Test and benchmarks of expectation value calculation in QuTiP

In [1]:

    

from qutip import *


    

In [2]:

    

from qutip.cyQ.spmatfuncs import cy_expect


    

In [3]:

    

N = 50


    

In [4]:

    

op = rand_herm(N)
ket = rand_ket(N)
rho = ket2dm(ket)

ket_data = ket.full()
op_data = op.data


    

In [5]:

    

sop = spre(op)
sop_data = sop.data


    

In [6]:

    

rho_vec = operator_to_vector(rho)
rho_vec_data = rho_vec.full()


    

In [7]:

    

expect(op, ket)


    

    
Out[7]:





-7.758978511084069

In [8]:

    

expect(op, rho)


    

    
Out[8]:





-7.75897851108407

In [9]:

    

cy_expect(op_data.data, op_data.indices, op_data.indptr, ket_data, 1)


    

    
Out[9]:





-7.758978511084069

In [10]:

    

expect_psi(op_data, ket_data, 1)


    

    
Out[10]:





-7.758978511084069

In [11]:

    

expect_rho_vec(sop_data, rho_vec_data, 1)


    

    
Out[11]:





-7.75897851108407

In [12]:

    

%%timeit

expect(op, ket)


    

    




1000 loops, best of 3: 688 µs per loop

In [13]:

    

%%timeit

cy_expect(op_data.data, op_data.indices, op_data.indptr, ket_data, 1)


    

    




10000 loops, best of 3: 34 µs per loop

In [14]:

    

%%timeit

expect_psi(op_data, ket_data, 1)


    

    




10000 loops, best of 3: 30.2 µs per loop

In [15]:

    

%%timeit

expect(op, rho)


    

    




1000 loops, best of 3: 802 µs per loop

In [16]:

    

%%timeit

expect_rho_vec(sop_data, rho_vec_data)


    

    




1000 loops, best of 3: 640 µs per loop

In [17]:

    

from qutip.ipynbtools import version_table

version_table()


    

    
Out[17]:




Software
Version
matplotlib
1.4.x
Numpy
1.8.0.dev-75cdf3d
Python
3.3.1 (default, Apr 17 2013, 22:30:32) 
[GCC 4.7.3]
IPython
1.0.dev
OS
posix [linux]
SciPy
0.13.0.dev-d74fd00
QuTiP
2.3.0.dev-7f0ed30
Cython
0.19.1
Thu Jul 04 15:50:58 2013 JST

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