MinimumEigensolverResult
MinimumEigensolverResult(a_dict=None)
Minimum Eigensolver Result.
Attributes
aux_operator_eigenvalues
Union[None, numpy.ndarray]
return aux operator eigen values
Return type
Optional[ndarray]
eigenstate
Union[None, numpy.ndarray]
return eigen state
Return type
Optional[ndarray]
eigenvalue
Union[None, float]
returns eigen value
Return type
Optional[float]
Methods
__getitem__
MinimumEigensolverResult.__getitem__(key)
Return type
object
__len__
MinimumEigensolverResult.__len__()
clear
MinimumEigensolverResult.clear()
Return type
None
combine
MinimumEigensolverResult.combine(result)
Any property from the argument that exists in the receiver is updated. :type result: AlgorithmResult :param result: Argument result with properties to be set.
Raises
TypeError – Argument is None
Return type
None
copy
MinimumEigensolverResult.copy()
from_dict
static MinimumEigensolverResult.from_dict(a_dict)
fromkeys
classmethod MinimumEigensolverResult.fromkeys(iterable, value=None)
get
MinimumEigensolverResult.get(k[, d]) → D[k] if k in D, else d. d defaults to None.
items
MinimumEigensolverResult.items() → a set-like object providing a view on D’s items
keys
MinimumEigensolverResult.keys() → a set-like object providing a view on D’s keys
pop
MinimumEigensolverResult.pop(key, default=None)
If key is not found, d is returned if given, otherwise KeyError is raised.
Return type
object
popitem
MinimumEigensolverResult.popitem()
as a 2-tuple; but raise KeyError if D is empty.
Return type
Tuple[object, object]
setdefault
MinimumEigensolverResult.setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D
update
MinimumEigensolverResult.update(*args, **kwargs)
If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
Return type
None
values
MinimumEigensolverResult.values() → an object providing a view on D’s values