# This file is part of QuTiP: Quantum Toolbox in Python.
#
# Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the QuTiP: Quantum Toolbox in Python nor the names
# of its contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
###############################################################################
"""The Quantum Object (Qobj) class, for representing quantum states and
operators, and related functions.
"""
__all__ = ['Qobj', 'qobj_list_evaluate', 'ptrace', 'dag', 'isequal',
'issuper', 'isoper', 'isoperket', 'isoperbra', 'isket', 'isbra',
'isherm', 'shape', 'dims']
import warnings
import types
try:
import builtins
except:
import __builtin__ as builtins
# import math functions from numpy.math: required for td string evaluation
from numpy import (arccos, arccosh, arcsin, arcsinh, arctan, arctan2, arctanh,
ceil, copysign, cos, cosh, degrees, e, exp, expm1, fabs,
floor, fmod, frexp, hypot, isinf, isnan, ldexp, log, log10,
log1p, modf, pi, radians, sin, sinh, sqrt, tan, tanh, trunc)
import numpy as np
import scipy.sparse as sp
import scipy.linalg as la
import qutip.settings as settings
from qutip import __version__
from qutip.fastsparse import fast_csr_matrix, fast_identity
from qutip.cy.ptrace import _ptrace
from qutip.permute import _permute
from qutip.sparse import (sp_eigs, sp_expm, sp_fro_norm, sp_max_norm,
sp_one_norm, sp_L2_norm)
from qutip.dimensions import (type_from_dims, enumerate_flat,
collapse_dims_super)
from qutip.cy.spmath import (zcsr_transpose, zcsr_adjoint, zcsr_isherm,
zcsr_trace, zcsr_proj, zcsr_inner)
from qutip.cy.spmatfuncs import zcsr_mat_elem
from qutip.cy.sparse_utils import cy_tidyup
import sys
if sys.version_info.major >= 3:
from itertools import zip_longest
elif sys.version_info.major < 3:
from itertools import izip_longest
zip_longest = izip_longest
# OPENMP stuff
from qutip.cy.openmp.utilities import use_openmp
if settings.has_openmp:
from qutip.cy.openmp.omp_sparse_utils import omp_tidyup
[docs]class Qobj(object):
"""A class for representing quantum objects, such as quantum operators
and states.
The Qobj class is the QuTiP representation of quantum operators and state
vectors. This class also implements math operations +,-,* between Qobj
instances (and / by a C-number), as well as a collection of common
operator/state operations. The Qobj constructor optionally takes a
dimension ``list`` and/or shape ``list`` as arguments.
Parameters
----------
inpt : array_like
Data for vector/matrix representation of the quantum object.
dims : list
Dimensions of object used for tensor products.
shape : list
Shape of underlying data structure (matrix shape).
copy : bool
Flag specifying whether Qobj should get a copy of the
input data, or use the original.
fast : bool
Flag for fast qobj creation when running ode solvers.
This parameter is used internally only.
Attributes
----------
data : array_like
Sparse matrix characterizing the quantum object.
dims : list
List of dimensions keeping track of the tensor structure.
shape : list
Shape of the underlying `data` array.
type : str
Type of quantum object: 'bra', 'ket', 'oper', 'operator-ket',
'operator-bra', or 'super'.
superrep : str
Representation used if `type` is 'super'. One of 'super'
(Liouville form) or 'choi' (Choi matrix with tr = dimension).
isherm : bool
Indicates if quantum object represents Hermitian operator.
isunitary : bool
Indictaes if quantum object represents unitary operator.
iscp : bool
Indicates if the quantum object represents a map, and if that map is
completely positive (CP).
ishp : bool
Indicates if the quantum object represents a map, and if that map is
hermicity preserving (HP).
istp : bool
Indicates if the quantum object represents a map, and if that map is
trace preserving (TP).
iscptp : bool
Indicates if the quantum object represents a map that is completely
positive and trace preserving (CPTP).
isket : bool
Indicates if the quantum object represents a ket.
isbra : bool
Indicates if the quantum object represents a bra.
isoper : bool
Indicates if the quantum object represents an operator.
issuper : bool
Indicates if the quantum object represents a superoperator.
isoperket : bool
Indicates if the quantum object represents an operator in column vector
form.
isoperbra : bool
Indicates if the quantum object represents an operator in row vector
form.
Methods
-------
copy()
Create copy of Qobj
conj()
Conjugate of quantum object.
cosm()
Cosine of quantum object.
dag()
Adjoint (dagger) of quantum object.
dnorm()
Diamond norm of quantum operator.
dual_chan()
Dual channel of quantum object representing a CP map.
eigenenergies(sparse=False, sort='low', eigvals=0, tol=0, maxiter=100000)
Returns eigenenergies (eigenvalues) of a quantum object.
eigenstates(sparse=False, sort='low', eigvals=0, tol=0, maxiter=100000)
Returns eigenenergies and eigenstates of quantum object.
expm()
Matrix exponential of quantum object.
full(order='C')
Returns dense array of quantum object `data` attribute.
groundstate(sparse=False, tol=0, maxiter=100000)
Returns eigenvalue and eigenket for the groundstate of a quantum
object.
inv()
Return a Qobj corresponding to the matrix inverse of the operator.
matrix_element(bra, ket)
Returns the matrix element of operator between `bra` and `ket` vectors.
norm(norm='tr', sparse=False, tol=0, maxiter=100000)
Returns norm of a ket or an operator.
permute(order)
Returns composite qobj with indices reordered.
proj()
Computes the projector for a ket or bra vector.
ptrace(sel)
Returns quantum object for selected dimensions after performing
partial trace.
sinm()
Sine of quantum object.
sqrtm()
Matrix square root of quantum object.
tidyup(atol=1e-12)
Removes small elements from quantum object.
tr()
Trace of quantum object.
trans()
Transpose of quantum object.
transform(inpt, inverse=False)
Performs a basis transformation defined by `inpt` matrix.
trunc_neg(method='clip')
Removes negative eigenvalues and returns a new Qobj that is
a valid density operator.
unit(norm='tr', sparse=False, tol=0, maxiter=100000)
Returns normalized quantum object.
"""
__array_priority__ = 100 # sets Qobj priority above numpy arrays
def __init__(self, inpt=None, dims=[[], []], shape=[],
type=None, isherm=None, copy=True,
fast=False, superrep=None, isunitary=None):
"""
Qobj constructor.
