# Quantum phase transitions in the Bose-Hubbard model

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'                  : 20000000,
'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 matplotlib.pyplot as plt
import pyalps.plot as aplt

plt.xlabel('Hopping $t/U$')
plt.ylabel('Superfluid density $\\rho _s$')