulated to give the ground state using the principles of diffusion Monte Carlo imaginary time evolution. Instead of following paths that make up ring polymers, one can start the paths from points given by an analytical wave function. Unlike diffusion Monte Carlo, which can simulate imaginary time evolution for an arbitrarily long time, the path integral ground state method can evolve for a finite imaginary time. This evolution can still be long enough to project out the ground state, with the benefit that the process is very stable.

Institute of Physics eBooks•Aug 1, 2025•0 citations

Green’s function Monte Carlo method with exact imaginary-time propagation

Kevin E. Schmidt, Parhat Niyaz, Amanda L. Vaught +1 more

We present a general formulation of the Green's function Monte Carlo method in imaginary-time quantum Monte Carlo which employs exact propagators. This algorithm has no time-step errors and is obtained by minimal modifications of the time-independent Green's function Monte Carlo method. We describe how the method can be applied to the many-body Schrödinger equation, lattice Hamiltonians, and simple field theories. Our modification of the Green's function Monte Carlo algorithm is applied to the ground state of liquid 4He. We calculate the zero-temperature imaginary-time diffusion constant and relate that to the effective mass of a mass-four "impurity" atom in liquid 4He.

Physical Review E•Jan 1, 2005•44 citations

Monte Carlo Calculation of Three-Body Scattering

Narayan Krishna Mahale

We construct an eigenvalue problem by confining many-body system to a bounded domain with the boundary condition that the wave function vanishes. By changing the boundary, however, the eigenvalues of the energy can be varied continuously. The D-matrix is defined for a series of bounded problems with the same value for the ground state energy. The D-matrix is related to the S-matrix, enabling us to calculate the S-matrix at a given energy. The Schrodinger equation for the system is transformed to a diffusion equation by regarding time as imaginary. Initial ensemble, representing an approximate wave function, is evolved, through Monte Carlo simulation of random walks and branching, to the ground state ensemble. The limitations of investigation are: 1. Ingoing and outgoing channels have two fragments. 2. The interaction between the fragments is negligible outside the boundary mentioned above. 3. The particles are bosons or we know the zeros of the wave function. First we consider the scattering of a particle by a potential, which is equivalent to the two-body problem, in one dimension. Here we use the Poschl-Teller potential for which the exact solution is known. We use this case to investigate a new sampling method and study of various parameters. Next we consider three particles in one dimension. Here we take interaction to be a potential well, where at least one of the interactions is attractive so that a two-body bound state is possible.

•Jan 31, 2019•0 citations

Quantum Monte Carlo simulations of many- dimensional harmonic oscillators: A study of 2 nd and 4 th order propagators

Brent-Jon Bjorgvin Magnusson

Because the Schrodinger equation cannot be solved analytically for systems larger than hydrogen, approximate solution methods must be applied to study systems of chemical interest. Quantum Monte Carlo is one of these methods. Quantum Monte Carlo simulations involve the diffusion of walkers through configuration space and necessitate the use of one of two imaginary time propagators, 2nd or 4th order. The choice of the propagator implemented depends on a balance between accuracy and efficiency. Obtaining accurate solutions is the primary objective; once this is accomplished, finding ways to make the simulations efficient is the next intention. Using a model system of a multidimensional harmonic oscillator, this study intends to compare and contrast both 2nd and 4th order propagators' diminished accuracy as the dimensionality of the system is increased. Two phases of the propagation are sampled to determine accuracy: a single propagation step and the asymptotic limit. To test the accuracy after one step, we start a simulation by placing walkers at the origin, moving them forward one time step in imaginary time, creating normalized histograms of their positions and comparing these with the known result for the imaginary time propagation of the delta function centered at x=0. The convergence of the simulation is determined by observing the change in distribution from the known asymptotic limit distribution. This is done by distributing the walkers in the known asymptotic distribution and observing the change in average weight upon further propagation. A converged distribution’s average weight will not change.