"""
self._isherm = isherm
self._type = type
self.superrep = superrep
self._isunitary = isunitary
if fast == 'mc':
# fast Qobj construction for use in mcsolve with ket output
self._data = inpt
self.dims = dims
self._isherm = False
return
if fast == 'mc-dm':
# fast Qobj construction for use in mcsolve with dm output
self._data = inpt
self.dims = dims
self._isherm = True
return
if isinstance(inpt, Qobj):
# if input is already Qobj then return identical copy
self._data = fast_csr_matrix((inpt.data.data, inpt.data.indices,
inpt.data.indptr),
shape=inpt.shape, copy=copy)
if not np.any(dims):
# Dimensions of quantum object used for keeping track of tensor
# components
self.dims = inpt.dims
else:
self.dims = dims
self.superrep = inpt.superrep
self._isunitary = inpt._isunitary
elif inpt is None:
# initialize an empty Qobj with correct dimensions and shape
if any(dims):
N, M = np.prod(dims[0]), np.prod(dims[1])
self.dims = dims
elif shape:
N, M = shape
self.dims = [[N], [M]]
else:
N, M = 1, 1
self.dims = [[N], [M]]
self._data = fast_csr_matrix(shape=(N, M))
elif isinstance(inpt, list) or isinstance(inpt, tuple):
# case where input is a list
data = np.array(inpt)
if len(data.shape) == 1:
# if list has only one dimension (i.e [5,4])
data = data.transpose()
_tmp = sp.csr_matrix(data, dtype=complex)
self._data = fast_csr_matrix((_tmp.data, _tmp.indices, _tmp.indptr),
shape=_tmp.shape)
if not np.any(dims):
self.dims = [[int(data.shape[0])], [int(data.shape[1])]]
else:
self.dims = dims
elif isinstance(inpt, np.ndarray) or sp.issparse(inpt):
# case where input is array or sparse
if inpt.ndim == 1:
inpt = inpt[:, np.newaxis]
do_copy = copy
if not isinstance(inpt, fast_csr_matrix):
_tmp = sp.csr_matrix(inpt, dtype=complex, copy=do_copy)
_tmp.sort_indices() #Make sure indices are sorted.
do_copy = 0
else:
_tmp = inpt
self._data = fast_csr_matrix((_tmp.data, _tmp.indices, _tmp.indptr),
shape=_tmp.shape, copy=do_copy)
if not np.any(dims):
self.dims = [[int(inpt.shape[0])], [int(inpt.shape[1])]]
else:
self.dims = dims
elif isinstance(inpt, (int, float, complex,
np.integer, np.floating, np.complexfloating)):
# if input is int, float, or complex then convert to array
_tmp = sp.csr_matrix([[inpt]], dtype=complex)
self._data = fast_csr_matrix((_tmp.data, _tmp.indices, _tmp.indptr),
shape=_tmp.shape)
if not np.any(dims):
self.dims = [[1], [1]]
else:
self.dims = dims
else:
warnings.warn("Initializing Qobj from unsupported type: %s" %
builtins.type(inpt))
inpt = np.array([[0]])
_tmp = sp.csr_matrix(inpt, dtype=complex, copy=copy)
self._data = fast_csr_matrix((_tmp.data, _tmp.indices, _tmp.indptr),
shape=_tmp.shape)
self.dims = [[int(inpt.shape[0])], [int(inpt.shape[1])]]
if type == 'super':
# Type is not super, i.e. dims not explicitly passed, but oper shape
if dims == [[], []] and self.shape[0] == self.shape[1]:
sub_shape = np.sqrt(self.shape[0])
# check if root of shape is int
if (sub_shape % 1) != 0:
raise Exception('Invalid shape for a super operator.')
else:
sub_shape = int(sub_shape)
self.dims = [[[sub_shape], [sub_shape]]]*2
if superrep:
self.superrep = superrep
else:
if self.type == 'super' and self.superrep is None:
self.superrep = 'super'
# clear type cache
self._type = None
[docs] def copy(self):
"""Create identical copy"""
return Qobj(inpt=self)
def get_data(self):
return self._data
# Here we perfrom a check of the csr matrix type during setting of Q.data
def set_data(self, data):
if not isinstance(data, fast_csr_matrix):
raise TypeError('Qobj data must be in fast_csr format.')
else:
self._data = data
data = property(get_data, set_data)
def __add__(self, other):
"""
ADDITION with Qobj on LEFT [ ex. Qobj+4 ]
"""
self._isunitary = None
if isinstance(other, eseries):
return other.__radd__(self)
if not isinstance(other, Qobj):
if isinstance(other, (int, float, complex, np.integer, np.floating,
np.complexfloating, np.ndarray, list, tuple)) \
or sp.issparse(other):
other = Qobj(other)
else:
return NotImplemented
if np.prod(other.shape) == 1 and np.prod(self.shape) != 1:
# case for scalar quantum object
dat = other.data[0, 0]
if dat == 0:
return self
out = Qobj()
if self.type in ['oper', 'super']:
out.data = self.data + dat * fast_identity(
self.shape[0])
else:
out.data = self.data
out.data.data = out.data.data + dat
out.dims = self.dims
if settings.auto_tidyup: out.tidyup()
if isinstance(dat, (int, float)):
out._isherm = self._isherm
else:
# We use _isherm here to prevent recalculating on self and
# other, relying on that bool(None) == False.
out._isherm = (True if self._isherm and other._isherm
else out.isherm)
out.superrep = self.superrep
return out
elif np.prod(self.shape) == 1 and np.prod(other.shape) != 1:
# case for scalar quantum object
dat = self.data[0, 0]
if dat == 0:
return other
out = Qobj()
if other.type in ['oper', 'super']:
out.data = dat * fast_identity(other.shape[0]) + other.data
else:
out.data = other.data
out.data.data = out.data.data + dat
out.dims = other.dims
if settings.auto_tidyup: out.tidyup()
if isinstance(dat, complex):
out._isherm = out.isherm
else:
out._isherm = self._isherm
out.superrep = self.superrep
return out
elif self.dims != other.dims:
raise TypeError('Incompatible quantum object dimensions')
elif self.shape != other.shape:
raise TypeError('Matrix shapes do not match')
else: # case for matching quantum objects
out = Qobj()
out.data = self.data + other.data
out.dims = self.dims
if settings.auto_tidyup: out.tidyup()
if self.type in ['ket', 'bra', 'operator-ket', 'operator-bra']:
out._isherm = False
elif self._isherm is None or other._isherm is None:
out._isherm = out.isherm
elif not self._isherm and not other._isherm:
out._isherm = out.isherm
else:
out._isherm = self._isherm and other._isherm
if self.superrep and other.superrep:
if self.superrep != other.superrep:
msg = ("Adding superoperators with different " +
"representations")
warnings.warn(msg)
out.superrep = self.superrep
return out
def __radd__(self, other):
"""
ADDITION with Qobj on RIGHT [ ex. 4+Qobj ]
"""
return self + other
def __sub__(self, other):
"""
SUBTRACTION with Qobj on LEFT [ ex. Qobj-4 ]
"""
return self + (-other)
def __rsub__(self, other):
"""
SUBTRACTION with Qobj on RIGHT [ ex. 4-Qobj ]
"""
return (-self) + other
def __mul__(self, other):
"""
MULTIPLICATION with Qobj on LEFT [ ex. Qobj*4 ]
"""
self._isunitary = None
if isinstance(other, Qobj):
if self.dims[1] == other.dims[0]:
out = Qobj()
out.data = self.data * other.data
dims = [self.dims[0], other.dims[1]]
out.dims = dims
if settings.auto_tidyup: out.tidyup()
if (settings.auto_tidyup_dims
and not isinstance(dims[0][0], list)
and not isinstance(dims[1][0], list)):
