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qobj.py
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qobj.py
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# 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.
###############################################################################
"""Main module for QuTiP, consisting of the Quantum Object (Qobj) class and
its methods.
"""
import warnings
import types
import pickle
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.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, sp_inf_norm)
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).
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.
iscp : bool
Indicates if the quantum object represents a map, and if that map is
completely positive (CP).
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
-------
conj()
Conjugate of quantum object.
dag()
Adjoint (dagger) of quantum object.
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()
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.
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.
ptrace(sel)
Returns quantum object for selected dimensions after performing
partial trace.
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.
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, fast=False, superrep=None):
"""
Qobj constructor.
"""
self._isherm = None
self._type = None
self.superrep = None
if fast == 'mc':
# fast Qobj construction for use in mcsolve with ket output
self.data = sp.csr_matrix(inpt, dtype=complex)
self.dims = dims
self._isherm = False
return
if fast == 'mc-dm':
# fast Qobj construction for use in mcsolve with dm output
self.data = sp.csr_matrix(inpt, dtype=complex)
self.dims = dims
self._isherm = True
return
if isinstance(inpt, Qobj):
# if input is already Qobj then return identical copy
# make sure matrix is sparse (safety check)
self.data = sp.csr_matrix(inpt.data, dtype=complex)
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
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 = sp.csr_matrix((N, M), dtype=complex)
elif isinstance(inpt, list) or isinstance(inpt, tuple):
# case where input is a list
if len(np.array(inpt).shape) == 1:
# if list has only one dimension (i.e [5,4])
inpt = np.array([inpt]).transpose()
else: # if list has two dimensions (i.e [[5,4]])
inpt = np.array(inpt)
self.data = sp.csr_matrix(inpt, dtype=complex)
if not np.any(dims):
self.dims = [[int(inpt.shape[0])], [int(inpt.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]
self.data = sp.csr_matrix(inpt, dtype=complex)
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.int64)):
# if input is int, float, or complex then convert to array
self.data = sp.csr_matrix([[inpt]], dtype=complex)
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]])
self.data = sp.csr_matrix(inpt, dtype=complex)
self.dims = [[int(inpt.shape[0])], [int(inpt.shape[1])]]
if type == 'super':
if self.type == 'oper':
self.dims = [[[d] for d in self.dims[0]],
[[d] for d in self.dims[1]]]
if superrep:
self.superrep = superrep
else:
if self.type == 'super' and self.superrep is None:
self.superrep = 'super'
# clear type cache
self._type = None
def __add__(self, other):
"""
ADDITION with Qobj on LEFT [ ex. Qobj+4 ]
"""
if isinstance(other, eseries):
return other.__radd__(self)
if not isinstance(other, Qobj):
other = Qobj(other)
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 * sp.identity(
self.shape[0], dtype=complex, format='csr')
else:
out.data = self.data
out.data.data = out.data.data + dat
out.dims = self.dims
if isinstance(dat, (int, float)):
out._isherm = self._isherm
else:
out._isherm = out.isherm
out.superrep = self.superrep
return out.tidyup() if settings.auto_tidyup else 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 * sp.identity(other.shape[0], dtype=complex,
format='csr') + other.data
else:
out.data = other.data
out.data.data = out.data.data + dat
out.dims = other.dims
if isinstance(dat, complex):
out._isherm = out.isherm
else:
out._isherm = self._isherm
out.superrep = self.superrep
return out.tidyup() if settings.auto_tidyup else 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 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.tidyup() if settings.auto_tidyup else 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 ]
"""
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 (not isinstance(dims[0][0], list) and
not isinstance(dims[1][0], list)):
r = range(len(dims[0]))
mask = [dims[0][n] == dims[1][n] == 1 for n in r]
out.dims = [max([1], [dims[0][n]
for n in r if not mask[n]]),
max([1], [dims[1][n]
for n in r if not mask[n]])]
else:
out.dims = dims
out._isherm = out.isherm
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.tidyup() if settings.auto_tidyup else 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
