DWA-01 Revisiting MC05
Quantum phase transitions in the Bose-Hubbard model
Attention: this implementation of the directed worm algorithm is an experimental code and should be used only for the Bose Hubbard model without interactions between neighbors. Otherwise the code may crash or yield incorrect results.
As an example of the dwa QMC code we will study a quantum phase transition in the Bose-Hubbard mode.
Superfluid density in the Bose Hubbard model
Preparing and running the simulation from the command line
The parameter file parm1a sets up Monte Carlo simulations of the quantum Bose Hubbard model on a square lattice with 4x4 sites for a couple of hopping parameters (t=0.01, 0.02, …, 0.1) using the dwa code.
LATTICE="square lattice";
L=4;
MODEL="boson Hubbard";
Nmax = 2;
U = 1.0;
mu = 0.5;
T = 0.1;
SWEEPS=5000000;
THERMALIZATION=100000;
SKIP=500;
MEASURE[Winding Number]=1
{ t=0.01; }
{ t=0.02; }
{ t=0.03; }
{ t=0.04; }
{ t=0.05; }
{ t=0.06; }
{ t=0.07; }
{ t=0.08; }
{ t=0.09; }
{ t=0.1; }
Using the standard sequence of commands you can run the simulation using the quantum dwa code
parameter2xml parm1a
dwa --Tmin 5 --write-xml parm1a.in.xml
Preparing and running the simulation using Python
To set up and run the simulation in Python we use the script tutorial1a.py. The first parts of this script imports the required modules and then prepares the input files as a list of Python dictionaries:
import pyalps
parms = []
for t in [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1]:
parms.append(
{
'LATTICE' : "square lattice",
'MODEL' : "boson Hubbard",
'T' : 0.1,
'L' : 4 ,
't' : t ,
'mu' : 0.5,
'U' : 1.0 ,
'Nmax' : 2 ,
'THERMALIZATION' : 100000,
'SWEEPS' : 2000000,
'SKIP' : 500,
'MEASURE[Winding Number]' : 1
}
)
We next convert this into a job file in XML format and run the worm simulation:
input_file = pyalps.writeInputFiles('parm1a', parms)
res = pyalps.runApplication('dwa', input_file, Tmin=5, writexml=True)
We now have the same output files as in the command line version.
Evaluating the simulation and preparing plots using Python
To load the results and prepare plots we load the results from the output files and collect the magntization density as a function of magnetic field from all output files starting with parm1a. The script is again in tutorial1a.py
import pyalps
import matplotlib.pyplot as plt
import pyalps.plot as aplt
data = pyalps.loadMeasurements(pyalps.getResultFiles(prefix='parm1a'),'Stiffness')
rhos = pyalps.collectXY(data,x='t',y='Stiffness')
plt.figure()
aplt.plot(rhos)
plt.xlabel('Hopping $t/U$')
plt.ylabel('Superfluid density $\\rho _s$')
plt.show()
The transition from the Mott insulator to the superfluid
We next want to pin down the location of the phase transition more accurately. For this we simulate a two-dimensional square lattice for various system sizes and look for a crossing of the quantity $\rho_s L$.
Preparing and running the simulation from the command line
In the parameter file parm1b we focus on the region around the critical point for three system sizes L=4,6, and 8:
LATTICE="square lattice";
MODEL="boson Hubbard";
Nmax =2;
U = 1.0;
mu = 0.5;
T = 0.05;
SWEEPS=2000000;
THERMALIZATION=150000;
SKIP=500;
{ L=4; t=0.01; }
{ L=4; t=0.02; }
{ L=4; t=0.03; }
{ L=4; t=0.04; }
{ L=4; t=0.05; }
{ L=4; t=0.06; }
{ L=4; t=0.07; }
{ L=4; t=0.08; }
{ L=4; t=0.09; }
{ L=4; t=0.1; }
{ L=6; t=0.01; }
{ L=6; t=0.02; }
{ L=6; t=0.03; }
{ L=6; t=0.04; }
{ L=6; t=0.05; }
{ L=6; t=0.06; }
{ L=6; t=0.07; }
{ L=6; t=0.08; }
{ L=6; t=0.09; }
{ L=6; t=0.1; }
{ L=8; t=0.01; }
{ L=8; t=0.02; }
{ L=8; t=0.03; }
{ L=8; t=0.04; }
{ L=8; t=0.05; }
{ L=8; t=0.06; }
{ L=8; t=0.07; }
{ L=8; t=0.08; }
{ L=8; t=0.09; }
{ L=8; t=0.1; }
Using the standard sequence of commands you can run the simulation using the quantum dwa code
parameter2xml parm1b
dwa --Tmin 5 --write-xml parm1b.in.xml
Preparing and running the simulation using Python
To set up and run the simulation in Python we use the script tutorial1b.py. The first parts of this script imports the required modules and then prepares the input files as a list of Python dictionaries:
parms = []
for L in [4,6,8]:
for t in [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1]:
parms.append(
{
'LATTICE' : "square lattice",
'MODEL' : "boson Hubbard",
'T' : 0.1,
'L' : L ,
't' : t ,
'mu' : 0.5,
'U' : 1.0 ,
'Nmax' : 2 ,
'THERMALIZATION' : 100000,
'SWEEPS' : 2000000,
'SKIP' : 500,
'MEASURE[Winding Number]': 1
}
)
We next convert this into a job file in XML format and run the worm simulation:
input_file = pyalps.writeInputFiles('parm1b', parms)
res = pyalps.runApplication('dwa', input_file, Tmin=5, writexml=True)
We now have the same output files as in the command line version.
Evaluating the simulation and preparing plots using Python
To load the results and prepare plots we load the results from the output files and collect the magntization density as a function of magnetic field from all output files starting with parm1b. The script is again in tutorial1b.py
import pyalps
import matplotlib.pyplot as plt
import pyalps.plot as aplt
data = pyalps.loadMeasurements(pyalps.getResultFiles(prefix='parm1b'),'Stiffness')
rhos = pyalps.collectXY(data,x='t',y='Stiffness',foreach=['L'])
for rho in rhos:
rho.y = rho.y * float(rho.props['L'])
plt.figure()
aplt.plot(rhos)
plt.xlabel('Hopping $t/U$')
plt.ylabel('$\\rho _sL$')
plt.legend()
plt.title('Scaling plot for Bose-Hubbard model')
plt.show()
Note the legend and labels that are nicely set up.
Contributors
- Matthias Troyer
- Ping Nang Ma