# If neither left or right is a superoperator,
# we should implicitly partial trace over
# matching dimensions of 1.
# Using izip_longest allows for the left and right dims
# to have uneven length (non-square Qobjs).
# We use None as padding so that it doesn't match anything,
# and will never cause a partial trace on the other side.
mask = [l == r == 1 for l, r in zip_longest(dims[0],
dims[1],
fillvalue=None)]
# To ensure that there are still any dimensions left, we
# use max() to add a dimensions list of [1] if all matching
# dims are traced out of that side.
out.dims = [max([1],
[dim for dim, m in zip(dims[0], mask)
if not m]),
max([1],
[dim for dim, m in zip(dims[1], mask)
if not m])]
else:
out.dims = dims
out._isherm = None
if self.superrep and other.superrep:
if self.superrep != other.superrep:
msg = ("Multiplying superoperators with different " +
"representations")
warnings.warn(msg)
out.superrep = self.superrep
return out
elif np.prod(self.shape) == 1:
out = Qobj(other)
out.data *= self.data[0, 0]
out.superrep = other.superrep
return out.tidyup() if settings.auto_tidyup else out
elif np.prod(other.shape) == 1:
out = Qobj(self)
out.data *= other.data[0, 0]
out.superrep = self.superrep
return out.tidyup() if settings.auto_tidyup else out
elif self.shape[1] == 1 and other.shape[0] == 1:
out = Qobj()
out.data = self.data * other.data
out.dims = [self.dims[0], other.dims[1]]
return out.tidyup() if settings.auto_tidyup else out
else:
raise TypeError("Incompatible Qobj shapes")
elif isinstance(other, np.ndarray):
if other.dtype == 'object':
return np.array([self * item for item in other],
dtype=object)
else:
return self.data * other
elif isinstance(other, list):
# if other is a list, do element-wise multiplication
return np.array([self * item for item in other],
dtype=object)
elif isinstance(other, eseries):
return other.__rmul__(self)
elif isinstance(other, (int, float, complex,
np.integer, np.floating, np.complexfloating)):
out = Qobj()
out.data = self.data * other
out.dims = self.dims
out.superrep = self.superrep
if settings.auto_tidyup: out.tidyup()
if isinstance(other, complex):
out._isherm = out.isherm
else:
out._isherm = self._isherm
return out
else:
return NotImplemented
def __rmul__(self, other):
"""
MULTIPLICATION with Qobj on RIGHT [ ex. 4*Qobj ]
"""
if isinstance(other, np.ndarray):
if other.dtype == 'object':
return np.array([item * self for item in other],
dtype=object)
else:
return other * self.data
elif isinstance(other, list):
# if other is a list, do element-wise multiplication
return np.array([item * self for item in other],
dtype=object)
elif isinstance(other, eseries):
return other.__mul__(self)
elif isinstance(other, (int, float, complex,
np.integer, np.floating, np.complexfloating)):
out = Qobj()
out.data = other * self.data
out.dims = self.dims
out.superrep = self.superrep
if settings.auto_tidyup: out.tidyup()
if isinstance(other, complex):
out._isherm = out.isherm
else:
out._isherm = self._isherm
return out
else:
raise TypeError("Incompatible object for multiplication")
def __truediv__(self, other):
return self.__div__(other)
def __div__(self, other):
"""
DIVISION (by numbers only)
"""
if isinstance(other, Qobj): # if both are quantum objects
raise TypeError("Incompatible Qobj shapes " +
"[division with Qobj not implemented]")
if isinstance(other, (int, float, complex,
np.integer, np.floating, np.complexfloating)):
out = Qobj()
out.data = self.data / other
out.dims = self.dims
if settings.auto_tidyup: out.tidyup()
if isinstance(other, complex):
out._isherm = out.isherm
else:
out._isherm = self._isherm
out.superrep = self.superrep
return out
else:
raise TypeError("Incompatible object for division")
def __neg__(self):
"""
NEGATION operation.
"""
out = Qobj()
out.data = -self.data
out.dims = self.dims
out.superrep = self.superrep
if settings.auto_tidyup: out.tidyup()
out._isherm = self._isherm
out._isunitary = self._isunitary
return out
def __getitem__(self, ind):
"""
GET qobj elements.
"""
out = self.data[ind]
if sp.issparse(out):
return np.asarray(out.todense())
else:
return out
def __eq__(self, other):
"""
EQUALITY operator.
"""
if (isinstance(other, Qobj) and
self.dims == other.dims and
not np.any(np.abs((self.data - other.data).data) >
settings.atol)):
return True
else:
return False
def __ne__(self, other):
"""
INEQUALITY operator.
"""
return not (self == other)
def __pow__(self, n, m=None): # calculates powers of Qobj
"""
POWER operation.
"""
if self.type not in ['oper', 'super']:
raise Exception("Raising a qobj to some power works only for " +
"operators and super-operators (square matrices).")
if m is not None:
raise NotImplementedError("modulo is not implemented for Qobj")
try:
data = self.data ** n
out = Qobj(data, dims=self.dims)
out.superrep = self.superrep
return out.tidyup() if settings.auto_tidyup else out
except:
raise ValueError('Invalid choice of exponent.')
def __abs__(self):
return abs(self.data)
def __str__(self):
s = ""
t = self.type
shape = self.shape
if self.type in ['oper', 'super']:
s += ("Quantum object: " +
"dims = " + str(self.dims) +
", shape = " + str(shape) +
", type = " + t +
", isherm = " + str(self.isherm) +
(
", superrep = {0.superrep}".format(self)
if t == "super" and self.superrep != "super"
else ""
) + "\n")
else:
s += ("Quantum object: " +
"dims = " + str(self.dims) +
", shape = " + str(shape) +
", type = " + t + "\n")
s += "Qobj data =\n"
if shape[0] > 10000 or shape[1] > 10000:
# if the system is huge, don't attempt to convert to a
# dense matrix and then to string, because it is pointless
# and is likely going to produce memory errors. Instead print the
# sparse data string representation
s += str(self.data)
elif all(np.imag(self.data.data) == 0):
s += str(np.real(self.full()))
else:
s += str(self.full())
return s
def __repr__(self):
# give complete information on Qobj without print statement in
# command-line we cant realistically serialize a Qobj into a string,
# so we simply return the informal __str__ representation instead.)
return self.__str__()
def __call__(self, other):
"""
Acts this Qobj on another Qobj either by left-multiplication,
or by vectorization and devectorization, as
appropriate.
"""
if not isinstance(other, Qobj):
raise TypeError("Only defined for quantum objects.")
if self.type == "super":
if other.type == "ket":
other = qutip.states.ket2dm(other)
if other.type == "oper":
return qutip.superoperator.vector_to_operator(
self * qutip.superoperator.operator_to_vector(other)
)
else:
raise TypeError("Can only act super on oper or ket.")
elif self.type == "oper":
if other.type == "ket":
return self * other
else:
raise TypeError("Can only act oper on ket.")
def __getstate__(self):
# defines what happens when Qobj object gets pickled
self.__dict__.update({'qutip_version': __version__[:5]})
return self.__dict__
def __setstate__(self, state):
# defines what happens when loading a pickled Qobj
if 'qutip_version' in state.keys():
del state['qutip_version']
(self.__dict__).update(state)
def _repr_latex_(self):
"""
Generate a LaTeX representation of the Qobj instance. Can be used for
formatted output in ipython notebook.
