"""
Module for the creation of composite quantum objects via the tensor product.
"""
__all__ = [
'tensor', 'super_tensor', 'composite', 'tensor_swap', 'tensor_contract',
'expand_operator'
]
import numpy as np
from functools import partial
from .operators import qeye
from .qobj import Qobj
from .superoperator import operator_to_vector, reshuffle
from .dimensions import (
flatten, enumerate_flat, unflatten, deep_remove, dims_to_tensor_shape,
dims_idxs_to_tensor_idxs
)
from . import data as _data
from .. import settings
class _reverse_partial_tensor:
""" Picklable lambda op: tensor(op, right) """
def __init__(self, right):
self.right = right
def __call__(self, op):
return tensor(op, self.right)
[docs]def tensor(*args):
"""Calculates the tensor product of input operators.
Parameters
----------
args : array_like
``list`` or ``array`` of quantum objects for tensor product.
Returns
-------
obj : qobj
A composite quantum object.
Examples
--------
>>> tensor([sigmax(), sigmax()]) # doctest: +SKIP
Quantum object: dims = [[2, 2], [2, 2]], \
shape = [4, 4], type = oper, isHerm = True
Qobj data =
[[ 0.+0.j 0.+0.j 0.+0.j 1.+0.j]
[ 0.+0.j 0.+0.j 1.+0.j 0.+0.j]
[ 0.+0.j 1.+0.j 0.+0.j 0.+0.j]
[ 1.+0.j 0.+0.j 0.+0.j 0.+0.j]]
"""
from .cy.qobjevo import QobjEvo
if not args:
raise TypeError("Requires at least one input argument")
if len(args) == 1 and isinstance(args[0], (Qobj, QobjEvo)):
return args[0].copy()
if len(args) == 1:
try:
args = tuple(args[0])
except TypeError:
raise TypeError("requires Qobj or QobjEvo operands") from None
if not all(isinstance(q, (Qobj, QobjEvo)) for q in args):
raise TypeError("requires Qobj or QobjEvo operands")
if any(isinstance(q, QobjEvo) for q in args):
# First make tensor from pairs only
if len(args) >= 3:
return tensor(args[0], tensor(args[1:]))
left, right = args
if isinstance(left, Qobj):
return right.linear_map(partial(tensor, left))
if isinstance(right, Qobj):
return left.linear_map(_reverse_partial_tensor(right))
left_t = left.linear_map(_reverse_partial_tensor(qeye(right.dims[0])))
right_t = right.linear_map(partial(tensor, qeye(left.dims[1])))
return left_t @ right_t
if not all(q.superrep == args[0].superrep for q in args[1:]):
raise TypeError("".join([
"In tensor products of superroperators,",
" all must have the same representation"
]))
isherm = args[0]._isherm
isunitary = args[0]._isunitary
out_data = args[0].data
dims_l = [args[0]._dims[0]]
dims_r = [args[0]._dims[1]]
for arg in args[1:]:
out_data = _data.kron(out_data, arg.data)
# If both _are_ Hermitian and/or unitary, then so is the output, but if
# both _aren't_, then output still can be.
isherm = (isherm and arg._isherm) or None
isunitary = (isunitary and arg._isunitary) or None
dims_l.append(arg._dims[0])
dims_r.append(arg._dims[1])
return Qobj(out_data,
dims=[dims_l, dims_r],
isherm=isherm,
isunitary=isunitary,
copy=False)
[docs]def super_tensor(*args):
"""
Calculate the tensor product of input superoperators, by tensoring together
the underlying Hilbert spaces on which each vectorized operator acts.
Parameters
----------
args : array_like
``list`` or ``array`` of quantum objects with ``type="super"``.
Returns
-------
obj : qobj
A composite quantum object.
"""
if isinstance(args[0], list):
args = args[0]
# Check if we're tensoring vectors or superoperators.
if all(arg.issuper for arg in args):
if not all(arg.superrep == "super" for arg in args):
raise TypeError(
"super_tensor on type='super' is only implemented for "
"superrep='super'."
)
# Reshuffle the superoperators.
shuffled_ops = list(map(reshuffle, args))
# Tensor the result.
shuffled_tensor = tensor(shuffled_ops)
# Unshuffle and return.
out = reshuffle(shuffled_tensor)
out.superrep = args[0].superrep
return out
if all(arg.isoperket for arg in args):
# Reshuffle the superoperators.
shuffled_ops = list(map(reshuffle, args))
# Tensor the result.
shuffled_tensor = tensor(shuffled_ops)
# Unshuffle and return.
out = reshuffle(shuffled_tensor)
return out
if all(arg.isoperbra for arg in args):
return super_tensor(*(arg.dag() for arg in args)).dag()
raise TypeError(
"All arguments must be the same type, "
"either super, operator-ket or operator-bra."
)
def _isoperlike(q):
return q.isoper or q.issuper
def _isketlike(q):
return q.isket or q.isoperket
def _isbralike(q):
return q.isbra or q.isoperbra
[docs]def composite(*args):
"""
Given two or more operators, kets or bras, returns the Qobj
corresponding to a composite system over each argument.