•Jan 1, 2008•0 citations
A wave function written in analytical form is a simplistic version of the true wave function. Normally, a wave function evolves in time in an oscillatory fashion, each oscillation corresponds to an energy eigenvalue—quantities we can't compute in a many-body system. In imaginary time, however, the oscillations turn into decaying amplitudes, the higher the energy the faster it decays. This gives us the change to project out a low-energy state. The algorithm that does imaginary-time evolution is called diffusion Monte Carlo. It won't lead to a better analytical form of a wave function, but nevertheless lets us compute its properties, which, after all, is all we want.
Direct sampling of multi-dimensional systems with quantum Monte Carlo methods allows exact account of many-body effects or particle correlations. The most straightforward approach to solve the Schrödinger equation, Diffusion Monte Carlo, has been used in several benchmark cases for other methods to pursue. Its robustness is based on direct sampling of a positive probability density for diffusion in imaginary time. It has been argued that the corresponding real time diffusion can not be realised, because the corresponding oscillating complex valued distribution can not be used to drive diffusion. Here, we demonstrate that this can be done by turning the distribution piecewise positive and normalizable, and also, by using four types of walkers. This study is a proof of concept demonstration using the well-known and transparent case: one-dimensional harmonic oscillator. Furthermore, we show that our novel method can be used to find not only the ground state but also excited states and even the time evolution of a given wave function. Considering fermionic systems, this method may turn out to be feasible for finding the wave function nodes for other approaches.
By decomposing the important sampled imaginary time Schr\"odinger evolution operator to fourth order with positive coefficients, we derived a number of distinct fourth-order diffusion Monte Carlo algorithms. These sophisticated algorithms require higher derivatives of the drift velocity and local energy and are more complicated to program. However, they allowed very large time steps to be used, converged faster with lesser correlations, and virtually eliminated the step size error. We demonstrated the effectiveness of these quartic algorithms by solving for the ground-state energy and radial density distribution of bulk liquid helium.
Path integral Monte Carlo is paths in imaginary time, so it can be re-formulated to give the ground state using the principles of diffusion Monte Carlo imaginary time evolution. Instead of following paths that make up ring polymers, one can start the paths from points given by an analytical wave function. Unlike diffusion Monte Carlo, which can simulate imaginary time evolution for an arbitrarily long time, the path integral ground state method can evolve for a finite imaginary time. This evolution can still be long enough to project out the ground state, with the benefit that the process is very stable.
We present a general formulation of the Green's function Monte Carlo method in imaginary-time quantum Monte Carlo which employs exact propagators. This algorithm has no time-step errors and is obtained by minimal modifications of the time-independent Green's function Monte Carlo method. We describe how the method can be applied to the many-body Schrödinger equation, lattice Hamiltonians, and simple field theories. Our modification of the Green's function Monte Carlo algorithm is applied to the ground state of liquid 4He. We calculate the zero-temperature imaginary-time diffusion constant and relate that to the effective mass of a mass-four "impurity" atom in liquid 4He.
We construct an eigenvalue problem by confining many-body system to a bounded domain with the boundary condition that the wave function vanishes. By changing the boundary, however, the eigenvalues of the energy can be varied continuously. The D-matrix is defined for a series of bounded problems with the same value for the ground state energy. The D-matrix is related to the S-matrix, enabling us to calculate the S-matrix at a given energy. The Schrodinger equation for the system is transformed to a diffusion equation by regarding time as imaginary. Initial ensemble, representing an approximate wave function, is evolved, through Monte Carlo simulation of random walks and branching, to the ground state ensemble. The limitations of investigation are: 1. Ingoing and outgoing channels have two fragments. 2. The interaction between the fragments is negligible outside the boundary mentioned above. 3. The particles are bosons or we know the zeros of the wave function. First we consider the scattering of a particle by a potential, which is equivalent to the two-body problem, in one dimension. Here we use the Poschl-Teller potential for which the exact solution is known. We use this case to investigate a new sampling method and study of various parameters. Next we consider three particles in one dimension. Here we take interaction to be a potential well, where at least one of the interactions is attractive so that a two-body bound state is possible.
Because the Schrodinger equation cannot be solved analytically for systems larger than hydrogen, approximate solution methods must be applied to study systems of chemical interest. Quantum Monte Carlo is one of these methods. Quantum Monte Carlo simulations involve the diffusion of walkers through configuration space and necessitate the use of one of two imaginary time propagators, 2nd or 4th order. The choice of the propagator implemented depends on a balance between accuracy and efficiency. Obtaining accurate solutions is the primary objective; once this is accomplished, finding ways to make the simulations efficient is the next intention. Using a model system of a multidimensional harmonic oscillator, this study intends to compare and contrast both 2nd and 4th order propagators' diminished accuracy as the dimensionality of the system is increased. Two phases of the propagation are sampled to determine accuracy: a single propagation step and the asymptotic limit. To test the accuracy after one step, we start a simulation by placing walkers at the origin, moving them forward one time step in imaginary time, creating normalized histograms of their positions and comparing these with the known result for the imaginary time propagation of the delta function centered at x=0. The convergence of the simulation is determined by observing the change in distribution from the known asymptotic limit distribution. This is done by distributing the walkers in the known asymptotic distribution and observing the change in average weight upon further propagation. A converged distribution’s average weight will not change.