"""
t = self.type
shape = self.shape
s = r''
if self.type in ['oper', 'super']:
s += ("Quantum object: " +
"dims = " + str(self.dims) +
", shape = " + str(shape) +
", type = " + t +
", isherm = " + str(self.isherm) +
(
", superrep = {0.superrep}".format(self)
if t == "super" and self.superrep != "super"
else ""
))
else:
s += ("Quantum object: " +
"dims = " + str(self.dims) +
", shape = " + str(shape) +
", type = " + t)
M, N = self.data.shape
s += r'\begin{equation*}\left(\begin{array}{*{11}c}'
def _format_float(value):
if value == 0.0:
return "0.0"
elif abs(value) > 1000.0 or abs(value) < 0.001:
return ("%.3e" % value).replace("e", r"\times10^{") + "}"
elif abs(value - int(value)) < 0.001:
return "%.1f" % value
else:
return "%.3f" % value
def _format_element(m, n, d):
s = " & " if n > 0 else ""
if type(d) == str:
return s + d
else:
if abs(np.imag(d)) < settings.atol:
return s + _format_float(np.real(d))
elif abs(np.real(d)) < settings.atol:
return s + _format_float(np.imag(d)) + "j"
else:
s_re = _format_float(np.real(d))
s_im = _format_float(np.imag(d))
if np.imag(d) > 0.0:
return (s + "(" + s_re + "+" + s_im + "j)")
else:
return (s + "(" + s_re + s_im + "j)")
if M > 10 and N > 10:
# truncated matrix output
for m in range(5):
for n in range(5):
s += _format_element(m, n, self.data[m, n])
s += r' & \cdots'
for n in range(N - 5, N):
s += _format_element(m, n, self.data[m, n])
s += r'\\'
for n in range(5):
s += _format_element(m, n, r'\vdots')
s += r' & \ddots'
for n in range(N - 5, N):
s += _format_element(m, n, r'\vdots')
s += r'\\'
for m in range(M - 5, M):
for n in range(5):
s += _format_element(m, n, self.data[m, n])
s += r' & \cdots'
for n in range(N - 5, N):
s += _format_element(m, n, self.data[m, n])
s += r'\\'
elif M > 10 and N <= 10:
# truncated vertically elongated matrix output
for m in range(5):
for n in range(N):
s += _format_element(m, n, self.data[m, n])
s += r'\\'
for n in range(N):
s += _format_element(m, n, r'\vdots')
s += r'\\'
for m in range(M - 5, M):
for n in range(N):
s += _format_element(m, n, self.data[m, n])
s += r'\\'
elif M <= 10 and N > 10:
# truncated horizontally elongated matrix output
for m in range(M):
for n in range(5):
s += _format_element(m, n, self.data[m, n])
s += r' & \cdots'
for n in range(N - 5, N):
s += _format_element(m, n, self.data[m, n])
s += r'\\'
else:
# full output
for m in range(M):
for n in range(N):
s += _format_element(m, n, self.data[m, n])
s += r'\\'
s += r'\end{array}\right)\end{equation*}'
return s
[docs] def dag(self):
"""Adjoint operator of quantum object.
"""
out = Qobj()
out.data = zcsr_adjoint(self.data)
out.dims = [self.dims[1], self.dims[0]]
out._isherm = self._isherm
out.superrep = self.superrep
return out
[docs] def dual_chan(self):
"""Dual channel of quantum object representing a completely positive
map.
"""
# Uses the technique of Johnston and Kribs (arXiv:1102.0948), which
# is only valid for completely positive maps.
if not self.iscp:
raise ValueError("Dual channels are only implemented for CP maps.")
J = sr.to_choi(self)
tensor_idxs = enumerate_flat(J.dims)
J_dual = tensor.tensor_swap(J, *(
list(zip(tensor_idxs[0][1], tensor_idxs[0][0])) +
list(zip(tensor_idxs[1][1], tensor_idxs[1][0]))
)).trans()
J_dual.superrep = 'choi'
return J_dual
[docs] def conj(self):
"""Conjugate operator of quantum object.
"""
out = Qobj()
out.data = self.data.conj()
out.dims = [self.dims[0], self.dims[1]]
return out
[docs] def norm(self, norm=None, sparse=False, tol=0, maxiter=100000):
"""Norm of a quantum object.
Default norm is L2-norm for kets and trace-norm for operators.
Other ket and operator norms may be specified using the `norm` and
argument.
Parameters
----------
norm : str
Which norm to use for ket/bra vectors: L2 'l2', max norm 'max',
or for operators: trace 'tr', Frobius 'fro', one 'one', or max
'max'.
sparse : bool
Use sparse eigenvalue solver for trace norm. Other norms are not
affected by this parameter.
tol : float
Tolerance for sparse solver (if used) for trace norm. The sparse
solver may not converge if the tolerance is set too low.
maxiter : int
Maximum number of iterations performed by sparse solver (if used)
for trace norm.
Returns
-------
norm : float
The requested norm of the operator or state quantum object.
Notes
-----
The sparse eigensolver is much slower than the dense version.
Use sparse only if memory requirements demand it.
"""
if self.type in ['oper', 'super']:
if norm is None or norm == 'tr':
_op = self*self.dag()
vals = sp_eigs(_op.data, _op.isherm, vecs=False,
sparse=sparse, tol=tol, maxiter=maxiter)
return np.sum(np.sqrt(np.abs(vals)))
elif norm == 'fro':
return sp_fro_norm(self.data)
elif norm == 'one':
return sp_one_norm(self.data)
elif norm == 'max':
return sp_max_norm(self.data)
else:
raise ValueError(
"For matrices, norm must be 'tr', 'fro', 'one', or 'max'.")
else:
if norm is None or norm == 'l2':
return sp_L2_norm(self.data)
elif norm == 'max':
return sp_max_norm(self.data)
else:
raise ValueError("For vectors, norm must be 'l2', or 'max'.")
[docs] def proj(self):
"""Form the projector from a given ket or bra vector.
Parameters
----------
Q : :class:`qutip.Qobj`
Input bra or ket vector
Returns
-------
P : :class:`qutip.Qobj`
Projection operator.
"""
if self.isket:
_out = zcsr_proj(self.data, 1)
_dims = [self.dims[0], self.dims[0]]
elif self.isbra:
_out = zcsr_proj(self.data, 0)
_dims = [self.dims[1], self.dims[1]]
else:
raise TypeError('Projector can only be formed from a bra or ket.')
return Qobj(_out, dims=_dims)
[docs] def tr(self):
"""Trace of a quantum object.
Returns
-------
trace : float
Returns the trace of the quantum object.
"""
return zcsr_trace(self.data, self.isherm)
[docs] def purity(self):
"""Calculate purity of a quantum object.
Returns
-------
state_purity : float
Returns the purity of a quantum object.
For a pure state, the purity is 1.
For a mixed state of dimension `d`, 1/d<=purity<1.
"""
rho = self
if (rho.type == "super"):
raise TypeError('Purity is defined on a density matrix or state.')
if rho.type == "ket" or rho.type == "bra":
state_purity = (rho*rho.dag()).tr()
if rho.type == "oper":
state_purity = (rho*rho).tr()
return state_purity
[docs] def full(self, order='C', squeeze=False):
"""Dense array from quantum object.
Parameters
----------
order : str {'C', 'F'}
Return array in C (default) or Fortran ordering.
squeeze : bool {False, True}
Squeeze output array.
Returns
-------
data : array
Array of complex data from quantum objects `data` attribute.
"""
if squeeze:
return self.data.toarray(order=order).squeeze()
else:
return self.data.toarray(order=order)
def __array__(self, *arg, **kwarg):
"""Numpy array from Qobj
For compatibility with np.array
"""
return self.full()
[docs] def diag(self):
"""Diagonal elements of quantum object.
Returns
-------
diags : array
Returns array of ``real`` values if operators is Hermitian,
otherwise ``complex`` values are returned.
"""
out = self.data.diagonal()
if np.any(np.imag(out) > settings.atol) or not self.isherm:
return out
else:
return np.real(out)
[docs] def expm(self, method='dense'):
"""Matrix exponential of quantum operator.
