- 1 Directed worm algorithm
- 1.1 Theory
- 1.2 The dwa code: options
- 1.3 The dwa code: starting the simulation
- 1.4 The dwa code: output
Directed worm algorithm
Quantum statistical mechanics at finite temperature
At finite temperature T, the physics is essentially captured by the partition function
and physical quantities such as the local density
for some configuration in the complete configuration space, with inverse temperature . Here, the units will be cleverly normalized later on.
Feynmann perturbation in the path-integral representation
Decompose , where is purely diagonal in the basis of choice, and being off-diagonal.
Feynmann perturbation in the path-integral representation defines the configuration weight:
The derivation can be found in chapter 2.1-2.2 of my thesis.
Quantum Monte Carlo (Directed Worm Algorithm)
The Quantum Monte Carlo simulation is in fact a Markov chain random walk in the (worldlines) configuration space, importance sampled by the configuration weight which is just a positive number assigned to some particular configuration for instance shown here.
How is being assigned depends on the model Hamiltonian as well as the ergodic algorithm that satisfies detailed balance.
For the directed worm algorithm, the configuration is updated with the worm transversing to and from the extended configuration space to ensure ergodicty. In addition, is the number of particles (or state) at site i with time 0.
Each configuration update is known as a Monte Carlo sweep.
The complete step-by-step description of the directed worm algorithm can be found in chapter 2.3 of my thesis, and the code implementation in the following.
The dwa code: options
Monte Carlo options
|THERMALIZATION||0|| 1) number of Monte Carlo configuration updates (sweeps) needed for thermalization |
2) no measurements are performed in the thermalization stage
|SWEEPS||1000000||total number of Monte Carlo configuration updates (sweeps) after thermalization|
|SKIP||1.||number of Monte Carlo configuration updates (sweeps) per measurement|
ALPS lattice library options
|LATTICE||--||which lattice do you want?|
|L||--||length of lattice|
A first quick guide to the ALPS Lattice library can be found here.
Boson Hubbard model options
|MODEL||--||set as "boson Hubbard"|
|Nmax||--||maximum number of bosons allowed per site|
|U||0.||onsite interaction strength|
The following definitions for mu are allowed:
- mu="0.5 - 0.001*((x-(L-1)/2.)*(x-(L-1)/2.) + (y-(L-1)/2.)*(y-(L-1)/2.) + (z-(L-1)/2.)*(z-(L-1)/2.)"
|MEASURE||true||shall we measure the common observables?|
|MEASURE[Simulation Speed]||true||shall we measure the simulation performance?|
More measurement options
|MEASURE[Total Particle Number^2]||false||measure_number2_|
|MEASURE[Local Kink: Number]||false||measure_local_num_kinks_|
The dwa code: starting the simulation
in command line
An example is found here.
An example is found here.
The dwa code: output
List of measurement observables
When the measurement mode is turned on, the following is a list of common observables available to the user.
|Observable||Boolean control||Binning analysis||Remark|
|Total Particle Number||—||detailed||measure always|
|Density||—||detailed||measure if lattice is homoogeneous|
|Energy Density||—||detailed||measure if lattice is homoogeneous|
|Energy Density:Vertex||—||detailed||measure if lattice is homoogeneous|
|Energy Density:Onsite||—||detailed||measure if lattice is homoogeneous|
|Total Particle Number^2||measure_number2_||detailed||—|
|Density^2||measure_density2_||detailed||measure if lattice is homogeneous|
|Energy Density^2||measure_energy_density2_||detailed||measure if lattice is homogeneous|
|Winding Number^2||measure_winding_number_||detailed||measure if lattice is periodic:|
|Stiffness (superfluid density)||measure_winding_number_||detailed||measure if lattice is periodic:|
|Green Function:0||—||detailed||measure always|
|Green Function:1||—||detailed||measure always|
|Green Function:TOF||measure_green_function_||simple||measure if tof_phase != 0|
|Momentum Distribution:0||—||detailed||measure if tof_phase == 0|
|Momentum Distribution:TOF:0||—||detailed||measure if tof_phase != 0|
Evaluating the simulation in Python
An example is found here.
Extracting and visualizing the worldlines configuration in Python
Illustrating from this example, we want to, for instance, extract the worldlines configuration of task 30 after the first run.
import pyalps.dwa; wl = pyalps.dwa.extract_worldlines('parm1b.task30.out.run1.h5');
We can easily visualise, for instance, the cross-sectional worldlines configuration of this 8x8 lattice at y=4:
pyalps.dwa.show_worldlines(wl, reshape = [8,8], at = '[:,4]');
One can easily extract the instantaneous state of the worldlines configuration at time 0 via:
import numpy states = numpy.array(wl.states());
It is also possible to easily sketch your own (extended) worldlines configuration using Python, but that will not be discussed here. In practice, you can even produce your own movie that illustrates how the worm propagates from one worldlines configuration to another.
Interested people are kindly requested to forward further enquiries to the ALPS mailing list via here.
© 2013 by Matthias Troyer, Ping Nang Ma.