Dear Santu,

At least, loop with

'local_S'        : 0.5,
'L'              : 40,
'J'              : -1 ,
'Jxy'            : 0,

generates consistent results with spinmc.

As for qwl, it produces results larger than the others, but I don't know if
the difference is significant as it does not give the statistical error.

Best,
Synge


> 2018/08/27 17:24、S Baidya <santubaidya2009@gmail.com>のメール:
>
> Dear ALPS users,
>
>    I calculated specific heat capacity for square lattice with similar parameters, for spinmc (classical monte carlo) and qwl (qmc) for a square lattice.
>
> For spinmc,
>
>   #prepare the input parameters
> parms = []
> for l in [40]:
>     for t in [5.0,4.5,4.0,3.5,3.0,2.9,2.8,2.7]:
>         parms.append(
>             {
>               'LATTICE'        : "square lattice",
>               'T'              : t,
>               'S'              : 0.5,
>               'J'              : 1 ,
>               'THERMALIZATION' : 1000,
>               'SWEEPS'         : 400000,
>               'UPDATE'         : "cluster",
>               'MODEL'          : "Ising",
>               'L'              : l
>             }
>     )
>     for t in [2.6, 2.5, 2.4, 2.3, 2.2, 2.1, 2.0, 1.9, 1.8, 1.7, 1.6, 1.5, 1.2]:
>         parms.append(
>             {
>               'LATTICE'        : "square lattice",
>               'T'              : t,
>               'S'              : 0.5,
>               'J'              : 1 ,
>               'THERMALIZATION' : 1000,
>               'SWEEPS'         : 40000,
>               'UPDATE'         : "cluster",
>               'MODEL'          : "Ising",
>               'L'              : l
>             }
>     )
>
> #write the input file and run the simulation
> input_file = pyalps.writeInputFiles('parm7a',parms)
> pyalps.runApplication('spinmc',input_file,Tmin=5)
>
>
>
> For qwl,
>
> #prepare the input parameters
> parms = [{
>           'LATTICE'        : "square lattice",
>           'MODEL'          : "spin",
>           'local_S'        : 0.5,
>           'L'              : 40,
>           'J'              : -1 ,
>           'Jxy'            : 0,
>           'CUTOFF'         : 1000
>         }]
>
> #write the input file and run the simulation
> input_file = pyalps.writeInputFiles('parm6a',parms)
> res = pyalps.runApplication('qwl',input_file)
>
> #run the evaluation and load all the plots
> data = pyalps.evaluateQWL(pyalps.getResultFiles(prefix='parm6a'),DELTA_T=0.1, T_MIN=0.1, T_MAX=10.0)
>
>
> I hoped that the two results should not show completely different specific heat per site curve with temperature. But, they are totally different. Can anyone please tell me if it is expected that classical and quantum result are completely different.
>
>
> Thanking you,
>
> Santu Baidya
>
>
>
>
>
>
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