Input operator must be square.
Parameters
----------
method : str {'dense', 'sparse'}
Use set method to use to calculate the matrix exponentiation. The
available choices includes 'dense' and 'sparse'. Since the
exponential of a matrix is nearly always dense, method='dense'
is set as default.s
Returns
-------
oper : :class:`qutip.Qobj`
Exponentiated quantum operator.
Raises
------
TypeError
Quantum operator is not square.
"""
if self.dims[0][0] != self.dims[1][0]:
raise TypeError('Invalid operand for matrix exponential')
if method == 'dense':
F = sp_expm(self.data, sparse=False)
elif method == 'sparse':
F = sp_expm(self.data, sparse=True)
else:
raise ValueError("method must be 'dense' or 'sparse'.")
out = Qobj(F, dims=self.dims)
return out.tidyup() if settings.auto_tidyup else out
[docs] def check_herm(self):
"""Check if the quantum object is hermitian.
Returns
-------
isherm : bool
Returns the new value of isherm property.
"""
self._isherm = None
return self.isherm
[docs] def sqrtm(self, sparse=False, tol=0, maxiter=100000):
"""Sqrt of a quantum operator.
Operator must be square.
Parameters
----------
sparse : bool
Use sparse eigenvalue/vector solver.
tol : float
Tolerance used by sparse solver (0 = machine precision).
maxiter : int
Maximum number of iterations used by sparse solver.
Returns
-------
oper : :class:`qutip.Qobj`
Matrix square root of operator.
Raises
------
TypeError
Quantum object is not square.
Notes
-----
The sparse eigensolver is much slower than the dense version.
Use sparse only if memory requirements demand it.
"""
if self.dims[0][0] == self.dims[1][0]:
evals, evecs = sp_eigs(self.data, self.isherm, sparse=sparse,
tol=tol, maxiter=maxiter)
numevals = len(evals)
dV = sp.spdiags(np.sqrt(evals, dtype=complex), 0, numevals,
numevals, format='csr')
if self.isherm:
spDv = dV.dot(evecs.T.conj().T)
else:
spDv = dV.dot(np.linalg.inv(evecs.T))
out = Qobj(evecs.T.dot(spDv), dims=self.dims)
return out.tidyup() if settings.auto_tidyup else out
else:
raise TypeError('Invalid operand for matrix square root')
[docs] def cosm(self):
"""Cosine of a quantum operator.
Operator must be square.
Returns
-------
oper : :class:`qutip.Qobj`
Matrix cosine of operator.
Raises
------
TypeError
Quantum object is not square.
Notes
-----
Uses the Q.expm() method.
"""
if self.dims[0][0] == self.dims[1][0]:
return 0.5 * ((1j * self).expm() + (-1j * self).expm())
else:
raise TypeError('Invalid operand for matrix square root')
[docs] def sinm(self):
"""Sine of a quantum operator.
Operator must be square.
Returns
-------
oper : :class:`qutip.Qobj`
Matrix sine of operator.
Raises
------
TypeError
Quantum object is not square.
Notes
-----
Uses the Q.expm() method.
"""
if self.dims[0][0] == self.dims[1][0]:
return -0.5j * ((1j * self).expm() - (-1j * self).expm())
else:
raise TypeError('Invalid operand for matrix square root')
[docs] def inv(self, sparse=False):
"""Matrix inverse of a quantum operator
Operator must be square.
Returns
-------
oper : :class:`qutip.Qobj`
Matrix inverse of operator.
Raises
------
TypeError
Quantum object is not square.
"""
if self.shape[0] != self.shape[1]:
raise TypeError('Invalid operand for matrix inverse')
if sparse:
inv_mat = sp.linalg.inv(sp.csc_matrix(self.data))
else:
inv_mat = np.linalg.inv(self.full())
return Qobj(inv_mat, dims=self.dims[::-1])
[docs] def unit(self, inplace=False,
norm=None, sparse=False,
tol=0, maxiter=100000):
"""Operator or state normalized to unity.
Uses norm from Qobj.norm().
Parameters
----------
inplace : bool
Do an in-place normalization
norm : str
Requested norm for states / operators.
sparse : bool
Use sparse eigensolver for trace norm. Does not affect other norms.
tol : float
Tolerance used by sparse eigensolver.
maxiter : int
Number of maximum iterations performed by sparse eigensolver.
Returns
-------
oper : :class:`qutip.Qobj`
Normalized quantum object if not in-place,
else None.
"""
if inplace:
nrm = self.norm(norm=norm, sparse=sparse,
tol=tol, maxiter=maxiter)
self.data /= nrm
elif not inplace:
out = self / self.norm(norm=norm, sparse=sparse,
tol=tol, maxiter=maxiter)
if settings.auto_tidyup:
return out.tidyup()
else:
return out
else:
raise Exception('inplace kwarg must be bool.')
[docs] def ptrace(self, sel, sparse=None):
"""Partial trace of the quantum object.
Parameters
----------
sel : int/list
An ``int`` or ``list`` of components to keep after partial trace.
Returns
-------
oper : :class:`qutip.Qobj`
Quantum object representing partial trace with selected components
remaining.
Notes
-----
This function is identical to the :func:`qutip.qobj.ptrace` function
that has been deprecated.
"""
if sparse is None:
if self.isket:
sparse = False
elif (self.data.nnz / (self.shape[0] * self.shape[1])) >= 0.1:
sparse = False
if sparse:
q = Qobj()
q.data, q.dims, _ = _ptrace(self, sel)
return q.tidyup() if settings.auto_tidyup else q
else:
return _ptrace_dense(self, sel)
[docs] def permute(self, order):
"""Permutes a composite quantum object.
Parameters
----------
order : list/array
List specifying new tensor order.
Returns
-------
P : :class:`qutip.Qobj`
Permuted quantum object.
"""
q = Qobj()
q.data, q.dims = _permute(self, order)
q.data.sort_indices()
return q
[docs] def tidyup(self, atol=settings.auto_tidyup_atol):
"""Removes small elements from the quantum object.
Parameters
----------
atol : float
Absolute tolerance used by tidyup. Default is set
via qutip global settings parameters.
Returns
-------
oper : :class:`qutip.Qobj`
Quantum object with small elements removed.
"""
if self.data.nnz:
# This does the tidyup and returns True if
# The sparse data needs to be shortened
if use_openmp() and self.data.nnz > 500:
if omp_tidyup(self.data.data,atol, self.data.nnz,
settings.num_cpus):
self.data.eliminate_zeros()
else:
if cy_tidyup(self.data.data,atol, self.data.nnz):
self.data.eliminate_zeros()
return self
else:
return self
[docs] def trunc_neg(self, method="clip"):
"""Truncates negative eigenvalues and renormalizes.
Returns a new Qobj by removing the negative eigenvalues
of this instance, then renormalizing to obtain a valid density
operator.
Parameters
----------
method : str
Algorithm to use to remove negative eigenvalues. "clip"
simply discards negative eigenvalues, then renormalizes.
"sgs" uses the SGS algorithm (doi:10/bb76) to find the
positive operator that is nearest in the Shatten 2-norm.
Returns
-------
oper : :class:`qutip.Qobj`
A valid density operator.
