def super_tensor(*args):
"""Calculates 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
elif 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
elif all(arg.isoperbra for arg in args):
return super_tensor(*(arg.dag() for arg in args)).dag()
else:
raise TypeError(
"All arguments must be the same type, "
"either super, operator-ket or operator-bra."
)
def super_tensor(*args):
"""Calculates 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
elif 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
elif all(arg.isoperbra for arg in args):
return super_tensor(*(arg.dag() for arg in args)).dag()
else:
raise TypeError(
"All arguments must be the same type, "
"either super, operator-ket or operator-bra."
)
def super_tensor(*args):
"""Calculates 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]
if not all(arg.type == "super" and arg.superrep == "super" for arg in args):
raise TypeError(
"super_tensor 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
def super_tensor(*args):
"""Calculates 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]
if not all(arg.type == "super" and arg.superrep == "super"
for arg in args):
raise TypeError("super_tensor 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