else:
raise TypeError("Incompatible Qobj shapes")
elif isinstance(other, (list, np.ndarray)):
# if other is a list, do element-wise multiplication
return np.array([self * item for item in other])
elif isinstance(other, eseries):
return other.__rmul__(self)
elif isinstance(other, (int, float, complex, np.int64)):
out = Qobj()
out.data = self.data * other
out.dims = self.dims
out.superrep = self.superrep
if isinstance(other, complex):
out._isherm = out.isherm
else:
out._isherm = self._isherm
return out.tidyup() if settings.auto_tidyup else out
else:
raise TypeError("Incompatible object for multiplication")
def __rmul__(self, other):
"""
MULTIPLICATION with Qobj on RIGHT [ ex. 4*Qobj ]
"""
if isinstance(other, (list, np.ndarray)):
# if other is a list, do element-wise multiplication
return np.array([item * self for item in other])
if isinstance(other, eseries):
return other.__mul__(self)
if isinstance(other, (int, float, complex, np.int64)):
out = Qobj()
out.data = other * self.data
out.dims = self.dims
out.superrep = self.superrep
if isinstance(other, complex):
out._isherm = out.isherm
else:
out._isherm = self._isherm
return out.tidyup() if settings.auto_tidyup else 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.int64)):
out = Qobj()
out.data = self.data / other
out.dims = self.dims
if isinstance(other, complex):
out._isherm = out.isherm
else:
out._isherm = self._isherm
out.superrep = self.superrep
return out.tidyup() if settings.auto_tidyup else 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
out._isherm = self._isherm
return out.tidyup() if settings.auto_tidyup else 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 __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 == 1:
# truncated column vector output
for m in range(5):
s += _format_element(m, 0, self.data[m, 0])
s += r'\\'
s += _format_element(m, 0, r'\vdots')
s += r'\\'
for m in range(M - 5, M):
s += _format_element(m, 0, self.data[m, 0])
s += r'\\'
elif M == 1 and N > 10:
# truncated row vector output
for n in range(5):
s += _format_element(0, n, self.data[0, n])
s += r' & \cdots'
for n in range(N - 5, N):
s += _format_element(0, n, self.data[0, 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
def dag(self):
"""Adjoint operator of quantum object.
"""
out = Qobj()
out.data = self.data.T.conj().tocsr()
out.dims = [self.dims[1], self.dims[0]]
out._isherm = self._isherm
return out
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
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':
vals = sp_eigs(self.data, self.isherm, vecs=False,
sparse=sparse, tol=tol, maxiter=maxiter)
return np.sum(sqrt(abs(vals) ** 2))
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'.")
def tr(self):
"""Trace of a quantum object.
Returns
-------
trace: float
Returns ``real`` if operator is Hermitian, returns ``complex``
otherwise.
"""
if self.isherm:
return float(np.real(np.sum(self.data.diagonal())))
else:
return complex(np.sum(self.data.diagonal()))
def full(self, squeeze=False):
"""Dense array from quantum object.
Returns
-------
data : array
Array of complex data from quantum objects `data` attribute.
"""
if squeeze:
return self.data.toarray().squeeze()
else:
return self.data.toarray()
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)
def expm(self):
"""Matrix exponential of quantum operator.
Input operator must be square.
Returns
-------
oper : qobj
Exponentiated quantum operator.
Raises
------
TypeError
Quantum operator is not square.
"""
if self.dims[0][0] == self.dims[1][0]:
F = sp_expm(self.data)
out = Qobj(F, dims=self.dims)
return out.tidyup() if settings.auto_tidyup else out
else:
raise TypeError('Invalid operand for matrix exponential')
def checkherm(self):
"""Check if the quantum object is hermitian.
Returns
-------
isherm: bool
Returns the new value of isherm property.
"""
self._isherm = None
return self.isherm
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: 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')
def unit(self, norm=None, sparse=False, tol=0, maxiter=100000):
"""Operator or state normalized to unity.
Uses norm from Qobj.norm().
Parameters
----------
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 : qobj
Normalized quantum object.
"""
out = self / self.norm(norm=norm, sparse=sparse,
tol=tol, maxiter=maxiter)
if settings.auto_tidyup:
return out.tidyup()
else:
return out
def ptrace(self, sel):
"""Partial trace of the quantum object.
Parameters
----------
sel : int/list
An ``int`` or ``list`` of components to keep after partial trace.
Returns
-------
oper: 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.
"""
q = Qobj()
q.data, q.dims, _ = _ptrace(self, sel)
return q.tidyup() if settings.auto_tidyup else q
def permute(self, order):
"""Permutes a composite quantum object.
Parameters
----------