"""
if not self.isherm:
raise ValueError("Must be a Hermitian operator to remove negative "
"eigenvalues.")
if method not in ('clip', 'sgs'):
raise ValueError("Method {} not recognized.".format(method))
eigvals, eigstates = self.eigenstates()
if all([eigval >= 0 for eigval in eigvals]):
# All positive, so just renormalize.
return self.unit()
idx_nonzero = eigvals != 0
eigvals = eigvals[idx_nonzero]
eigstates = eigstates[idx_nonzero]
if method == 'clip':
eigvals[eigvals < 0] = 0
elif method == 'sgs':
eigvals = eigvals[::-1]
eigstates = eigstates[::-1]
acc = 0.0
dim = self.shape[0]
n_eigs = len(eigvals)
for idx in reversed(range(n_eigs)):
if eigvals[idx] + acc / (idx + 1) >= 0:
break
else:
acc += eigvals[idx]
eigvals[idx] = 0.0
eigvals[:idx+1] += acc / (idx + 1)
return sum([
val * qutip.states.ket2dm(state)
for val, state in zip(eigvals, eigstates)
], Qobj(np.zeros(self.shape), dims=self.dims)
).unit()
[docs] def matrix_element(self, bra, ket):
"""Calculates a matrix element.
Gives the matrix element for the quantum object sandwiched between a
`bra` and `ket` vector.
Parameters
-----------
bra : :class:`qutip.Qobj`
Quantum object of type 'bra' or 'ket'
ket : :class:`qutip.Qobj`
Quantum object of type 'ket'.
Returns
-------
elem : complex
Complex valued matrix element.
Note
----
It is slightly more computationally efficient to use a ket
vector for the 'bra' input.
"""
if not self.isoper:
raise TypeError("Can only get matrix elements for an operator.")
else:
if bra.isbra and ket.isket:
return zcsr_mat_elem(self.data, bra.data, ket.data, 1)
elif bra.isket and ket.isket:
return zcsr_mat_elem(self.data, bra.data, ket.data, 0)
else:
err = "Can only calculate matrix elements for bra"
err += " and ket vectors."
raise TypeError(err)
[docs] def overlap(self, other):
"""Overlap between two state vectors or two operators.
Gives the overlap (inner product) between the current bra or ket Qobj
and and another bra or ket Qobj. It gives the Hilbert-Schmidt overlap
when one of the Qobj is an operator/density matrix.
Parameters
-----------
other : :class:`qutip.Qobj`
Quantum object for a state vector of type 'ket', 'bra' or density
matrix.
Returns
-------
overlap : complex
Complex valued overlap.
Raises
------
TypeError
Can only calculate overlap between a bra, ket and density matrix
quantum objects.
Notes
-----
Since QuTiP mainly deals with ket vectors, the most efficient inner
product call is the ket-ket version that computes the product
<self|other> with both vectors expressed as kets.
"""
if isinstance(other, Qobj):
if self.isbra:
if other.isket:
return zcsr_inner(self.data, other.data, 1)
elif other.isbra:
# Since we deal mainly with ket vectors, the bra-bra combo
# is not common, and not optimized.
return zcsr_inner(self.data, other.dag().data, 1)
elif other.isoper:
return (qutip.states.ket2dm(self).dag() * other).tr()
else:
err = "Can only calculate overlap for state vector Qobjs"
raise TypeError(err)
elif self.isket:
if other.isbra:
return zcsr_inner(other.data, self.data, 1)
elif other.isket:
return zcsr_inner(self.data, other.data, 0)
elif other.isoper:
return (qutip.states.ket2dm(self).dag() * other).tr()
else:
err = "Can only calculate overlap for state vector Qobjs"
raise TypeError(err)
elif self.isoper:
if other.isket or other.isbra:
return (self.dag() * qutip.states.ket2dm(other)).tr()
elif other.isoper:
return (self.dag() * other).tr()
else:
err = "Can only calculate overlap for state vector Qobjs"
raise TypeError(err)
raise TypeError("Can only calculate overlap for state vector Qobjs")
[docs] def eigenstates(self, sparse=False, sort='low',
eigvals=0, tol=0, maxiter=100000):
"""Eigenstates and eigenenergies.
Eigenstates and eigenenergies are defined for operators and
superoperators only.
Parameters
----------
sparse : bool
Use sparse Eigensolver
sort : str
Sort eigenvalues (and vectors) 'low' to high, or 'high' to low.
eigvals : int
Number of requested eigenvalues. Default is all eigenvalues.
tol : float
Tolerance used by sparse Eigensolver (0 = machine precision).
The sparse solver may not converge if the tolerance is set too low.
maxiter : int
Maximum number of iterations performed by sparse solver (if used).
Returns
-------
eigvals : array
Array of eigenvalues for operator.
eigvecs : array
Array of quantum operators representing the oprator eigenkets.
Order of eigenkets is determined by order of eigenvalues.
Notes
-----
The sparse eigensolver is much slower than the dense version.
Use sparse only if memory requirements demand it.
"""
evals, evecs = sp_eigs(self.data, self.isherm, sparse=sparse,
sort=sort, eigvals=eigvals, tol=tol,
maxiter=maxiter)
new_dims = [self.dims[0], [1] * len(self.dims[0])]
ekets = np.array([Qobj(vec, dims=new_dims) for vec in evecs],
dtype=object)
norms = np.array([ket.norm() for ket in ekets])
return evals, ekets / norms
[docs] def eigenenergies(self, sparse=False, sort='low',
eigvals=0, tol=0, maxiter=100000):
"""Eigenenergies of a quantum object.
Eigenenergies (eigenvalues) are defined for operators or superoperators
only.
Parameters
----------
sparse : bool
Use sparse Eigensolver
sort : str
Sort eigenvalues 'low' to high, or 'high' to low.
eigvals : int
Number of requested eigenvalues. Default is all eigenvalues.
tol : float
Tolerance used by sparse Eigensolver (0=machine precision).
The sparse solver may not converge if the tolerance is set too low.
maxiter : int
Maximum number of iterations performed by sparse solver (if used).
Returns
-------
eigvals : array
Array of eigenvalues for operator.
Notes
-----
The sparse eigensolver is much slower than the dense version.
Use sparse only if memory requirements demand it.
"""
return sp_eigs(self.data, self.isherm, vecs=False, sparse=sparse,
sort=sort, eigvals=eigvals, tol=tol, maxiter=maxiter)
[docs] def groundstate(self, sparse=False, tol=0, maxiter=100000, safe=True):
"""Ground state Eigenvalue and Eigenvector.
Defined for quantum operators or superoperators only.
Parameters
----------
sparse : bool
Use sparse Eigensolver
tol : float
Tolerance used by sparse Eigensolver (0 = machine precision).
The sparse solver may not converge if the tolerance is set too low.
maxiter : int
Maximum number of iterations performed by sparse solver (if used).
safe : bool (default=True)
Check for degenerate ground state
Returns
-------
eigval : float
Eigenvalue for the ground state of quantum operator.
eigvec : :class:`qutip.Qobj`
Eigenket for the ground state of quantum operator.
Notes
-----
The sparse eigensolver is much slower than the dense version.
Use sparse only if memory requirements demand it.
"""
if safe:
evals = 2
else:
evals = 1
grndval, grndvec = sp_eigs(self.data, self.isherm, sparse=sparse,
eigvals=evals, tol=tol, maxiter=maxiter)
if safe:
if tol == 0: tol = 1e-15
if (grndval[1]-grndval[0]) <= 10*tol:
print("WARNING: Ground state may be degenerate. "
"Use Q.eigenstates()")
new_dims = [self.dims[0], [1] * len(self.dims[0])]
grndvec = Qobj(grndvec[0], dims=new_dims)
grndvec = grndvec / grndvec.norm()
return grndval[0], grndvec
[docs] def trans(self):
"""Transposed operator.