For ordinary operators and vectors, this is the tensor product,
while for superoperators and vectorized operators, this is
the column-reshuffled tensor product.
If a mix of Qobjs supported on Hilbert and Liouville spaces
are passed in, the former are promoted. Ordinary operators
are assumed to be unitaries, and are promoted using ``to_super``,
while kets and bras are promoted by taking their projectors and
using ``operator_to_vector(ket2dm(arg))``.
"""
import qutip.core.superop_reps
# First step will be to ensure everything is a Qobj at all.
if not all(isinstance(arg, Qobj) for arg in args):
raise TypeError("All arguments must be Qobjs.")
# Next, figure out if we have something oper-like (isoper or issuper),
# or something ket-like (isket or isoperket). Bra-like we'll deal with
# by turning things into ket-likes and back.
if all(map(_isoperlike, args)):
if any(arg.issuper for arg in args):
# to_super will promote 'oper' and leave 'super' untouched
return super_tensor(*map(qutip.core.superop_reps.to_super, args))
return tensor(*args)
if all(map(_isketlike, args)):
if any(arg.isoperket for arg in args):
return super_tensor(*(
arg if arg.isoperket else operator_to_vector(arg.proj())
for arg in args
))
return tensor(*args)
if all(map(_isbralike, args)):
# Turn into ket-likes and recurse.
return composite(*(arg.dag() for arg in args)).dag()
raise TypeError("Unsupported Qobj types [{}].".format(
", ".join(arg.type for arg in args)
))
def _tensor_contract_single(arr, i, j):
"""
Contracts a dense tensor along a single index pair.
"""
if arr.shape[i] != arr.shape[j]:
raise ValueError("Cannot contract over indices of different length.")
idxs = np.arange(arr.shape[i])
sl = tuple(slice(None, None, None) if idx not in (i, j) else idxs
for idx in range(arr.ndim))
contract_at = i if j == i + 1 else 0
return np.sum(arr[sl], axis=contract_at)
def _tensor_contract_dense(arr, *pairs):
"""
Contracts a dense tensor along one or more index pairs,
keeping track of how the indices are relabeled by the removal
of other indices.
"""
axis_idxs = list(range(arr.ndim))
for pair in pairs:
# axis_idxs.index effectively evaluates the mapping from original index
# labels to the labels after contraction.
arr = _tensor_contract_single(arr, *map(axis_idxs.index, pair))
axis_idxs.remove(pair[0])
axis_idxs.remove(pair[1])
return arr
def tensor_swap(q_oper, *pairs):
"""Transposes one or more pairs of indices of a Qobj.
Note that this uses dense representations and thus
should *not* be used for very large Qobjs.
Parameters
----------
pairs : tuple
One or more tuples ``(i, j)`` indicating that the
``i`` and ``j`` dimensions of the original qobj
should be swapped.
Returns
-------
sqobj : Qobj
The original Qobj with all named index pairs swapped with each other
"""
dims = q_oper.dims
tensor_pairs = dims_idxs_to_tensor_idxs(dims, pairs)
data = q_oper.full()
# Reshape into tensor indices
data = data.reshape(dims_to_tensor_shape(dims))
# Now permute the dims list so we know how to get back.
flat_dims = flatten(dims)
perm = list(range(len(flat_dims)))
for i, j in pairs:
flat_dims[i], flat_dims[j] = flat_dims[j], flat_dims[i]
for i, j in tensor_pairs:
perm[i], perm[j] = perm[j], perm[i]
dims = unflatten(flat_dims, enumerate_flat(dims))
# Next, permute the actual indices of the dense tensor.
data = data.transpose(perm)
# Reshape back, using the left and right of dims.
data = data.reshape(list(map(np.prod, dims)))
return Qobj(data, dims=dims, superrep=q_oper.superrep, copy=False)
[docs]def tensor_contract(qobj, *pairs):
"""Contracts a qobj along one or more index pairs.
Note that this uses dense representations and thus
should *not* be used for very large Qobjs.
Parameters
----------
qobj: Qobj
Operator to contract subspaces on.
pairs : tuple
One or more tuples ``(i, j)`` indicating that the
``i`` and ``j`` dimensions of the original qobj
should be contracted.
Returns
-------
cqobj : Qobj
The original Qobj with all named index pairs contracted
away.