Returns
-------
oper : :class:`qutip.Qobj`
Transpose of input operator.
"""
out = Qobj()
out.data = zcsr_transpose(self.data)
out.dims = [self.dims[1], self.dims[0]]
return out
[docs] def eliminate_states(self, states_inds, normalize=False):
"""Creates a new quantum object with states in state_inds eliminated.
Parameters
----------
states_inds : list of integer
The states that should be removed.
normalize : True / False
Weather or not the new Qobj instance should be normalized (default
is False). For Qobjs that represents density matrices or state
vectors normalized should probably be set to True, but for Qobjs
that represents operators in for example an Hamiltonian, normalize
should be False.
Returns
-------
q : :class:`qutip.Qobj`
A new instance of :class:`qutip.Qobj` that contains only the states
corresponding to indices that are **not** in `state_inds`.
Notes
-----
Experimental.
"""
keep_indices = np.array([s not in states_inds
for s in range(self.shape[0])]).nonzero()[0]
return self.extract_states(keep_indices, normalize=normalize)
[docs] def dnorm(self, B=None):
"""Calculates the diamond norm, or the diamond distance to another
operator.
Parameters
----------
B : :class:`qutip.Qobj` or None
If B is not None, the diamond distance d(A, B) = dnorm(A - B)
between this operator and B is returned instead of the diamond norm.
Returns
-------
d : float
Either the diamond norm of this operator, or the diamond distance
from this operator to B.
"""
return mts.dnorm(self, B)
@property
def ishp(self):
# FIXME: this needs to be cached in the same ways as isherm.
if self.type in ["super", "oper"]:
try:
J = sr.to_choi(self)
return J.isherm
except:
return False
else:
return False
@property
def iscp(self):
# FIXME: this needs to be cached in the same ways as isherm.
if self.type in ["super", "oper"]:
try:
J = (
self
# We can test with either Choi or chi, since the basis
# transformation between them is unitary and hence
# preserves the CP and TP conditions.
if self.superrep in ('choi', 'chi')
else sr.to_choi(self)
)
# If J isn't hermitian, then that could indicate either
# that J is not normal, or is normal,
# but has complex eigenvalues.
# In either case, it makes no sense to then demand that the
# eigenvalues be non-negative.
if not J.isherm:
return False
eigs = J.eigenenergies()
return all(eigs >= -settings.atol)
except:
return False
else:
return False
@property
def istp(self):
import qutip.superop_reps as sr
if self.type in ["super", "oper"]:
try:
# Normalize to a super of type choi or chi.
# We can test with either Choi or chi, since the basis
# transformation between them is unitary and hence
# preserves the CP and TP conditions.
if self.type == "super" and self.superrep in ('choi', 'chi'):
qobj = self
else:
qobj = sr.to_choi(self)
# Possibly collapse dims.
if any([len(index) > 1 for super_index in qobj.dims
for index in super_index]):
qobj = Qobj(qobj, dims=collapse_dims_super(qobj.dims))
else:
qobj = qobj
# We use the condition from John Watrous' lecture notes,
# Tr_1(J(Phi)) = identity_2.
tr_oper = qobj.ptrace([0])
ident = ops.identity(tr_oper.shape[0])
return isequal(tr_oper, ident)
except:
return False
else:
return False
@property
def iscptp(self):
from qutip.superop_reps import to_choi
if self.type == "super" or self.type == "oper":
reps = ('choi', 'chi')
q_oper = to_choi(self) if self.superrep not in reps else self
return q_oper.iscp and q_oper.istp
else:
return False
@property
def isherm(self):
if self._isherm is not None:
# used previously computed value
return self._isherm
self._isherm = bool(zcsr_isherm(self.data))
return self._isherm
@isherm.setter
def isherm(self, isherm):
self._isherm = isherm
[docs] def check_isunitary(self):
"""
Checks whether qobj is a unitary matrix
"""
if self.isoper:
eye_data = fast_identity(self.shape[0])
return not (np.any(np.abs((self.data*self.dag().data
- eye_data).data)
> settings.atol)
or
np.any(np.abs((self.dag().data*self.data
- eye_data).data) >
settings.atol)
)
else:
return False
@property
def isunitary(self):
if self._isunitary is not None:
# used previously computed value
return self._isunitary
self._isunitary = self.check_isunitary()
return self._isunitary
@isunitary.setter
def isunitary(self, isunitary):
self._isunitary = isunitary
@property
def type(self):
if not self._type:
self._type = type_from_dims(self.dims)
return self._type
@property
def shape(self):
if self.data.shape == (1, 1):
return tuple([np.prod(self.dims[0]), np.prod(self.dims[1])])
else:
return tuple(self.data.shape)
@property
def isbra(self):
return self.type == 'bra'
@property
def isket(self):
return self.type == 'ket'
@property
def isoperbra(self):
return self.type == 'operator-bra'
@property
def isoperket(self):
return self.type == 'operator-ket'
@property
def isoper(self):
return self.type == 'oper'
@property
def issuper(self):
return self.type == 'super'
[docs] @staticmethod
def evaluate(qobj_list, t, args):
"""Evaluate a time-dependent quantum object in list format. For
example,
qobj_list = [H0, [H1, func_t]]
is evaluated to
Qobj(t) = H0 + H1 * func_t(t, args)
and
qobj_list = [H0, [H1, 'sin(w * t)']]
is evaluated to
Qobj(t) = H0 + H1 * sin(args['w'] * t)
Parameters
----------
qobj_list : list
A nested list of Qobj instances and corresponding time-dependent
coefficients.
t : float
The time for which to evaluate the time-dependent Qobj instance.
args : dictionary
A dictionary with parameter values required to evaluate the
time-dependent Qobj intance.
Returns
-------
output : :class:`qutip.Qobj`
A Qobj instance that represents the value of qobj_list at time t.
"""
q_sum = 0
if isinstance(qobj_list, Qobj):
q_sum = qobj_list
elif isinstance(qobj_list, list):
for q in qobj_list:
if isinstance(q, Qobj):
q_sum += q
elif (isinstance(q, list) and len(q) == 2 and
isinstance(q[0], Qobj)):
if isinstance(q[1], types.FunctionType):
q_sum += q[0] * q[1](t, args)
elif isinstance(q[1], str):
args['t'] = t
q_sum += q[0] * float(eval(q[1], globals(), args))
else:
raise TypeError('Unrecognized format for ' +
'specification of time-dependent Qobj')
else:
raise TypeError('Unrecognized format for specification ' +
'of time-dependent Qobj')
else:
raise TypeError(
'Unrecongized format for specification of time-dependent Qobj')
return q_sum
# -----------------------------------------------------------------------------
# This functions evaluates a time-dependent quantum object on the list-string
# and list-function formats that are used by the time-dependent solvers.
# Although not used directly in by those solvers, it can for test purposes be
# conventient to be able to evaluate the expressions passed to the solver for
# arbitrary value of time. This function provides this functionality.
#
def qobj_list_evaluate(qobj_list, t, args):
"""
Depracated: See Qobj.evaluate
"""
warnings.warn("Deprecated: Use Qobj.evaluate", DeprecationWarning)
return Qobj.evaluate(qobj_list, t, args)
# -----------------------------------------------------------------------------
#
# A collection of tests used to determine the type of quantum objects, and some
# functions for increased compatibility with quantum optics toolbox.
#
def dag(A):
"""Adjont operator (dagger) of a quantum object.
Parameters
----------
A : :class:`qutip.Qobj`
Input quantum object.
Returns
-------
oper : :class:`qutip.Qobj`
Adjoint of input operator
Notes
-----
This function is for legacy compatibility only. It is recommended to use
the ``dag()`` Qobj method.
"""
if not isinstance(A, Qobj):
raise TypeError("Input is not a quantum object")
return A.dag()
def ptrace(Q, sel):
"""Partial trace of the Qobj with selected components remaining.
Parameters
----------
Q : :class:`qutip.Qobj`
Composite quantum object.
sel : int/list
An ``int`` or ``list`` of components to keep after partial trace.
Returns
-------
oper : :class:`qutip.Qobj`
Quantum object representing partial trace with selected components
remaining.
Notes
-----
This function is for legacy compatibility only. It is recommended to use
the ``ptrace()`` Qobj method.
"""
if not isinstance(Q, Qobj):
raise TypeError("Input is not a quantum object")
return Q.ptrace(sel)
def _ptrace_dense(Q, sel):
rd = np.asarray(Q.dims[0], dtype=np.int32).ravel()
nd = len(rd)
if isinstance(sel, int):
sel = np.array([sel])
else:
sel = np.asarray(sel)
sel = list(np.sort(sel))
dkeep = (rd[sel]).tolist()
qtrace = list(set(np.arange(nd)) - set(sel))
dtrace = (rd[qtrace]).tolist()
rd = list(rd)
if isket(Q):
vmat = (Q.full()
.reshape(rd)
.transpose(sel + qtrace)
.reshape([np.prod(dkeep), np.prod(dtrace)]))
rhomat = vmat.dot(vmat.conj().T)
else:
rhomat = np.trace(Q.full()
.reshape(rd + rd)
.transpose(qtrace + [nd + q for q in qtrace] +
sel + [nd + q for q in sel])
.reshape([np.prod(dtrace),
np.prod(dtrace),
np.prod(dkeep),
np.prod(dkeep)]))
return Qobj(rhomat, dims=[dkeep, dkeep])
def dims(inpt):
"""Returns the dims attribute of a quantum object.
Parameters
----------
inpt : :class:`qutip.Qobj`
Input quantum object.
Returns
-------
dims : list
A ``list`` of the quantum objects dimensions.
Notes
-----
This function is for legacy compatibility only. Using the `Qobj.dims`
attribute is recommended.
"""
if isinstance(inpt, Qobj):
return inpt.dims
else:
raise TypeError("Input is not a quantum object")
def shape(inpt):
"""Returns the shape attribute of a quantum object.
Parameters
----------
inpt : :class:`qutip.Qobj`
Input quantum object.
Returns
-------
shape : list
A ``list`` of the quantum objects shape.
Notes
-----
This function is for legacy compatibility only. Using the `Qobj.shape`
attribute is recommended.
"""
if isinstance(inpt, Qobj):
return Qobj.shape
else:
return np.shape(inpt)
def isket(Q):
"""
Determines if given quantum object is a ket-vector.
Parameters
----------
Q : :class:`qutip.Qobj`
Quantum object
Returns
-------
isket : bool
True if qobj is ket-vector, False otherwise.
Examples
--------
>>> psi = basis(5,2)
>>> isket(psi)
True
Notes
-----
This function is for legacy compatibility only. Using the `Qobj.isket`
attribute is recommended.
"""
return True if isinstance(Q, Qobj) and Q.isket else False
def isbra(Q):
"""Determines if given quantum object is a bra-vector.
Parameters
----------
Q : :class:`qutip.Qobj`
Quantum object
Returns
-------
isbra : bool
True if Qobj is bra-vector, False otherwise.
Examples
--------
>>> psi = basis(5,2)
>>> isket(psi)
False
Notes
-----
This function is for legacy compatibility only. Using the `Qobj.isbra`
attribute is recommended.
"""
return True if isinstance(Q, Qobj) and Q.isbra else False
def isoperket(Q):
"""Determines if given quantum object is an operator in column vector form
(operator-ket).
Parameters
----------
Q : :class:`qutip.Qobj`
Quantum object
Returns
-------
isoperket : bool
True if Qobj is operator-ket, False otherwise.
Notes
-----
This function is for legacy compatibility only. Using the `Qobj.isoperket`
attribute is recommended.
"""
return True if isinstance(Q, Qobj) and Q.isoperket else False
def isoperbra(Q):
"""Determines if given quantum object is an operator in row vector form
(operator-bra).
Parameters
----------
Q : :class:`qutip.Qobj`
Quantum object
Returns
-------
isoperbra : bool
True if Qobj is operator-bra, False otherwise.
Notes
-----
This function is for legacy compatibility only. Using the `Qobj.isoperbra`
attribute is recommended.
"""
return True if isinstance(Q, Qobj) and Q.isoperbra else False
def isoper(Q):
"""Determines if given quantum object is a operator.
Parameters
----------
Q : :class:`qutip.Qobj`
Quantum object
Returns
-------
isoper : bool
True if Qobj is operator, False otherwise.
Examples
--------
>>> a = destroy(5)
>>> isoper(a)
True
Notes
-----
This function is for legacy compatibility only. Using the `Qobj.isoper`
attribute is recommended.
"""
return True if isinstance(Q, Qobj) and Q.isoper else False
def issuper(Q):
"""Determines if given quantum object is a super-operator.
Parameters
----------
Q : :class:`qutip.Qobj`
Quantum object
Returns
-------
issuper : bool
True if Qobj is superoperator, False otherwise.
Notes
-----
This function is for legacy compatibility only. Using the `Qobj.issuper`
attribute is recommended.
"""
return True if isinstance(Q, Qobj) and Q.issuper else False
def isequal(A, B, tol=None):
"""Determines if two qobj objects are equal to within given tolerance.
Parameters
----------
A : :class:`qutip.Qobj`
Qobj one
B : :class:`qutip.Qobj`
Qobj two
tol : float
Tolerence for equality to be valid
Returns
-------
isequal : bool
True if qobjs are equal, False otherwise.
Notes
-----
This function is for legacy compatibility only. Instead, it is recommended
to use the equality operator of Qobj instances instead: A == B.
"""
if tol is None:
tol = settings.atol
if not isinstance(A, Qobj) or not isinstance(B, Qobj):
return False
if A.dims != B.dims:
return False
Adat = A.data
Bdat = B.data
elems = (Adat - Bdat).data
if np.any(np.abs(elems) > tol):
return False
return True
def isherm(Q):
"""Determines if given operator is Hermitian.
Parameters
----------
Q : :class:`qutip.Qobj`
Quantum object
Returns
-------
isherm : bool
True if operator is Hermitian, False otherwise.
Examples
--------
>>> a = destroy(4)
>>> isherm(a)
False
Notes
-----
This function is for legacy compatibility only. Using the `Qobj.isherm`
attribute is recommended.
"""
return True if isinstance(Q, Qobj) and Q.isherm else False
# TRAILING IMPORTS
# We do a few imports here to avoid circular dependencies.
from qutip.eseries import eseries
import qutip.superop_reps as sr
import qutip.tensor as tensor
import qutip.operators as ops
import qutip.metrics as mts
import qutip.states
import qutip.superoperator