"""
# Record and label the original dims.
dims = qobj.dims
dims_idxs = enumerate_flat(dims)
tensor_dims = dims_to_tensor_shape(dims)
# Convert to dense first, since sparse won't support the reshaping we need.
qtens = qobj.data.to_array()
# Reshape by the flattened dims.
qtens = qtens.reshape(tensor_dims)
# Contract out the indices from the flattened object.
# Note that we need to feed pairs through dims_idxs_to_tensor_idxs
# to ensure that we are contracting the right indices.
qtens = _tensor_contract_dense(qtens,
*dims_idxs_to_tensor_idxs(dims, pairs))
# Remove the contracted indexes from dims so we know how to
# reshape back.
# This concerns dims, and not the tensor indices, so we need
# to make sure to use the original dims indices and not the ones
# generated by dims_to_* functions.
contracted_idxs = deep_remove(dims_idxs, *flatten(list(map(list, pairs))))
contracted_dims = unflatten(flatten(dims), contracted_idxs)
# We don't need to check for tensor idxs versus dims idxs here,
# as column- versus row-stacking will never move an index for the
# vectorized operator spaces all the way from the left to the right.
l_mtx_dims, r_mtx_dims = map(np.prod, map(flatten, contracted_dims))
# Reshape back into a 2D matrix.
qmtx = qtens.reshape((l_mtx_dims, r_mtx_dims))
# Return back as a qobj.
return Qobj(qmtx, dims=contracted_dims, superrep=qobj.superrep, copy=False)
def _check_oper_dims(oper, dims=None, targets=None):
"""
Check if the given operator is valid.
Parameters
----------
oper : :class:`.Qobj`
The quantum object to be checked.
dims : list, optional
A list of integer for the dimension of each composite system.
e.g ``[2, 2, 2, 2, 2]`` for 5 qubits system.
targets : int or list of int, optional
The indices of subspace that are acted on.
"""
# if operator matches N
if not isinstance(oper, Qobj) or oper.dims[0] != oper.dims[1]:
raise ValueError(
"The operator is not an "
"Qobj with the same input and output dimensions.")
# if operator dims matches the target dims
if dims is not None and targets is not None:
targ_dims = [dims[t] for t in targets]
if oper.dims[0] != targ_dims:
raise ValueError(
"The operator dims {} do not match "
"the target dims {}.".format(
oper.dims[0], targ_dims))
def _targets_to_list(targets, oper=None, N=None):
"""
transform targets to a list and check validity.
Parameters
----------
targets : int or list of int
The indices of subspace that are acted on.
oper : :class:`.Qobj`, optional
An operator, the type of the :class:`.Qobj`
has to be an operator
and the dimension matches the tensored qubit Hilbert space
e.g. dims = ``[[2, 2, 2], [2, 2, 2]]``
N : int, optional
The number of subspace in the system.
"""
# if targets is a list of integer
if targets is None:
targets = list(range(len(oper.dims[0])))
if not hasattr(targets, '__iter__'):
targets = [targets]
if not all([isinstance(t, int) for t in targets]):
raise TypeError(
"targets should be "
"an integer or a list of integer")
# if targets has correct length
if oper is not None:
req_num = len(oper.dims[0])
if len(targets) != req_num:
raise ValueError(
"The given operator needs {} "
"target qutbis, "
"but {} given.".format(
req_num, len(targets)))
# if targets is smaller than N
if N is not None:
if not all([t < N for t in targets]):
raise ValueError("Targets must be smaller than N={}.".format(N))
return targets
def expand_operator(oper, dims, targets, dtype=None):
"""
Expand an operator to one that acts on a system with desired dimensions.
e.g.
```
expand_operator(oper, [2, 3, 4, 5], 2) ==
tensor(qeye(2), qeye(3), oper, qeye(5))
expand_operator(tensor(oper1, oper2), [2, 3, 4, 5], [2, 0]) ==
tensor(oper2, qeye(3), oper1, qeye(5))
```
Parameters
----------
oper : :class:`.Qobj`
An operator that act on the subsystem, has to be an operator and the
dimension matches the tensored dims Hilbert space
e.g. oper.dims = ``[[2, 3], [2, 3]]``
dims : list
A list of integer for the dimension of each composite system.
E.g ``[2, 3, 2, 3, 4]``.
targets : int or list of int
The indices of subspace that are acted on.
dtype : str, optional
Data type of the output :class:`.Qobj`. By default it uses the data
type specified in settings. If no data type is specified
in settings it uses the ``CSR`` data type.
Returns
-------
expanded_oper : :class:`.Qobj`
The expanded operator acting on a system with the desired dimension.
"""
from .operators import identity
dtype = dtype or settings.core["default_dtype"] or _data.CSR
oper = oper.to(dtype)
N = len(dims)
targets = _targets_to_list(targets, oper=oper, N=N)
_check_oper_dims(oper, dims=dims, targets=targets)
# Generate the correct order for permutation,
# eg. if N = 5, targets = [3,0], the order is [1,2,3,0,4].
# If the operator is cnot,
# this order means that the 3rd qubit controls the 0th qubit.
new_order = [0] * N
for i, t in enumerate(targets):
new_order[t] = i
# allocate the rest qutbits (not targets) to the empty
# position in new_order
rest_pos = [q for q in list(range(N)) if q not in targets]
rest_qubits = list(range(len(targets), N))
for i, ind in enumerate(rest_pos):
new_order[ind] = rest_qubits[i]
id_list = [identity(dims[i]) for i in rest_pos]
return tensor([oper] + id_list).permute(new_order)